diff --git a/Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml b/Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml index 1f28523d81..170ac4917d 100644 --- a/Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml +++ b/Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml @@ -1,21 +1,21 @@ -OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results -PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active -NetworkExpansion: 0 # Transmission network expansionl; 0 = not active; 1 = active systemwide -Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic -Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide -EnergyShareRequirement: 1 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide -CapacityReserveMargin: 1 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide -CO2Cap: 2 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint -StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) -MinCapReq: 1 # Activate minimum technology carveout constraints; 0 = not active; 1 = active -MaxCapReq: 1 # Activate maximum technology carveout constraints; 0 = not active; 1 = active -Solver: HiGHS # Available solvers: Gurobi, CPLEX, Clps -ParameterScale: 1 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide -WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active -UCommit: 2 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering -TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. -TimeDomainReduction: 1 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) -ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output -ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value -ModelingToGenerateAlternativeIterations: 3 # Number of MGA iterations with maximization and minimization objective -MethodofMorris: 0 #Flag for turning on the Method of Morris analysis +NetworkExpansion: 0 +TimeDomainReductionFolder: "TDR_Results" +ModelingToGenerateAlternativeIterations: 3 +ParameterScale: 1 +EnergyShareRequirement: 1 +PrintModel: 0 +TimeDomainReduction: 0 +Trans_Loss_Segments: 1 +CapacityReserveMargin: 1 +ModelingtoGenerateAlternativeSlack: 0.1 +Solver: "HiGHS" +MethodofMorris: 0 +Reserves: 0 +StorageLosses: 1 +OverwriteResults: 0 +UCommit: 2 +ModelingToGenerateAlternatives: 0 +MaxCapReq: 1 +MinCapReq: 1 +CO2Cap: 2 +WriteShadowPrices: 1 diff --git a/Project.toml b/Project.toml index 51cd3acab9..f42c8f9bcd 100644 --- a/Project.toml +++ b/Project.toml @@ -13,14 +13,25 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7" +Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" +GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" +GMT = "5752ebe1-31b9-557e-87aa-f909b540aa54" +GeoMakie = "db073c08-6b98-4ee5-b6a4-5efafb3259c6" HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b" +JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" JuMP = "4076af6c-e467-56ae-b986-b466b2749572" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee" +Plotly = "58dd65bb-95f3-509e-9936-c39a10fdeae7" +PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0" +PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd" +ScikitLearn = "3646fa90-6ef7-5e7e-9f22-8aca16db6324" Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" +TickTock = "9ff05d80-102d-5586-aa04-3a8bd1a90d20" +VegaDatasets = "0ae4a718-28b7-58ec-9efb-cded64d6d5b4" YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6" [compat] diff --git a/Tutorials/.ipynb_checkpoints/Tutorial_0_Introduction-checkpoint.ipynb b/Tutorials/.ipynb_checkpoints/Tutorial_0_Introduction-checkpoint.ipynb new file mode 100644 index 0000000000..58137acefd --- /dev/null +++ b/Tutorials/.ipynb_checkpoints/Tutorial_0_Introduction-checkpoint.ipynb @@ -0,0 +1,115 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f8b3c984", + "metadata": {}, + "source": [ + "# Tutorial 0: Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "5fe615ef", + "metadata": {}, + "source": [ + "Welcome to the GenX tutorials! In the following tutorials, we outline some important features of GenX and how to run them." + ] + }, + { + "cell_type": "markdown", + "id": "283cfb5a", + "metadata": {}, + "source": [ + "Running these notebooks follows similar steps to those in the GenX documentation here. In this tutorial, we go through setting up GenX on Jupyter and navigating to the `Tutorials` file from there." + ] + }, + { + "cell_type": "markdown", + "id": "4984f841", + "metadata": {}, + "source": [ + "First, as in the documentation, open a terminal window on your computer and navigate to GenX. Then, navigate to Julia and open a project using the command `julia --project=.`.\n" + ] + }, + { + "cell_type": "markdown", + "id": "906b7b33", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "14bd5e78", + "metadata": {}, + "source": [ + "If you don't already have the package IJulia, add it here using `import(Pkg); Pkg.add(\"IJulia\")`:" + ] + }, + { + "cell_type": "markdown", + "id": "50fe200d", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "0e1b2998", + "metadata": {}, + "source": [ + "After IJulia is added, open a Jupyter notebook with `using IJulia; notebook()`:" + ] + }, + { + "cell_type": "markdown", + "id": "af32af4d", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "762ecdbb", + "metadata": {}, + "source": [ + "This will open a notebook in your default browser. From there, navigate through the GenX directory to the `Tutorials` folder and you're good to go!" + ] + }, + { + "cell_type": "markdown", + "id": "97b2b84b", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "c6ada492", + "metadata": {}, + "source": [ + "**For a more detailed explanation of IJulia, Jupyter, and downloading packages, see here.**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.2", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Tutorials/.ipynb_checkpoints/Tutorial_1_Configuring_Settings-checkpoint.ipynb b/Tutorials/.ipynb_checkpoints/Tutorial_1_Configuring_Settings-checkpoint.ipynb new file mode 100644 index 0000000000..c687f7ae89 --- /dev/null +++ b/Tutorials/.ipynb_checkpoints/Tutorial_1_Configuring_Settings-checkpoint.ipynb @@ -0,0 +1,373 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "78a5ccd4-f602-4381-aadc-d468cff03ba3", + "metadata": {}, + "source": [ + "# Tutorial 1: Configuring Settings" + ] + }, + { + "cell_type": "markdown", + "id": "c78bfdbd", + "metadata": {}, + "source": [ + "GenX is easy to customize to fit a variety of problems. In this tutorial, we show which settings are available to change, what their defaults are, and how to change them in your code." + ] + }, + { + "cell_type": "markdown", + "id": "d6814a53", + "metadata": {}, + "source": [ + "## What settings are there?" + ] + }, + { + "cell_type": "markdown", + "id": "69655945", + "metadata": {}, + "source": [ + "There are 21 settings available to edit in GenX, found in the file `genx_settings.yml`. These settings are described here in the documentation. The file is located in the `Settings` folder in the working directory. To change the location of the file, edit the `settings_path` variable in `Run.jl` within your directory." + ] + }, + { + "cell_type": "markdown", + "id": "6e11cc81", + "metadata": {}, + "source": [ + "Most settings are set as either 0 or 1, which correspond to whether or not to include a specifc feature. For example, to use `TimeDomainReduction`, you would set its parameter to 0 within `genx_settings.yml`. If you would like to run GenX without it, you would set its parameter to 1." + ] + }, + { + "cell_type": "markdown", + "id": "4c88b3b9", + "metadata": {}, + "source": [ + "Other settings, such as `CO2Cap`, have more options corresponding to integers, while some settings such as `ModelingtoGenerateAlternativeSlack` take a numerical input directly (in this case, the slack value). Two settings, `Solver` and `TimeDomainReductionFolder` take in text as input. To learn more about different solvers, read here. For `TimeDomainReductionFolder`, specify the name of the directory you wish to see the results in. For a more comprehensive description of the input options, see the documentation linked above." + ] + }, + { + "cell_type": "markdown", + "id": "bbd18f76", + "metadata": {}, + "source": [ + "Below is the settings file for `SmallNewEngland/OneZone`:" + ] + }, + { + "cell_type": "markdown", + "id": "57ee59c3", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "c37c756e", + "metadata": {}, + "source": [ + "All `genx_settings.yml` files in `Example_Systems` specify most parameters. When configuring your own settings, however, it is not necessary to input all parameters as defaults are specified for each one in `configure_settings.jl`." + ] + }, + { + "cell_type": "markdown", + "id": "16ec73eb", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "527e600d", + "metadata": {}, + "source": [ + "To open `genx_settings.yml` in Jupyter, use the function `YAML.load(open(...))` and navigate to file in the desired directory:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "751b0e46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dict{Any, Any} with 21 entries:\n", + " \"NetworkExpansion\" => 0\n", + " \"TimeDomainReductionFolder\" => \"TDR_Results\"\n", + " \"ModelingToGenerateAlternativeIterations\" => 3\n", + " \"ParameterScale\" => 1\n", + " \"EnergyShareRequirement\" => 1\n", + " \"PrintModel\" => 0\n", + " \"TimeDomainReduction\" => 1\n", + " \"Trans_Loss_Segments\" => 1\n", + " \"CapacityReserveMargin\" => 1\n", + " \"ModelingtoGenerateAlternativeSlack\" => 0.1\n", + " \"Solver\" => \"HiGHS\"\n", + " \"MethodofMorris\" => 0\n", + " \"Reserves\" => 0\n", + " \"StorageLosses\" => 1\n", + " \"OverwriteResults\" => 0\n", + " \"UCommit\" => 2\n", + " \"ModelingToGenerateAlternatives\" => 0\n", + " \"MaxCapReq\" => 1\n", + " \"MinCapReq\" => 1\n", + " \"CO2Cap\" => 2\n", + " \"WriteShadowPrices\" => 1" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "using YAML\n", + "genx_settings_SNE = YAML.load(open(\"../Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml\"))" + ] + }, + { + "cell_type": "markdown", + "id": "dcdd15fd", + "metadata": {}, + "source": [ + "Since all settings have defaults, you only need to specify the settings you would like to change. In fact, you can leave your settings file completely blank and it will still run! Let's try editing `genx_settings` in `SmallNewEngland/OneZone` to contain no parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1ea1703", + "metadata": {}, + "outputs": [], + "source": [ + "new_params = Dict() # Empty dictionary\n", + "YAML.write_file(\"../Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml\", new_params)" + ] + }, + { + "cell_type": "markdown", + "id": "fbc6ce5d", + "metadata": {}, + "source": [ + "The empty file will look like this:" + ] + }, + { + "cell_type": "markdown", + "id": "e24980d7", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "67a336a9", + "metadata": {}, + "source": [ + "Now, run GenX and output the file `capacity.csv` from the `Results` folder:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "236c0180", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Configuring Settings\n", + "Time Series Data Already Clustered.\n", + "Configuring Solver\n", + "Loading Inputs\n", + "Reading Input CSV Files\n", + "Network.csv Successfully Read!\n", + "Load_data.csv Successfully Read!\n", + "Fuels_data.csv Successfully Read!\n", + "Generators_data.csv Successfully Read!\n", + "Generators_variability.csv Successfully Read!\n", + "Validating time basis\n", + "Capacity_reserve_margin.csv Successfully Read!\n", + "Minimum_capacity_requirement.csv Successfully Read!\n", + "Maximum_capacity_requirement.csv Successfully Read!\n", + "Energy_share_requirement.csv Successfully Read!\n", + "CO2_cap.csv Successfully Read!\n", + "CSV Files Successfully Read In From /Users/mayamutic/Desktop/GenX/Example_Systems/SmallNewEngland/OneZone\n", + "Generating the Optimization Model\n", + "Discharge Module\n", + "Non-served Energy Module\n", + "Investment Discharge Module\n", + "Unit Commitment Module\n", + "Emissions Module (for CO2 Policy modularization\n", + "Dispatchable Resources Module\n", + "Storage Resources Module\n", + "Storage Investment Module\n", + "Storage Core Resources Module\n", + "Storage Resources with Symmetric Charge/Discharge Capacity Module\n", + "Thermal (Unit Commitment) Resources Module\n", + "C02 Policies Module\n", + "Energy Share Requirement Policies Module\n", + "Capacity Reserve Margin Policies Module\n", + "Minimum Capacity Requirement Module\n", + "Maximum Capacity Requirement Module\n", + "Time elapsed for model building is\n", + "4.274147083\n", + "Solving Model\n", + "Running HiGHS 1.6.0: Copyright (c) 2023 HiGHS under MIT licence terms\n", + "Presolving model\n", + "36012 rows, 17529 cols, 136567 nonzeros\n", + "34549 rows, 16070 cols, 136337 nonzeros\n", + "Presolve : Reductions: rows 34549(-6123); columns 16070(-2422); elements 136337(-29881)\n", + "Solving the presolved LP\n", + "Using EKK dual simplex solver - serial\n", + " Iteration Objective Infeasibilities num(sum)\n", + " 0 -1.9058295039e-01 Ph1: 12(61.9533); Du: 1(0.190583) 0s\n", + " 16420 9.7160052378e+03 Pr: 0(0); Du: 0(3.13359e-06) 4s\n", + "Using EKK primal simplex solver\n", + " Iteration Objective Infeasibilities num(sum)\n", + " 16420 9.7160052378e+03 Pr: 0(0); Du: 1(0.000200549) 4s\n", + " 16421 9.7160052378e+03 Pr: 0(0); Du: 0(6.09353e-15) 4s\n", + "Solving the original LP from the solution after postsolve\n", + "Model status : Optimal\n", + "Simplex iterations: 16421\n", + "Objective value : 9.7160052378e+03\n", + "HiGHS run time : 4.25\n", + "LP solved for primal\n", + "Writing Output\n", + "Time elapsed for writing costs is\n", + "0.659734\n", + "Time elapsed for writing storage is\n", + "0.078838791\n", + "Time elapsed for writing nse is\n", + "0.317711209\n", + "Time elapsed for writing power balance is\n", + "0.178140584\n", + "Time elapsed for writing emissions is\n", + "0.30562725\n", + "Time elapsed for writing reliability is\n", + "0.141396375\n", + "Time elapsed for writing storage duals is\n", + "0.326884042\n", + "Time elapsed for writing commitment is\n", + "0.028738458\n", + "Time elapsed for writing startup is\n", + "0.028648041\n", + "Time elapsed for writing shutdown is\n", + "0.024390583\n", + "Time elapsed for writing time weights is\n", + "0.048884292\n", + "Time elapsed for writing reserve margin is\n", + "0.124729875\n", + "Time elapsed for writing capacity value is\n", + "1.293372\n", + "Time elapsed for writing net revenue is\n", + "0.369404208\n", + "Wrote outputs to /Users/mayamutic/Desktop/GenX/Example_Systems/SmallNewEngland/OneZone/Results_2\n", + "Time elapsed for writing is\n", + "7.758552166\n" + ] + } + ], + "source": [ + "include(\"../Example_Systems/SmallNewEngland/OneZone/Run.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b8ea6030", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
5×14 DataFrame
RowResourceZoneStartCapRetCapNewCapEndCapStartEnergyCapRetEnergyCapNewEnergyCapEndEnergyCapStartChargeCapRetChargeCapNewChargeCapEndChargeCap
String31String3Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64
1natural_gas_combined_cycle10.00.015113.515113.50.00.00.00.00.00.00.00.0
2solar_pv10.00.031519.931519.90.00.00.00.00.00.00.00.0
3onshore_wind10.00.026083.726083.70.00.00.00.00.00.00.00.0
4battery10.00.08609.668609.660.00.034158.134158.10.00.00.00.0
5Totaln/a0.00.081326.881326.80.00.034158.134158.10.00.00.00.0
" + ], + "text/latex": [ + "\\begin{tabular}{r|cccccccc}\n", + "\t& Resource & Zone & StartCap & RetCap & NewCap & EndCap & StartEnergyCap & \\\\\n", + "\t\\hline\n", + "\t& String31 & String3 & Float64 & Float64 & Float64 & Float64 & Float64 & \\\\\n", + "\t\\hline\n", + "\t1 & natural\\_gas\\_combined\\_cycle & 1 & 0.0 & 0.0 & 15113.5 & 15113.5 & 0.0 & $\\dots$ \\\\\n", + "\t2 & solar\\_pv & 1 & 0.0 & 0.0 & 31519.9 & 31519.9 & 0.0 & $\\dots$ \\\\\n", + "\t3 & onshore\\_wind & 1 & 0.0 & 0.0 & 26083.7 & 26083.7 & 0.0 & $\\dots$ \\\\\n", + "\t4 & battery & 1 & 0.0 & 0.0 & 8609.66 & 8609.66 & 0.0 & $\\dots$ \\\\\n", + "\t5 & Total & n/a & 0.0 & 0.0 & 81326.8 & 81326.8 & 0.0 & $\\dots$ \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m5×14 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m Resource \u001b[0m\u001b[1m Zone \u001b[0m\u001b[1m StartCap \u001b[0m\u001b[1m RetCap \u001b[0m\u001b[1m NewCap \u001b[0m\u001b[1m EndCa\u001b[0m ⋯\n", + " │\u001b[90m String31 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float\u001b[0m ⋯\n", + "─────┼──────────────────────────────────────────────────────────────────────────\n", + " 1 │ natural_gas_combined_cycle 1 0.0 0.0 15113.5 15113 ⋯\n", + " 2 │ solar_pv 1 0.0 0.0 31519.9 31519\n", + " 3 │ onshore_wind 1 0.0 0.0 26083.7 26083\n", + " 4 │ battery 1 0.0 0.0 8609.66 8609\n", + " 5 │ Total n/a 0.0 0.0 81326.8 81326 ⋯\n", + "\u001b[36m 9 columns omitted\u001b[0m" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "using CSV\n", + "using DataFrames\n", + "results = CSV.read(open(\"../Example_Systems/SmallNewEngland/OneZone/Results_2/capacity.csv\"),DataFrame)" + ] + }, + { + "cell_type": "markdown", + "id": "75d31b5a", + "metadata": {}, + "source": [ + "As you can see, this runs without a problem! To try with your own parameters, edit the `new_params` dictionary with whatever parameters you'd like to try and run the cells again." + ] + }, + { + "cell_type": "markdown", + "id": "f4f9679f", + "metadata": {}, + "source": [ + "Finally, let's rewite `genx_settings.yml` to put the original settings in the example back:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41b5d0c7", + "metadata": {}, + "outputs": [], + "source": [ + "YAML.write_file(\"../Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml\", genx_settings_SNE)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.2", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Tutorials/.ipynb_checkpoints/Tutorial_2_Network_Visualization-checkpoint.ipynb b/Tutorials/.ipynb_checkpoints/Tutorial_2_Network_Visualization-checkpoint.ipynb new file mode 100644 index 0000000000..d814592144 --- /dev/null +++ b/Tutorials/.ipynb_checkpoints/Tutorial_2_Network_Visualization-checkpoint.ipynb @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3ce667ff", + "metadata": {}, + "source": [ + "# Tutorial 2: Network Visualization" + ] + }, + { + "cell_type": "markdown", + "id": "71b0f263", + "metadata": {}, + "source": [ + "To run GenX, there are five mandatory input files: `Fuels_data.csv`, `Network.csv`, `Load_data.csv`, `Generators_variability.csv`, and `Generators_data.csv`. Detailed descriptions of these files can be found here in the documentation under \"Inputs\". This tutorial helps visualize the file `Network.csv` using the example system `SmallNewEngland/ThreeZones`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fa98763b", + "metadata": {}, + "outputs": [], + "source": [ + "using Plots\n", + "using PlotlyJS\n", + "using CSV\n", + "using DataFrames" + ] + }, + { + "cell_type": "markdown", + "id": "d5f37531", + "metadata": {}, + "source": [ + "The input file `Network.csv` contains the nodes of your network, how they connect to each other, and some important features about them. Below is the network file for `SmallNewEngland/ThreeZones`:" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "id": "bcaa924c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
3×15 DataFrame
RowColumn1Network_zonesNetwork_Linesz1z2z3Line_Max_Flow_MWtransmission_path_namedistance_mileLine_Loss_PercentageLine_Max_Reinforcement_MWLine_Reinforcement_Cost_per_MWyrDerateCapRes_1CapRes_1CapRes_Excl_1
String3String3String3String3String3String3String7String15String15String15String7String7String7String3Int64?
1MAz111-102950MA_to_CT123.05840.0123058372950120600.9500
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" + ], + "text/latex": [ + "\\begin{tabular}{r|cccccccc}\n", + "\t& Column1 & Network\\_zones & Network\\_Lines & z1 & z2 & z3 & Line\\_Max\\_Flow\\_MW & \\\\\n", + "\t\\hline\n", + "\t& String3 & String3 & String3 & String3 & String3 & String3 & String7 & \\\\\n", + "\t\\hline\n", + "\t1 & MA & z1 & 1 & 1 & -1 & 0 & 2950 & $\\dots$ \\\\\n", + "\t2 & CT & z2 & 2 & 1 & 0 & -1 & 2000 & $\\dots$ \\\\\n", + "\t3 & ME & z3 & & & & & & $\\dots$ \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m3×15 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m Column1 \u001b[0m\u001b[1m Network_zones \u001b[0m\u001b[1m Network_Lines \u001b[0m\u001b[1m z1 \u001b[0m\u001b[1m z2 \u001b[0m\u001b[1m z3 \u001b[0m\u001b[1m Line_\u001b[0m ⋯\n", + " │\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m Strin\u001b[0m ⋯\n", + "─────┼──────────────────────────────────────────────────────────────────────────\n", + " 1 │ MA z1 1 1 -1 0 2950 ⋯\n", + " 2 │ CT z2 2 1 0 -1 2000\n", + " 3 │ ME z3\n", + "\u001b[36m 9 columns omitted\u001b[0m" + ] + }, + "execution_count": 241, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "network = CSV.read(\"Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Network.csv\",DataFrame,missingstring=\"NA\")" + ] + }, + { + "cell_type": "markdown", + "id": "b9aafe7b", + "metadata": {}, + "source": [ + "Here, MA, CT, and ME are the abbreviations for states Massachusetts, Connecticut, and Maine. However, since the US region of New England contains other states as well, ME in this case is also used to refer to those states." + ] + }, + { + "cell_type": "markdown", + "id": "ca18429c", + "metadata": {}, + "source": [ + "Columns `z1, z2, z3` specify an adjacency matrix forming the network of the three regions. In this case, there are only two network lines, specified in the `Network_Lines` columns. The `z1` column indicates that the first node, MA, is the source of both lines as both rows have value 1. Columns `z2` and `z3` both have values of -1, which means both nodes CT and ME recieve energy from node MA:" + ] + }, + { + "cell_type": "markdown", + "id": "6a52eb00", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d89f8c96", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "@webio": { + "lastCommId": "22b36260144444988f1a70d85ef0b17a", + "lastKernelId": "0d15a65c-9b8a-4242-bdb1-4d39948d79a9" + }, + "kernelspec": { + "display_name": "Julia 1.9.2", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Tutorials/.ipynb_checkpoints/Tutorial_3_K-means_Time_Domain_Reduction-checkpoint.ipynb b/Tutorials/.ipynb_checkpoints/Tutorial_3_K-means_Time_Domain_Reduction-checkpoint.ipynb new file mode 100644 index 0000000000..cf59fac6f4 --- /dev/null +++ b/Tutorials/.ipynb_checkpoints/Tutorial_3_K-means_Time_Domain_Reduction-checkpoint.ipynb @@ -0,0 +1,158764 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7054e9e0", + "metadata": {}, + "source": [ + "# Tutorial 3: K-Means and Time Domain Reduction" + ] + }, + { + "cell_type": "markdown", + "id": "944dff9e", + "metadata": {}, + "source": [ + "A good tool to reduce computation time of GenX is to use Time Domain Reduction (TDR). Time Domain Reduction is a method that selects a smaller set of time steps from the data in a way that reduces computation time while still capturing the main information of the model. In this tutorial, we go over how TDR works in GenX and how it uses K-means clustering to choose the optimal time steps." + ] + }, + { + "cell_type": "markdown", + "id": "9cb4e61f", + "metadata": {}, + "source": [ + "### Time Domain Reduction" + ] + }, + { + "cell_type": "markdown", + "id": "4456f70f", + "metadata": {}, + "source": [ + "To see how Time Domain Reduction works, let's look at the `Load_data` in `Example_Systems_Tutorials/SmallNewEngland/OneZone/`:" + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "id": "5f8730d4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m registry at `~/.julia/registries/General.toml`\n", + "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m TickTock ─ v1.3.0\n", + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/Desktop/GenX/Project.toml`\n", + " \u001b[90m[9ff05d80] \u001b[39m\u001b[92m+ TickTock v1.3.0\u001b[39m\n", + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/Desktop/GenX/Manifest.toml`\n", + " \u001b[90m[9ff05d80] \u001b[39m\u001b[92m+ TickTock v1.3.0\u001b[39m\n", + "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n", + "\u001b[32m ✓ \u001b[39mTickTock\n", + "\u001b[33m ✓ \u001b[39mGenX\n", + " 2 dependencies successfully precompiled in 7 seconds. 128 already precompiled.\n", + " \u001b[33m1\u001b[39m dependency precompiled but a different version is currently loaded. Restart julia to access the new version\n" + ] + } + ], + "source": [ + "# First, load all packages needed\n", + "using DataFrames\n", + "using CSV\n", + "using VegaLite\n", + "using YAML\n", + "using TickTock\n", + "using PlotlyJS\n", + "using Plots\n", + "using Clustering\n", + "using ScikitLearn\n", + "@sk_import datasets: (make_blobs)" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "id": "328f601e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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This file shows the number of time steps used in GenX before applying TDR, i.e. every hour in a year, totaling 8,760 hours. This means that there is only one representative period, as seen in `Rep_Periods`." + ] + }, + { + "cell_type": "markdown", + "id": "2fc22b2f", + "metadata": {}, + "source": [ + "Below shows the load per timestep in megawatts:" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "id": "6b684248", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.vega.v5+json": { + "$schema": "https://vega.github.io/schema/vega/v5.json", + "axes": [ + { + "aria": false, + "domain": false, + "grid": true, + "gridScale": "y", + "labels": false, + "maxExtent": 0, + "minExtent": 0, + "orient": "bottom", + "scale": "x", + "tickCount": { + "signal": "ceil(width/40)" + }, + "ticks": false, + "zindex": 0 + }, + { + "aria": false, + "domain": false, + "grid": true, + "gridScale": "x", + "labels": false, + "maxExtent": 0, + "minExtent": 0, + "orient": "left", + "scale": "y", + "tickCount": { + "signal": "ceil(height/40)" + }, + "ticks": false, + "zindex": 0 + }, + { + "grid": false, 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", + "image/svg+xml": [ + "05001,0001,5002,0002,5003,0003,5004,0004,5005,0005,5006,0006,5007,0007,5008,0008,5009,000Time_Index02,0004,0006,0008,00010,00012,00014,00016,00018,00020,00022,00024,000Load_MW_z1MW Load per hour, No TDR" + ], + "text/plain": [ + "@vlplot(\n", + " title=\"MW Load per hour, No TDR\",\n", + " width=600,\n", + " height=400,\n", + " linewidth=0.01,\n", + " mark=\"line\",\n", + " encoding={\n", + " x={\n", + " field=\"Time_Index\"\n", + " },\n", + " y={\n", + " field=\"Load_MW_z1\"\n", + " }\n", + " },\n", + " data={\n", + " values=...\n", + " }\n", + ")" + ] + }, + "execution_count": 260, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loads |>\n", + "@vlplot(:line, \n", + " x=:Time_Index, y=:Load_MW_z1, title=\"MW Load per hour, No TDR\",\n", + " width=600,height=400,linewidth=.01)" + ] + }, + { + "cell_type": "markdown", + "id": "86afb434", + "metadata": {}, + "source": [ + "As in Tutorial 1, we can open the `genx_settings.yml` file for `SmallNewEngland/OneZone` to see how `TimeDomainReduction` is set. If it's set to 1, this means TDR is being used." + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "id": "ebb8fde0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dict{Any, Any} with 21 entries:\n", + " \"NetworkExpansion\" => 0\n", + " \"TimeDomainReductionFolder\" => \"TDR_Results\"\n", + " \"ModelingToGenerateAlternativeIterations\" => 3\n", + " \"ParameterScale\" => 1\n", + " \"EnergyShareRequirement\" => 1\n", + " \"PrintModel\" => 0\n", + " \"TimeDomainReduction\" => 1\n", + " \"Trans_Loss_Segments\" => 1\n", + " \"CapacityReserveMargin\" => 1\n", + " \"ModelingtoGenerateAlternativeSlack\" => 0.1\n", + " \"Solver\" => \"HiGHS\"\n", + " \"MethodofMorris\" => 0\n", + " \"Reserves\" => 0\n", + " \"StorageLosses\" => 1\n", + " \"OverwriteResults\" => 0\n", + " \"UCommit\" => 2\n", + " \"ModelingToGenerateAlternatives\" => 0\n", + " \"MaxCapReq\" => 1\n", + " \"MinCapReq\" => 1\n", + " \"CO2Cap\" => 2\n", + " \"WriteShadowPrices\" => 1" + ] + }, + "execution_count": 303, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genx_settings_SNE = YAML.load(open(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml\"))" + ] + }, + { + "cell_type": "markdown", + "id": "1ad130ab", + "metadata": {}, + "source": [ + "To visualize how TDR decreases computation time, let's start by running `SmallNewEngland/OneZone` without TDR. We'll use the package `TickTock` to track the runtime." + ] + }, + { + "cell_type": "markdown", + "id": "a2c0a168", + "metadata": {}, + "source": [ + "To run GenX without TDR, we start by editing the settings to set `TimeDomainReduction` to 0:" + ] + }, + { + "cell_type": "code", + "execution_count": 304, + "id": "d6893321", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dict{Any, Any} with 21 entries:\n", + " \"NetworkExpansion\" => 0\n", + " \"TimeDomainReductionFolder\" => \"TDR_Results\"\n", + " \"ModelingToGenerateAlternativeIterations\" => 3\n", + " \"ParameterScale\" => 1\n", + " \"EnergyShareRequirement\" => 1\n", + " \"PrintModel\" => 0\n", + " \"TimeDomainReduction\" => 0\n", + " \"Trans_Loss_Segments\" => 1\n", + " \"CapacityReserveMargin\" => 1\n", + " \"ModelingtoGenerateAlternativeSlack\" => 0.1\n", + " \"Solver\" => \"HiGHS\"\n", + " \"MethodofMorris\" => 0\n", + " \"Reserves\" => 0\n", + " \"StorageLosses\" => 1\n", + " \"OverwriteResults\" => 0\n", + " \"UCommit\" => 2\n", + " \"ModelingToGenerateAlternatives\" => 0\n", + " \"MaxCapReq\" => 1\n", + " \"MinCapReq\" => 1\n", + " \"CO2Cap\" => 2\n", + " \"WriteShadowPrices\" => 1" + ] + }, + "execution_count": 304, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genx_settings_SNE[\"TimeDomainReduction\"] = 0\n", + "genx_settings_SNE ## Output settings" + ] + }, + { + "cell_type": "markdown", + "id": "8706d925", + "metadata": {}, + "source": [ + "Then we write the edited settings to the file path:" + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "id": "7768ac6a", + "metadata": {}, + "outputs": [], + "source": [ + "YAML.write_file(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml\", genx_settings_SNE)" + ] + }, + { + "cell_type": "markdown", + "id": "1865ad33", + "metadata": {}, + "source": [ + "And run it using `include`. (Note: this process will take a few minutes):" + ] + }, + { + "cell_type": "code", + "execution_count": 306, + "id": "137e9c95", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Configuring Settings\n", + "Configuring Solver\n", + "Loading Inputs\n", + "Reading Input CSV Files\n", + "Network.csv Successfully Read!\n", + "Load_data.csv Successfully Read!\n", + "Fuels_data.csv Successfully Read!\n", + "Generators_data.csv Successfully Read!\n", + "Generators_variability.csv Successfully Read!\n", + "Validating time basis\n", + "Capacity_reserve_margin.csv Successfully Read!\n", + "Minimum_capacity_requirement.csv Successfully Read!\n", + "Maximum_capacity_requirement.csv Successfully Read!\n", + "Energy_share_requirement.csv Successfully Read!\n", + "CO2_cap.csv Successfully Read!\n", + "CSV Files Successfully Read In From /Users/mayamutic/Desktop/GenX/Example_Systems/SmallNewEngland/OneZone\n", + "Generating the Optimization Model\n", + "Discharge Module\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m started timer at: 2024-01-14T18:50:47.180\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Non-served Energy Module\n", + "Investment Discharge Module\n", + "Unit Commitment Module\n", + "Emissions Module (for CO2 Policy modularization\n", + "Dispatchable Resources Module\n", + "Storage Resources Module\n", + "Storage Investment Module\n", + "Storage Core Resources Module\n", + "Storage Resources with Symmetric Charge/Discharge Capacity Module\n", + "Thermal (Unit Commitment) Resources Module\n", + "C02 Policies Module\n", + "Energy Share Requirement Policies Module\n", + "Capacity Reserve Margin Policies Module\n", + "Minimum Capacity Requirement Module\n", + "Maximum Capacity Requirement Module\n", + "Time elapsed for model building is\n", + "0.555159583\n", + "Solving Model\n", + "Running HiGHS 1.6.0: Copyright (c) 2023 HiGHS under MIT licence terms\n", + "Presolving model\n", + "170620 rows, 83017 cols, 646999 nonzeros\n", + "161830 rows, 74231 cols, 653419 nonzeros\n", + "Presolve : Reductions: rows 161830(-30906); columns 74231(-13381); elements 653419(-134175)\n", + "Solving the presolved LP\n", + "Using EKK dual simplex solver - serial\n", + " Iteration Objective Infeasibilities num(sum)\n", + " 0 -4.2035774355e+00 Ph1: 204(998.471); Du: 25(4.20358) 0s\n", + " 12911 3.5764275943e+03 Pr: 11955(9388.12); Du: 0(0.00728457) 5s\n", + " 27245 4.4018819620e+03 Pr: 15248(18133.3); Du: 0(0.0164783) 10s\n", + " 33480 4.6172015768e+03 Pr: 19961(178631); Du: 0(0.00123592) 16s\n", + " 41671 6.5841177886e+03 Pr: 19497(1.94677e+06); Du: 0(0.00303983) 21s\n", + " 45369 7.2446943919e+03 Pr: 47955(198575); Du: 0(0.00363254) 26s\n", + " 48032 7.4871889951e+03 Pr: 41159(54384.3); Du: 0(0.00323346) 31s\n", + " 51113 7.7263507185e+03 Pr: 31599(1.02636e+07); Du: 0(0.00359186) 37s\n", + " 54158 7.9912685513e+03 Pr: 16427(56826.4); Du: 0(0.00373523) 43s\n", + " 57341 8.2279131748e+03 Pr: 25016(70269.9); Du: 0(0.00401519) 49s\n", + " 60295 8.4411702548e+03 Pr: 22461(18551); Du: 0(0.00387185) 54s\n", + " 63129 8.6669193880e+03 Pr: 46660(9.78873e+06); Du: 0(0.0038402) 60s\n", + " 65751 8.8788576826e+03 Pr: 27101(119570); Du: 0(0.00396439) 65s\n", + " 68330 9.0772379783e+03 Pr: 15287(17459.6); Du: 0(0.0041059) 71s\n", + " 70852 9.2285381286e+03 Pr: 19466(50196.1); Du: 0(0.00439293) 76s\n", + " 73853 9.3631903691e+03 Pr: 38866(92264.6); Du: 0(0.00418896) 82s\n", + " 76708 9.5539040461e+03 Pr: 16172(14127.5); Du: 0(0.0045444) 87s\n", + " 79303 9.7553740900e+03 Pr: 26742(56495.4); Du: 0(0.0042862) 93s\n", + " 81856 9.9044159014e+03 Pr: 10467(3884.72); Du: 0(0.00444058) 98s\n", + " 84716 1.0129741603e+04 Pr: 62822(3.23315e+06); Du: 0(0.00483053) 104s\n", + " 87705 1.0220537743e+04 Pr: 31318(150590); Du: 0(0.00477644) 110s\n", + " 90348 1.0279428078e+04 Pr: 10918(5037.23); Du: 0(0.00476901) 115s\n", + " 93645 1.0357677034e+04 Pr: 12137(29845.6); Du: 0(0.00488115) 121s\n", + " 96623 1.0433817391e+04 Pr: 7282(4202.53); Du: 0(0.00443161) 126s\n", + " 99565 1.0485702387e+04 Pr: 25971(1.39873e+07); Du: 0(0.00416339) 132s\n", + " 102156 1.0527662238e+04 Pr: 21719(228836); Du: 0(0.0040159) 137s\n", + " 105159 1.0568969436e+04 Pr: 28146(190800); Du: 0(0.00408142) 143s\n", + " 107981 1.0591229947e+04 Pr: 8709(6382.63); Du: 0(0.00420473) 149s\n", + " 108857 1.0592942277e+04 Pr: 0(0); Du: 0(6.25e-06) 151s\n", + " 108857 1.0592942277e+04 Pr: 0(0); Du: 0(6.25e-06) 151s\n", + "Using EKK primal simplex solver\n", + " Iteration Objective Infeasibilities num(sum)\n", + " 108857 1.0592942277e+04 Pr: 0(0); Du: 1(0.0001) 151s\n", + " 108858 1.0592942277e+04 Pr: 0(0); Du: 0(2.99577e-15) 151s\n", + "Solving the original LP from the solution after postsolve\n", + "Model status : Optimal\n", + "Simplex iterations: 108858\n", + "Objective value : 1.0592942277e+04\n", + "HiGHS run time : 151.71\n", + "LP solved for primal\n", + "Writing Output\n", + "Time elapsed for writing costs is\n", + "0.09726075\n", + "Time elapsed for writing storage is\n", + "0.021158625\n", + "Time elapsed for writing nse is\n", + "0.057492916\n", + "Time elapsed for writing power balance is\n", + "0.054992458\n", + "Time elapsed for writing emissions is\n", + "0.10993575\n", + "Time elapsed for writing reliability is\n", + "0.014614333\n", + "Time elapsed for writing storage duals is\n", + "0.02906075\n", + "Time elapsed for writing commitment is\n", + "0.02163175\n", + "Time elapsed for writing startup is\n", + "0.066653583\n", + "Time elapsed for writing shutdown is\n", + "0.065220833\n", + "Time elapsed for writing time weights is\n", + "0.002030875\n", + "Time elapsed for writing reserve margin is\n", + "0.008165709\n", + "Time elapsed for writing capacity value is\n", + "0.025880458\n", + "Time elapsed for writing net revenue is\n", + "0.030976584\n", + "Wrote outputs to /Users/mayamutic/Desktop/GenX/Example_Systems/SmallNewEngland/OneZone/Results_6\n", + "Time elapsed for writing is\n", + "1.055933958\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m 153.662797875s: 2 minutes, 33 seconds, 662 milliseconds\n" + ] + } + ], + "source": [ + "tick()\n", + "include(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Run.jl\")\n", + "tock()" + ] + }, + { + "cell_type": "markdown", + "id": "bd41ae1e", + "metadata": {}, + "source": [ + "This took a little while to run, and would take even longer for larger systems. Let's see how we can get the run time down using Time Domain Reduction. The next sections go over how K-means clustering is used to perform TDR, and how to interpret the resulting files in GenX." + ] + }, + { + "cell_type": "markdown", + "id": "da668a37", + "metadata": {}, + "source": [ + "### K-means clustering" + ] + }, + { + "cell_type": "markdown", + "id": "379831dc", + "metadata": {}, + "source": [ + "Let's go over how TDR works. To perform TDR, GenX uses K-means clustering. K-means is an optimization method that clusters data into several groups based on their proximity to \"centers\" determined by the algorithm. Clustering data is " + ] + }, + { + "cell_type": "markdown", + "id": "d1505608", + "metadata": {}, + "source": [ + "To be more specific, k-means finds a set number of groups such that the variance between the distance of each point in the group to the mean of the group is minimized. \n", + "\n", + "$$\n", + "\\begin{align*}\n", + "\\mathop{\\arg \\min}\\limits_{\\mathbf{S}} & \\sum_{i = 1}^k \\sum_{x \\in S_i} ||x - \\mu_i||^2 \\\\\n", + "\\end{align*}\n", + "$$\n", + "\n", + "Where $\\mathbf{S} = \\{S_1, ... , S_k\\}$ are the clusters, and $\\mu_i$ is the mean of each cluster i.e. the mean of the distances between each point in the cluster. By taking the argmin over $\\mathbf{S}$, the points $x$ are clustered into groups where their distance to the center is the smallest." + ] + }, + { + "cell_type": "markdown", + "id": "8f6a69ea", + "metadata": {}, + "source": [ + "GenX uses the package `Clustering.jl`, with documentation here. As an example, using the package `ScikitLearn.jl`, let's generate data that can cluster easily." + ] + }, + { + "cell_type": "code", + "execution_count": 457, + "id": "69582aa0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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", + "image/svg+xml": [ + "-50510-10-50510Before K-means Clustering" + ], + "text/html": [ + "
\n", + " \n" + ] + }, + "execution_count": 458, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plotlyjs()\n", + "Plots.scatter(b[!,\"x1\"],b[!,\"x2\"],legend=false,title=\"Before K-means Clustering\")" + ] + }, + { + "cell_type": "markdown", + "id": "f82515f7", + "metadata": {}, + "source": [ + "Now we use the function `kmeans`, which is also used in `src/time_domain_reduction` in GenX." + ] + }, + { + "cell_type": "code", + "execution_count": 460, + "id": "ad777278", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KmeansResult{Matrix{Float64}, Float64, Int64}([-0.5983234957845184 7.533257370256171 … 7.527880189472178 -5.971220015686362; -1.2278405241483499 9.450101620955774 … 0.6882845988483258 -9.984381244149827], [1, 5, 1, 1, 4, 2, 1, 1, 1, 4 … 1, 1, 4, 2, 1, 5, 1, 4, 4, 1], [1.3255250419151228, 4.997847745986178, 8.091409099122394, 0.03714790494307074, 3.2626391548434412, 1.9889458740610166, 5.599991619086529, 5.546511424821204, 5.6808996899043525, 0.1286842679680973 … 2.501523413471922, 1.5778169684187557, 3.852127483939782, 0.5750996178271066, 10.710858491505114, 1.8568275420579425, 4.101412120977988, 1.5756217190165671, 0.005038328827794203, 5.487009130161922], [40, 20, 6, 20, 14], [40, 20, 6, 20, 14], 284.066131030526, 4, true)" + ] + }, + "execution_count": 460, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "R = kmeans(transpose(Matrix(b)),5)" + ] + }, + { + "cell_type": "markdown", + "id": "993370fe", + "metadata": {}, + "source": [ + "`kmeans` returns three outputs: assignments, centers, and counts. Assignments shows to which cluster each points belongs, centers shows where the center coordinates of each cluster are, and counts shows how many points belong to each cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 470, + "id": "c8f956cc", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 5, 1, 1, 4, 2, 1, 1, 1, 4, 2, 2, 4, 1, 4, 1, 3, 4, 4, 1, 2, 2, 1, 5, 4, 2, 2, 1, 2, 4, 3, 1, 5, 5, 1, 1, 4, 1, 1, 2, 5, 1, 5, 3, 5, 1, 2, 1, 5, 2, 5, 4, 4, 1, 2, 2, 1, 1, 5, 3, 2, 1, 1, 5, 4, 1, 2, 4, 1, 5, 2, 2, 4, 1, 4, 3, 2, 4, 1, 1, 1, 5, 1, 2, 1, 1, 1, 1, 4, 3, 1, 1, 4, 2, 1, 5, 1, 4, 4, 1]\n" + ] + } + ], + "source": [ + "println(R.assignments)" + ] + }, + { + "cell_type": "code", + "execution_count": 463, + "id": "c7b9ab46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2×5 Matrix{Float64}:\n", + " -0.598323 7.53326 -3.98791 7.52788 -5.97122\n", + " -1.22784 9.4501 -8.69267 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", + "image/svg+xml": [ + "-3036-12.5-10.0-7.5-5.0-2.50.02.5After K-means Clustering" + ], + "text/html": [ + "
\n", + " \n" + ] + }, + "execution_count": 453, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plotlyjs()\n", + "Plots.scatter(b[!,\"x1\"],b[!,\"x2\"],legend=false,marker_z=R.assignments,c=:lightrainbow,title=\"After K-means Clustering\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3b7ee69", + "metadata": {}, + "outputs": [], + "source": [ + "# Ask F: how are representative sets selected?" + ] + }, + { + "cell_type": "markdown", + "id": "b8b957e2", + "metadata": {}, + "source": [ + "### Results of Time Domain Reduction" + ] + }, + { + "cell_type": "markdown", + "id": "e2e1b15c", + "metadata": {}, + "source": [ + "To visualize the results of TDR, we'll set TDR = 1 back in the `genx_settings.yml` file in `Example_Systems_Tutorials/SmallNewEngland/OneZone/`:" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "id": "cbc4c84f", + "metadata": {}, + "outputs": [], + "source": [ + "genx_settings_SNE[\"TimeDomainReduction\"] = 1;" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "id": "5ffa9e4a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dict{Any, Any} with 21 entries:\n", + " \"NetworkExpansion\" => 0\n", + " \"TimeDomainReductionFolder\" => \"TDR_Results\"\n", + " \"ModelingToGenerateAlternativeIterations\" => 3\n", + " \"ParameterScale\" => 1\n", + " \"EnergyShareRequirement\" => 1\n", + " \"PrintModel\" => 0\n", + " \"TimeDomainReduction\" => 1\n", + " \"Trans_Loss_Segments\" => 1\n", + " \"CapacityReserveMargin\" => 1\n", + " \"ModelingtoGenerateAlternativeSlack\" => 0.1\n", + " \"Solver\" => \"HiGHS\"\n", + " \"MethodofMorris\" => 0\n", + " \"Reserves\" => 0\n", + " \"StorageLosses\" => 1\n", + " \"OverwriteResults\" => 0\n", + " \"UCommit\" => 2\n", + " \"ModelingToGenerateAlternatives\" => 0\n", + " \"MaxCapReq\" => 1\n", + " \"MinCapReq\" => 1\n", + " \"CO2Cap\" => 2\n", + " \"WriteShadowPrices\" => 1" + ] + }, + "execution_count": 296, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genx_settings_SNE" + ] + }, + { + "cell_type": "code", + "execution_count": 475, + "id": "8a61066a", + "metadata": {}, + "outputs": [], + "source": [ + "YAML.write_file(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml\", genx_settings_SNE)" + ] + }, + { + "cell_type": "markdown", + "id": "035bd2cb", + "metadata": {}, + "source": [ + "And run GenX again with TDR:" + ] + }, + { + "cell_type": "code", + "execution_count": 473, + "id": "19c3a604", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m started timer at: 2024-01-15T19:59:06.005\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Configuring Settings\n", + "Configuring Solver\n", + "Loading Inputs\n", + "Reading Input CSV Files\n", + "Network.csv Successfully Read!\n", + "Load_data.csv Successfully Read!\n", + "Fuels_data.csv Successfully Read!\n", + "Generators_data.csv Successfully Read!\n", + "Generators_variability.csv Successfully Read!\n", + "Validating time basis\n", + "CSV Files Successfully Read In From /Users/mayamutic/Desktop/GenX/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone\n", + "Generating the Optimization Model\n", + "Discharge Module\n", + "Non-served Energy Module\n", + "Investment Discharge Module\n", + "Emissions Module (for CO2 Policy modularization\n", + "Dispatchable Resources Module\n", + "Storage Resources Module\n", + "Storage Investment Module\n", + "Storage Core Resources Module\n", + "Storage Resources with Symmetric Charge/Discharge Capacity Module\n", + "Thermal (No Unit Commitment) Resources Module\n", + "Time elapsed for model building is\n", + "1.478717916\n", + "Solving Model\n", + "Running HiGHS 1.6.0: Copyright (c) 2023 HiGHS under MIT licence terms\n", + "Presolving model\n", + "109294 rows, 56737 cols, 292840 nonzeros\n", + "104401 rows, 51844 cols, 296648 nonzeros\n", + "Presolve : Reductions: rows 104401(-27006); columns 51844(-9487); elements 296648(-105909)\n", + "Solving the presolved LP\n", + "Using EKK dual simplex solver - serial\n", + " Iteration Objective Infeasibilities num(sum)\n", + " 0 -4.2724711023e+02 Ph1: 141(124.391); Du: 25(427.247) 0s\n", + " 13227 2.8278685533e+09 Pr: 9996(2.28476e+07); Du: 0(1.41046e-05) 5s\n", + " 63706 5.1701547992e+09 Pr: 0(0); Du: 0(9.6273e-13) 8s\n", + "Solving the original LP from the solution after postsolve\n", + "Model status : Optimal\n", + "Simplex iterations: 63706\n", + "Objective value : 5.1701547992e+09\n", + "HiGHS run time : 8.48\n", + "LP solved for primal\n", + "Writing Output\n", + "Time elapsed for writing costs is\n", + "0.089931958\n", + "Time elapsed for writing storage is\n", + "0.024121417\n", + "Time elapsed for writing nse is\n", + "0.072039042\n", + "Time elapsed for writing power balance is\n", + "0.085300792\n", + "Time elapsed for writing emissions is\n", + "0.111281875\n", + "Time elapsed for writing reliability is\n", + "0.032329834\n", + "Time elapsed for writing storage duals is\n", + "0.025279625\n", + "Time elapsed for writing time weights is\n", + "0.00560975\n", + "Time elapsed for writing net revenue is\n", + "0.028684708\n", + "Wrote outputs to /Users/mayamutic/Desktop/GenX/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Results\n", + "Time elapsed for writing is\n", + "0.979934208\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m 16.213814459s: 16 seconds, 213 milliseconds\n" + ] + } + ], + "source": [ + "tick()\n", + "include(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Run.jl\")\n", + "tock()" + ] + }, + { + "cell_type": "markdown", + "id": "7a098f2a", + "metadata": {}, + "source": [ + "Csv files with the results of TDR are generated automatically in a folder called `TDR_results` found within the same folder containing the input csv files, in this case `Example_Systems/SmallNewEngland/OneZone`. The csv files in this folder show the files used in `Run.jl` that have been pared down from the initial input files." + ] + }, + { + "cell_type": "markdown", + "id": "ab5f572f", + "metadata": {}, + "source": [ + "As an example, consider the input file `Fuels_data.csv`:" + ] + }, + { + "cell_type": "code", + "execution_count": 472, + "id": "fb178f63", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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The number of hours per representative period is set in `time_domain_reduction_settings.yml`. Also specified in the file is the minimum and maximum number of clusters we would like to have (in this case 6 and 11). The k-means algorithm will then select the number of clusters that should be sufficient to capture the GenX model in fewer time steps (in this case 11)." + ] + }, + { + "cell_type": "markdown", + "id": "db916072", + "metadata": {}, + "source": [ + "Below, we create arrays out of the representative weeks and plot them on the same plot used in the beginning of this tutorial:" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "id": "d6c7df93", + "metadata": {}, + "outputs": [], + "source": [ + "# To do: explain how to find periods using weights\n", + "\n", + "week1 = [repeat([\"Week 1\"],168) loads[1:168,end-1] loads[1:168,end]]\n", + "week2 = [repeat([\"Week 2\"],168) loads[1009:1176,end-1] loads[1009:1176,end]];\n", + "week3 = [repeat([\"Week 3\"],168) loads[1849:2016,end-1] loads[1849:2016,end]];\n", + "week4 = [repeat([\"Week 4\"],168) loads[2353:2520,end-1] loads[2353:2520,end]];\n", + "week5 = [repeat([\"Week 5\"],168) loads[3865:4032,end-1] loads[3865:4032,end]];\n", + "week6 = [repeat([\"Week 6\"],168) loads[4537:4704,end-1] loads[4537:4704,end]];\n", + "week7 = [repeat([\"Week 7\"],168) loads[4705:4872,end-1] loads[4705:4872,end]];\n", + "week8 = [repeat([\"Week 8\"],168) loads[7057:7224,end-1] loads[7057:7224,end]];\n", + "week9 = [repeat([\"Week 9\"],168) loads[7516:7683,end-1] loads[7516:7683,end]];\n", + "week10 = [repeat([\"Week 10\"],168) loads[7897:8064,end-1] loads[7897:8064,end]];\n", + "week11 = [repeat([\"Week 11\"],168) loads[8401:8568,end-1] loads[8401:8568,end]];" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "id": "5dd13d8a", + "metadata": {}, + "outputs": [], + "source": [ + "loads_plot = [repeat([\"Total\"],8760) loads[!,end-1] loads[!,end]];" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "id": "a7dd236a", + "metadata": {}, + "outputs": [], + "source": [ + "loads_with_TDR = [loads_TP; week1; week2; week3; week4; week5; week6; week7; week8; week9; week10; week11];" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "id": "8f46b99f", + "metadata": {}, + "outputs": [], + "source": [ + "loads_with_TDR = DataFrame(loads_with_TDR ,[\"week\",\"hour\", \"MW\"])\n", + "loads_with_TDR[!,:hour] = convert.(Int64,loads_with_TDR[!,:hour]);\n", + "loads_with_TDR[!,:MW] = convert.(Float64,loads_with_TDR[!,:MW]);" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "id": "8d389d51", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.vega.v5+json": { + "$schema": "https://vega.github.io/schema/vega/v5.json", + "axes": [ + { + "aria": false, + "domain": false, + "grid": true, + "gridScale": "y", + "labels": false, + "maxExtent": 0, + "minExtent": 0, + "orient": "bottom", + "scale": "x", + "tickCount": { + "signal": "ceil(width/40)" + }, + "ticks": false, + "zindex": 0 + }, + { + "aria": false, + "domain": false, + "grid": true, + "gridScale": "x", + "labels": false, + "maxExtent": 0, + "minExtent": 0, + "orient": "left", + "scale": "y", + 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", + "image/svg+xml": [ + "05001,0001,5002,0002,5003,0003,5004,0004,5005,0005,5006,0006,5007,0007,5008,0008,5009,000Time Step (hours)02,0004,0006,0008,00010,00012,00014,00016,00018,00020,00022,00024,000Load (MW)TotalWeek 1Week 2Week 3Week 4Week 5Week 6Week 7Week 8Week 9Week 10Week 11weekMW Load per hour with TDR Representative Weeks" + ], + "text/plain": [ + "@vlplot(\n", + " title=\"MW Load per hour with TDR Representative Weeks\",\n", + " width=845,\n", + " height=400,\n", + " mark={\n", + " type=\"line\"\n", + " },\n", + " encoding={\n", + " x={\n", + " title=\"Time Step (hours)\",\n", + " labels=\"week:n\",\n", + " field=\"hour\"\n", + " },\n", + " y={\n", + " title=\"Load (MW)\",\n", + " field=\"MW\"\n", + " },\n", + " color={\n", + " scale={\n", + " scheme=\"paired\"\n", + " },\n", + " sort=\"decsending\",\n", + " field=\"week\",\n", + " type=\"nominal\"\n", + " }\n", + " },\n", + " data={\n", + " values=...\n", + " }\n", + ")" + ] + }, + "execution_count": 254, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loads_with_TDR |>\n", + "@vlplot( \n", + " x={:hour,title=\"Time Step (hours)\",labels=\"week:n\"}, y={:MW,title=\"Load (MW)\"}, \n", + " color={\"week:n\", scale={scheme=\"paired\"},sort=\"decsending\"}, title=\"MW Load per hour with TDR Representative Weeks\",\n", + " width=845,height=400,mark={\n", + " :line\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3638c66a", + "metadata": {}, + "outputs": [], + "source": [ + "## For F: Is there a way to compare results to see similarities?" + ] + } + ], + "metadata": { + "@webio": { + "lastCommId": "f7fffbebf5b748078dd261c3af65bacc", + "lastKernelId": "1e8374ca-71e7-44d8-88e7-e43f681fa41c" + }, + "kernelspec": { + "display_name": "Julia 1.9.2", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.2" + } + }, + "nbformat": 4, + 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b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Generators_data.csv new file mode 100644 index 0000000000..1aa6863cee --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Generators_data.csv @@ -0,0 +1,5 @@ +Resource,Zone,THERM,MUST_RUN,STOR,FLEX,HYDRO,VRE,LDS,Num_VRE_Bins,New_Build,Existing_Cap_MW,Existing_Cap_MWh,Existing_Charge_Cap_MW,Max_Cap_MW,Max_Cap_MWh,Max_Charge_Cap_MW,Min_Cap_MW,Min_Cap_MWh,Min_Charge_Cap_MW,Inv_Cost_per_MWyr,Inv_Cost_per_MWhyr,Inv_Cost_Charge_per_MWyr,Fixed_OM_Cost_per_MWyr,Fixed_OM_Cost_per_MWhyr,Fixed_OM_Cost_Charge_per_MWyr,Var_OM_Cost_per_MWh,Var_OM_Cost_per_MWh_In,Heat_Rate_MMBTU_per_MWh,Fuel,Cap_Size,Start_Cost_per_MW,Start_Fuel_MMBTU_per_MW,Up_Time,Down_Time,Ramp_Up_Percentage,Ramp_Dn_Percentage,Hydro_Energy_to_Power_Ratio,Min_Power,Self_Disch,Eff_Up,Eff_Down,Min_Duration,Max_Duration,Max_Flexible_Demand_Advance,Max_Flexible_Demand_Delay,Flexible_Demand_Energy_Eff,Reg_Max,Rsv_Max,Reg_Cost,Rsv_Cost,MinCapTag_1,MinCapTag_2,MinCapTag_3,MGA,Resource_Type,CapRes_1,ESR_1,ESR_2,region,cluster 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/dev/null +++ b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Generators_variability.csv @@ -0,0 +1,8761 @@ +Time_Index,natural_gas_combined_cycle,solar_pv,onshore_wind,battery +1,1,0,0.889717042,1 +2,1,0,0.877715468,1 +3,1,0,0.903424203,1 +4,1,0,0.895153165,1 +5,1,0,0.757258117,1 +6,1,0,0.630928695,1 +7,1,0,0.557177782,1 +8,1,0,0.6072492,1 +9,1,0.1779,0.423417866,1 +10,1,0.429,0.007470775,1 +11,1,0.5748,0.002535942,1 +12,1,0.6484,0.002153709,1 +13,1,0.6208,0.00445132,1 +14,1,0.596,0.007711587,1 +15,1,0.5013,0.100848213,1 +16,1,0.3311,0.201802149,1 +17,1,0.0642,0.141933054,1 +18,1,0,0.567022562,1 +19,1,0,0.946024895,1 +20,1,0,0.923394203,1 +21,1,0,0.953386247,1 +22,1,0,0.929205418,1 +23,1,0,0.849528909,1 +24,1,0,0.665570974,1 +25,1,0,0.527450681,1 +26,1,0,0.64820224,1 +27,1,0,0.636301041,1 +28,1,0,0.914841771,1 +29,1,0,0.857677341,1 +30,1,0,0.824768543,1 +31,1,0,0.586626828,1 +32,1,0,0.485967815,1 +33,1,0.16,0.565889001,1 +34,1,0.3418,0.741932392,1 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b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Method_of_morris_range.csv @@ -0,0 +1,9 @@ +Resource,Zone,ID,Lower_bound,Upper_bound,Parameter,Group,p_steps,total_num_trajectory,num_trajectory,len_design_mat,policy +natural_gas_combined_cycle,1,1,-5,5,Inv_Cost_per_MWyr,ng,10,2,2,15,reference +solar_pv,1,2,-10,10,Inv_Cost_per_MWyr,solar,20,,,, +onshore_wind,1,3,-7,7,Inv_Cost_per_MWyr,wind,14,,,, +battery,1,4,-20,20,Inv_Cost_per_MWyr,battery,40,,,, +natural_gas_combined_cycle,1,1,-5,5,Fixed_OM_Cost_per_MWyr,ng,10,,,, +solar_pv,1,2,-10,10,Fixed_OM_Cost_per_MWyr,solar,20,,,, +onshore_wind,1,3,-7,7,Fixed_OM_Cost_per_MWyr,wind,14,,,, +battery,1,4,-20,20,Fixed_OM_Cost_per_MWyr,battery,40,,,, diff --git a/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..8593a5abcc --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,ConstraintDescription,Min_MW +1,PV,5000 +2,Wind,10000 +3,Batteries,6000 diff --git a/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Network.csv b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Network.csv new file mode 100644 index 0000000000..51ec108565 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Network.csv @@ -0,0 +1,2 @@ +,Network_zones,Network_Lines,z1,Line_Max_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1 +NE,z1,1,1,2950,MA_to_CT,123.0584,0.012305837,2950,12060,0.95,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/README.md b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/README.md new file mode 100644 index 0000000000..7990f97771 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/README.md @@ -0,0 +1,15 @@ +# Small New England: One Zone + +**SmallNewEngland** is set of a simplified versions of the more detailed example system RealSystemExample. It is condensed for easy comprehension and quick testing of different components of the GenX. **SmallNewEngland/OneZone** is our most basic model, a one-year example with hourly resolution containing only one zone representing New England. The model includes only natural gas, solar PV, wind, and lithium-ion battery storage with no initial capacity. + +To run the model, first navigate to the example directory at `GenX/Example_Systems/SmallNewEngland/OneZone`: + +`cd("Example_Systems/SmallNewEngland/OneZone")` + +Next, ensure that your settings in `GenX_settings.yml` are correct. The default settings use the solver Gurobi (`Solver: Gurobi`), time domain reduced input data (`TimeDomainReduction: 1`). Other optional policies include minimum capacity requirements, a capacity reserve margin, and more. A rate-based carbon cap of 50 gCO2 per kWh is specified in the `CO2_cap.csv` input file. + +Once the settings are confirmed, run the model with the `Run.jl` script in the example directory: + +`include("Run.jl")` + +Once the model has completed, results will write to the `Results` directory. \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Run.jl b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/clp_settings.yml new file mode 100644 index 0000000000..491a9bb27e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/genx_settings.yml new file mode 100644 index 0000000000..aff369aa2f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/genx_settings.yml @@ -0,0 +1,21 @@ +MacOrWindows: Mac # Set to either "Mac" (also works for Linux) or "Windows" to ensure use of proper file directory separator "\" or "/ +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 0 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 0 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 0 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 0 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 0 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: HiGHS # Available solvers: Gurobi, CPLEX, Clp, Cbc +ParameterScale: 1 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 0 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 1 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 #Number of MGA Iterations +MethodofMorris: 1 #Flag for turning on the Method of Morris analysis diff --git a/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..e0f2ec151b --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/gurobi_settings.yml @@ -0,0 +1,15 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 4 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: -1 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. diff --git a/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/highs_settings.yml b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/highs_settings.yml new file mode 100644 index 0000000000..f447d088e3 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/highs_settings.yml @@ -0,0 +1,11 @@ +# HiGHS Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Primal feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +Optimal_Tol: 1.0e-05 # Dual feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +TimeLimit: 1.0e23 # Time limit # [type: double, advanced: false, range: [0, inf], default: inf] +Pre_Solve: choose # Presolve option: "off", "choose" or "on" # [type: string, advanced: false, default: "choose"] +Method: ipm #HiGHS-specific solver settings # Solver option: "simplex", "choose" or "ipm" # [type: string, advanced: false, default: "choose"] + +# run the crossover routine for ipx +# [type: string, advanced: "on", range: {"off", "on"}, default: "off"] +run_crossover: "off" diff --git a/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/time_domain_reduction_settings.yml b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/time_domain_reduction_settings.yml new file mode 100644 index 0000000000..02ce3d964d --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/Settings/time_domain_reduction_settings.yml @@ -0,0 +1,151 @@ +##### +# +# TIME DOMAIN REDUCTION SETTINGS +# +# Set parameters here that organize how your full timeseries +# data will be divided into representative period clusters. +# Ensure that time_domain_reduction is set to 1 in GenX_settings.yml +# before running. Run within GenX or use PreCluster.jl to test and +# examine representative period output before proceeding. +# Specify your data input directory as inpath within Run_test.jl +# or PreCluster.jl. +# +##### + + # - TimestepsPerRepPeriod + # Typically 168 timesteps (e.g., hours) per period, this designates + # the length of each representative period. +TimestepsPerRepPeriod: 168 + + # - ClusterMethod + # Either 'kmeans' or 'kmedoids', this designates the method used to cluster + # periods and determine each point's representative period. +ClusterMethod: 'kmeans' + + # - ScalingMethod + # Either 'N' or 'S', this designates directs the module to normalize ([0,1]) + # or standardize (mean 0, variance 1) the input data. +ScalingMethod: "S" + + # - MaxPeriods + # The maximum number of periods - both clustered periods and extreme periods - + # that may be used to represent the input data. If IterativelyAddPeriods is on and the + # error threshold is never met, this will be the total number of periods. +MaxPeriods: 11 + + # - MinPeriods + # The minimum number of periods used to represent the input data. If using + # UseExtremePeriods, this must be at least the number of extreme periods requests. If + # IterativelyAddPeriods if off, this will be the total number of periods. +MinPeriods: 6 + + # - IterativelyAddPeriods + # Either 'yes' or 'no', this designates whether or not to add periods + # until the error threshold between input data and represented data is met or the maximum + # number of periods is reached. +IterativelyAddPeriods: 1 + + # - IterateMethod + # Either 'cluster' or 'extreme', this designates whether to add clusters to + # the kmeans/kmedoids method or to set aside the worst-fitting periods as a new extreme periods. + # The default option is 'cluster'. +IterateMethod: "cluster" + + # - Threshold + # Iterative period addition will end if the period farthest (Euclidean Distance) + # from its representative period is within this percentage of the total possible error (for normalization) + # or ~95% of the total possible error (for standardization). E.g., for a threshold of 0.01, + # every period must be within 1% of the spread of possible error before the clustering + # iterations will terminate (or until the max number of periods is reached). +Threshold: 0.05 + + # - nReps + # The number of times to repeat each kmeans/kmedoids clustering at the same setting. +nReps: 100 + + # - LoadWeight + # Default 1, this is an optional multiplier on load columns in order to prioritize + # better fits for load profiles over resource capacity factor profiles. +LoadWeight: 1 + + # - WeightTotal + # Default 8760, the sum to which the relative weights of representative periods will be scaled. +WeightTotal: 8760 + + # - ClusterFuelPrices + # Either 'yes' ro 'no', this indicates whether or not to use the fuel price + # time series in Fuels_data.csv in the clustering process. If 'no', this function will still write + # Fuels_data_clustered.csv with reshaped fuel prices based on the number and size of the + # representative weeks, assuming a constant time series of fuel prices with length equal to the + # number of timesteps in the raw input data. +ClusterFuelPrices: 1 + + # - MultiStageConcatenate + # (Only considered if MultiStage = 1 in genx_settings.yml) + # If 1, this designates that the model should time domain reduce the input data + # of all model stages together. Else if 0, the model will time domain reduce each + # stage separately +MultiStageConcatenate: 0 + + # - UseExtremePeriods + # Either 'yes' or 'no', this designates whether or not to include + # outliers (by performance or load/resource extreme) as their own representative periods. + # This setting automatically includes the periods with maximum load, minimum solar cf and + # minimum wind cf as extreme periods. +UseExtremePeriods: 1 + +# STILL IN DEVELOPMENT - Currently just uses integral max load, integral min PV and wind. +# - ExtremePeriods +# Use this to define which periods to be included among the final representative periods +# as "Extreme Periods". +# Select by profile type: load ("Load"), solar PV capacity factors ("PV"), and wind capacity factors ("Wind"). +# Select whether to examine these profiles by zone ("Zone") or across the whole system ("System"). +# Select whether to look for absolute max/min at the timestep level ("Absolute") +# or max/min sum across the period ("Integral"). +# Select whether you want the maximum ("Max") or minimum ("Min") (of the prior type) for each profile type. +ExtremePeriods: + Load: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 1 + Min: 0 + Integral: + Max: 0 + Min: 0 + PV: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + Wind: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 diff --git a/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/testing_Method_of_Morris.xlsx b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/testing_Method_of_Morris.xlsx new file mode 100644 index 0000000000..a450e26b5d Binary files /dev/null and b/Tutorials/Example_Systems_Tutorials/MethodofMorrisExample/OneZone/testing_Method_of_Morris.xlsx differ diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/CO2_cap.csv new file mode 100644 index 0000000000..6ee92395f5 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/CO2_cap.csv @@ -0,0 +1,2 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_Mtons_1 +NENG,z1,1,12.19 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..3fd78a581b --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Capacity_reserve_margin.csv @@ -0,0 +1,2 @@ +,Network_zones,CapRes_1 +NENG,z1,0.193 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Energy_share_requirement.csv new file mode 100644 index 0000000000..cf06f3ca29 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Energy_share_requirement.csv @@ -0,0 +1,2 @@ +,Network_zones,ESR_1,ESR_2 +NENG,z1,0.37,0.44 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Fuels_data.csv new file mode 100644 index 0000000000..5448cd0e9f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Fuels_data.csv @@ -0,0 +1,8762 @@ 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b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Generators_data.csv new file mode 100644 index 0000000000..37ceee0dde --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Generators_data.csv @@ -0,0 +1,59 @@ 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+,,,,,,,,8731,16745 +,,,,,,,,8732,16333 +,,,,,,,,8733,15872 +,,,,,,,,8734,15076 +,,,,,,,,8735,14028 +,,,,,,,,8736,12925 +,,,,,,,,8737,12109 +,,,,,,,,8738,11651 +,,,,,,,,8739,11416 +,,,,,,,,8740,11377 +,,,,,,,,8741,11585 +,,,,,,,,8742,12213 +,,,,,,,,8743,13260 +,,,,,,,,8744,14083 +,,,,,,,,8745,14788 +,,,,,,,,8746,15316 +,,,,,,,,8747,15553 +,,,,,,,,8748,15509 +,,,,,,,,8749,15257 +,,,,,,,,8750,15002 +,,,,,,,,8751,14842 +,,,,,,,,8752,14886 +,,,,,,,,8753,15965 +,,,,,,,,8754,16933 +,,,,,,,,8755,16440 +,,,,,,,,8756,15059 +,,,,,,,,8757,14359 +,,,,,,,,8758,13687 +,,,,,,,,8759,12986 +,,,,,,,,8760,12287 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..7c781f09f5 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,Constraint_Description,Min_MW +1,ME_Wind,8000 +2,ME_OSW,5000 +3,MA_Solar,3200 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/README.md b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/README.md new file mode 100644 index 0000000000..9c7a9d18e7 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/README.md @@ -0,0 +1,19 @@ +# Real System Example: Single Zones + +**RealSystemExample/ISONE_Singlezone** is detailed example system approximating the ISO New England region with one zone that represents the whole ISO-NE. +They are total of 58 different resources modeled in the system. + +To run the model, first navigate to the example directory at `GenX/Example_Systems/RealSystemExample/ISONE_Singlezone`: + +`cd("Example_Systems/RealSystemExample/ISONE_Singlezone")` + +Next, ensure that your settings in `GenX_settings.yml` are correct. +The default settings use the solver Gurobi (`Solver: Gurobi`). +`TimeDomainReduction` is turned on (`1`), which reduces the full-year input data to 11 one-weeks periods of 168 hours. +Other optional policies include minimum capacity requirements, a capacity reserve margin, and more. + +Once the settings are confirmed, run the model with the `Run.jl` script in the example directory: + +`julia --project=/path/to/GenX Run.jl` + +Once the model has completed, results will write to the `Results` directory. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Reserves.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Reserves.csv new file mode 100644 index 0000000000..a29823e971 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Run.jl b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/genx_settings.yml new file mode 100644 index 0000000000..fb282a9546 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/genx_settings.yml @@ -0,0 +1,21 @@ +OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 0 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 1 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 1 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 1 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 1 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: HiGHS #Windows users, please make sure to install SCIP solver if using SCIP here; # Available solvers: Gurobi, CPLEX, CLP, SCIP +ParameterScale: 1 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 2 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 1 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 # Number of MGA iterations with maximization and minimization objective +MethodofMorris: 0 #Flag for turning on the Method of Morris analysis diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..29adc6ec34 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/gurobi_settings.yml @@ -0,0 +1,15 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: 1 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/highs_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/highs_settings.yml new file mode 100644 index 0000000000..e086d75a31 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/highs_settings.yml @@ -0,0 +1,12 @@ +# HiGHS Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Primal feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +Optimal_Tol: 1.0e-05 # Dual feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +TimeLimit: 1.0e23 # Time limit # [type: double, advanced: false, range: [0, inf], default: inf] +Pre_Solve: choose # Presolve option: "off", "choose" or "on" # [type: string, advanced: false, default: "choose"] +Method: ipm #HiGHS-specific solver settings # Solver option: "simplex", "choose" or "ipm" # [type: string, advanced: false, default: "choose"] + +#highs-specific solver settings +# run the crossover routine for ipx +# [type: string, advanced: "on", range: {"off", "on"}, default: "off"] +run_crossover: "off" diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/scip_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/scip_settings.yml new file mode 100644 index 0000000000..2779d54826 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/scip_settings.yml @@ -0,0 +1,5 @@ +# SCIP Solver Parameters + +#SCIP-specific solver settings +Dispverblevel: 0 +limitsgap: 0.05 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/time_domain_reduction_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/time_domain_reduction_settings.yml new file mode 100644 index 0000000000..f7768df774 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Singlezone/Settings/time_domain_reduction_settings.yml @@ -0,0 +1,144 @@ +##### +# +# TIME DOMAIN REDUCTION SETTINGS +# +# Set parameters here that organize how your full timeseries +# data will be divided into representative period clusters. +# Ensure that time_domain_reduction is set to 1 in GenX_settings.yml +# before running. Run within GenX or use PreCluster.jl to test and +# examine representative period output before proceeding. +# Specify your data input directory as inpath within Run_test.jl +# or PreCluster.jl. +# +##### + + # - TimestepsPerRepPeriod + # Typically 168 timesteps (e.g., hours) per period, this designates + # the length of each representative period. +TimestepsPerRepPeriod: 168 + + # - ClusterMethod + # Either 'kmeans' or 'kmedoids', this designates the method used to cluster + # periods and determine each point's representative period. +ClusterMethod: 'kmeans' + + # - ScalingMethod + # Either 'N' or 'S', this designates directs the module to normalize ([0,1]) + # or standardize (mean 0, variance 1) the input data. +ScalingMethod: "S" + + # - MaxPeriods + # The maximum number of periods - both clustered periods and extreme periods - + # that may be used to represent the input data. If IterativelyAddPeriods is on and the + # error threshold is never met, this will be the total number of periods. +MaxPeriods: 11 + + # - MinPeriods + # The minimum number of periods used to represent the input data. If using + # UseExtremePeriods, this must be at least the number of extreme periods requests. If + # IterativelyAddPeriods if off, this will be the total number of periods. +MinPeriods: 8 + + # - IterativelyAddPeriods + # Either 'yes' or 'no', this designates whether or not to add periods + # until the error threshold between input data and represented data is met or the maximum + # number of periods is reached. +IterativelyAddPeriods: 1 + + # - IterateMethod + # Either 'cluster' or 'extreme', this designates whether to add clusters to + # the kmeans/kmedoids method or to set aside the worst-fitting periods as a new extreme periods. + # The default option is 'cluster'. +IterateMethod: "cluster" + + # - Threshold + # Iterative period addition will end if the period farthest (Euclidean Distance) + # from its representative period is within this percentage of the total possible error (for normalization) + # or ~95% of the total possible error (for standardization). E.g., for a threshold of 0.01, + # every period must be within 1% of the spread of possible error before the clustering + # iterations will terminate (or until the max number of periods is reached). +Threshold: 0.05 + + # - nReps + # The number of times to repeat each kmeans/kmedoids clustering at the same setting. +nReps: 100 + + # - LoadWeight + # Default 1, this is an optional multiplier on load columns in order to prioritize + # better fits for load profiles over resource capacity factor profiles. +LoadWeight: 1 + + # - WeightTotal + # Default 8760, the sum to which the relative weights of representative periods will be scaled. +WeightTotal: 8760 + + # - ClusterFuelPrices + # Either 'yes' ro 'no', this indicates whether or not to use the fuel price + # time series in Fuels_data.csv in the clustering process. If 'no', this function will still write + # Fuels_data_clustered.csv with reshaped fuel prices based on the number and size of the + # representative weeks, assuming a constant time series of fuel prices with length equal to the + # number of timesteps in the raw input data. +ClusterFuelPrices: 1 + + # - UseExtremePeriods + # Either 'yes' or 'no', this designates whether or not to include + # outliers (by performance or load/resource extreme) as their own representative periods. + # This setting automatically includes the periods with maximum load, minimum solar cf and + # minimum wind cf as extreme periods. +UseExtremePeriods: 1 + +# STILL IN DEVELOPMENT - Currently just uses integral max load, integral min PV and wind. +# - ExtremePeriods +# Use this to define which periods to be included among the final representative periods +# as "Extreme Periods". +# Select by profile type: load ("Load"), solar PV capacity factors ("PV"), and wind capacity factors ("Wind"). +# Select whether to examine these profiles by zone ("Zone") or across the whole system ("System"). +# Select whether to look for absolute max/min at the timestep level ("Absolute") +# or max/min sum across the period ("Integral"). +# Select whether you want the maximum ("Max") or minimum ("Min") (of the prior type) for each profile type. +ExtremePeriods: + Load: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 1 + Min: 0 + Integral: + Max: 0 + Min: 0 + PV: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + Wind: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/CO2_cap.csv new file mode 100644 index 0000000000..97ba02e752 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/CO2_cap.csv @@ -0,0 +1,4 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_Mtons_1 +NENGREST,z1,1,8.59 +NENG_CT,z2,1,2.31 +NENG_ME,z3,1,1.29 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..c4ce6c337d --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Capacity_reserve_margin.csv @@ -0,0 +1,4 @@ +,Network_zones,CapRes_1 +NENGREST,z1,0.156 +NENG_CT,z2,0.156 +NENG_ME,z3,0.156 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Energy_share_requirement.csv new file mode 100644 index 0000000000..749ddfeaf1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Energy_share_requirement.csv @@ -0,0 +1,4 @@ +,Network_zones,ESR_1,ESR_2 +NENGREST,z1,0.259,0.348 +NENG_CT,z2,0.44,0.44 +NENG_ME,z3,0.776,0.776 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Fuels_data.csv new file mode 100644 index 0000000000..c2f3d9f85f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Fuels_data.csv @@ -0,0 +1,482 @@ +Time_Index,NENG_CT_NG,NENG_ME_NG,NENGREST_NG,NENGREST_NG_ccs90,west_north_central_coal,new_england_uranium,NENG_CT_NG_ccs90,NENG_ME_ccs90,NENGREST_ccs90,NENG_CT_NG_ccs100,NENGREST_NG_ccs100,NENG_ME_NG_ccs90,NENG_ME_NG_ccs100,NENG_ME_ccs100,NENGREST_ccs100,None +0,0.05306,0.05306,0.05306,0.05306,0.09552,0,0.00531,0.00531,0.00531,0,0,0.00531,0,0,0,0 +1,3.14,3.14,3.69,3.69,1.67,0.69,3.14,3.14,3.69,1.71,1.71,3.69,1.71,1.71,2.26,0 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a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Generators_data.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Generators_data.csv new file mode 100644 index 0000000000..a2c2ee4f93 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Generators_data.csv @@ -0,0 +1,62 @@ 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diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Generators_variability.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Generators_variability.csv new file mode 100644 index 0000000000..f9a8b5e4a3 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Generators_variability.csv @@ -0,0 +1,481 @@ 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end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Load_data.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Load_data.csv new file mode 100644 index 0000000000..aaf1897ccb --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Load_data.csv @@ -0,0 +1,481 @@ +Voll,Demand_Segment,Cost_of_Demand_Curtailment_per_MW,Max_Demand_Curtailment,$/MWh,Rep_Periods,Timesteps_per_Rep_Period,Sub_Weights,Time_Index,Load_MW_z1,Load_MW_z2,Load_MW_z3 +2000,1,1,1,2000,5,96,2112,1,8953,2557,1220 +,2,0.9,0.04,1800,,,2880,2,8388,2395,1144 +,3,0.55,0.024,1100,,,1632,3,8016,2290,1093 +,4,0.2,0.003,400,,,2016,4,7790,2225,1062 +,,,,,,,96,5,7739,2210,1055 +,,,,,,,,6,7807,2230,1065 +,,,,,,,,7,8107,2316,1105 +,,,,,,,,8,8824,2520,1203 +,,,,,,,,9,9731,2779,1327 +,,,,,,,,10,10541,3011,1437 +,,,,,,,,11,11119,3175,1515 +,,,,,,,,12,11425,3262,1557 +,,,,,,,,13,11542,3296,1574 +,,,,,,,,14,11550,3299,1575 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+,,,,,,,,470,12644,3611,1724 +,,,,,,,,471,12722,3633,1735 +,,,,,,,,472,12820,3661,1748 +,,,,,,,,473,12921,3690,1762 +,,,,,,,,474,12885,3680,1757 +,,,,,,,,475,12514,3574,1707 +,,,,,,,,476,12068,3446,1645 +,,,,,,,,477,11994,3426,1635 +,,,,,,,,478,11578,3306,1579 +,,,,,,,,479,10569,3018,1441 +,,,,,,,,480,9506,2715,1296 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..858294b6fc --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,Constraint_Description,Min_MW +1,ME_Wind,8000 +2,ME_OSW,5000 +3,MA_Solar,3200 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Network.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Network.csv new file mode 100644 index 0000000000..a26c95a6be --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Network.csv @@ -0,0 +1,4 @@ +,Network_zones,Network_Lines,z1,z2,z3,Line_Max_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1 +NENGREST,z1,1,1,-1,0,2950,NENGREST_to_NENG_CT,123.0584,0.012305837,2950,12060,0.95,0,0 +NENG_CT,z2,2,1,0,-1,2000,NENGREST_to_NENG_ME,196.5385,0.019653847,2000,19261,0.95,0,0 +NENG_ME,z3,,,,,,,,,,,,, \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Period_map.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Period_map.csv new file mode 100644 index 0000000000..c11379f0a7 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Period_map.csv @@ -0,0 +1,92 @@ +Period_Index,Rep_Period,Rep_Period_Index +1,4,1 +2,4,1 +3,4,1 +4,4,1 +5,4,1 +6,4,1 +7,4,1 +8,4,1 +9,4,1 +10,4,1 +11,4,1 +12,4,1 +13,4,1 +14,4,1 +15,4,1 +16,4,1 +17,4,1 +18,4,1 +19,4,1 +20,4,1 +21,4,1 +22,4,1 +23,24,2 +24,24,2 +25,24,2 +26,24,2 +27,24,2 +28,24,2 +29,24,2 +30,24,2 +31,24,2 +32,24,2 +33,24,2 +34,24,2 +35,24,2 +36,24,2 +37,24,2 +38,24,2 +39,24,2 +40,24,2 +41,24,2 +42,24,2 +43,24,2 +44,24,2 +45,24,2 +46,24,2 +47,24,2 +48,24,2 +49,24,2 +50,24,2 +51,24,2 +52,24,2 +53,51,3 +54,51,3 +55,51,3 +56,51,3 +57,51,3 +58,51,3 +59,51,3 +60,51,3 +61,51,3 +62,51,3 +63,51,3 +64,51,3 +65,51,3 +66,51,3 +67,51,3 +68,51,3 +69,51,3 +70,77,4 +71,77,4 +72,77,4 +73,77,4 +74,77,4 +75,77,4 +76,77,4 +77,77,4 +78,77,4 +79,77,4 +80,77,4 +81,77,4 +82,77,4 +83,77,4 +84,77,4 +85,77,4 +86,77,4 +87,77,4 +88,77,4 +89,77,4 +90,77,4 +91,69,5 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/README.md b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/README.md new file mode 100644 index 0000000000..2b174cb111 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/README.md @@ -0,0 +1,21 @@ +# Real System Example: Three Zones + +**RealSystemExample/ISONE_Trizone** is more detailed example system approximating the ISO New England region with three zones representing Connecticut, and Maine and rest of ISO New England (MA, VT, NH, RI). +There are a total of 58 different resources modeled in the system. +The temporal resolution of the model inputs is specified to be 480 hours or 20 days. + +To run the model, first navigate to the example directory at `GenX/Example_Systems/RealSystemExample/ISONE_Trizone`: + +`cd("Example_Systems/RealSystemExample/ISONE_Trizone")` + +Next, ensure that your settings in `GenX_settings.yml` are correct. +The default settings use the solver HiGHS (`Solver: highs`). +Other optional policies include minimum capacity requirements, a capacity reserve margin, and more. + +Since these input data are already clustered, time domain reduction must be turned off (`TimeDomainReduction: 0`). + +Once the settings are confirmed, run the model with the `Run.jl` script in the example directory: + +`include("Run.jl")` + +Once the model has completed, results will write to the `Results` directory. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Reserves.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Reserves.csv new file mode 100644 index 0000000000..a29823e971 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Run.jl b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/genx_settings.yml new file mode 100644 index 0000000000..49ecaed411 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/genx_settings.yml @@ -0,0 +1,21 @@ +OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 1 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 1 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 1 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 1 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 1 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 1 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: HiGHS #Windows users, please make sure to install SCIP solver if using SCIP here; # Available solvers: Gurobi, CPLEX, CLP, SCIP +ParameterScale: 1 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 2 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 0 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 # Number of MGA iterations with maximization and minimization objective +MethodofMorris: 0 #Flag for turning on the Method of Morris analysis diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..2076ca9f54 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/gurobi_settings.yml @@ -0,0 +1,15 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-06 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: 0 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/highs_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/highs_settings.yml new file mode 100644 index 0000000000..acb78928d4 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/highs_settings.yml @@ -0,0 +1,13 @@ +# HiGHS Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Primal feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +Optimal_Tol: 1.0e-05 # Dual feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +TimeLimit: 1.0e23 # Time limit # [type: double, advanced: false, range: [0, inf], default: inf] +Pre_Solve: choose # Presolve option: "off", "choose" or "on" # [type: string, advanced: false, default: "choose"] +Method: ipm #HiGHS-specific solver settings # Solver option: "simplex", "choose" or "ipm" # [type: string, advanced: false, default: "choose"] + +#HiGHS-specific solver settings + +# Run the crossover routine for IPX +# [type: string, advanced: "on", range: {"off", "on"}, default: "off"] +run_crossover: "off" diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/scip_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/scip_settings.yml new file mode 100644 index 0000000000..2779d54826 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone/Settings/scip_settings.yml @@ -0,0 +1,5 @@ +# SCIP Solver Parameters + +#SCIP-specific solver settings +Dispverblevel: 0 +limitsgap: 0.05 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/CO2_cap.csv new file mode 100644 index 0000000000..97ba02e752 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/CO2_cap.csv @@ -0,0 +1,4 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_Mtons_1 +NENGREST,z1,1,8.59 +NENG_CT,z2,1,2.31 +NENG_ME,z3,1,1.29 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..766b9ffed0 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Capacity_reserve_margin.csv @@ -0,0 +1,4 @@ +,Network_zones,CapRes_1 +NENGREST,z1,0.193 +NENG_CT,z2,0.193 +NENG_ME,z3,0.193 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Energy_share_requirement.csv new file mode 100644 index 0000000000..749ddfeaf1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Energy_share_requirement.csv @@ -0,0 +1,4 @@ +,Network_zones,ESR_1,ESR_2 +NENGREST,z1,0.259,0.348 +NENG_CT,z2,0.44,0.44 +NENG_ME,z3,0.776,0.776 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Fuels_data.csv new file mode 100644 index 0000000000..5448cd0e9f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Fuels_data.csv @@ -0,0 +1,8762 @@ +Time_Index,NENG_CT_NG,NENG_ME_NG,NENGREST_NG,west_north_central_coal,new_england_uranium,NENG_CT_NG_ccs90,NENG_ME_NG_ccs90,NENGREST_NG_ccs90,NENG_CT_NG_ccs100,NENG_ME_NG_ccs100,NENGREST_NG_ccs100,None +0,0.05306,0.05306,0.05306,0.09552,0,0.00531,0.00531,0.00531,0,0,0,0 +1,5.45,5.45,5.28,1.67,0.69,5.45,5.45,5.28,4.02,4.02,3.85,0 +2,5.45,5.45,5.28,1.67,0.69,5.45,5.45,5.28,4.02,4.02,3.85,0 +3,5.45,5.45,5.28,1.67,0.69,5.45,5.45,5.28,4.02,4.02,3.85,0 +4,5.45,5.45,5.28,1.67,0.69,5.45,5.45,5.28,4.02,4.02,3.85,0 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+,,,,,,,,8758,9018,3452,1217 +,,,,,,,,8759,8551,3281,1154 +,,,,,,,,8760,8089,3106,1092 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..858294b6fc --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,Constraint_Description,Min_MW +1,ME_Wind,8000 +2,ME_OSW,5000 +3,MA_Solar,3200 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Network.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Network.csv new file mode 100644 index 0000000000..a26c95a6be --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Network.csv @@ -0,0 +1,4 @@ +,Network_zones,Network_Lines,z1,z2,z3,Line_Max_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1 +NENGREST,z1,1,1,-1,0,2950,NENGREST_to_NENG_CT,123.0584,0.012305837,2950,12060,0.95,0,0 +NENG_CT,z2,2,1,0,-1,2000,NENGREST_to_NENG_ME,196.5385,0.019653847,2000,19261,0.95,0,0 +NENG_ME,z3,,,,,,,,,,,,, \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/README.md b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/README.md new file mode 100644 index 0000000000..10fe014add --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/README.md @@ -0,0 +1,19 @@ +# Real System Example: Three Zones + +**RealSystemExample/ISONE_Trizone_FullTimeseries** is more detailed example system approximating the ISO New England region with three zones representing Connecticut, and Maine and rest of ISO New England (MA, VT, NH, RI). +They are total of 58 different resources modeled in the system. +This is full-year (8760 hour) case. + +To run the model, first navigate to the example directory at `GenX/Example_Systems/RealSystemExample/ISONE_Trizone_FullTimeseries`: + +`cd("Example_Systems/RealSystemExample/ISONE_Trizone_FullTimeseries")` + +Next, ensure that your settings in `GenX_settings.yml` are correct. +The default settings use the solver HiGHS (`Solver: highs`). +Other optional policies include minimum capacity requirements, a capacity reserve margin, and more. + +Once the settings are confirmed, run the model with the `Run.jl` script in the example directory: + +`include("Run.jl")` + +Once the model has completed, results will write to the `Results` directory. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Reserves.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Reserves.csv new file mode 100644 index 0000000000..a29823e971 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Run.jl b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/genx_settings.yml new file mode 100644 index 0000000000..fdd2db0d31 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/genx_settings.yml @@ -0,0 +1,21 @@ +OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 1 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 1 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 1 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 1 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 1 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: HiGHS # Available solvers: Gurobi, CPLEX, CLP, SCIP +ParameterScale: 1 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 0 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 0 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 # Number of MGA iterations with maximization and minimization objective +MethodofMorris: 0 #Flag for turning on the Method of Morris analysis diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..fd38fb298e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/gurobi_settings.yml @@ -0,0 +1,15 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-06 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: 0 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/highs_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/highs_settings.yml new file mode 100644 index 0000000000..2c9034ee1a --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/highs_settings.yml @@ -0,0 +1,11 @@ +# HiGHS Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Primal feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +Optimal_Tol: 1.0e-05 # Dual feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +TimeLimit: 1.0e23 # Time limit # [type: double, advanced: false, range: [0, inf], default: inf] +Pre_Solve: choose # Presolve option: "off", "choose" or "on" # [type: string, advanced: false, default: "choose"] +Method: ipm #HiGHS-specific solver settings # Solver option: "simplex", "choose" or "ipm" # [type: string, advanced: false, default: "choose"] + +# run the crossover routine for ipx +# [type: string, advanced: "on", range: {"off", "on"}, default: "off"] +run_crossover: "off" diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/scip_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/scip_settings.yml new file mode 100644 index 0000000000..2779d54826 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/scip_settings.yml @@ -0,0 +1,5 @@ +# SCIP Solver Parameters + +#SCIP-specific solver settings +Dispverblevel: 0 +limitsgap: 0.05 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/time_domain_reduction_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/time_domain_reduction_settings.yml new file mode 100644 index 0000000000..e7723f6a6e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_FullTimeseries/Settings/time_domain_reduction_settings.yml @@ -0,0 +1,151 @@ +##### +# +# TIME DOMAIN REDUCTION SETTINGS +# +# Set parameters here that organize how your full timeseries +# data will be divided into representative period clusters. +# Ensure that time_domain_reduction is set to 1 in GenX_settings.yml +# before running. Run within GenX or use PreCluster.jl to test and +# examine representative period output before proceeding. +# Specify your data input directory as inpath within Run_test.jl +# or PreCluster.jl. +# +##### + + # - TimestepsPerRepPeriod + # Typically 168 timesteps (e.g., hours) per period, this designates + # the length of each representative period. +TimestepsPerRepPeriod: 168 + + # - ClusterMethod + # Either 'kmeans' or 'kmedoids', this designates the method used to cluster + # periods and determine each point's representative period. +ClusterMethod: 'kmeans' + + # - ScalingMethod + # Either 'N' or 'S', this designates directs the module to normalize ([0,1]) + # or standardize (mean 0, variance 1) the input data. +ScalingMethod: "S" + + # - MaxPeriods + # The maximum number of periods - both clustered periods and extreme periods - + # that may be used to represent the input data. If IterativelyAddPeriods is on and the + # error threshold is never met, this will be the total number of periods. +MaxPeriods: 11 + + # - MinPeriods + # The minimum number of periods used to represent the input data. If using + # UseExtremePeriods, this must be at least the number of extreme periods requests. If + # IterativelyAddPeriods if off, this will be the total number of periods. +MinPeriods: 8 + + # - IterativelyAddPeriods + # Either 'yes' or 'no', this designates whether or not to add periods + # until the error threshold between input data and represented data is met or the maximum + # number of periods is reached. +IterativelyAddPeriods: 1 + + # - IterateMethod + # Either 'cluster' or 'extreme', this designates whether to add clusters to + # the kmeans/kmedoids method or to set aside the worst-fitting periods as a new extreme periods. + # The default option is 'cluster'. +IterateMethod: "cluster" + + # - Threshold + # Iterative period addition will end if the period farthest (Euclidean Distance) + # from its representative period is within this percentage of the total possible error (for normalization) + # or ~95% of the total possible error (for standardization). E.g., for a threshold of 0.01, + # every period must be within 1% of the spread of possible error before the clustering + # iterations will terminate (or until the max number of periods is reached). +Threshold: 0.05 + + # - nReps + # The number of times to repeat each kmeans/kmedoids clustering at the same setting. +nReps: 100 + + # - LoadWeight + # Default 1, this is an optional multiplier on load columns in order to prioritize + # better fits for load profiles over resource capacity factor profiles. +LoadWeight: 1 + + # - WeightTotal + # Default 8760, the sum to which the relative weights of representative periods will be scaled. +WeightTotal: 8760 + + # - ClusterFuelPrices + # Either 'yes' ro 'no', this indicates whether or not to use the fuel price + # time series in Fuels_data.csv in the clustering process. If 'no', this function will still write + # Fuels_data_clustered.csv with reshaped fuel prices based on the number and size of the + # representative weeks, assuming a constant time series of fuel prices with length equal to the + # number of timesteps in the raw input data. +ClusterFuelPrices: 1 + + # - MultiStageConcatenate + # (Only considered if MultiStage = 1 in genx_settings.yml) + # If 1, this designates that the model should time domain reduce the input data + # of all model stages together. Else if 0, the model will time domain reduce each + # stage separately +MultiStageConcatenate: 0 + + # - UseExtremePeriods + # Either 'yes' or 'no', this designates whether or not to include + # outliers (by performance or load/resource extreme) as their own representative periods. + # This setting automatically includes the periods with maximum load, minimum solar cf and + # minimum wind cf as extreme periods. +UseExtremePeriods: 1 + +# STILL IN DEVELOPMENT - Currently just uses integral max load, integral min PV and wind. +# - ExtremePeriods +# Use this to define which periods to be included among the final representative periods +# as "Extreme Periods". +# Select by profile type: load ("Load"), solar PV capacity factors ("PV"), and wind capacity factors ("Wind"). +# Select whether to examine these profiles by zone ("Zone") or across the whole system ("System"). +# Select whether to look for absolute max/min at the timestep level ("Absolute") +# or max/min sum across the period ("Integral"). +# Select whether you want the maximum ("Max") or minimum ("Min") (of the prior type) for each profile type. +ExtremePeriods: + Load: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 1 + Min: 0 + Integral: + Max: 0 + Min: 0 + PV: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + Wind: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/CO2_cap.csv new file mode 100644 index 0000000000..97ba02e752 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/CO2_cap.csv @@ -0,0 +1,4 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_Mtons_1 +NENGREST,z1,1,8.59 +NENG_CT,z2,1,2.31 +NENG_ME,z3,1,1.29 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..766b9ffed0 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Capacity_reserve_margin.csv @@ -0,0 +1,4 @@ +,Network_zones,CapRes_1 +NENGREST,z1,0.193 +NENG_CT,z2,0.193 +NENG_ME,z3,0.193 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Energy_share_requirement.csv new file mode 100644 index 0000000000..749ddfeaf1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Energy_share_requirement.csv @@ -0,0 +1,4 @@ +,Network_zones,ESR_1,ESR_2 +NENGREST,z1,0.259,0.348 +NENG_CT,z2,0.44,0.44 +NENG_ME,z3,0.776,0.776 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Fuels_data.csv new file mode 100644 index 0000000000..5448cd0e9f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Fuels_data.csv @@ -0,0 +1,8762 @@ +Time_Index,NENG_CT_NG,NENG_ME_NG,NENGREST_NG,west_north_central_coal,new_england_uranium,NENG_CT_NG_ccs90,NENG_ME_NG_ccs90,NENGREST_NG_ccs90,NENG_CT_NG_ccs100,NENG_ME_NG_ccs100,NENGREST_NG_ccs100,None +0,0.05306,0.05306,0.05306,0.09552,0,0.00531,0.00531,0.00531,0,0,0,0 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b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Generators_data.csv @@ -0,0 +1,59 @@ 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a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Generators_variability.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Generators_variability.csv new file mode 100644 index 0000000000..e167444dd7 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Generators_variability.csv @@ -0,0 +1,8761 @@ 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+,,,,,,,,8758,9018,3452,1217 +,,,,,,,,8759,8551,3281,1154 +,,,,,,,,8760,8089,3106,1092 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..858294b6fc --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,Constraint_Description,Min_MW +1,ME_Wind,8000 +2,ME_OSW,5000 +3,MA_Solar,3200 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Network.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Network.csv new file mode 100644 index 0000000000..13317c8d9f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Network.csv @@ -0,0 +1,4 @@ +,Network_zones,Network_Lines,z1,z2,z3,Line_Max_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1,Line_Max_Flow_Possible_MW,WACC,Capital_Recovery_Period +NENGREST,z1,1,1,-1,0,2950,NENGREST_to_NENG_CT,123.0584,0.012305837,2950,12060,0.95,0,0,5900,0.062,30 +NENG_CT,z2,2,1,0,-1,2000,NENGREST_to_NENG_ME,196.5385,0.019653847,2000,19261,0.95,0,0,4000,0.062,30 +NENG_ME,z3,,,,,,,,,,,,,,,, diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Reserves.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Reserves.csv new file mode 100644 index 0000000000..a29823e971 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p1/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/CO2_cap.csv new file mode 100644 index 0000000000..97ba02e752 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/CO2_cap.csv @@ -0,0 +1,4 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_Mtons_1 +NENGREST,z1,1,8.59 +NENG_CT,z2,1,2.31 +NENG_ME,z3,1,1.29 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..766b9ffed0 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Capacity_reserve_margin.csv @@ -0,0 +1,4 @@ +,Network_zones,CapRes_1 +NENGREST,z1,0.193 +NENG_CT,z2,0.193 +NENG_ME,z3,0.193 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Energy_share_requirement.csv new file mode 100644 index 0000000000..749ddfeaf1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Energy_share_requirement.csv @@ -0,0 +1,4 @@ +,Network_zones,ESR_1,ESR_2 +NENGREST,z1,0.259,0.348 +NENG_CT,z2,0.44,0.44 +NENG_ME,z3,0.776,0.776 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Fuels_data.csv new file mode 100644 index 0000000000..5448cd0e9f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Fuels_data.csv @@ -0,0 +1,8762 @@ +Time_Index,NENG_CT_NG,NENG_ME_NG,NENGREST_NG,west_north_central_coal,new_england_uranium,NENG_CT_NG_ccs90,NENG_ME_NG_ccs90,NENGREST_NG_ccs90,NENG_CT_NG_ccs100,NENG_ME_NG_ccs100,NENGREST_NG_ccs100,None +0,0.05306,0.05306,0.05306,0.09552,0,0.00531,0.00531,0.00531,0,0,0,0 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b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Generators_data.csv @@ -0,0 +1,59 @@ 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a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Generators_variability.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Generators_variability.csv new file mode 100644 index 0000000000..e167444dd7 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Generators_variability.csv @@ -0,0 +1,8761 @@ 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+,,,,,,,,8758,9018,3452,1217 +,,,,,,,,8759,8551,3281,1154 +,,,,,,,,8760,8089,3106,1092 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..858294b6fc --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,Constraint_Description,Min_MW +1,ME_Wind,8000 +2,ME_OSW,5000 +3,MA_Solar,3200 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Network.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Network.csv new file mode 100644 index 0000000000..13317c8d9f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Network.csv @@ -0,0 +1,4 @@ +,Network_zones,Network_Lines,z1,z2,z3,Line_Max_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1,Line_Max_Flow_Possible_MW,WACC,Capital_Recovery_Period +NENGREST,z1,1,1,-1,0,2950,NENGREST_to_NENG_CT,123.0584,0.012305837,2950,12060,0.95,0,0,5900,0.062,30 +NENG_CT,z2,2,1,0,-1,2000,NENGREST_to_NENG_ME,196.5385,0.019653847,2000,19261,0.95,0,0,4000,0.062,30 +NENG_ME,z3,,,,,,,,,,,,,,,, diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Reserves.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Reserves.csv new file mode 100644 index 0000000000..a29823e971 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p2/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/CO2_cap.csv new file mode 100644 index 0000000000..97ba02e752 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/CO2_cap.csv @@ -0,0 +1,4 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_Mtons_1 +NENGREST,z1,1,8.59 +NENG_CT,z2,1,2.31 +NENG_ME,z3,1,1.29 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..766b9ffed0 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Capacity_reserve_margin.csv @@ -0,0 +1,4 @@ +,Network_zones,CapRes_1 +NENGREST,z1,0.193 +NENG_CT,z2,0.193 +NENG_ME,z3,0.193 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Energy_share_requirement.csv new file mode 100644 index 0000000000..749ddfeaf1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Energy_share_requirement.csv @@ -0,0 +1,4 @@ +,Network_zones,ESR_1,ESR_2 +NENGREST,z1,0.259,0.348 +NENG_CT,z2,0.44,0.44 +NENG_ME,z3,0.776,0.776 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Fuels_data.csv new file mode 100644 index 0000000000..5448cd0e9f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Fuels_data.csv @@ -0,0 +1,8762 @@ +Time_Index,NENG_CT_NG,NENG_ME_NG,NENGREST_NG,west_north_central_coal,new_england_uranium,NENG_CT_NG_ccs90,NENG_ME_NG_ccs90,NENGREST_NG_ccs90,NENG_CT_NG_ccs100,NENG_ME_NG_ccs100,NENGREST_NG_ccs100,None +0,0.05306,0.05306,0.05306,0.09552,0,0.00531,0.00531,0.00531,0,0,0,0 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b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Generators_data.csv @@ -0,0 +1,59 @@ 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a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Generators_variability.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Generators_variability.csv new file mode 100644 index 0000000000..e167444dd7 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Generators_variability.csv @@ -0,0 +1,8761 @@ 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+,,,,,,,,8758,9018,3452,1217 +,,,,,,,,8759,8551,3281,1154 +,,,,,,,,8760,8089,3106,1092 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..858294b6fc --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,Constraint_Description,Min_MW +1,ME_Wind,8000 +2,ME_OSW,5000 +3,MA_Solar,3200 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Network.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Network.csv new file mode 100644 index 0000000000..13317c8d9f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Network.csv @@ -0,0 +1,4 @@ +,Network_zones,Network_Lines,z1,z2,z3,Line_Max_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1,Line_Max_Flow_Possible_MW,WACC,Capital_Recovery_Period +NENGREST,z1,1,1,-1,0,2950,NENGREST_to_NENG_CT,123.0584,0.012305837,2950,12060,0.95,0,0,5900,0.062,30 +NENG_CT,z2,2,1,0,-1,2000,NENGREST_to_NENG_ME,196.5385,0.019653847,2000,19261,0.95,0,0,4000,0.062,30 +NENG_ME,z3,,,,,,,,,,,,,,,, diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Reserves.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Reserves.csv new file mode 100644 index 0000000000..a29823e971 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Inputs/Inputs_p3/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/README.md b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/README.md new file mode 100644 index 0000000000..c17393ad55 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/README.md @@ -0,0 +1,19 @@ +# Multi-Stage Real System Example: Three Zones + +**RealSystemExample/ISONE_Trizone_MultiStage** is more detailed example system approximating the ISO New England region with three zones representing Connecticut, and Maine and rest of ISO New England (MA, VT, NH, RI). +They are total of 58 different resources modeled in the system. +Each stage models a year consisting of 8 to 11 representative weeks of 168 hours. +These are generated by the time domain reduction (clustering) procedure. +To run the model, first navigate to the example directory at `GenX/Example_Systems/RealSystemExample/ISONE_Trizone_MultiStage`: + +`cd("Example_Systems/RealSystemExample/ISONE_Trizone_MultiStage")` + +Next, ensure that your settings in `GenX_settings.yml` are correct. +The default settings use the solver HiGHS (`Solver: highs`), and time domain reduced input data (`TimeDomainReduction: 1`). +Other optional policies include minimum capacity requirements, a capacity reserve margin, and more. + +Once the settings are confirmed, run the model with the `Run_multi_stage.jl` script in the example directory: + +`include("Run_multi_stage.jl")` + +Once the model has completed, results will write to the `Results` directory. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Run.jl b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Run_multi_stage.jl b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Run_multi_stage.jl new file mode 100644 index 0000000000..5eb39e9d7f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Run_multi_stage.jl @@ -0,0 +1,97 @@ +cd(dirname(@__FILE__)) +settings_path = joinpath(pwd(), "Settings") + +environment_path = "../../../package_activate.jl" +#include(environment_path) #Run this line to activate the Julia virtual environment for GenX; skip it, if the appropriate package versions are installed + +### Set relevant directory paths +src_path = "../../../src/" + +inpath = pwd() + +### Load GenX +println("Loading packages") +push!(LOAD_PATH, src_path) + +#using Pkg +#Pkg.activate("GenXJulEnv") + +using GenX +using YAML + +genx_settings = joinpath(settings_path, "genx_settings.yml") #Settings YAML file path +mysetup = configure_settings(genx_settings) # mysetup dictionary stores settings and GenX-specific parameters + +multistage_settings = joinpath(settings_path, "multi_stage_settings.yml") # Multi stage settings YAML file path +mysetup["MultiStageSettingsDict"] = YAML.load(open(multistage_settings)) + +### Cluster time series inputs if necessary and if specified by the user +tdr_settings = joinpath(settings_path, "time_domain_reduction_settings.yml") # Multi stage settings YAML file path +TDRSettingsDict = YAML.load(open(tdr_settings)) +TDRpath = joinpath(inpath, "Inputs", "Inputs_p1", mysetup["TimeDomainReductionFolder"]) +if mysetup["TimeDomainReduction"] == 1 + if (!isfile(TDRpath*"/Load_data.csv")) || (!isfile(TDRpath*"/Generators_variability.csv")) || (!isfile(TDRpath*"/Fuels_data.csv")) + if (mysetup["MultiStage"] == 1) && (TDRSettingsDict["MultiStageConcatenate"] == 0) + println("Clustering Time Series Data (Individually)...") + for stage_id in 1:mysetup["MultiStageSettingsDict"]["NumStages"] + cluster_inputs(inpath, settings_path, mysetup, stage_id) + end + else + println("Clustering Time Series Data (Grouped)...") + cluster_inputs(inpath, settings_path, mysetup) + end + else + println("Time Series Data Already Clustered.") + end +end + +### Configure solver +println("Configuring Solver") +OPTIMIZER = configure_solver(mysetup["Solver"], settings_path) + +### Load Inputs +model_dict=Dict() +inputs_dict=Dict() + +for t in 1:mysetup["MultiStageSettingsDict"]["NumStages"] + + # Step 0) Set Model Year + mysetup["MultiStageSettingsDict"]["CurStage"] = t + + # Step 1) Load Inputs + global inpath_sub = string("$inpath/Inputs/Inputs_p",t) + + inputs_dict[t] = load_inputs(mysetup, inpath_sub) + inputs_dict[t] = configure_multi_stage_inputs(inputs_dict[t],mysetup["MultiStageSettingsDict"],mysetup["NetworkExpansion"]) + + # Step 2) Generate model + model_dict[t] = generate_model(mysetup, inputs_dict[t], OPTIMIZER) +end + +### Solve model +println("Solving Model") + +# Step 3) Run DDP Algorithm +model_dict, mystats_d, inputs_dict = run_ddp(model_dict, mysetup, inputs_dict) + +# Step 4) Write final outputs from each stage +outpath = string("$inpath/Results") +if !haskey(mysetup, "OverwriteResults") || mysetup["OverwriteResults"] == 1 + # Overwrite existing results if dir exists + # This is the default behaviour when there is no flag, to avoid breaking existing code + if !(isdir(outpath)) + mkdir(outpath) + end +else + # Find closest unused ouput directory name and create it + outpath = choose_output_dir(outpath) + mkdir(outpath) +end + +for p in 1:mysetup["MultiStageSettingsDict"]["NumStages"] + outpath_cur = string("$outpath/Results_p$p") + write_outputs(model_dict[p], outpath_cur, mysetup, inputs_dict[p]) +end + +# Step 5) Write DDP summary outputs +write_multi_stage_outputs(mystats_d, outpath, mysetup, inputs_dict) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/genx_settings.yml new file mode 100644 index 0000000000..4e9221eccd --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/genx_settings.yml @@ -0,0 +1,22 @@ +#MacOrWindows: Mac # Set to either "Mac" (also works for Linux) or "Windows" to ensure use of proper file directory separator "\" or "/ +OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 1 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 0 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 0 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 0 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 0 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: HiGHS # Available solvers: Gurobi, CPLEX, CLPs +ParameterScale: 1 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 2 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 1 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 +MultiStage: 1 # 0 = Single-stage GenX, 1 = Multi-stage GenX diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..88bdc9b9fd --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/gurobi_settings.yml @@ -0,0 +1,15 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 200000 # Limits total time solver. (110000 before change 12102021) +Pre_Solve: 0 # Controls presolve level. (1 before change 12102021) +Method: -1 # Algorithm used to solve continuous models (including MIP root relaxation). (2 before change 12102021) + +#Gurobi-specific solver settings +MIPGap: 0.001 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). (1e-4 before change 12102021) +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: 0 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/highs_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/highs_settings.yml new file mode 100644 index 0000000000..f447d088e3 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/highs_settings.yml @@ -0,0 +1,11 @@ +# HiGHS Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Primal feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +Optimal_Tol: 1.0e-05 # Dual feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +TimeLimit: 1.0e23 # Time limit # [type: double, advanced: false, range: [0, inf], default: inf] +Pre_Solve: choose # Presolve option: "off", "choose" or "on" # [type: string, advanced: false, default: "choose"] +Method: ipm #HiGHS-specific solver settings # Solver option: "simplex", "choose" or "ipm" # [type: string, advanced: false, default: "choose"] + +# run the crossover routine for ipx +# [type: string, advanced: "on", range: {"off", "on"}, default: "off"] +run_crossover: "off" diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/multi_stage_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/multi_stage_settings.yml new file mode 100644 index 0000000000..b03d1dc74e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/multi_stage_settings.yml @@ -0,0 +1,5 @@ +NumStages: 3 # Number of model investment planning stages +StageLengths: [10,10,10] # Length of each model stage (years) +WACC: 0.045 # Weighted average cost of capital +ConvergenceTolerance: 0.01 # Relative optimality gap used for convergence +Myopic: 0 # 1 = myopic, 0 = dual dynamic programming diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/time_domain_reduction_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/time_domain_reduction_settings.yml new file mode 100644 index 0000000000..411e0d0fdb --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/ISONE_Trizone_MultiStage/Settings/time_domain_reduction_settings.yml @@ -0,0 +1,151 @@ +##### +# +# TIME DOMAIN REDUCTION SETTINGS +# +# Set parameters here that organize how your full timeseries +# data will be divided into representative period clusters. +# Ensure that time_domain_reduction is set to 1 in GenX_settings.yml +# before running. Run within GenX or use PreCluster.jl to test and +# examine representative period output before proceeding. +# Specify your data input directory as inpath within Run_test.jl +# or PreCluster.jl. +# +##### + + # - TimestepsPerRepPeriod + # Typically 168 timesteps (e.g., hours) per period, this designates + # the length of each representative period. +TimestepsPerRepPeriod: 168 + + # - ClusterMethod + # Either 'kmeans' or 'kmedoids', this designates the method used to cluster + # periods and determine each point's representative period. +ClusterMethod: 'kmeans' + + # - ScalingMethod + # Either 'N' or 'S', this designates directs the module to normalize ([0,1]) + # or standardize (mean 0, variance 1) the input data. +ScalingMethod: "S" + + # - MaxPeriods + # The maximum number of periods - both clustered periods and extreme periods - + # that may be used to represent the input data. If IterativelyAddPeriods is on and the + # error threshold is never met, this will be the total number of periods. +MaxPeriods: 10 + + # - MinPeriods + # The minimum number of periods used to represent the input data. If using + # UseExtremePeriods, this must be at least the number of extreme periods requests. If + # IterativelyAddPeriods if off, this will be the total number of periods. +MinPeriods: 10 + + # - IterativelyAddPeriods + # Either 'yes' or 'no', this designates whether or not to add periods + # until the error threshold between input data and represented data is met or the maximum + # number of periods is reached. +IterativelyAddPeriods: 1 + + # - IterateMethod + # Either 'cluster' or 'extreme', this designates whether to add clusters to + # the kmeans/kmedoids method or to set aside the worst-fitting periods as a new extreme periods. + # The default option is 'cluster'. +IterateMethod: "cluster" + + # - Threshold + # Iterative period addition will end if the period farthest (Euclidean Distance) + # from its representative period is within this percentage of the total possible error (for normalization) + # or ~95% of the total possible error (for standardization). E.g., for a threshold of 0.01, + # every period must be within 1% of the spread of possible error before the clustering + # iterations will terminate (or until the max number of periods is reached). +Threshold: 0.05 + + # - nReps + # The number of times to repeat each kmeans/kmedoids clustering at the same setting. +nReps: 100 + + # - LoadWeight + # Default 1, this is an optional multiplier on load columns in order to prioritize + # better fits for load profiles over resource capacity factor profiles. +LoadWeight: 1 + + # - WeightTotal + # Default 8760, the sum to which the relative weights of representative periods will be scaled. +WeightTotal: 8760 + + # - ClusterFuelPrices + # Either 'yes' ro 'no', this indicates whether or not to use the fuel price + # time series in Fuels_data.csv in the clustering process. If 'no', this function will still write + # Fuels_data_clustered.csv with reshaped fuel prices based on the number and size of the + # representative weeks, assuming a constant time series of fuel prices with length equal to the + # number of timesteps in the raw input data. +ClusterFuelPrices: 1 + + # - MultiStageConcatenate + # (Only considered if MultiStage = 1 in genx_settings.yml) + # If 1, this designates that the model should time domain reduce the input data + # of all model stages together. Else if 0, the model will time domain reduce each + # stage separately +MultiStageConcatenate: 0 + + # - UseExtremePeriods + # Either 'yes' or 'no', this designates whether or not to include + # outliers (by performance or load/resource extreme) as their own representative periods. + # This setting automatically includes the periods with maximum load, minimum solar cf and + # minimum wind cf as extreme periods. +UseExtremePeriods: 1 + +# STILL IN DEVELOPMENT - Currently just uses integral max load, integral min PV and wind. +# - ExtremePeriods +# Use this to define which periods to be included among the final representative periods +# as "Extreme Periods". +# Select by profile type: load ("Load"), solar PV capacity factors ("PV"), and wind capacity factors ("Wind"). +# Select whether to examine these profiles by zone ("Zone") or across the whole system ("System"). +# Select whether to look for absolute max/min at the timestep level ("Absolute") +# or max/min sum across the period ("Integral"). +# Select whether you want the maximum ("Max") or minimum ("Min") (of the prior type) for each profile type. +ExtremePeriods: + Load: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 1 + Min: 0 + Integral: + Max: 0 + Min: 0 + PV: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + Wind: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/.~lock.Fuels_data_clustered.csv# b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/.~lock.Fuels_data_clustered.csv# new file mode 100755 index 0000000000..fd489000cd --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/.~lock.Fuels_data_clustered.csv# @@ -0,0 +1 @@ +,samie,samie-Inspiron-5737,08.04.2021 07:19,file:///home/samie/.config/libreoffice/4; \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/CO2_cap.csv new file mode 100644 index 0000000000..97ba02e752 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/CO2_cap.csv @@ -0,0 +1,4 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_Mtons_1 +NENGREST,z1,1,8.59 +NENG_CT,z2,1,2.31 +NENG_ME,z3,1,1.29 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..766b9ffed0 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Capacity_reserve_margin.csv @@ -0,0 +1,4 @@ +,Network_zones,CapRes_1 +NENGREST,z1,0.193 +NENG_CT,z2,0.193 +NENG_ME,z3,0.193 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Energy_share_requirement.csv new file mode 100644 index 0000000000..749ddfeaf1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Energy_share_requirement.csv @@ -0,0 +1,4 @@ +,Network_zones,ESR_1,ESR_2 +NENGREST,z1,0.259,0.348 +NENG_CT,z2,0.44,0.44 +NENG_ME,z3,0.776,0.776 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Fuels_data.csv new file mode 100644 index 0000000000..5448cd0e9f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Fuels_data.csv @@ -0,0 +1,8762 @@ +Time_Index,NENG_CT_NG,NENG_ME_NG,NENGREST_NG,west_north_central_coal,new_england_uranium,NENG_CT_NG_ccs90,NENG_ME_NG_ccs90,NENGREST_NG_ccs90,NENG_CT_NG_ccs100,NENG_ME_NG_ccs100,NENGREST_NG_ccs100,None +0,0.05306,0.05306,0.05306,0.09552,0,0.00531,0.00531,0.00531,0,0,0,0 +1,5.45,5.45,5.28,1.67,0.69,5.45,5.45,5.28,4.02,4.02,3.85,0 +2,5.45,5.45,5.28,1.67,0.69,5.45,5.45,5.28,4.02,4.02,3.85,0 +3,5.45,5.45,5.28,1.67,0.69,5.45,5.45,5.28,4.02,4.02,3.85,0 +4,5.45,5.45,5.28,1.67,0.69,5.45,5.45,5.28,4.02,4.02,3.85,0 +5,5.45,5.45,5.28,1.67,0.69,5.45,5.45,5.28,4.02,4.02,3.85,0 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+,,,,,,,,8758,9018,3452,1217 +,,,,,,,,8759,8551,3281,1154 +,,,,,,,,8760,8089,3106,1092 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..858294b6fc --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,Constraint_Description,Min_MW +1,ME_Wind,8000 +2,ME_OSW,5000 +3,MA_Solar,3200 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Network.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Network.csv new file mode 100644 index 0000000000..a26c95a6be --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Network.csv @@ -0,0 +1,4 @@ +,Network_zones,Network_Lines,z1,z2,z3,Line_Max_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1 +NENGREST,z1,1,1,-1,0,2950,NENGREST_to_NENG_CT,123.0584,0.012305837,2950,12060,0.95,0,0 +NENG_CT,z2,2,1,0,-1,2000,NENGREST_to_NENG_ME,196.5385,0.019653847,2000,19261,0.95,0,0 +NENG_ME,z3,,,,,,,,,,,,, \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/README.md b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/README.md new file mode 100644 index 0000000000..b452e45a95 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/README.md @@ -0,0 +1,17 @@ +# Real System Example: Three Zones + +**RealSystemExample/ISONE_Trizone** is more detailed example system approximating the ISO New England region with three zones representing Connecticut, and Maine and rest of ISO New England (MA, VT, NH, RI). +They are total of 58 different resources modeled in the system. +The temporal resolution of the model inputs is specified to be 480 hours or 20 days. + +To run the model, first navigate to the example directory at `GenX/Example_Systems/RealSystemExample/ISONE_Trizone`: + +`cd("Example_Systems/RealSystemExample/ISONE_Trizone")` + +Next, ensure that your settings in `GenX_settings.yml` are correct. The default settings use the solver Gurobi (`Solver: Gurobi`), time domain reduced input data (`TimeDomainReduction: 0`). Other optional policies include minimum capacity requirements, a capacity reserve margin, and more. + +Once the settings are confirmed, run the model with the `Run.jl` script in the example directory: + +`include("Run.jl")` + +Once the model has completed, results will write to the `Results` directory. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Reserves.csv b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Reserves.csv new file mode 100644 index 0000000000..a29823e971 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Run.jl b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/genx_settings.yml new file mode 100644 index 0000000000..8e0b91e6a7 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/genx_settings.yml @@ -0,0 +1,21 @@ +OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 1 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 1 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 1 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 1 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 1 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: HiGHS # Available solvers: Gurobi, CPLEX, CLP, SCIP +ParameterScale: 1 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 0 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 1 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 1 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 # Number of MGA iterations with maximization and minimization objective +MethodofMorris: 0 #Flag for turning on the Method of Morris analysis diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..e5442fdfce --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/gurobi_settings.yml @@ -0,0 +1,15 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-06 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: 0 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/highs_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/highs_settings.yml new file mode 100644 index 0000000000..f447d088e3 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/highs_settings.yml @@ -0,0 +1,11 @@ +# HiGHS Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Primal feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +Optimal_Tol: 1.0e-05 # Dual feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +TimeLimit: 1.0e23 # Time limit # [type: double, advanced: false, range: [0, inf], default: inf] +Pre_Solve: choose # Presolve option: "off", "choose" or "on" # [type: string, advanced: false, default: "choose"] +Method: ipm #HiGHS-specific solver settings # Solver option: "simplex", "choose" or "ipm" # [type: string, advanced: false, default: "choose"] + +# run the crossover routine for ipx +# [type: string, advanced: "on", range: {"off", "on"}, default: "off"] +run_crossover: "off" diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/scip_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/scip_settings.yml new file mode 100644 index 0000000000..2779d54826 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/scip_settings.yml @@ -0,0 +1,5 @@ +# SCIP Solver Parameters + +#SCIP-specific solver settings +Dispverblevel: 0 +limitsgap: 0.05 diff --git a/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/time_domain_reduction_settings.yml b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/time_domain_reduction_settings.yml new file mode 100644 index 0000000000..e7723f6a6e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RealSystemExample/MGA_ISONE_Trizone_FullTimeseries/Settings/time_domain_reduction_settings.yml @@ -0,0 +1,151 @@ +##### +# +# TIME DOMAIN REDUCTION SETTINGS +# +# Set parameters here that organize how your full timeseries +# data will be divided into representative period clusters. +# Ensure that time_domain_reduction is set to 1 in GenX_settings.yml +# before running. Run within GenX or use PreCluster.jl to test and +# examine representative period output before proceeding. +# Specify your data input directory as inpath within Run_test.jl +# or PreCluster.jl. +# +##### + + # - TimestepsPerRepPeriod + # Typically 168 timesteps (e.g., hours) per period, this designates + # the length of each representative period. +TimestepsPerRepPeriod: 168 + + # - ClusterMethod + # Either 'kmeans' or 'kmedoids', this designates the method used to cluster + # periods and determine each point's representative period. +ClusterMethod: 'kmeans' + + # - ScalingMethod + # Either 'N' or 'S', this designates directs the module to normalize ([0,1]) + # or standardize (mean 0, variance 1) the input data. +ScalingMethod: "S" + + # - MaxPeriods + # The maximum number of periods - both clustered periods and extreme periods - + # that may be used to represent the input data. If IterativelyAddPeriods is on and the + # error threshold is never met, this will be the total number of periods. +MaxPeriods: 11 + + # - MinPeriods + # The minimum number of periods used to represent the input data. If using + # UseExtremePeriods, this must be at least the number of extreme periods requests. If + # IterativelyAddPeriods if off, this will be the total number of periods. +MinPeriods: 8 + + # - IterativelyAddPeriods + # Either 'yes' or 'no', this designates whether or not to add periods + # until the error threshold between input data and represented data is met or the maximum + # number of periods is reached. +IterativelyAddPeriods: 1 + + # - IterateMethod + # Either 'cluster' or 'extreme', this designates whether to add clusters to + # the kmeans/kmedoids method or to set aside the worst-fitting periods as a new extreme periods. + # The default option is 'cluster'. +IterateMethod: "cluster" + + # - Threshold + # Iterative period addition will end if the period farthest (Euclidean Distance) + # from its representative period is within this percentage of the total possible error (for normalization) + # or ~95% of the total possible error (for standardization). E.g., for a threshold of 0.01, + # every period must be within 1% of the spread of possible error before the clustering + # iterations will terminate (or until the max number of periods is reached). +Threshold: 0.05 + + # - nReps + # The number of times to repeat each kmeans/kmedoids clustering at the same setting. +nReps: 100 + + # - LoadWeight + # Default 1, this is an optional multiplier on load columns in order to prioritize + # better fits for load profiles over resource capacity factor profiles. +LoadWeight: 1 + + # - WeightTotal + # Default 8760, the sum to which the relative weights of representative periods will be scaled. +WeightTotal: 8760 + + # - ClusterFuelPrices + # Either 'yes' ro 'no', this indicates whether or not to use the fuel price + # time series in Fuels_data.csv in the clustering process. If 'no', this function will still write + # Fuels_data_clustered.csv with reshaped fuel prices based on the number and size of the + # representative weeks, assuming a constant time series of fuel prices with length equal to the + # number of timesteps in the raw input data. +ClusterFuelPrices: 1 + + # - MultiStageConcatenate + # (Only considered if MultiStage = 1 in genx_settings.yml) + # If 1, this designates that the model should time domain reduce the input data + # of all model stages together. Else if 0, the model will time domain reduce each + # stage separately +MultiStageConcatenate: 0 + + # - UseExtremePeriods + # Either 'yes' or 'no', this designates whether or not to include + # outliers (by performance or load/resource extreme) as their own representative periods. + # This setting automatically includes the periods with maximum load, minimum solar cf and + # minimum wind cf as extreme periods. +UseExtremePeriods: 1 + +# STILL IN DEVELOPMENT - Currently just uses integral max load, integral min PV and wind. +# - ExtremePeriods +# Use this to define which periods to be included among the final representative periods +# as "Extreme Periods". +# Select by profile type: load ("Load"), solar PV capacity factors ("PV"), and wind capacity factors ("Wind"). +# Select whether to examine these profiles by zone ("Zone") or across the whole system ("System"). +# Select whether to look for absolute max/min at the timestep level ("Absolute") +# or max/min sum across the period ("Integral"). +# Select whether you want the maximum ("Max") or minimum ("Min") (of the prior type) for each profile type. +ExtremePeriods: + Load: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 1 + Min: 0 + Integral: + Max: 0 + Min: 0 + PV: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + Wind: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p1/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p1/CO2_cap.csv new file mode 100644 index 0000000000..03f2e270da --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p1/CO2_cap.csv @@ -0,0 +1,2 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_tons_MWh_1,CO_2_Max_Mtons_1 +NE,z1,1,0.1,0.018 diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p1/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p1/Fuels_data.csv new file mode 100644 index 0000000000..3c5940a57d --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p1/Fuels_data.csv @@ -0,0 +1,8762 @@ +Time_Index,ng,coal,biomass,uranium,ng_ccs,h2,None +0,0.05307,0.0972,0.04169,0,0.00531,0,0 +1,3.6,1.95,1.02,0.66,4.1,20,0 +2,3.6,1.95,1.02,0.66,4.1,20,0 +3,3.6,1.95,1.02,0.66,4.1,20,0 +4,3.6,1.95,1.02,0.66,4.1,20,0 +5,3.6,1.95,1.02,0.66,4.1,20,0 +6,3.6,1.95,1.02,0.66,4.1,20,0 +7,3.6,1.95,1.02,0.66,4.1,20,0 +8,3.6,1.95,1.02,0.66,4.1,20,0 +9,3.6,1.95,1.02,0.66,4.1,20,0 +10,3.6,1.95,1.02,0.66,4.1,20,0 +11,3.6,1.95,1.02,0.66,4.1,20,0 +12,3.6,1.95,1.02,0.66,4.1,20,0 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a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p1/Generators_variability.csv b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p1/Generators_variability.csv new file mode 100644 index 0000000000..841370fac2 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p1/Generators_variability.csv @@ -0,0 +1,8761 @@ +Time_Index,NGCC_bf,NGCC_gf,NGCT_bf,NGCT_gf,NGST,Coal,Nuclear,Biomass,SolarPV,Wind,Hydro,PHS,Li-ion,Retro_NGCC_CCS,Retro_NGCC_H2,Retro_Coal_TES,Retro_Coal_SMR +1,1,1,1,1,1,1,1,1,0,0.889717042,0.625293813,1,1,1,1,1,1 +2,1,1,1,1,1,1,1,1,0,0.877715468,0.625293813,1,1,1,1,1,1 +3,1,1,1,1,1,1,1,1,0,0.903424203,0.625293813,1,1,1,1,1,1 +4,1,1,1,1,1,1,1,1,0,0.895153165,0.625293813,1,1,1,1,1,1 +5,1,1,1,1,1,1,1,1,0,0.757258117,0.625293813,1,1,1,1,1,1 +6,1,1,1,1,1,1,1,1,0,0.630928695,0.625293813,1,1,1,1,1,1 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a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p1/Network.csv b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p1/Network.csv new file mode 100644 index 0000000000..7e3790f604 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p1/Network.csv @@ -0,0 +1,2 @@ +,Network_zones,Network_Lines,z1,Line_Max_Flow_MW,Line_Min_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1,Line_Max_Flow_Possible_MW,Capital_Recovery_Period,WACC +NE,z1,1,1,2950,-2950,MA_to_CT,123.0584,0.012305837,2950,12060,0.95,0,0,7000,30,0.062 diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p2/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p2/CO2_cap.csv new file mode 100644 index 0000000000..1b5869ce2e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p2/CO2_cap.csv @@ -0,0 +1,2 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_tons_MWh_1,CO_2_Max_Mtons_1 +NE,z1,1,0.05,0.018 diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p2/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p2/Fuels_data.csv new file mode 100644 index 0000000000..3c5940a57d --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p2/Fuels_data.csv @@ -0,0 +1,8762 @@ +Time_Index,ng,coal,biomass,uranium,ng_ccs,h2,None +0,0.05307,0.0972,0.04169,0,0.00531,0,0 +1,3.6,1.95,1.02,0.66,4.1,20,0 +2,3.6,1.95,1.02,0.66,4.1,20,0 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file mode 100644 index 0000000000..487d5f963d --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p2/Generators_data.csv @@ -0,0 +1,18 @@ +Resource,Zone,THERM,MUST_RUN,STOR,FLEX,HYDRO,VRE,Num_VRE_Bins,LDS,RETRO,Num_RETRO_Sources,New_Build,Existing_Cap_MW,Existing_Cap_MWh,Existing_Charge_Cap_MW,Max_Cap_MW,Max_Cap_MWh,Max_Charge_Cap_MW,Min_Cap_MW,Min_Cap_MWh,Min_Charge_Cap_MW,Inv_Cost_per_MWyr,Inv_Cost_per_MWhyr,Inv_Cost_Charge_per_MWyr,Fixed_OM_Cost_per_MWyr,Fixed_OM_Cost_per_MWhyr,Fixed_OM_Cost_Charge_per_MWyr,Var_OM_Cost_per_MWh,Var_OM_Cost_per_MWh_In,Heat_Rate_MMBTU_per_MWh,Fuel,Cap_Size,Start_Cost_per_MW,Start_Fuel_MMBTU_per_MW,Up_Time,Down_Time,Ramp_Up_Percentage,Ramp_Dn_Percentage,Hydro_Energy_to_Power_Ratio,Min_Power,Self_Disch,Eff_Up,Eff_Down,Min_Duration,Max_Duration,Max_Flexible_Demand_Advance,Max_Flexible_Demand_Delay,Flexible_Demand_Energy_Eff,Reg_Max,Rsv_Max,Reg_Cost,Rsv_Cost,MinCapTag_1,MinCapTag_2,MinCapTag_3,MGA,Resource_Type,CapRes_1,ESR_1,ESR_2,region,cluster,WACC,Capital_Recovery_Period,Lifetime,Min_Retired_Cap_MW,Min_Retired_Energy_Cap_MW,Min_Retired_Charge_Cap_MW,Retro1_Source,Retro1_Efficiency,Retro1_Inv_Cost_per_MWyr,Retro2_Source,Retro2_Efficiency,Retro2_Inv_Cost_per_MWyr +NGCC_bf,1,1,0,0,0,0,0,0,0,0,0,0,3400,0,0,-1,-1,-1,0,0,0,40000,0,0,12000,0,0,3,0,7.3,ng,200,70,1,6,2,0.64,0.64,0,0.4,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,NGCC,0.93,0,0,MO,1,0.04,30,30,0,0,0,None,0,-1,None,0,-1 +NGCC_gf,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,-1,-1,-1,0,0,0,40000,0,0,12000,0,0,3,0,7.3,ng,200,70,1,6,2,0.64,0.64,0,0.4,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,NGCC,0.93,0,0,MO,1,0.04,30,30,0,0,0,None,0,-1,None,0,-1 +NGCT_bf,1,1,0,0,0,0,0,0,0,0,0,0,8200,0,0,-1,-1,-1,0,0,0,30000,0,0,11000,0,0,4,0,14.3,ng,100,90,1,6,1,0.64,0.64,0,0.2,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,NGCT,0.93,0,0,MO,1,0.04,30,30,0,0,0,None,0,-1,None,0,-1 +NGCT_gf,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,-1,-1,-1,0,0,0,30000,0,0,11000,0,0,4,0,14.3,ng,100,90,1,6,1,0.64,0.64,0,0.2,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,NGCT,0.93,0,0,MO,1,0.04,30,30,0,0,0,None,0,-1,None,0,-1 +NGST,1,1,0,0,0,0,0,0,0,0,0,0,1060,0,0,-1,-1,-1,0,0,0,0,0,0,11000,0,0,4,0,12.1,ng,500,90,1,6,1,0.64,0.64,0,0.2,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,NGST,0.93,0,0,MO,1,0.04,40,40,0,0,0,None,0,-1,None,0,-1 +Coal,1,1,0,0,0,0,0,0,0,0,0,0,14700,0,0,-1,-1,-1,0,0,0,0,0,0,30000,0,0,5,0,10.4,coal,500,150,10,6,6,0.64,0.64,0,0.4,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,Coal,0.93,0,0,MO,1,0.04,40,40,0,0,0,None,0,-1,None,0,-1 +Nuclear,1,1,1,0,0,0,0,0,0,0,0,0,2360,0,0,-1,-1,-1,0,0,0,0,0,0,100000,0,0,2,0,7.43,uranium,1000,0,0,0,0,0.64,0.64,0,0,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,Nuclear,0.93,0,0,MO,1,0.04,40,40,0,0,0,None,0,-1,None,0,-1 +Biomass,1,1,0,0,0,0,0,0,0,0,0,-1,20,0,0,-1,-1,-1,0,0,0,0,0,0,100000,0,0,5,0,13,biomass,100,90,2,6,2,0.64,0.64,0,0.4,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,Biomass,0.93,0,0,MO,1,0.04,30,30,0,0,0,None,0,-1,None,0,-1 +SolarPV,1,0,0,0,0,0,1,1,0,0,0,1,40,0,0,-1,-1,-1,0,0,0,36000,0,0,10000,0,0,0,0,0,None,1,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,0,0,1,SolarPV,0.8,1,1,MO,1,0.04,20,20,0,0,0,None,0,-1,None,0,-1 +Wind,1,0,0,0,0,0,1,1,0,0,0,1,5720,0,0,-1,-1,-1,0,0,0,60000,0,0,40000,0,0,0.1,0,0,None,1,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,Wind,0.8,1,1,MO,1,0.04,20,20,0,0,0,None,0,-1,None,0,-1 +Hydro,1,0,0,0,0,1,0,0,0,0,0,-1,480,0,0,-1,-1,-1,0,0,0,0,0,0,18000,0,0,0.1,0,0,None,1600,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,Hydro,0.8,1,1,MO,1,0.04,40,40,0,0,0,None,0,-1,None,0,-1 +PHS,1,0,0,1,0,0,0,0,1,0,0,0,480,0,0,-1,-1,-1,0,0,0,0,0,0,15000,5000,0,0.1,0.1,0,None,1,0,0,0,0,1,1,0,0,0,0.9,0.9,1,10,0,0,1,0,0,0,0,0,0,1,0,PHS,0.95,0,0,MO,0,0.04,40,40,0,0,0,None,0,-1,None,0,-1 +Li-ion,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,-1,-1,-1,0,0,0,20000,25000,0,5000,6000,0,0.15,0.15,0,None,1,0,0,0,0,1,1,0,0,0,0.9,0.9,1,10,0,0,1,0,0,0,0,0,0,1,0,Li-ion,0.95,0,0,MO,0,0.04,20,20,0,0,0,None,0,-1,None,0,-1 +Retro_NGCC_CCS,1,1,0,0,0,0,0,0,0,1,2,1,0,0,0,-1,-1,-1,0,0,0,60000,0,0,16000,0,0,3,0,7.3,ng_ccs,200,70,1,6,2,0.64,0.64,0,0.4,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,NGCC_CCS,0.93,0,0,MO,1,0.04,20,20,0,0,0,NGCC_bf,0.85,60000,NGCC_gf,0.9,55000 +Retro_NGCC_H2,1,1,0,0,0,0,0,0,0,1,2,1,0,0,0,-1,-1,-1,0,0,0,40000,0,0,12000,0,0,3,0,7.3,h2,200,70,1,6,2,0.64,0.64,0,0.4,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,Hydrogen,0.93,0,0,MO,1,0.04,20,20,0,0,0,NGCC_bf,0.85,40000,NGCC_gf,0.9,35000 +Retro_Coal_TES,1,0,0,2,0,0,0,0,0,1,1,1,0,0,0,-1,-1,-1,0,0,0,0,200,220,260,10,0,0.1,0.1,0,None,1,0,0,0,0,1,1,0,0,0,0.995,0.55,1,10,0,0,1,0,0,0,0,0,0,1,0,TES,0.95,0,0,MO,0,0.04,20,20,0,0,0,Coal,0.85,33300,None,0,-1 +Retro_Coal_SMR,1,1,1,0,0,0,0,0,0,1,1,1,0,0,0,-1,-1,-1,0,0,0,0,0,0,6200,0,0,0.75,0,7.43,uranium,200,0,0,0,0,0.64,0.64,0,0,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,Nuclear,0.93,0,0,MO,1,0.04,20,20,0,0,0,Coal,0.85,195000,None,0,-1 diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p2/Generators_variability.csv b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p2/Generators_variability.csv new file mode 100644 index 0000000000..841370fac2 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p2/Generators_variability.csv @@ -0,0 +1,8761 @@ +Time_Index,NGCC_bf,NGCC_gf,NGCT_bf,NGCT_gf,NGST,Coal,Nuclear,Biomass,SolarPV,Wind,Hydro,PHS,Li-ion,Retro_NGCC_CCS,Retro_NGCC_H2,Retro_Coal_TES,Retro_Coal_SMR +1,1,1,1,1,1,1,1,1,0,0.889717042,0.625293813,1,1,1,1,1,1 +2,1,1,1,1,1,1,1,1,0,0.877715468,0.625293813,1,1,1,1,1,1 +3,1,1,1,1,1,1,1,1,0,0.903424203,0.625293813,1,1,1,1,1,1 +4,1,1,1,1,1,1,1,1,0,0.895153165,0.625293813,1,1,1,1,1,1 +5,1,1,1,1,1,1,1,1,0,0.757258117,0.625293813,1,1,1,1,1,1 +6,1,1,1,1,1,1,1,1,0,0.630928695,0.625293813,1,1,1,1,1,1 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+,,,,,,,8760,37776.08364 diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p2/Network.csv b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p2/Network.csv new file mode 100644 index 0000000000..7e3790f604 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p2/Network.csv @@ -0,0 +1,2 @@ +,Network_zones,Network_Lines,z1,Line_Max_Flow_MW,Line_Min_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1,Line_Max_Flow_Possible_MW,Capital_Recovery_Period,WACC +NE,z1,1,1,2950,-2950,MA_to_CT,123.0584,0.012305837,2950,12060,0.95,0,0,7000,30,0.062 diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p3/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p3/CO2_cap.csv new file mode 100644 index 0000000000..d79ae4a617 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p3/CO2_cap.csv @@ -0,0 +1,2 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_tons_MWh_1,CO_2_Max_Mtons_1 +NE,z1,1,0.01,0.018 diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p3/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p3/Fuels_data.csv new file mode 100644 index 0000000000..3c5940a57d --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p3/Fuels_data.csv @@ -0,0 +1,8762 @@ +Time_Index,ng,coal,biomass,uranium,ng_ccs,h2,None +0,0.05307,0.0972,0.04169,0,0.00531,0,0 +1,3.6,1.95,1.02,0.66,4.1,20,0 +2,3.6,1.95,1.02,0.66,4.1,20,0 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file mode 100644 index 0000000000..487d5f963d --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p3/Generators_data.csv @@ -0,0 +1,18 @@ +Resource,Zone,THERM,MUST_RUN,STOR,FLEX,HYDRO,VRE,Num_VRE_Bins,LDS,RETRO,Num_RETRO_Sources,New_Build,Existing_Cap_MW,Existing_Cap_MWh,Existing_Charge_Cap_MW,Max_Cap_MW,Max_Cap_MWh,Max_Charge_Cap_MW,Min_Cap_MW,Min_Cap_MWh,Min_Charge_Cap_MW,Inv_Cost_per_MWyr,Inv_Cost_per_MWhyr,Inv_Cost_Charge_per_MWyr,Fixed_OM_Cost_per_MWyr,Fixed_OM_Cost_per_MWhyr,Fixed_OM_Cost_Charge_per_MWyr,Var_OM_Cost_per_MWh,Var_OM_Cost_per_MWh_In,Heat_Rate_MMBTU_per_MWh,Fuel,Cap_Size,Start_Cost_per_MW,Start_Fuel_MMBTU_per_MW,Up_Time,Down_Time,Ramp_Up_Percentage,Ramp_Dn_Percentage,Hydro_Energy_to_Power_Ratio,Min_Power,Self_Disch,Eff_Up,Eff_Down,Min_Duration,Max_Duration,Max_Flexible_Demand_Advance,Max_Flexible_Demand_Delay,Flexible_Demand_Energy_Eff,Reg_Max,Rsv_Max,Reg_Cost,Rsv_Cost,MinCapTag_1,MinCapTag_2,MinCapTag_3,MGA,Resource_Type,CapRes_1,ESR_1,ESR_2,region,cluster,WACC,Capital_Recovery_Period,Lifetime,Min_Retired_Cap_MW,Min_Retired_Energy_Cap_MW,Min_Retired_Charge_Cap_MW,Retro1_Source,Retro1_Efficiency,Retro1_Inv_Cost_per_MWyr,Retro2_Source,Retro2_Efficiency,Retro2_Inv_Cost_per_MWyr +NGCC_bf,1,1,0,0,0,0,0,0,0,0,0,0,3400,0,0,-1,-1,-1,0,0,0,40000,0,0,12000,0,0,3,0,7.3,ng,200,70,1,6,2,0.64,0.64,0,0.4,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,NGCC,0.93,0,0,MO,1,0.04,30,30,0,0,0,None,0,-1,None,0,-1 +NGCC_gf,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,-1,-1,-1,0,0,0,40000,0,0,12000,0,0,3,0,7.3,ng,200,70,1,6,2,0.64,0.64,0,0.4,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,NGCC,0.93,0,0,MO,1,0.04,30,30,0,0,0,None,0,-1,None,0,-1 +NGCT_bf,1,1,0,0,0,0,0,0,0,0,0,0,8200,0,0,-1,-1,-1,0,0,0,30000,0,0,11000,0,0,4,0,14.3,ng,100,90,1,6,1,0.64,0.64,0,0.2,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,NGCT,0.93,0,0,MO,1,0.04,30,30,0,0,0,None,0,-1,None,0,-1 +NGCT_gf,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,-1,-1,-1,0,0,0,30000,0,0,11000,0,0,4,0,14.3,ng,100,90,1,6,1,0.64,0.64,0,0.2,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,NGCT,0.93,0,0,MO,1,0.04,30,30,0,0,0,None,0,-1,None,0,-1 +NGST,1,1,0,0,0,0,0,0,0,0,0,0,1060,0,0,-1,-1,-1,0,0,0,0,0,0,11000,0,0,4,0,12.1,ng,500,90,1,6,1,0.64,0.64,0,0.2,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,NGST,0.93,0,0,MO,1,0.04,40,40,0,0,0,None,0,-1,None,0,-1 +Coal,1,1,0,0,0,0,0,0,0,0,0,0,14700,0,0,-1,-1,-1,0,0,0,0,0,0,30000,0,0,5,0,10.4,coal,500,150,10,6,6,0.64,0.64,0,0.4,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,Coal,0.93,0,0,MO,1,0.04,40,40,0,0,0,None,0,-1,None,0,-1 +Nuclear,1,1,1,0,0,0,0,0,0,0,0,0,2360,0,0,-1,-1,-1,0,0,0,0,0,0,100000,0,0,2,0,7.43,uranium,1000,0,0,0,0,0.64,0.64,0,0,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,Nuclear,0.93,0,0,MO,1,0.04,40,40,0,0,0,None,0,-1,None,0,-1 +Biomass,1,1,0,0,0,0,0,0,0,0,0,-1,20,0,0,-1,-1,-1,0,0,0,0,0,0,100000,0,0,5,0,13,biomass,100,90,2,6,2,0.64,0.64,0,0.4,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,Biomass,0.93,0,0,MO,1,0.04,30,30,0,0,0,None,0,-1,None,0,-1 +SolarPV,1,0,0,0,0,0,1,1,0,0,0,1,40,0,0,-1,-1,-1,0,0,0,36000,0,0,10000,0,0,0,0,0,None,1,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,0,0,1,SolarPV,0.8,1,1,MO,1,0.04,20,20,0,0,0,None,0,-1,None,0,-1 +Wind,1,0,0,0,0,0,1,1,0,0,0,1,5720,0,0,-1,-1,-1,0,0,0,60000,0,0,40000,0,0,0.1,0,0,None,1,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,Wind,0.8,1,1,MO,1,0.04,20,20,0,0,0,None,0,-1,None,0,-1 +Hydro,1,0,0,0,0,1,0,0,0,0,0,-1,480,0,0,-1,-1,-1,0,0,0,0,0,0,18000,0,0,0.1,0,0,None,1600,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,Hydro,0.8,1,1,MO,1,0.04,40,40,0,0,0,None,0,-1,None,0,-1 +PHS,1,0,0,1,0,0,0,0,1,0,0,0,480,0,0,-1,-1,-1,0,0,0,0,0,0,15000,5000,0,0.1,0.1,0,None,1,0,0,0,0,1,1,0,0,0,0.9,0.9,1,10,0,0,1,0,0,0,0,0,0,1,0,PHS,0.95,0,0,MO,0,0.04,40,40,0,0,0,None,0,-1,None,0,-1 +Li-ion,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,-1,-1,-1,0,0,0,20000,25000,0,5000,6000,0,0.15,0.15,0,None,1,0,0,0,0,1,1,0,0,0,0.9,0.9,1,10,0,0,1,0,0,0,0,0,0,1,0,Li-ion,0.95,0,0,MO,0,0.04,20,20,0,0,0,None,0,-1,None,0,-1 +Retro_NGCC_CCS,1,1,0,0,0,0,0,0,0,1,2,1,0,0,0,-1,-1,-1,0,0,0,60000,0,0,16000,0,0,3,0,7.3,ng_ccs,200,70,1,6,2,0.64,0.64,0,0.4,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,NGCC_CCS,0.93,0,0,MO,1,0.04,20,20,0,0,0,NGCC_bf,0.85,60000,NGCC_gf,0.9,55000 +Retro_NGCC_H2,1,1,0,0,0,0,0,0,0,1,2,1,0,0,0,-1,-1,-1,0,0,0,40000,0,0,12000,0,0,3,0,7.3,h2,200,70,1,6,2,0.64,0.64,0,0.4,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,Hydrogen,0.93,0,0,MO,1,0.04,20,20,0,0,0,NGCC_bf,0.85,40000,NGCC_gf,0.9,35000 +Retro_Coal_TES,1,0,0,2,0,0,0,0,0,1,1,1,0,0,0,-1,-1,-1,0,0,0,0,200,220,260,10,0,0.1,0.1,0,None,1,0,0,0,0,1,1,0,0,0,0.995,0.55,1,10,0,0,1,0,0,0,0,0,0,1,0,TES,0.95,0,0,MO,0,0.04,20,20,0,0,0,Coal,0.85,33300,None,0,-1 +Retro_Coal_SMR,1,1,1,0,0,0,0,0,0,1,1,1,0,0,0,-1,-1,-1,0,0,0,0,0,0,6200,0,0,0.75,0,7.43,uranium,200,0,0,0,0,0.64,0.64,0,0,0,1,1,0,0,0,0,1,0.25,0.5,0,0,0,0,0,1,Nuclear,0.93,0,0,MO,1,0.04,20,20,0,0,0,Coal,0.85,195000,None,0,-1 diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p3/Generators_variability.csv b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p3/Generators_variability.csv new file mode 100644 index 0000000000..841370fac2 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p3/Generators_variability.csv @@ -0,0 +1,8761 @@ +Time_Index,NGCC_bf,NGCC_gf,NGCT_bf,NGCT_gf,NGST,Coal,Nuclear,Biomass,SolarPV,Wind,Hydro,PHS,Li-ion,Retro_NGCC_CCS,Retro_NGCC_H2,Retro_Coal_TES,Retro_Coal_SMR +1,1,1,1,1,1,1,1,1,0,0.889717042,0.625293813,1,1,1,1,1,1 +2,1,1,1,1,1,1,1,1,0,0.877715468,0.625293813,1,1,1,1,1,1 +3,1,1,1,1,1,1,1,1,0,0.903424203,0.625293813,1,1,1,1,1,1 +4,1,1,1,1,1,1,1,1,0,0.895153165,0.625293813,1,1,1,1,1,1 +5,1,1,1,1,1,1,1,1,0,0.757258117,0.625293813,1,1,1,1,1,1 +6,1,1,1,1,1,1,1,1,0,0.630928695,0.625293813,1,1,1,1,1,1 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+,,,,,,,8727,45439.1012 +,,,,,,,8728,47179.45811 +,,,,,,,8729,49186.31364 +,,,,,,,8730,49711.92706 +,,,,,,,8731,49329.80089 +,,,,,,,8732,48442.32008 +,,,,,,,8733,46989.30705 +,,,,,,,8734,45153.4948 +,,,,,,,8735,43671.92338 +,,,,,,,8736,42636.35346 +,,,,,,,8737,41460.67106 +,,,,,,,8738,41287.39365 +,,,,,,,8739,41588.99746 +,,,,,,,8740,42346.40186 +,,,,,,,8741,43956.07771 +,,,,,,,8742,45793.6376 +,,,,,,,8743,46748.00656 +,,,,,,,8744,47349.70588 +,,,,,,,8745,47821.35238 +,,,,,,,8746,47826.26163 +,,,,,,,8747,47355.414 +,,,,,,,8748,46948.65823 +,,,,,,,8749,46467.06025 +,,,,,,,8750,46334.2071 +,,,,,,,8751,46454.65738 +,,,,,,,8752,47637.41089 +,,,,,,,8753,48934.60868 +,,,,,,,8754,48836.60861 +,,,,,,,8755,47681.69046 +,,,,,,,8756,46646.17088 +,,,,,,,8757,45601.30369 +,,,,,,,8758,44468.10578 +,,,,,,,8759,44113.61854 +,,,,,,,8760,42927.36778 diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p3/Network.csv b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p3/Network.csv new file mode 100644 index 0000000000..7e3790f604 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Inputs/Inputs_p3/Network.csv @@ -0,0 +1,2 @@ +,Network_zones,Network_Lines,z1,Line_Max_Flow_MW,Line_Min_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1,Line_Max_Flow_Possible_MW,Capital_Recovery_Period,WACC +NE,z1,1,1,2950,-2950,MA_to_CT,123.0584,0.012305837,2950,12060,0.95,0,0,7000,30,0.062 diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/README.txt b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/README.txt new file mode 100644 index 0000000000..fecb650604 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/README.txt @@ -0,0 +1,14 @@ +Multi-Stage Retrofit Example +---------------------------- +Description: One zone, few technologies, retrofit options include CCS, H2, SMR, and TES. + +All changes necessary in the inputs in enable retrofit modeling are located in Generators_data.csv. + +New fields: + + - RETRO: Indicates that this resource is a retrofit option. + - Num_RETRO_Sources: Indicates number of source technologies that can have capacity transferred to this retrofit resource type. +For i in 1:n source technologies, + - Retro[i]_Source: Resource name of source technology i. + - Retro[i]_Efficiency: Ratio of incoming retrofit capacity to retiring source capacity (e.g., 0.93 could mean that retiring 100 MW of NGCC result in 93 MW added of Retro_NGCC_CCS). + - Retro[i]_Inv_Cost_per_MWyr: Annualized investment cost ($/MWyr) of retrofitting capacity from source technology i to this retrofit technology. diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Run.jl b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/clp_settings.yml new file mode 100644 index 0000000000..6499a358c6 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-7 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/cplex_settings.yml new file mode 100644 index 0000000000..cb76831e55 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/cplex_settings.yml @@ -0,0 +1,13 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-06 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-4 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +#AggFill: # Allowed fill during presolve aggregation. +#PreDual: # Presolve dualization. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-4 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 #-1 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +#BarObjRng: # Sets the maximum absolute value of the objective function. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/genx_settings.yml new file mode 100644 index 0000000000..d35cb95713 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/genx_settings.yml @@ -0,0 +1,22 @@ +#MacOrWindows: Mac # Set to either "Mac" (also works for Linux) or "Windows" to ensure use of proper file directory separator "\" or "/ +OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 0 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 0 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 0 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 2 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 0 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: Gurobi # Available solvers: Gurobi, CPLEX, Clp, Cbc +ParameterScale: 0 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 2 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 1 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 +MultiStage: 1 diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..e97c759323 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/gurobi_settings.yml @@ -0,0 +1,16 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-06 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-6 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 4 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-4 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: -1 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. +OutputFlag: 0 # Controls Gurobi output.s diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/multi_stage_settings.yml b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/multi_stage_settings.yml new file mode 100644 index 0000000000..a818567e17 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/multi_stage_settings.yml @@ -0,0 +1,5 @@ +NumStages: 3 # Number of model investment stages +StageLengths: [5,5,5] # Length of each model stage (years) +WACC: 0.045 # Weighted average cost of capital +ConvergenceTolerance: 0.01 # Relative optimality gap used for convergence +Myopic: 0 # If using multi-stage GenX, 1 = myopic, 0 = dual dynamic programming diff --git a/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/time_domain_reduction_settings.yml b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/time_domain_reduction_settings.yml new file mode 100644 index 0000000000..ce6617e7b9 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/RetrofitExample/RetrofitExample_MultiStage/Settings/time_domain_reduction_settings.yml @@ -0,0 +1,151 @@ +##### +# +# TIME DOMAIN REDUCTION SETTINGS +# +# Set parameters here that organize how your full timeseries +# data will be divided into representative period clusters. +# Ensure that time_domain_reduction is set to 1 in GenX_settings.yml +# before running. Run within GenX or use PreCluster.jl to test and +# examine representative period output before proceeding. +# Specify your data input directory as inpath within Run_test.jl +# or PreCluster.jl. +# +##### + + # - TimestepsPerRepPeriod + # Typically 168 timesteps (e.g., hours) per period, this designates + # the length of each representative period. +TimestepsPerRepPeriod: 168 + + # - ClusterMethod + # Either 'kmeans' or 'kmedoids', this designates the method used to cluster + # periods and determine each point's representative period. +ClusterMethod: 'kmeans' + + # - ScalingMethod + # Either 'N' or 'S', this designates directs the module to normalize ([0,1]) + # or standardize (mean 0, variance 1) the input data. +ScalingMethod: "S" + + # - MaxPeriods + # The maximum number of periods - both clustered periods and extreme periods - + # that may be used to represent the input data. If IterativelyAddPeriods is on and the + # error threshold is never met, this will be the total number of periods. +MaxPeriods: 11 + + # - MinPeriods + # The minimum number of periods used to represent the input data. If using + # UseExtremePeriods, this must be at least the number of extreme periods requests. If + # IterativelyAddPeriods if off, this will be the total number of periods. +MinPeriods: 6 + + # - IterativelyAddPeriods + # Either 'yes' or 'no', this designates whether or not to add periods + # until the error threshold between input data and represented data is met or the maximum + # number of periods is reached. +IterativelyAddPeriods: 1 + + # - IterateMethod + # Either 'cluster' or 'extreme', this designates whether to add clusters to + # the kmeans/kmedoids method or to set aside the worst-fitting periods as a new extreme periods. + # The default option is 'cluster'. +IterateMethod: "cluster" + + # - Threshold + # Iterative period addition will end if the period farthest (Euclidean Distance) + # from its representative period is within this percentage of the total possible error (for normalization) + # or ~95% of the total possible error (for standardization). E.g., for a threshold of 0.01, + # every period must be within 1% of the spread of possible error before the clustering + # iterations will terminate (or until the max number of periods is reached). +Threshold: 0.05 + + # - nReps + # The number of times to repeat each kmeans/kmedoids clustering at the same setting. +nReps: 100 + + # - LoadWeight + # Default 1, this is an optional multiplier on load columns in order to prioritize + # better fits for load profiles over resource capacity factor profiles. +LoadWeight: 1 + + # - WeightTotal + # Default 8760, the sum to which the relative weights of representative periods will be scaled. +WeightTotal: 8760 + + # - ClusterFuelPrices + # Either 1 (yes) or 0 (no), this indicates whether or not to use the fuel price + # time series in Fuels_data.csv in the clustering process. If 'no', this function will still write + # Fuels_data_clustered.csv with reshaped fuel prices based on the number and size of the + # representative weeks, assuming a constant time series of fuel prices with length equal to the + # number of timesteps in the raw input data. +ClusterFuelPrices: 1 + + # - MultiStageConcatenate + # (Only considered if MultiStage = 1 in genx_settings.yml) + # If 1, this designates that the model should time domain reduce the input data + # of all model stages together. Else if 0, the model will time domain reduce each + # stage separately +MultiStageConcatenate: 1 + + # - UseExtremePeriods + # Either 'yes' or 'no', this designates whether or not to include + # outliers (by performance or load/resource extreme) as their own representative periods. + # This setting automatically includes the periods with maximum load, minimum solar cf and + # minimum wind cf as extreme periods. +UseExtremePeriods: 1 + +# STILL IN DEVELOPMENT - Currently just uses integral max load, integral min PV and wind. +# - ExtremePeriods +# Use this to define which periods to be included among the final representative periods +# as "Extreme Periods". +# Select by profile type: load ("Load"), solar PV capacity factors ("PV"), and wind capacity factors ("Wind"). +# Select whether to examine these profiles by zone ("Zone") or across the whole system ("System"). +# Select whether to look for absolute max/min at the timestep level ("Absolute") +# or max/min sum across the period ("Integral"). +# Select whether you want the maximum ("Max") or minimum ("Min") (of the prior type) for each profile type. +ExtremePeriods: + Load: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 1 + Min: 0 + Integral: + Max: 0 + Min: 0 + PV: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + Wind: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/CO2_cap.csv new file mode 100644 index 0000000000..1b5869ce2e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/CO2_cap.csv @@ -0,0 +1,2 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_tons_MWh_1,CO_2_Max_Mtons_1 +NE,z1,1,0.05,0.018 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..1cda938c80 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Capacity_reserve_margin.csv @@ -0,0 +1,2 @@ +,Network_zones,CapRes_1 +NE,z1,0.156 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Energy_share_requirement.csv new file mode 100644 index 0000000000..50c97b4b39 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Energy_share_requirement.csv @@ -0,0 +1,2 @@ +,Network_zones,ESR_1,ESR_2 +NE,z1,0.259,0.348 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Fuels_data.csv new file mode 100644 index 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b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Generators_data.csv new file mode 100644 index 0000000000..9393ab0727 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Generators_data.csv @@ -0,0 +1,5 @@ +Resource,Zone,THERM,MUST_RUN,STOR,FLEX,HYDRO,VRE,LDS,Num_VRE_Bins,New_Build,Existing_Cap_MW,Existing_Cap_MWh,Existing_Charge_Cap_MW,Max_Cap_MW,Max_Cap_MWh,Max_Charge_Cap_MW,Min_Cap_MW,Min_Cap_MWh,Min_Charge_Cap_MW,Inv_Cost_per_MWyr,Inv_Cost_per_MWhyr,Inv_Cost_Charge_per_MWyr,Fixed_OM_Cost_per_MWyr,Fixed_OM_Cost_per_MWhyr,Fixed_OM_Cost_Charge_per_MWyr,Var_OM_Cost_per_MWh,Var_OM_Cost_per_MWh_In,Heat_Rate_MMBTU_per_MWh,Fuel,Cap_Size,Start_Cost_per_MW,Start_Fuel_MMBTU_per_MW,Up_Time,Down_Time,Ramp_Up_Percentage,Ramp_Dn_Percentage,Hydro_Energy_to_Power_Ratio,Min_Power,Self_Disch,Eff_Up,Eff_Down,Min_Duration,Max_Duration,Max_Flexible_Demand_Advance,Max_Flexible_Demand_Delay,Flexible_Demand_Energy_Eff,Reg_Max,Rsv_Max,Reg_Cost,Rsv_Cost,MaxCapTag_1,MaxCapTag_2,MaxCapTag_3,MinCapTag_1,MinCapTag_2,MinCapTag_3,MGA,Resource_Type,CapRes_1,ESR_1,ESR_2,region,cluster 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--- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Generators_variability.csv @@ -0,0 +1,8761 @@ +Time_Index,natural_gas_combined_cycle,solar_pv,onshore_wind,battery +1,1,0,0.889717042,1 +2,1,0,0.877715468,1 +3,1,0,0.903424203,1 +4,1,0,0.895153165,1 +5,1,0,0.757258117,1 +6,1,0,0.630928695,1 +7,1,0,0.557177782,1 +8,1,0,0.6072492,1 +9,1,0.1779,0.423417866,1 +10,1,0.429,0.007470775,1 +11,1,0.5748,0.002535942,1 +12,1,0.6484,0.002153709,1 +13,1,0.6208,0.00445132,1 +14,1,0.596,0.007711587,1 +15,1,0.5013,0.100848213,1 +16,1,0.3311,0.201802149,1 +17,1,0.0642,0.141933054,1 +18,1,0,0.567022562,1 +19,1,0,0.946024895,1 +20,1,0,0.923394203,1 +21,1,0,0.953386247,1 +22,1,0,0.929205418,1 +23,1,0,0.849528909,1 +24,1,0,0.665570974,1 +25,1,0,0.527450681,1 +26,1,0,0.64820224,1 +27,1,0,0.636301041,1 +28,1,0,0.914841771,1 +29,1,0,0.857677341,1 +30,1,0,0.824768543,1 +31,1,0,0.586626828,1 +32,1,0,0.485967815,1 +33,1,0.16,0.565889001,1 +34,1,0.3418,0.741932392,1 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new file mode 100644 index 0000000000..ddf55ebace --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Maximum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MaxCapReqConstraint,ConstraintDescription,Max_MW +1,PV,50000 +2,Wind,100000 +3,Batteries,60000 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..8593a5abcc --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,ConstraintDescription,Min_MW +1,PV,5000 +2,Wind,10000 +3,Batteries,6000 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Network.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Network.csv new file mode 100644 index 0000000000..51ec108565 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Network.csv @@ -0,0 +1,2 @@ +,Network_zones,Network_Lines,z1,Line_Max_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1 +NE,z1,1,1,2950,MA_to_CT,123.0584,0.012305837,2950,12060,0.95,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/README.md b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/README.md new file mode 100644 index 0000000000..12e70c5fe7 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/README.md @@ -0,0 +1,15 @@ +# Small New England: One Zone + +**SmallNewEngland** is set of a simplified versions of the more detailed example system RealSystemExample. It is condensed for easy comprehension and quick testing of different components of the GenX. **SmallNewEngland/OneZone** is our most basic model, a one-year example with hourly resolution containing only one zone representing New England. The model includes only natural gas, solar PV, wind, and lithium-ion battery storage with no initial capacity. + +To run the model, first navigate to the example directory at `GenX/Example_Systems/SmallNewEngland/OneZone`: + +`cd("Example_Systems/SmallNewEngland/OneZone")` + +Next, ensure that your settings in `GenX_settings.yml` are correct. The default settings use the solver Gurobi (`Solver: Gurobi`), time domain reduced input data (`TimeDomainReduction: 1`). Other optional policies include minimum capacity requirements, a capacity reserve margin, and more. A rate-based carbon cap of 50 gCO2 per kWh is specified in the `CO2_cap.csv` input file. + +Once the settings are confirmed, run the model with the `Run.jl` script in the example directory: + +`include("Run.jl")` + +Once the model has completed, results will write to the `Results` directory. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Reserves.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Reserves.csv new file mode 100644 index 0000000000..b96bce1284 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Run.jl b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml new file mode 100644 index 0000000000..170ac4917d --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml @@ -0,0 +1,21 @@ +NetworkExpansion: 0 +TimeDomainReductionFolder: "TDR_Results" +ModelingToGenerateAlternativeIterations: 3 +ParameterScale: 1 +EnergyShareRequirement: 1 +PrintModel: 0 +TimeDomainReduction: 0 +Trans_Loss_Segments: 1 +CapacityReserveMargin: 1 +ModelingtoGenerateAlternativeSlack: 0.1 +Solver: "HiGHS" +MethodofMorris: 0 +Reserves: 0 +StorageLosses: 1 +OverwriteResults: 0 +UCommit: 2 +ModelingToGenerateAlternatives: 0 +MaxCapReq: 1 +MinCapReq: 1 +CO2Cap: 2 +WriteShadowPrices: 1 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..0aa53a56d3 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/gurobi_settings.yml @@ -0,0 +1,15 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: 0 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/highs_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/highs_settings.yml new file mode 100644 index 0000000000..e244a0b4cf --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/highs_settings.yml @@ -0,0 +1,13 @@ +# HiGHS Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Primal feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +Optimal_Tol: 1.0e-05 # Dual feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +TimeLimit: 1.0e23 # Time limit # [type: double, advanced: false, range: [0, inf], default: inf] +Pre_Solve: choose # Presolve option: "off", "choose" or "on" # [type: string, advanced: false, default: "choose"] +Method: choose #HiGHS-specific solver settings # Solver option: "simplex", "choose" or "ipm" # [type: string, advanced: false, default: "choose"] + +#highs-specific solver settings + +# run the crossover routine for ipx +# [type: string, advanced: "on", range: {"off", "on"}, default: "off"] +run_crossover: "on" diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/scip_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/scip_settings.yml new file mode 100644 index 0000000000..2779d54826 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/scip_settings.yml @@ -0,0 +1,5 @@ +# SCIP Solver Parameters + +#SCIP-specific solver settings +Dispverblevel: 0 +limitsgap: 0.05 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/time_domain_reduction_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/time_domain_reduction_settings.yml new file mode 100644 index 0000000000..a1db56f2be --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/time_domain_reduction_settings.yml @@ -0,0 +1,151 @@ +##### +# +# TIME DOMAIN REDUCTION SETTINGS +# +# Set parameters here that organize how your full timeseries +# data will be divided into representative period clusters. +# Ensure that time_domain_reduction is set to 1 in GenX_settings.yml +# before running. Run within GenX or use PreCluster.jl to test and +# examine representative period output before proceeding. +# Specify your data input directory as inpath within Run_test.jl +# or PreCluster.jl. +# +##### + + # - TimestepsPerRepPeriod + # Typically 168 timesteps (e.g., hours) per period, this designates + # the length of each representative period. +TimestepsPerRepPeriod: 168 + + # - ClusterMethod + # Either 'kmeans' or 'kmedoids', this designates the method used to cluster + # periods and determine each point's representative period. +ClusterMethod: 'kmeans' + + # - ScalingMethod + # Either 'N' or 'S', this designates directs the module to normalize ([0,1]) + # or standardize (mean 0, variance 1) the input data. +ScalingMethod: "S" + + # - MaxPeriods + # The maximum number of periods - both clustered periods and extreme periods - + # that may be used to represent the input data. If IterativelyAddPeriods is on and the + # error threshold is never met, this will be the total number of periods. +MaxPeriods: 11 + + # - MinPeriods + # The minimum number of periods used to represent the input data. If using + # UseExtremePeriods, this must be at least the number of extreme periods requests. If + # IterativelyAddPeriods if off, this will be the total number of periods. +MinPeriods: 8 + + # - IterativelyAddPeriods + # Either 'yes' or 'no', this designates whether or not to add periods + # until the error threshold between input data and represented data is met or the maximum + # number of periods is reached. +IterativelyAddPeriods: 1 + + # - IterateMethod + # Either 'cluster' or 'extreme', this designates whether to add clusters to + # the kmeans/kmedoids method or to set aside the worst-fitting periods as a new extreme periods. + # The default option is 'cluster'. +IterateMethod: "cluster" + + # - Threshold + # Iterative period addition will end if the period farthest (Euclidean Distance) + # from its representative period is within this percentage of the total possible error (for normalization) + # or ~95% of the total possible error (for standardization). E.g., for a threshold of 0.01, + # every period must be within 1% of the spread of possible error before the clustering + # iterations will terminate (or until the max number of periods is reached). +Threshold: 0.05 + + # - nReps + # The number of times to repeat each kmeans/kmedoids clustering at the same setting. +nReps: 100 + + # - LoadWeight + # Default 1, this is an optional multiplier on load columns in order to prioritize + # better fits for load profiles over resource capacity factor profiles. +LoadWeight: 1 + + # - WeightTotal + # Default 8760, the sum to which the relative weights of representative periods will be scaled. +WeightTotal: 8760 + + # - ClusterFuelPrices + # Either 1 (yes) or 0 (no), this indicates whether or not to use the fuel price + # time series in Fuels_data.csv in the clustering process. If 0, this function will still write + # Fuels_data_clustered.csv with reshaped fuel prices based on the number and size of the + # representative weeks, assuming a constant time series of fuel prices with length equal to the + # number of timesteps in the raw input data. +ClusterFuelPrices: 1 + + # - UseExtremePeriods + # Either 'yes' or 'no', this designates whether or not to include + # outliers (by performance or load/resource extreme) as their own representative periods. + # This setting automatically includes the periods with maximum load, minimum solar cf and + # minimum wind cf as extreme periods. +UseExtremePeriods: 1 + + # - MultiStageConcatenate + # (Only considered if MultiStage = 1 in genx_settings.yml) + # If 1, this designates that the model should time domain reduce the input data + # of all model stages together. Else if 0, the model will time domain reduce each + # stage separately +MultiStageConcatenate: 0 + +# STILL IN DEVELOPMENT - Currently just uses integral max load, integral min PV and wind. +# - ExtremePeriods +# Use this to define which periods to be included among the final representative periods +# as "Extreme Periods". +# Select by profile type: load ("Load"), solar PV capacity factors ("PV"), and wind capacity factors ("Wind"). +# Select whether to examine these profiles by zone ("Zone") or across the whole system ("System"). +# Select whether to look for absolute max/min at the timestep level ("Absolute") +# or max/min sum across the period ("Integral"). +# Select whether you want the maximum ("Max") or minimum ("Min") (of the prior type) for each profile type. +ExtremePeriods: + Load: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 1 + Min: 0 + Integral: + Max: 0 + Min: 0 + PV: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + Wind: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/CO2_cap.csv new file mode 100644 index 0000000000..49d1015799 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/CO2_cap.csv @@ -0,0 +1,2 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_tons_MWh_1,CO_2_Max_Mtons_1 +NE,z1,1,0.02,0.018 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..1cda938c80 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Capacity_reserve_margin.csv @@ -0,0 +1,2 @@ +,Network_zones,CapRes_1 +NE,z1,0.156 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Energy_share_requirement.csv new file mode 100644 index 0000000000..a08b5bc2b9 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Energy_share_requirement.csv @@ -0,0 +1,2 @@ +,Network_zones,RPS_1,RPS_2 +NE,z1,0.259,0.348 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Fuels_data.csv new file mode 100644 index 0000000000..0e2c1fbc90 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Fuels_data.csv @@ -0,0 +1,8762 @@ +Time_Index,NG,None +0,0.05306,0 +1,5.28,0 +2,5.28,0 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b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Generators_data.csv @@ -0,0 +1,7 @@ +Resource,Zone,THERM,MUST_RUN,STOR,FLEX,HYDRO,VRE,LDS,Num_VRE_Bins,New_Build,Existing_Cap_MW,Existing_Cap_MWh,Existing_Charge_Cap_MW,Max_Cap_MW,Max_Cap_MWh,Max_Charge_Cap_MW,Min_Cap_MW,Min_Cap_MWh,Min_Charge_Cap_MW,Inv_Cost_per_MWyr,Inv_Cost_per_MWhyr,Inv_Cost_Charge_per_MWyr,Fixed_OM_Cost_per_MWyr,Fixed_OM_Cost_per_MWhyr,Fixed_OM_Cost_Charge_per_MWyr,Var_OM_Cost_per_MWh,Var_OM_Cost_per_MWh_In,Heat_Rate_MMBTU_per_MWh,Fuel,Cap_Size,Start_Cost_per_MW,Start_Fuel_MMBTU_per_MW,Up_Time,Down_Time,Ramp_Up_Percentage,Ramp_Dn_Percentage,Hydro_Energy_to_Power_Ratio,Min_Power,Self_Disch,Eff_Up,Eff_Down,Min_Duration,Max_Duration,Max_Flexible_Demand_Advance,Max_Flexible_Demand_Delay,Flexible_Demand_Energy_Eff,Reg_Max,Rsv_Max,Reg_Cost,Rsv_Cost,MinCapTag_1,MinCapTag_2,MinCapTag_3,MGA,Resource_Type,CapRes_1,RPS_1,RPS_2,region,cluster 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-0,0 +1,4 @@ +MaxCapReqConstraint,ConstraintDescription,Max_MW +1,PV,50000 +2,Wind,100000 +3,Batteries,60000 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..8593a5abcc --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,ConstraintDescription,Min_MW +1,PV,5000 +2,Wind,10000 +3,Batteries,6000 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/README.md b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/README.md new file mode 100644 index 0000000000..1559780daa --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/README.md @@ -0,0 +1,15 @@ +# Small New England: One Zone + VRE Bins + +**SmallNewEngland** is set of a simplified versions of the more detailed example system RealSystemExample. It is condensed for easy comprehension and quick testing of different components of the GenX. **SmallNewEngland/OneZone_3VREBin** is an iteration on our most basic model. Like the basic model, this is a one-year example with hourly resolution containing only one zone representing New England. In addition, it divides its wind resource into three separate bins with different parameters as defined in `Generators_data.csv` and `Generators_variability.csv`. This division into bins of different costs, capacities, and capacity factors allows the user to model more realistic supply curves of each resource. + +To run the model, first navigate to the example directory at `GenX/Example_Systems/SmallNewEngland/OneZone_3VREBin`: + +`cd("Example_Systems/SmallNewEngland/OneZone_3VREBin")` + +Next, ensure that your settings in `GenX_settings.yml` are correct. The default settings use the solver Gurobi (`Solver: Gurobi`), time domain reduced input data (`TimeDomainReduction: 1`), and linearized unit commitment of thermal resources (`UCommit: 2`). Other optional policies include minimum capacity requirements, a capacity reserve margin, and more. A rate-based carbon cap of 20 gCO2 per kWh is specified in the `CO2_cap.csv` input file. + +Once the settings are confirmed, run the model with the `Run.jl` script in the example directory: + +`include("Run.jl")` + +Once the model has completed, results will write to the `Results` directory. \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Reserves.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Reserves.csv new file mode 100644 index 0000000000..b96bce1284 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Run.jl b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/genx_settings.yml new file mode 100644 index 0000000000..7dc9c1b419 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/genx_settings.yml @@ -0,0 +1,21 @@ +OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 0 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 0 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 0 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 2 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 0 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: HiGHS # Available solvers: Gurobi, CPLEX, CLP, SCIP +ParameterScale: 1 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 2 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 1 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 # Number of MGA iterations with maximization and minimization objective +MethodofMorris: 0 #Flag for turning on the Method of Morris analysis diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..fd4d9b8a83 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/gurobi_settings.yml @@ -0,0 +1,15 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 4 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: -1 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/highs_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/highs_settings.yml new file mode 100644 index 0000000000..a5e021ae70 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/highs_settings.yml @@ -0,0 +1,11 @@ +# HiGHS Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Primal feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +Optimal_Tol: 1.0e-05 # Dual feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +TimeLimit: 1.0e23 # Time limit # [type: double, advanced: false, range: [0, inf], default: inf] +Pre_Solve: choose # Presolve option: "off", "choose" or "on" # [type: string, advanced: false, default: "choose"] +Method: ipm #HiGHS-specific solver settings # Solver option: "simplex", "choose" or "ipm" # [type: string, advanced: false, default: "choose"] + +# run the crossover routine for ipx +# [type: string, advanced: "on", range: {"off", "on"}, default: "off"] +run_crossover: "on" diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/scip_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/scip_settings.yml new file mode 100644 index 0000000000..2779d54826 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/scip_settings.yml @@ -0,0 +1,5 @@ +# SCIP Solver Parameters + +#SCIP-specific solver settings +Dispverblevel: 0 +limitsgap: 0.05 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/time_domain_reduction_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/time_domain_reduction_settings.yml new file mode 100644 index 0000000000..02ce3d964d --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_3VREBin/Settings/time_domain_reduction_settings.yml @@ -0,0 +1,151 @@ +##### +# +# TIME DOMAIN REDUCTION SETTINGS +# +# Set parameters here that organize how your full timeseries +# data will be divided into representative period clusters. +# Ensure that time_domain_reduction is set to 1 in GenX_settings.yml +# before running. Run within GenX or use PreCluster.jl to test and +# examine representative period output before proceeding. +# Specify your data input directory as inpath within Run_test.jl +# or PreCluster.jl. +# +##### + + # - TimestepsPerRepPeriod + # Typically 168 timesteps (e.g., hours) per period, this designates + # the length of each representative period. +TimestepsPerRepPeriod: 168 + + # - ClusterMethod + # Either 'kmeans' or 'kmedoids', this designates the method used to cluster + # periods and determine each point's representative period. +ClusterMethod: 'kmeans' + + # - ScalingMethod + # Either 'N' or 'S', this designates directs the module to normalize ([0,1]) + # or standardize (mean 0, variance 1) the input data. +ScalingMethod: "S" + + # - MaxPeriods + # The maximum number of periods - both clustered periods and extreme periods - + # that may be used to represent the input data. If IterativelyAddPeriods is on and the + # error threshold is never met, this will be the total number of periods. +MaxPeriods: 11 + + # - MinPeriods + # The minimum number of periods used to represent the input data. If using + # UseExtremePeriods, this must be at least the number of extreme periods requests. If + # IterativelyAddPeriods if off, this will be the total number of periods. +MinPeriods: 6 + + # - IterativelyAddPeriods + # Either 'yes' or 'no', this designates whether or not to add periods + # until the error threshold between input data and represented data is met or the maximum + # number of periods is reached. +IterativelyAddPeriods: 1 + + # - IterateMethod + # Either 'cluster' or 'extreme', this designates whether to add clusters to + # the kmeans/kmedoids method or to set aside the worst-fitting periods as a new extreme periods. + # The default option is 'cluster'. +IterateMethod: "cluster" + + # - Threshold + # Iterative period addition will end if the period farthest (Euclidean Distance) + # from its representative period is within this percentage of the total possible error (for normalization) + # or ~95% of the total possible error (for standardization). E.g., for a threshold of 0.01, + # every period must be within 1% of the spread of possible error before the clustering + # iterations will terminate (or until the max number of periods is reached). +Threshold: 0.05 + + # - nReps + # The number of times to repeat each kmeans/kmedoids clustering at the same setting. +nReps: 100 + + # - LoadWeight + # Default 1, this is an optional multiplier on load columns in order to prioritize + # better fits for load profiles over resource capacity factor profiles. +LoadWeight: 1 + + # - WeightTotal + # Default 8760, the sum to which the relative weights of representative periods will be scaled. +WeightTotal: 8760 + + # - ClusterFuelPrices + # Either 'yes' ro 'no', this indicates whether or not to use the fuel price + # time series in Fuels_data.csv in the clustering process. If 'no', this function will still write + # Fuels_data_clustered.csv with reshaped fuel prices based on the number and size of the + # representative weeks, assuming a constant time series of fuel prices with length equal to the + # number of timesteps in the raw input data. +ClusterFuelPrices: 1 + + # - MultiStageConcatenate + # (Only considered if MultiStage = 1 in genx_settings.yml) + # If 1, this designates that the model should time domain reduce the input data + # of all model stages together. Else if 0, the model will time domain reduce each + # stage separately +MultiStageConcatenate: 0 + + # - UseExtremePeriods + # Either 'yes' or 'no', this designates whether or not to include + # outliers (by performance or load/resource extreme) as their own representative periods. + # This setting automatically includes the periods with maximum load, minimum solar cf and + # minimum wind cf as extreme periods. +UseExtremePeriods: 1 + +# STILL IN DEVELOPMENT - Currently just uses integral max load, integral min PV and wind. +# - ExtremePeriods +# Use this to define which periods to be included among the final representative periods +# as "Extreme Periods". +# Select by profile type: load ("Load"), solar PV capacity factors ("PV"), and wind capacity factors ("Wind"). +# Select whether to examine these profiles by zone ("Zone") or across the whole system ("System"). +# Select whether to look for absolute max/min at the timestep level ("Absolute") +# or max/min sum across the period ("Integral"). +# Select whether you want the maximum ("Max") or minimum ("Min") (of the prior type) for each profile type. +ExtremePeriods: + Load: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 1 + Min: 0 + Integral: + Max: 0 + Min: 0 + PV: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + Wind: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/CO2_cap.csv new file mode 100644 index 0000000000..f1f3ff4165 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/CO2_cap.csv @@ -0,0 +1,2 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_tons_MWh_1,CO_2_Max_Mtons_1 +NE,z1,1,1,1 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..1cda938c80 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Capacity_reserve_margin.csv @@ -0,0 +1,2 @@ +,Network_zones,CapRes_1 +NE,z1,0.156 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Energy_share_requirement.csv new file mode 100644 index 0000000000..50c97b4b39 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Energy_share_requirement.csv @@ -0,0 +1,2 @@ +,Network_zones,ESR_1,ESR_2 +NE,z1,0.259,0.348 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Fuels_data.csv new file mode 100644 index 0000000000..0e2c1fbc90 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Fuels_data.csv @@ -0,0 +1,8762 @@ +Time_Index,NG,None +0,0.05306,0 +1,5.28,0 +2,5.28,0 +3,5.28,0 +4,5.28,0 +5,5.28,0 +6,5.28,0 +7,5.28,0 +8,5.28,0 +9,5.28,0 +10,5.28,0 +11,5.28,0 +12,5.28,0 +13,5.28,0 +14,5.28,0 +15,5.28,0 +16,5.28,0 +17,5.28,0 +18,5.28,0 +19,5.28,0 +20,5.28,0 +21,5.28,0 +22,5.28,0 +23,5.28,0 +24,5.28,0 +25,5.28,0 +26,5.28,0 +27,5.28,0 +28,5.28,0 +29,5.28,0 +30,5.28,0 +31,5.28,0 +32,5.28,0 +33,5.28,0 +34,5.28,0 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+Resource,Zone,THERM,MUST_RUN,STOR,FLEX,HYDRO,VRE,Num_VRE_Bins,New_Build,Existing_Cap_MW,Existing_Cap_MWh,Existing_Charge_Cap_MW,Max_Cap_MW,Max_Cap_MWh,Max_Charge_Cap_MW,Min_Cap_MW,Min_Cap_MWh,Min_Charge_Cap_MW,Inv_Cost_per_MWyr,Inv_Cost_per_MWhyr,Inv_Cost_Charge_per_MWyr,Fixed_OM_Cost_per_MWyr,Fixed_OM_Cost_per_MWhyr,Fixed_OM_Cost_Charge_per_MWyr,Var_OM_Cost_per_MWh,Var_OM_Cost_per_MWh_In,Heat_Rate_MMBTU_per_MWh,Fuel,Cap_Size,Start_Cost_per_MW,Start_Fuel_MMBTU_per_MW,Up_Time,Down_Time,Ramp_Up_Percentage,Ramp_Dn_Percentage,Hydro_Energy_to_Power_Ratio,Min_Power,Self_Disch,Eff_Up,Eff_Down,Min_Duration,Max_Duration,Max_Flexible_Demand_Advance,Max_Flexible_Demand_Delay,Flexible_Demand_Energy_Eff,Reg_Max,Rsv_Max,Reg_Cost,Rsv_Cost,MinCapTag,MinCapTag_1,MinCapTag_2,MinCapTag_3,MGA,Resource_Type,CapRes_1,ESR_1,ESR_2,region,cluster,WACC,Capital_Recovery_Period,Lifetime,Min_Retired_Cap_MW,Min_Retired_Energy_Cap_MW,Min_Retired_Charge_Cap_MW,LDS 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b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Generators_variability.csv new file mode 100644 index 0000000000..e27e9ab755 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Generators_variability.csv @@ -0,0 +1,8761 @@ +Time_Index,natural_gas_combined_cycle,solar_pv,onshore_wind,battery +1,1,0,0.889717042,1 +2,1,0,0.877715468,1 +3,1,0,0.903424203,1 +4,1,0,0.895153165,1 +5,1,0,0.757258117,1 +6,1,0,0.630928695,1 +7,1,0,0.557177782,1 +8,1,0,0.6072492,1 +9,1,0.1779,0.423417866,1 +10,1,0.429,0.007470775,1 +11,1,0.5748,0.002535942,1 +12,1,0.6484,0.002153709,1 +13,1,0.6208,0.00445132,1 +14,1,0.596,0.007711587,1 +15,1,0.5013,0.100848213,1 +16,1,0.3311,0.201802149,1 +17,1,0.0642,0.141933054,1 +18,1,0,0.567022562,1 +19,1,0,0.946024895,1 +20,1,0,0.923394203,1 +21,1,0,0.953386247,1 +22,1,0,0.929205418,1 +23,1,0,0.849528909,1 +24,1,0,0.665570974,1 +25,1,0,0.527450681,1 +26,1,0,0.64820224,1 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a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..8593a5abcc --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,ConstraintDescription,Min_MW +1,PV,5000 +2,Wind,10000 +3,Batteries,6000 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Reserves.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Reserves.csv new file mode 100644 index 0000000000..b96bce1284 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p1/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/CO2_cap.csv new file mode 100644 index 0000000000..e8a5ff9305 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/CO2_cap.csv @@ -0,0 +1,2 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_tons_MWh_1,CO_2_Max_Mtons_1 +NE,z1,1,0.5,0.5 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..1cda938c80 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Capacity_reserve_margin.csv @@ -0,0 +1,2 @@ +,Network_zones,CapRes_1 +NE,z1,0.156 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Energy_share_requirement.csv new file mode 100644 index 0000000000..50c97b4b39 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Energy_share_requirement.csv @@ -0,0 +1,2 @@ +,Network_zones,ESR_1,ESR_2 +NE,z1,0.259,0.348 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Fuels_data.csv new file mode 100644 index 0000000000..0e2c1fbc90 --- /dev/null +++ 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a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Generators_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Generators_data.csv new file mode 100644 index 0000000000..343e52b9c5 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Generators_data.csv @@ -0,0 +1,5 @@ +Resource,Zone,THERM,MUST_RUN,STOR,FLEX,HYDRO,VRE,Num_VRE_Bins,New_Build,Existing_Cap_MW,Existing_Cap_MWh,Existing_Charge_Cap_MW,Max_Cap_MW,Max_Cap_MWh,Max_Charge_Cap_MW,Min_Cap_MW,Min_Cap_MWh,Min_Charge_Cap_MW,Inv_Cost_per_MWyr,Inv_Cost_per_MWhyr,Inv_Cost_Charge_per_MWyr,Fixed_OM_Cost_per_MWyr,Fixed_OM_Cost_per_MWhyr,Fixed_OM_Cost_Charge_per_MWyr,Var_OM_Cost_per_MWh,Var_OM_Cost_per_MWh_In,Heat_Rate_MMBTU_per_MWh,Fuel,Cap_Size,Start_Cost_per_MW,Start_Fuel_MMBTU_per_MW,Up_Time,Down_Time,Ramp_Up_Percentage,Ramp_Dn_Percentage,Hydro_Energy_to_Power_Ratio,Min_Power,Self_Disch,Eff_Up,Eff_Down,Min_Duration,Max_Duration,Max_Flexible_Demand_Advance,Max_Flexible_Demand_Delay,Flexible_Demand_Energy_Eff,Reg_Max,Rsv_Max,Reg_Cost,Rsv_Cost,MinCapTag,MinCapTag_1,MinCapTag_2,MinCapTag_3,MGA,Resource_Type,CapRes_1,ESR_1,ESR_2,region,cluster,WACC,Capital_Recovery_Period,Lifetime,Min_Retired_Cap_MW,Min_Retired_Energy_Cap_MW,Min_Retired_Charge_Cap_MW,LDS 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b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Generators_variability.csv new file mode 100644 index 0000000000..e27e9ab755 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Generators_variability.csv @@ -0,0 +1,8761 @@ +Time_Index,natural_gas_combined_cycle,solar_pv,onshore_wind,battery +1,1,0,0.889717042,1 +2,1,0,0.877715468,1 +3,1,0,0.903424203,1 +4,1,0,0.895153165,1 +5,1,0,0.757258117,1 +6,1,0,0.630928695,1 +7,1,0,0.557177782,1 +8,1,0,0.6072492,1 +9,1,0.1779,0.423417866,1 +10,1,0.429,0.007470775,1 +11,1,0.5748,0.002535942,1 +12,1,0.6484,0.002153709,1 +13,1,0.6208,0.00445132,1 +14,1,0.596,0.007711587,1 +15,1,0.5013,0.100848213,1 +16,1,0.3311,0.201802149,1 +17,1,0.0642,0.141933054,1 +18,1,0,0.567022562,1 +19,1,0,0.946024895,1 +20,1,0,0.923394203,1 +21,1,0,0.953386247,1 +22,1,0,0.929205418,1 +23,1,0,0.849528909,1 +24,1,0,0.665570974,1 +25,1,0,0.527450681,1 +26,1,0,0.64820224,1 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a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..8593a5abcc --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,ConstraintDescription,Min_MW +1,PV,5000 +2,Wind,10000 +3,Batteries,6000 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Reserves.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Reserves.csv new file mode 100644 index 0000000000..b96bce1284 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p2/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/CO2_cap.csv new file mode 100644 index 0000000000..cfb18ecf62 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/CO2_cap.csv @@ -0,0 +1,2 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Max_tons_MWh_1,CO_2_Max_Mtons_1 +NE,z1,1,0.05,0.05 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..1cda938c80 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Capacity_reserve_margin.csv @@ -0,0 +1,2 @@ +,Network_zones,CapRes_1 +NE,z1,0.156 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Energy_share_requirement.csv new file mode 100644 index 0000000000..50c97b4b39 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Energy_share_requirement.csv @@ -0,0 +1,2 @@ +,Network_zones,ESR_1,ESR_2 +NE,z1,0.259,0.348 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Fuels_data.csv new file mode 100644 index 0000000000..0e2c1fbc90 --- /dev/null +++ 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a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Generators_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Generators_data.csv new file mode 100644 index 0000000000..343e52b9c5 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Generators_data.csv @@ -0,0 +1,5 @@ +Resource,Zone,THERM,MUST_RUN,STOR,FLEX,HYDRO,VRE,Num_VRE_Bins,New_Build,Existing_Cap_MW,Existing_Cap_MWh,Existing_Charge_Cap_MW,Max_Cap_MW,Max_Cap_MWh,Max_Charge_Cap_MW,Min_Cap_MW,Min_Cap_MWh,Min_Charge_Cap_MW,Inv_Cost_per_MWyr,Inv_Cost_per_MWhyr,Inv_Cost_Charge_per_MWyr,Fixed_OM_Cost_per_MWyr,Fixed_OM_Cost_per_MWhyr,Fixed_OM_Cost_Charge_per_MWyr,Var_OM_Cost_per_MWh,Var_OM_Cost_per_MWh_In,Heat_Rate_MMBTU_per_MWh,Fuel,Cap_Size,Start_Cost_per_MW,Start_Fuel_MMBTU_per_MW,Up_Time,Down_Time,Ramp_Up_Percentage,Ramp_Dn_Percentage,Hydro_Energy_to_Power_Ratio,Min_Power,Self_Disch,Eff_Up,Eff_Down,Min_Duration,Max_Duration,Max_Flexible_Demand_Advance,Max_Flexible_Demand_Delay,Flexible_Demand_Energy_Eff,Reg_Max,Rsv_Max,Reg_Cost,Rsv_Cost,MinCapTag,MinCapTag_1,MinCapTag_2,MinCapTag_3,MGA,Resource_Type,CapRes_1,ESR_1,ESR_2,region,cluster,WACC,Capital_Recovery_Period,Lifetime,Min_Retired_Cap_MW,Min_Retired_Energy_Cap_MW,Min_Retired_Charge_Cap_MW,LDS 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b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Generators_variability.csv new file mode 100644 index 0000000000..e27e9ab755 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Generators_variability.csv @@ -0,0 +1,8761 @@ +Time_Index,natural_gas_combined_cycle,solar_pv,onshore_wind,battery +1,1,0,0.889717042,1 +2,1,0,0.877715468,1 +3,1,0,0.903424203,1 +4,1,0,0.895153165,1 +5,1,0,0.757258117,1 +6,1,0,0.630928695,1 +7,1,0,0.557177782,1 +8,1,0,0.6072492,1 +9,1,0.1779,0.423417866,1 +10,1,0.429,0.007470775,1 +11,1,0.5748,0.002535942,1 +12,1,0.6484,0.002153709,1 +13,1,0.6208,0.00445132,1 +14,1,0.596,0.007711587,1 +15,1,0.5013,0.100848213,1 +16,1,0.3311,0.201802149,1 +17,1,0.0642,0.141933054,1 +18,1,0,0.567022562,1 +19,1,0,0.946024895,1 +20,1,0,0.923394203,1 +21,1,0,0.953386247,1 +22,1,0,0.929205418,1 +23,1,0,0.849528909,1 +24,1,0,0.665570974,1 +25,1,0,0.527450681,1 +26,1,0,0.64820224,1 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a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..8593a5abcc --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,ConstraintDescription,Min_MW +1,PV,5000 +2,Wind,10000 +3,Batteries,6000 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Reserves.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Reserves.csv new file mode 100644 index 0000000000..b96bce1284 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Inputs/Inputs_p3/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/README.md b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/README.md new file mode 100644 index 0000000000..9c476e4d92 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/README.md @@ -0,0 +1,21 @@ +# Small New England: One Zone Mulit Period + +**SmallNewEngland** is set of a simplified versions of the more detailed example system RealSystemExample. It is condensed for easy comprehension and quick testing of different components of the GenX. **SmallNewEngland/OneZone_MultiStage** is a toy multi-stage model, with three model periods and only one zone representing New England. The model includes only natural gas, solar PV, wind, and lithium-ion battery storage. Existing capacities are included to demonstrate the endogenous retirement capabilities of mutli-period modeling in GenX, although these capacities do not reflect actual brownfield capacity in the New England region. + +To run the model, first navigate to the example directory at `GenX/Example_Systems/SmallNewEngland/OneZone_MultiStage`: + +`cd("Example_Systems/SmallNewEngland/OneZone_MultiStage")` + +Next, ensure that your settings in `GenX_settings.yml` are correct. The default settings use the solver Gurobi (`Solver: Gurobi`), and time domain reduced input data (`TimeDomainReduction: 1`). + +The `multi_stage_settings.yml` file contains settings parameters specific to multi-stage modeling. This example is configured for three model periods (`NumPeriods: 3`) of 10 years in length each (`PeriodLength: 10`). + +Multi-period modeling in GenX requires a separate set of model inputs for each period to be modeled, which are located in the directories `Inputs/Inputs_p$`, where `$` is the number of the model period. Although separate model periods can have different costs and policy parameters, the resources names and types, specified in each `Generators_data.csv` must be identical across model periods. In addition, multi-stage modeling with a single zone requires an additional input file, `Generators_data_multi_stage.csv`, also located in the `Inputs/` directory, which contains fields related to resource lifetimes, capital recovery periods, and endogenous retirements. + +A rate-based carbon cap becomes more stringent across the three model periods, declining from of 1,000 gCO2 per kWh in the first period, 500 gCO2 per kWh in the second period, and 50 gCO2 per kWh in the third period, as specified in the `CO2_cap.csv` input files in `Inputs/Inputs_p1`, `Inputs/Inputs_p2`, and `Inputs/Inputs_p3` respectively. + +Once the settings are confirmed, run the model with the `Run_multi_stage.jl` script in the example directory: + +`include("Run.jl")` + +Once the model has completed, results will write to the `Results/` directory. \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Run.jl b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/genx_settings.yml new file mode 100644 index 0000000000..9899e5bec3 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/genx_settings.yml @@ -0,0 +1,22 @@ +#MacOrWindows: Mac # Set to either "Mac" (also works for Linux) or "Windows" to ensure use of proper file directory separator "\" or "/ +OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 0 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 1 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 0 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 0 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 2 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 0 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: HiGHS # Available solvers: Gurobi, CPLEX, Clp, Cbc +ParameterScale: 1 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 2 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 1 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 +MultiStage: 1 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..6593f70370 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/gurobi_settings.yml @@ -0,0 +1,16 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 4 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: -1 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. +OutputFlag: 0 # Controls Gurobi output.s diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/highs_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/highs_settings.yml new file mode 100644 index 0000000000..9437f19683 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/highs_settings.yml @@ -0,0 +1,11 @@ +# HiGHS Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Primal feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +Optimal_Tol: 1.0e-05 # Dual feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +TimeLimit: 1.0e23 # Time limit # [type: double, advanced: false, range: [0, inf], default: inf] +Pre_Solve: choose # Presolve option: "off", "choose" or "on" # [type: string, advanced: false, default: "choose"] +Method: ipm #HiGHS-specific solver settings # Solver option: "simplex", "choose" or "ipm" # [type: string, advanced: false, default: "choose"] + +# run the crossover routine for ipx +# [type: string, advanced: "on", range: {"off", "on"}, default: "off"] +run_crossover: "on" diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/multi_stage_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/multi_stage_settings.yml new file mode 100644 index 0000000000..5254725c5b --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/multi_stage_settings.yml @@ -0,0 +1,5 @@ +NumStages: 3 # Number of model investment stages +StageLengths: [10,10,10] # Length of each model stage (years) +WACC: 0.045 # Weighted average cost of capital +ConvergenceTolerance: 0.01 # Relative optimality gap used for convergence +Myopic: 0 # If using multi-stage GenX, 1 = myopic, 0 = dual dynamic programming diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/time_domain_reduction_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/time_domain_reduction_settings.yml new file mode 100644 index 0000000000..02ce3d964d --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone_MultiStage/Settings/time_domain_reduction_settings.yml @@ -0,0 +1,151 @@ +##### +# +# TIME DOMAIN REDUCTION SETTINGS +# +# Set parameters here that organize how your full timeseries +# data will be divided into representative period clusters. +# Ensure that time_domain_reduction is set to 1 in GenX_settings.yml +# before running. Run within GenX or use PreCluster.jl to test and +# examine representative period output before proceeding. +# Specify your data input directory as inpath within Run_test.jl +# or PreCluster.jl. +# +##### + + # - TimestepsPerRepPeriod + # Typically 168 timesteps (e.g., hours) per period, this designates + # the length of each representative period. +TimestepsPerRepPeriod: 168 + + # - ClusterMethod + # Either 'kmeans' or 'kmedoids', this designates the method used to cluster + # periods and determine each point's representative period. +ClusterMethod: 'kmeans' + + # - ScalingMethod + # Either 'N' or 'S', this designates directs the module to normalize ([0,1]) + # or standardize (mean 0, variance 1) the input data. +ScalingMethod: "S" + + # - MaxPeriods + # The maximum number of periods - both clustered periods and extreme periods - + # that may be used to represent the input data. If IterativelyAddPeriods is on and the + # error threshold is never met, this will be the total number of periods. +MaxPeriods: 11 + + # - MinPeriods + # The minimum number of periods used to represent the input data. If using + # UseExtremePeriods, this must be at least the number of extreme periods requests. If + # IterativelyAddPeriods if off, this will be the total number of periods. +MinPeriods: 6 + + # - IterativelyAddPeriods + # Either 'yes' or 'no', this designates whether or not to add periods + # until the error threshold between input data and represented data is met or the maximum + # number of periods is reached. +IterativelyAddPeriods: 1 + + # - IterateMethod + # Either 'cluster' or 'extreme', this designates whether to add clusters to + # the kmeans/kmedoids method or to set aside the worst-fitting periods as a new extreme periods. + # The default option is 'cluster'. +IterateMethod: "cluster" + + # - Threshold + # Iterative period addition will end if the period farthest (Euclidean Distance) + # from its representative period is within this percentage of the total possible error (for normalization) + # or ~95% of the total possible error (for standardization). E.g., for a threshold of 0.01, + # every period must be within 1% of the spread of possible error before the clustering + # iterations will terminate (or until the max number of periods is reached). +Threshold: 0.05 + + # - nReps + # The number of times to repeat each kmeans/kmedoids clustering at the same setting. +nReps: 100 + + # - LoadWeight + # Default 1, this is an optional multiplier on load columns in order to prioritize + # better fits for load profiles over resource capacity factor profiles. +LoadWeight: 1 + + # - WeightTotal + # Default 8760, the sum to which the relative weights of representative periods will be scaled. +WeightTotal: 8760 + + # - ClusterFuelPrices + # Either 'yes' ro 'no', this indicates whether or not to use the fuel price + # time series in Fuels_data.csv in the clustering process. If 'no', this function will still write + # Fuels_data_clustered.csv with reshaped fuel prices based on the number and size of the + # representative weeks, assuming a constant time series of fuel prices with length equal to the + # number of timesteps in the raw input data. +ClusterFuelPrices: 1 + + # - MultiStageConcatenate + # (Only considered if MultiStage = 1 in genx_settings.yml) + # If 1, this designates that the model should time domain reduce the input data + # of all model stages together. Else if 0, the model will time domain reduce each + # stage separately +MultiStageConcatenate: 0 + + # - UseExtremePeriods + # Either 'yes' or 'no', this designates whether or not to include + # outliers (by performance or load/resource extreme) as their own representative periods. + # This setting automatically includes the periods with maximum load, minimum solar cf and + # minimum wind cf as extreme periods. +UseExtremePeriods: 1 + +# STILL IN DEVELOPMENT - Currently just uses integral max load, integral min PV and wind. +# - ExtremePeriods +# Use this to define which periods to be included among the final representative periods +# as "Extreme Periods". +# Select by profile type: load ("Load"), solar PV capacity factors ("PV"), and wind capacity factors ("Wind"). +# Select whether to examine these profiles by zone ("Zone") or across the whole system ("System"). +# Select whether to look for absolute max/min at the timestep level ("Absolute") +# or max/min sum across the period ("Integral"). +# Select whether you want the maximum ("Max") or minimum ("Min") (of the prior type) for each profile type. +ExtremePeriods: + Load: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 1 + Min: 0 + Integral: + Max: 0 + Min: 0 + PV: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + Wind: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Fuels_data.csv new file mode 100644 index 0000000000..64ea2d8074 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Fuels_data.csv @@ -0,0 +1,26 @@ +Time_Index,NG +0,0.05306 +1,5.28 +2,5.28 +3,5.28 +4,5.28 +5,5.28 +6,5.28 +7,5.28 +8,5.28 +9,5.28 +10,5.28 +11,5.28 +12,5.28 +13,5.28 +14,5.28 +15,5.28 +16,5.28 +17,5.28 +18,5.28 +19,5.28 +20,5.28 +21,5.28 +22,5.28 +23,5.28 +24,5.28 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Generators_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Generators_data.csv new file mode 100644 index 0000000000..df361f7590 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Generators_data.csv @@ -0,0 +1,5 @@ +Resource,Zone,THERM,MUST_RUN,STOR,FLEX,HYDRO,VRE,LDS,Num_VRE_Bins,New_Build,Existing_Cap_MW,Existing_Cap_MWh,Existing_Charge_Cap_MW,Max_Cap_MW,Max_Cap_MWh,Max_Charge_Cap_MW,Min_Cap_MW,Min_Cap_MWh,Min_Charge_Cap_MW,Inv_Cost_per_MWyr,Inv_Cost_per_MWhyr,Inv_Cost_Charge_per_MWyr,Fixed_OM_Cost_per_MWyr,Fixed_OM_Cost_per_MWhyr,Fixed_OM_Cost_Charge_per_MWyr,Var_OM_Cost_per_MWh,Var_OM_Cost_per_MWh_In,Heat_Rate_MMBTU_per_MWh,Fuel,Cap_Size,Ramp_Up_Percentage,Ramp_Dn_Percentage,Min_Power,Self_Disch,Eff_Up,Eff_Down,Min_Duration,Max_Duration,Resource_Type,region,cluster +natural_gas_combined_cycle,1,1,0,0,0,0,0,0,0,1,0,0,0,-1,-1,-1,0,0,0,65400,0,0,10287,0,0,3.55,0,7.43,NG,250,0.64,0.64,0.468,0,1,1,0,0,natural_gas_fired_combined_cycle,NE,1 +solar_pv,1,0,0,0,0,0,1,0,1,1,0,0,0,-1,-1,-1,0,0,0,85300,0,0,18760,0,0,0,0,9.13,None,0,1,1,0,0,1,1,0,0,solar_photovoltaic,NE,1 +onshore_wind,1,0,0,0,0,0,1,0,1,1,0,0,0,-1,-1,-1,0,0,0,97200,0,0,43205,0,0,0.1,0,9.12,None,0,1,1,0,0,1,1,0,0,onshore_wind_turbine,NE,1 +battery,1,0,0,1,0,0,0,0,0,1,0,0,0,-1,-1,-1,0,0,0,19584,22494,0,4895,5622,0,0.15,0.15,0,None,0,1,1,0,0,0.92,0.92,1,10,battery_mid,NE,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Generators_variability.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Generators_variability.csv new file mode 100644 index 0000000000..b2d9669ee8 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Generators_variability.csv @@ -0,0 +1,25 @@ +Time_Index,solar_pv,onshore_wind +1,0,0.889717042 +2,0,0.877715468 +3,0,0.903424203 +4,0,0.895153165 +5,0,0.757258117 +6,0,0.630928695 +7,0,0.557177782 +8,0,0.6072492 +9,0.1779,0.423417866 +10,0.429,0.007470775 +11,0.5748,0.002535942 +12,0.6484,0.002153709 +13,0.6208,0.00445132 +14,0.596,0.007711587 +15,0.5013,0.100848213 +16,0.3311,0.201802149 +17,0.0642,0.141933054 +18,0,0.567022562 +19,0,0.946024895 +20,0,0.923394203 +21,0,0.953386247 +22,0,0.929205418 +23,0,0.849528909 +24,0,0.665570974 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Load_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Load_data.csv new file mode 100644 index 0000000000..ca9ce8c10e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Load_data.csv @@ -0,0 +1,25 @@ +Voll,Demand_Segment,Cost_of_Demand_Curtailment_per_MW,Max_Demand_Curtailment,Rep_Periods,Timesteps_per_Rep_Period,Sub_Weights,Time_Index,Load_MW_z1 +50000,1,1,1,1,24,8760,1,14539 +,,,,,,,2,14539 +,,,,,,,3,9878 +,,,,,,,4,9878 +,,,,,,,5,14539 +,,,,,,,6,14539 +,,,,,,,7,9878 +,,,,,,,8,9878 +,,,,,,,9,14539 +,,,,,,,10,14539 +,,,,,,,11,9878 +,,,,,,,12,9878 +,,,,,,,13,14539 +,,,,,,,14,14539 +,,,,,,,15,9878 +,,,,,,,16,9878 +,,,,,,,17,14539 +,,,,,,,18,14539 +,,,,,,,19,9878 +,,,,,,,20,9878 +,,,,,,,21,14539 +,,,,,,,22,14539 +,,,,,,,23,9878 +,,,,,,,24,9878 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/README.md b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/README.md new file mode 100644 index 0000000000..7990f97771 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/README.md @@ -0,0 +1,15 @@ +# Small New England: One Zone + +**SmallNewEngland** is set of a simplified versions of the more detailed example system RealSystemExample. It is condensed for easy comprehension and quick testing of different components of the GenX. **SmallNewEngland/OneZone** is our most basic model, a one-year example with hourly resolution containing only one zone representing New England. The model includes only natural gas, solar PV, wind, and lithium-ion battery storage with no initial capacity. + +To run the model, first navigate to the example directory at `GenX/Example_Systems/SmallNewEngland/OneZone`: + +`cd("Example_Systems/SmallNewEngland/OneZone")` + +Next, ensure that your settings in `GenX_settings.yml` are correct. The default settings use the solver Gurobi (`Solver: Gurobi`), time domain reduced input data (`TimeDomainReduction: 1`). Other optional policies include minimum capacity requirements, a capacity reserve margin, and more. A rate-based carbon cap of 50 gCO2 per kWh is specified in the `CO2_cap.csv` input file. + +Once the settings are confirmed, run the model with the `Run.jl` script in the example directory: + +`include("Run.jl")` + +Once the model has completed, results will write to the `Results` directory. \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Run.jl b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/genx_settings.yml new file mode 100644 index 0000000000..704cea0516 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/genx_settings.yml @@ -0,0 +1,4 @@ +StorageLosses: 0 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +Solver: HiGHS #Windows users, please make sure to install SCIP solver if using SCIP here; Let us know of bugs # Available solvers: Gurobi, CPLEX, CLP, SCIP +UCommit: 0 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +MethodofMorris: 0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..29adc6ec34 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/gurobi_settings.yml @@ -0,0 +1,15 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: 1 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/highs_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/highs_settings.yml new file mode 100644 index 0000000000..d224097683 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/highs_settings.yml @@ -0,0 +1,13 @@ +# HiGHS Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Primal feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +Optimal_Tol: 1.0e-05 # Dual feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +TimeLimit: 1.0e23 # Time limit # [type: double, advanced: false, range: [0, inf], default: inf] +Pre_Solve: choose # Presolve option: "off", "choose" or "on" # [type: string, advanced: false, default: "choose"] +Method: choose #HiGHS-specific solver settings # Solver option: "simplex", "choose" or "ipm" # [type: string, advanced: false, default: "choose"] + +#HiGHS-specific solver settings + +# Run the crossover routine for IPX +# [type: bool, advanced: true, range: {false, true}, default: true] +run_crossover: "off" diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/scip_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/scip_settings.yml new file mode 100644 index 0000000000..2779d54826 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Simple_Test_Case/Settings/scip_settings.yml @@ -0,0 +1,5 @@ +# SCIP Solver Parameters + +#SCIP-specific solver settings +Dispverblevel: 0 +limitsgap: 0.05 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Fuels_data.csv new file mode 100644 index 0000000000..b2591f7b07 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Fuels_data.csv @@ -0,0 +1,26 @@ +Time_Index,NG,None +0,0,0 +1,0,0 +2,0,0 +3,0,0 +4,0,0 +5,0,0 +6,0,0 +7,0,0 +8,0,0 +9,0,0 +10,0,0 +11,0,0 +12,0,0 +13,0,0 +14,0,0 +15,0,0 +16,0,0 +17,0,0 +18,0,0 +19,0,0 +20,0,0 +21,0,0 +22,0,0 +23,0,0 +24,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Generators_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Generators_data.csv new file mode 100644 index 0000000000..827bed2f06 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Generators_data.csv @@ -0,0 +1,4 @@ +Resource,Zone,THERM,MUST_RUN,STOR,FLEX,HYDRO,VRE,LDS,Num_VRE_Bins,New_Build,Existing_Cap_MW,Existing_Cap_MWh,Existing_Charge_Cap_MW,Max_Cap_MW,Max_Cap_MWh,Max_Charge_Cap_MW,Min_Cap_MW,Min_Cap_MWh,Min_Charge_Cap_MW,Inv_Cost_per_MWyr,Inv_Cost_per_MWhyr,Inv_Cost_Charge_per_MWyr,Fixed_OM_Cost_per_MWyr,Fixed_OM_Cost_per_MWhyr,Fixed_OM_Cost_Charge_per_MWyr,Var_OM_Cost_per_MWh,Var_OM_Cost_per_MWh_In,Heat_Rate_MMBTU_per_MWh,Fuel,Cap_Size,Start_Cost_per_MW,Start_Fuel_MMBTU_per_MW,Up_Time,Down_Time,Ramp_Up_Percentage,Ramp_Dn_Percentage,Min_Power,Reg_Cost,Rsv_Cost,Hydro_Energy_to_Power_Ratio,Self_Disch,Eff_Up,Eff_Down,Min_Duration,Max_Duration,MGA,Resource_Type,region,cluster +thermal_plant,1,1,0,0,0,0,0,0,0,0,1,0,0,-1,-1,-1,0,0,0,0,0,0,0,0,0,10,0,1,NG,1,0,0,4,6,1,1,1.0,0,0,0,0,1,1,0,0,1,thermal_plant,NE,1 +solar_pv,1,0,0,0,0,0,1,0,1,1,0,0,0,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,1,None,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,1,solar_photovoltaic,NE,1 +dummy_storage,1,0,0,1,0,0,0,0,0,0,0,0,0,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,None,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,dummy_storage,NE,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Generators_variability.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Generators_variability.csv new file mode 100644 index 0000000000..6ae8ce2e4f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Generators_variability.csv @@ -0,0 +1,25 @@ +Time_Index,thermal_plant,solar_pv,dummy_storage +1,1,0,1 +2,1,0,1 +3,1,0,1 +4,1,0,1 +5,1,0,1 +6,1,0,1 +7,1,0,1 +8,1,0,1 +9,1,0,1 +10,1,0,1 +11,1,1,1 +12,1,1,1 +13,1,1,1 +14,1,0,1 +15,1,0,1 +16,1,0,1 +17,1,0,1 +18,1,0,1 +19,1,0,1 +20,1,0,1 +21,1,0,1 +22,1,0,1 +23,1,0,1 +24,1,0,1 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Load_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Load_data.csv new file mode 100644 index 0000000000..3354b835bb --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Load_data.csv @@ -0,0 +1,25 @@ +Voll,Demand_Segment,Cost_of_Demand_Curtailment_per_MW,Max_Demand_Curtailment,Rep_Periods,Timesteps_per_Rep_Period,Sub_Weights,Time_Index,Load_MW_z1 +1000,1,1,1,1,24,1,1,1 +,,,,,,,2,1 +,,,,,,,3,1 +,,,,,,,4,1 +,,,,,,,5,1 +,,,,,,,6,1 +,,,,,,,7,1 +,,,,,,,8,1 +,,,,,,,9,1 +,,,,,,,10,0 +,,,,,,,11,0 +,,,,,,,12,0 +,,,,,,,13,1 +,,,,,,,14,1 +,,,,,,,15,1 +,,,,,,,16,1 +,,,,,,,17,1 +,,,,,,,18,1 +,,,,,,,19,1 +,,,,,,,20,1 +,,,,,,,21,1 +,,,,,,,22,1 +,,,,,,,23,1 +,,,,,,,24,1 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/README.md b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/README.md new file mode 100644 index 0000000000..e21f24ff44 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/README.md @@ -0,0 +1,4 @@ +# Test Down_Time + +This demonstrates the function of the Down_Time input. +The thermal_plant is forced to be down for 6 hours even though it would otherwise be better to come online after the load drops for 3 hours. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Run.jl b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/genx_settings.yml new file mode 100644 index 0000000000..d6110bac0e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/genx_settings.yml @@ -0,0 +1,22 @@ +OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 0 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 0 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 0 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 0 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 0 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 0 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: HiGHS #Windows users, please make sure to install SCIP solver if using SCIP here; Let us know of bugs # Available solvers: Gurobi, CPLEX, CLP, SCIP +ParameterScale: 0 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 1 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +LongDurationStorage: 0 # Inter-period energy exchange for storage technologies; 0 = not active; 1 = active systemwide +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 0 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 # Number of MGA iterations with maximization and minimization objective +MethodofMorris: 0 #Flag for turning on the Method of Morris analysis diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..29adc6ec34 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/gurobi_settings.yml @@ -0,0 +1,15 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: 1 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/scip_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/scip_settings.yml new file mode 100644 index 0000000000..2779d54826 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Down_Time/Settings/scip_settings.yml @@ -0,0 +1,5 @@ +# SCIP Solver Parameters + +#SCIP-specific solver settings +Dispverblevel: 0 +limitsgap: 0.05 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Fuels_data.csv new file mode 100644 index 0000000000..b2591f7b07 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Fuels_data.csv @@ -0,0 +1,26 @@ +Time_Index,NG,None +0,0,0 +1,0,0 +2,0,0 +3,0,0 +4,0,0 +5,0,0 +6,0,0 +7,0,0 +8,0,0 +9,0,0 +10,0,0 +11,0,0 +12,0,0 +13,0,0 +14,0,0 +15,0,0 +16,0,0 +17,0,0 +18,0,0 +19,0,0 +20,0,0 +21,0,0 +22,0,0 +23,0,0 +24,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Generators_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Generators_data.csv new file mode 100644 index 0000000000..6cdc77b09b --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Generators_data.csv @@ -0,0 +1,4 @@ +Resource,Zone,THERM,MUST_RUN,STOR,FLEX,HYDRO,VRE,LDS,Num_VRE_Bins,New_Build,Existing_Cap_MW,Existing_Cap_MWh,Existing_Charge_Cap_MW,Max_Cap_MW,Max_Cap_MWh,Max_Charge_Cap_MW,Min_Cap_MW,Min_Cap_MWh,Min_Charge_Cap_MW,Inv_Cost_per_MWyr,Inv_Cost_per_MWhyr,Inv_Cost_Charge_per_MWyr,Fixed_OM_Cost_per_MWyr,Fixed_OM_Cost_per_MWhyr,Fixed_OM_Cost_Charge_per_MWyr,Var_OM_Cost_per_MWh,Var_OM_Cost_per_MWh_In,Heat_Rate_MMBTU_per_MWh,Fuel,Cap_Size,Start_Cost_per_MW,Start_Fuel_MMBTU_per_MW,Up_Time,Down_Time,Ramp_Up_Percentage,Ramp_Dn_Percentage,Min_Power,Hydro_Energy_to_Power_Ratio,Self_Disch,Eff_Up,Eff_Down,Min_Duration,Max_Duration,MGA,Resource_Type,region,cluster +thermal_plant,1,1,0,0,0,0,0,0,0,0,1,0,0,-1,-1,-1,0,0,0,0,0,0,0,0,0,10,0,1,NG,1,0,0,4,6,1,1,1.0,0,0,1,1,0,0,1,thermal_plant,NE,1 +solar_pv,1,0,0,0,0,0,1,0,1,1,0,0,0,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,1,None,0,0,0,0,0,1,1,0,0,0,1,1,0,0,1,solar_photovoltaic,NE,1 +dummy_storage,1,0,0,1,0,0,0,0,0,0,0,0,0,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,None,0,0,0,0,0,1,1,0,0,0,1,1,0,0,0,dummy_storage,NE,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Generators_variability.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Generators_variability.csv new file mode 100644 index 0000000000..9e626ff628 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Generators_variability.csv @@ -0,0 +1,25 @@ +Time_Index,thermal_plant,solar_pv,dummy_storage +1,1,1,1 +2,1,1,1 +3,1,1,1 +4,1,1,1 +5,1,1,1 +6,1,1,1 +7,1,1,1 +8,1,1,1 +9,1,1,1 +10,1,0,1 +11,1,1,1 +12,1,1,1 +13,1,1,1 +14,1,1,1 +15,1,1,1 +16,1,1,1 +17,1,1,1 +18,1,1,1 +19,1,1,1 +20,1,1,1 +21,1,1,1 +22,1,1,1 +23,1,1,1 +24,1,1,1 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Load_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Load_data.csv new file mode 100644 index 0000000000..d68ee0c3af --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Load_data.csv @@ -0,0 +1,25 @@ +Voll,Demand_Segment,Cost_of_Demand_Curtailment_per_MW,Max_Demand_Curtailment,Rep_Periods,Timesteps_per_Rep_Period,Sub_Weights,Time_Index,Load_MW_z1 +1000000,1,1,1,1,24,1,1,1 +,,,,,,,2,1 +,,,,,,,3,1 +,,,,,,,4,1 +,,,,,,,5,1 +,,,,,,,6,1 +,,,,,,,7,1 +,,,,,,,8,1 +,,,,,,,9,1 +,,,,,,,10,1 +,,,,,,,11,1 +,,,,,,,12,1 +,,,,,,,13,1 +,,,,,,,14,1 +,,,,,,,15,1 +,,,,,,,16,1 +,,,,,,,17,1 +,,,,,,,18,1 +,,,,,,,19,1 +,,,,,,,20,1 +,,,,,,,21,1 +,,,,,,,22,1 +,,,,,,,23,1 +,,,,,,,24,1 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/README.md b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/README.md new file mode 100644 index 0000000000..c835ec8fb5 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/README.md @@ -0,0 +1,4 @@ +# Test Up_Time + +This demonstrates the Up_Time input. +The thermal_plant is forced to be committed for 4 hours even though it would otherwise be more economical to only turn on for 1. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Run.jl b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/genx_settings.yml new file mode 100644 index 0000000000..d6110bac0e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/genx_settings.yml @@ -0,0 +1,22 @@ +OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 0 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 0 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 0 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 0 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 0 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 0 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: HiGHS #Windows users, please make sure to install SCIP solver if using SCIP here; Let us know of bugs # Available solvers: Gurobi, CPLEX, CLP, SCIP +ParameterScale: 0 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 1 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +LongDurationStorage: 0 # Inter-period energy exchange for storage technologies; 0 = not active; 1 = active systemwide +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 0 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 # Number of MGA iterations with maximization and minimization objective +MethodofMorris: 0 #Flag for turning on the Method of Morris analysis diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..5963760b6b --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/gurobi_settings.yml @@ -0,0 +1,15 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: 1 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/scip_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/scip_settings.yml new file mode 100644 index 0000000000..2779d54826 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/Test_Up_Time/Settings/scip_settings.yml @@ -0,0 +1,5 @@ +# SCIP Solver Parameters + +#SCIP-specific solver settings +Dispverblevel: 0 +limitsgap: 0.05 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/CO2_cap.csv new file mode 100644 index 0000000000..fbd59924ee --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/CO2_cap.csv @@ -0,0 +1,4 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Cap_Zone_2,CO_2_Cap_Zone_3,CO_2_Max_tons_MWh_1,CO_2_Max_tons_MWh_2,CO_2_Max_tons_MWh_3,CO_2_Max_Mtons_1,CO_2_Max_Mtons_2,CO_2_Max_Mtons_3 +MA,z1,1,0,0,0.05,0,0,0.018,0,0 +CT,z2,0,1,0,0,0.05,0,0,0.025,0 +ME,z3,0,0,1,0,0,0.05,0,0,0.025 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..55077ad095 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Capacity_reserve_margin.csv @@ -0,0 +1,4 @@ +,Network_zones,CapRes_1 +MA,z1,0.156 +CT,z2,0.156 +ME,z3,0.156 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Energy_share_requirement.csv new file mode 100644 index 0000000000..3d49badae7 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Energy_share_requirement.csv @@ -0,0 +1,4 @@ +,Network_zones,ESR_1,ESR_2 +MA,z1,0.259,0.348 +CT,z2,0.44,0.44 +ME,z3,0.776,0.776 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Fuels_data.csv new file mode 100644 index 0000000000..78227c2a5b --- /dev/null +++ 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b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Generators_data.csv @@ -0,0 +1,11 @@ +Resource,Zone,THERM,MUST_RUN,STOR,FLEX,HYDRO,VRE,LDS,Num_VRE_Bins,New_Build,Existing_Cap_MW,Existing_Cap_MWh,Existing_Charge_Cap_MW,Max_Cap_MW,Max_Cap_MWh,Max_Charge_Cap_MW,Min_Cap_MW,Min_Cap_MWh,Min_Charge_Cap_MW,Inv_Cost_per_MWyr,Inv_Cost_per_MWhyr,Inv_Cost_Charge_per_MWyr,Fixed_OM_Cost_per_MWyr,Fixed_OM_Cost_per_MWhyr,Fixed_OM_Cost_Charge_per_MWyr,Var_OM_Cost_per_MWh,Var_OM_Cost_per_MWh_In,Heat_Rate_MMBTU_per_MWh,Fuel,Cap_Size,Start_Cost_per_MW,Start_Fuel_MMBTU_per_MW,Up_Time,Down_Time,Ramp_Up_Percentage,Ramp_Dn_Percentage,Hydro_Energy_to_Power_Ratio,Min_Power,Self_Disch,Eff_Up,Eff_Down,Min_Duration,Max_Duration,Max_Flexible_Demand_Advance,Max_Flexible_Demand_Delay,Flexible_Demand_Energy_Eff,Reg_Max,Rsv_Max,Reg_Cost,Rsv_Cost,MinCapTag_1,MinCapTag_2,MinCapTag_3,MGA,Resource_Type,CapRes_1,ESR_1,ESR_2,region,cluster 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a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Generators_variability.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Generators_variability.csv new file mode 100644 index 0000000000..32860a8860 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Generators_variability.csv @@ -0,0 +1,8761 @@ +Time_Index,MA_natural_gas_combined_cycle,MA_solar_pv,CT_natural_gas_combined_cycle,CT_onshore_wind,CT_solar_pv,ME_natural_gas_combined_cycle,ME_onshore_wind,MA_battery,CT_battery,ME_battery +1,1,0,1,0.569944978,0,1,0.920104027,1,1,1 +2,1,0,1,0.623258889,0,1,0.882233679,1,1,1 +3,1,0,1,0.694188416,0,1,0.89507395,1,1,1 +4,1,0,1,0.647399664,0,1,0.87304908,1,1,1 +5,1,0,1,0.381773531,0,1,0.831012249,1,1,1 +6,1,0,1,0.244582877,0,1,0.776541591,1,1,1 +7,1,0,1,0.293396771,0,1,0.704862773,1,1,1 +8,1,0,1,0.37860319,0,1,0.64796108,1,1,1 +9,1,0.1779,1,0.329338044,0.2003,1,0.526324749,1,1,1 +10,1,0.429,1,1.52E-05,0.4221,1,0.079201177,1,1,1 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+8760,1,0,1,0.652051687,0,1,0.908408403,1,1,1 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Load_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Load_data.csv new file mode 100644 index 0000000000..cbb4f36bc8 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Load_data.csv @@ -0,0 +1,8761 @@ +Voll,Demand_Segment,Cost_of_Demand_Curtailment_per_MW,Max_Demand_Curtailment,$/MWh,Rep_Periods,Timesteps_per_Rep_Period,Sub_Weights,Time_Index,Load_MW_z1,Load_MW_z2,Load_MW_z3 +50000,1,1,1,2000,1,8760,8760,1,7850,2242,1070 +,2,0.9,0.04,1800,,,,2,7424,2120,1012 +,3,0.55,0.024,1100,,,,3,7107,2029,969 +,4,0.2,0.003,400,,,,4,6947,1984,947 +,,,,,,,,5,6922,1977,944 +,,,,,,,,6,7045,2012,960 +,,,,,,,,7,7307,2087,996 +,,,,,,,,8,7544,2154,1029 +,,,,,,,,9,7946,2269,1083 +,,,,,,,,10,8340,2382,1137 +,,,,,,,,11,8578,2449,1169 +,,,,,,,,12,8666,2474,1181 +,,,,,,,,13,8707,2487,1187 +,,,,,,,,14,8630,2464,1176 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+,,,,,,,,8748,10907,3115,1487 +,,,,,,,,8749,10730,3064,1463 +,,,,,,,,8750,10550,3013,1439 +,,,,,,,,8751,10438,2981,1423 +,,,,,,,,8752,10469,2990,1427 +,,,,,,,,8753,11228,3206,1531 +,,,,,,,,8754,11908,3401,1624 +,,,,,,,,8755,11562,3302,1576 +,,,,,,,,8756,9923,3797,1339 +,,,,,,,,8757,9461,3621,1277 +,,,,,,,,8758,9018,3452,1217 +,,,,,,,,8759,8551,3281,1154 +,,,,,,,,8760,8089,3106,1092 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..bd16edeeb3 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,ConstraintDescription,Min_MW +1,MA_PV,5000 +2,CT_Wind,10000 +3,All_Batteries,6000 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Network.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Network.csv new file mode 100644 index 0000000000..5cca655c66 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Network.csv @@ -0,0 +1,4 @@ +,Network_zones,Network_Lines,z1,z2,z3,Line_Max_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1 +MA,z1,1,1,-1,0,2950,MA_to_CT,123.0584,0.012305837,2950,12060,0.95,0,0 +CT,z2,2,1,0,-1,2000,MA_to_ME,196.5385,0.019653847,2000,19261,0.95,0,0 +ME,z3,,,,,,,,,,,,, \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/README.md b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/README.md new file mode 100644 index 0000000000..7ff8b89be5 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/README.md @@ -0,0 +1,15 @@ +# Small New England: Three Zones + +**SmallNewEngland** is set of a simplified versions of the more detailed example system RealSystemExample. It is condensed for easy comprehension and quick testing of different components of the GenX. **SmallNewEngland/ThreeZones**, a one-year example with hourly resolution, contains zones representing Massachusetts, Connecticut, and Maine. The ten represented resources include only natural gas, solar PV, wind, and lithium-ion battery storage. + +To run the model, first navigate to the example directory at `GenX/Example_Systems/SmallNewEngland/ThreeZones`: + +`cd("Example_Systems/SmallNewEngland/ThreeZones")` + +Next, ensure that your settings in `GenX_settings.yml` are correct. The default settings use the solver Gurobi (`Solver: Gurobi`), time domain reduced input data (`TimeDomainReduction: 1`). Other optional policies include minimum capacity requirements, a capacity reserve margin, and more. A rate-based carbon cap of 50 gCO2 per kWh is specified in the `CO2_cap.csv` input file. + +Once the settings are confirmed, run the model with the `Run.jl` script in the example directory: + +`include("Run.jl")` + +Once the model has completed, results will write to the `Results` directory. \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Reserves.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Reserves.csv new file mode 100644 index 0000000000..b96bce1284 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Run.jl b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/genx_settings.yml new file mode 100644 index 0000000000..a532e0646e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/genx_settings.yml @@ -0,0 +1,22 @@ +OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 1 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 0 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 0 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 2 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 1 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: HiGHS # Available solvers: Gurobi, CPLEX, CLP, SCIP +ParameterScale: 1 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 2 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 1 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 # Number of MGA iterations with maximization and minimization objective +MultiStage: 0 # Multi-stage modeling; 0 if single-stage; 1 if multi-stage. +MethodofMorris: 0 #Flag for turning on the Method of Morris analysis diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..fd4d9b8a83 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/gurobi_settings.yml @@ -0,0 +1,15 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 4 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: -1 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/highs_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/highs_settings.yml new file mode 100644 index 0000000000..e4f1ad0245 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/highs_settings.yml @@ -0,0 +1,11 @@ +# HiGHS Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Primal feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +Optimal_Tol: 1.0e-05 # Dual feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +TimeLimit: 1.0e23 # Time limit # [type: double, advanced: false, range: [0, inf], default: inf] +Pre_Solve: choose # Presolve option: "off", "choose" or "on" # [type: string, advanced: false, default: "choose"] +Method: ipm #HiGHS-specific solver settings # Solver option: "simplex", "choose" or "ipm" # [type: string, advanced: false, default: "choose"] + +# run the crossover routine for ipx +# [type: string, advanced: "on", range: {"off", "on"}, default: "off"] +run_crossover: "on" diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/scip_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/scip_settings.yml new file mode 100644 index 0000000000..2779d54826 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/scip_settings.yml @@ -0,0 +1,5 @@ +# SCIP Solver Parameters + +#SCIP-specific solver settings +Dispverblevel: 0 +limitsgap: 0.05 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/time_domain_reduction_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/time_domain_reduction_settings.yml new file mode 100644 index 0000000000..a1db56f2be --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Settings/time_domain_reduction_settings.yml @@ -0,0 +1,151 @@ +##### +# +# TIME DOMAIN REDUCTION SETTINGS +# +# Set parameters here that organize how your full timeseries +# data will be divided into representative period clusters. +# Ensure that time_domain_reduction is set to 1 in GenX_settings.yml +# before running. Run within GenX or use PreCluster.jl to test and +# examine representative period output before proceeding. +# Specify your data input directory as inpath within Run_test.jl +# or PreCluster.jl. +# +##### + + # - TimestepsPerRepPeriod + # Typically 168 timesteps (e.g., hours) per period, this designates + # the length of each representative period. +TimestepsPerRepPeriod: 168 + + # - ClusterMethod + # Either 'kmeans' or 'kmedoids', this designates the method used to cluster + # periods and determine each point's representative period. +ClusterMethod: 'kmeans' + + # - ScalingMethod + # Either 'N' or 'S', this designates directs the module to normalize ([0,1]) + # or standardize (mean 0, variance 1) the input data. +ScalingMethod: "S" + + # - MaxPeriods + # The maximum number of periods - both clustered periods and extreme periods - + # that may be used to represent the input data. If IterativelyAddPeriods is on and the + # error threshold is never met, this will be the total number of periods. +MaxPeriods: 11 + + # - MinPeriods + # The minimum number of periods used to represent the input data. If using + # UseExtremePeriods, this must be at least the number of extreme periods requests. If + # IterativelyAddPeriods if off, this will be the total number of periods. +MinPeriods: 8 + + # - IterativelyAddPeriods + # Either 'yes' or 'no', this designates whether or not to add periods + # until the error threshold between input data and represented data is met or the maximum + # number of periods is reached. +IterativelyAddPeriods: 1 + + # - IterateMethod + # Either 'cluster' or 'extreme', this designates whether to add clusters to + # the kmeans/kmedoids method or to set aside the worst-fitting periods as a new extreme periods. + # The default option is 'cluster'. +IterateMethod: "cluster" + + # - Threshold + # Iterative period addition will end if the period farthest (Euclidean Distance) + # from its representative period is within this percentage of the total possible error (for normalization) + # or ~95% of the total possible error (for standardization). E.g., for a threshold of 0.01, + # every period must be within 1% of the spread of possible error before the clustering + # iterations will terminate (or until the max number of periods is reached). +Threshold: 0.05 + + # - nReps + # The number of times to repeat each kmeans/kmedoids clustering at the same setting. +nReps: 100 + + # - LoadWeight + # Default 1, this is an optional multiplier on load columns in order to prioritize + # better fits for load profiles over resource capacity factor profiles. +LoadWeight: 1 + + # - WeightTotal + # Default 8760, the sum to which the relative weights of representative periods will be scaled. +WeightTotal: 8760 + + # - ClusterFuelPrices + # Either 1 (yes) or 0 (no), this indicates whether or not to use the fuel price + # time series in Fuels_data.csv in the clustering process. If 0, this function will still write + # Fuels_data_clustered.csv with reshaped fuel prices based on the number and size of the + # representative weeks, assuming a constant time series of fuel prices with length equal to the + # number of timesteps in the raw input data. +ClusterFuelPrices: 1 + + # - UseExtremePeriods + # Either 'yes' or 'no', this designates whether or not to include + # outliers (by performance or load/resource extreme) as their own representative periods. + # This setting automatically includes the periods with maximum load, minimum solar cf and + # minimum wind cf as extreme periods. +UseExtremePeriods: 1 + + # - MultiStageConcatenate + # (Only considered if MultiStage = 1 in genx_settings.yml) + # If 1, this designates that the model should time domain reduce the input data + # of all model stages together. Else if 0, the model will time domain reduce each + # stage separately +MultiStageConcatenate: 0 + +# STILL IN DEVELOPMENT - Currently just uses integral max load, integral min PV and wind. +# - ExtremePeriods +# Use this to define which periods to be included among the final representative periods +# as "Extreme Periods". +# Select by profile type: load ("Load"), solar PV capacity factors ("PV"), and wind capacity factors ("Wind"). +# Select whether to examine these profiles by zone ("Zone") or across the whole system ("System"). +# Select whether to look for absolute max/min at the timestep level ("Absolute") +# or max/min sum across the period ("Integral"). +# Select whether you want the maximum ("Max") or minimum ("Min") (of the prior type) for each profile type. +ExtremePeriods: + Load: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 1 + Min: 0 + Integral: + Max: 0 + Min: 0 + PV: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + Wind: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/CO2_cap.csv new file mode 100644 index 0000000000..6667d9f1df --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/CO2_cap.csv @@ -0,0 +1,4 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Cap_Zone_2,CO_2_Cap_Zone_3,CO_2_Max_tons_MWh_1,CO_2_Max_tons_MWh_2,CO_2_Max_tons_MWh_3,CO_2_Max_Mtons_1,CO_2_Max_Mtons_2,CO_2_Max_Mtons_3 +MA,z1,1,0,0,1,0,0,0.018,0,0 +CT,z2,0,1,0,0,1,0,0,0.025,0 +ME,z3,0,0,1,0,0,1,0,0,0.025 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..55077ad095 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Capacity_reserve_margin.csv @@ -0,0 +1,4 @@ +,Network_zones,CapRes_1 +MA,z1,0.156 +CT,z2,0.156 +ME,z3,0.156 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Energy_share_requirement.csv new file mode 100644 index 0000000000..3d49badae7 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Energy_share_requirement.csv @@ -0,0 +1,4 @@ +,Network_zones,ESR_1,ESR_2 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+Resource,Zone,THERM,MUST_RUN,STOR,FLEX,HYDRO,VRE,Num_VRE_Bins,New_Build,Existing_Cap_MW,Existing_Cap_MWh,Existing_Charge_Cap_MW,Max_Cap_MW,Max_Cap_MWh,Max_Charge_Cap_MW,Min_Cap_MW,Min_Cap_MWh,Min_Charge_Cap_MW,Inv_Cost_per_MWyr,Inv_Cost_per_MWhyr,Inv_Cost_Charge_per_MWyr,Fixed_OM_Cost_per_MWyr,Fixed_OM_Cost_per_MWhyr,Fixed_OM_Cost_Charge_per_MWyr,Var_OM_Cost_per_MWh,Var_OM_Cost_per_MWh_In,Heat_Rate_MMBTU_per_MWh,Fuel,Cap_Size,Start_Cost_per_MW,Start_Fuel_MMBTU_per_MW,Up_Time,Down_Time,Ramp_Up_Percentage,Ramp_Dn_Percentage,Hydro_Energy_to_Power_Ratio,Min_Power,Self_Disch,Eff_Up,Eff_Down,Min_Duration,Max_Duration,Max_Flexible_Demand_Advance,Max_Flexible_Demand_Delay,Flexible_Demand_Energy_Eff,Reg_Max,Rsv_Max,Reg_Cost,Rsv_Cost,MinCapTag,MinCapTag_1,MinCapTag_2,MinCapTag_3,MGA,Resource_Type,WACC,CapRes_1,ESR_1,ESR_2,Capital_Recovery_Period,Lifetime,Min_Retired_Cap_MW,Min_Retired_Energy_Cap_MW,Min_Retired_Charge_Cap_MW,region,cluster,LDS 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b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..bd16edeeb3 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,ConstraintDescription,Min_MW +1,MA_PV,5000 +2,CT_Wind,10000 +3,All_Batteries,6000 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Network.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Network.csv new file mode 100644 index 0000000000..a67a2667e5 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Network.csv @@ -0,0 +1,4 @@ +,Network_zones,Network_Lines,z1,z2,z3,Line_Max_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1,Line_Max_Flow_Possible_MW,WACC,Capital_Recovery_Period +MA,z1,1,1,-1,0,2950,MA_to_CT,123.0584,0.012305837,2950,12060,0.95,0,0,7000,0.062,30 +CT,z2,2,1,0,-1,2000,MA_to_ME,196.5385,0.019653847,2000,19261,0.95,0,0,5000,0.062,30 +ME,z3,,,,,,,,,,,,,,,, diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Reserves.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Reserves.csv new file mode 100644 index 0000000000..b96bce1284 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p1/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/CO2_cap.csv new file mode 100644 index 0000000000..d992c71d69 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/CO2_cap.csv @@ -0,0 +1,4 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Cap_Zone_2,CO_2_Cap_Zone_3,CO_2_Max_tons_MWh_1,CO_2_Max_tons_MWh_2,CO_2_Max_tons_MWh_3,CO_2_Max_Mtons_1,CO_2_Max_Mtons_2,CO_2_Max_Mtons_3 +MA,z1,1,0,0,0.5,0,0,0.018,0,0 +CT,z2,0,1,0,0,0.5,0,0,0.025,0 +ME,z3,0,0,1,0,0,0.5,0,0,0.025 \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..55077ad095 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Capacity_reserve_margin.csv @@ -0,0 +1,4 @@ +,Network_zones,CapRes_1 +MA,z1,0.156 +CT,z2,0.156 +ME,z3,0.156 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Energy_share_requirement.csv new file mode 100644 index 0000000000..3d49badae7 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Energy_share_requirement.csv @@ -0,0 +1,4 @@ +,Network_zones,ESR_1,ESR_2 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b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..bd16edeeb3 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,ConstraintDescription,Min_MW +1,MA_PV,5000 +2,CT_Wind,10000 +3,All_Batteries,6000 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Network.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Network.csv new file mode 100644 index 0000000000..a67a2667e5 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Network.csv @@ -0,0 +1,4 @@ +,Network_zones,Network_Lines,z1,z2,z3,Line_Max_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1,Line_Max_Flow_Possible_MW,WACC,Capital_Recovery_Period +MA,z1,1,1,-1,0,2950,MA_to_CT,123.0584,0.012305837,2950,12060,0.95,0,0,7000,0.062,30 +CT,z2,2,1,0,-1,2000,MA_to_ME,196.5385,0.019653847,2000,19261,0.95,0,0,5000,0.062,30 +ME,z3,,,,,,,,,,,,,,,, diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Reserves.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Reserves.csv new file mode 100644 index 0000000000..b96bce1284 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p2/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/CO2_cap.csv new file mode 100644 index 0000000000..fbd59924ee --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/CO2_cap.csv @@ -0,0 +1,4 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Cap_Zone_2,CO_2_Cap_Zone_3,CO_2_Max_tons_MWh_1,CO_2_Max_tons_MWh_2,CO_2_Max_tons_MWh_3,CO_2_Max_Mtons_1,CO_2_Max_Mtons_2,CO_2_Max_Mtons_3 +MA,z1,1,0,0,0.05,0,0,0.018,0,0 +CT,z2,0,1,0,0,0.05,0,0,0.025,0 +ME,z3,0,0,1,0,0,0.05,0,0,0.025 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..55077ad095 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Capacity_reserve_margin.csv @@ -0,0 +1,4 @@ +,Network_zones,CapRes_1 +MA,z1,0.156 +CT,z2,0.156 +ME,z3,0.156 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Energy_share_requirement.csv new file mode 100644 index 0000000000..3d49badae7 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Energy_share_requirement.csv @@ -0,0 +1,4 @@ +,Network_zones,ESR_1,ESR_2 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+,,,,,,,,8732,11486,3281,1566 +,,,,,,,,8733,11162,3188,1522 +,,,,,,,,8734,10603,3028,1445 +,,,,,,,,8735,9865,2818,1345 +,,,,,,,,8736,9090,2596,1239 +,,,,,,,,8737,8516,2432,1161 +,,,,,,,,8738,8194,2340,1117 +,,,,,,,,8739,8028,2293,1095 +,,,,,,,,8740,8002,2285,1090 +,,,,,,,,8741,8148,2327,1110 +,,,,,,,,8742,8589,2453,1171 +,,,,,,,,8743,9325,2663,1272 +,,,,,,,,8744,9904,2829,1350 +,,,,,,,,8745,10400,2970,1418 +,,,,,,,,8746,10771,3076,1469 +,,,,,,,,8747,10938,3124,1491 +,,,,,,,,8748,10907,3115,1487 +,,,,,,,,8749,10730,3064,1463 +,,,,,,,,8750,10550,3013,1439 +,,,,,,,,8751,10438,2981,1423 +,,,,,,,,8752,10469,2990,1427 +,,,,,,,,8753,11228,3206,1531 +,,,,,,,,8754,11908,3401,1624 +,,,,,,,,8755,11562,3302,1576 +,,,,,,,,8756,9923,3797,1339 +,,,,,,,,8757,9461,3621,1277 +,,,,,,,,8758,9018,3452,1217 +,,,,,,,,8759,8551,3281,1154 +,,,,,,,,8760,8090,3106,1092 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..bd16edeeb3 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,ConstraintDescription,Min_MW +1,MA_PV,5000 +2,CT_Wind,10000 +3,All_Batteries,6000 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Network.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Network.csv new file mode 100644 index 0000000000..a67a2667e5 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Network.csv @@ -0,0 +1,4 @@ +,Network_zones,Network_Lines,z1,z2,z3,Line_Max_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1,Line_Max_Flow_Possible_MW,WACC,Capital_Recovery_Period +MA,z1,1,1,-1,0,2950,MA_to_CT,123.0584,0.012305837,2950,12060,0.95,0,0,7000,0.062,30 +CT,z2,2,1,0,-1,2000,MA_to_ME,196.5385,0.019653847,2000,19261,0.95,0,0,5000,0.062,30 +ME,z3,,,,,,,,,,,,,,,, diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Reserves.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Reserves.csv new file mode 100644 index 0000000000..b96bce1284 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Inputs/Inputs_p3/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/README.md b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/README.md new file mode 100644 index 0000000000..895a1db94a --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/README.md @@ -0,0 +1,21 @@ +# Small New England: Three Zones Multi-Stage + +**SmallNewEngland** is set of a simplified versions of the more detailed example system RealSystemExample. It is condensed for easy comprehension and quick testing of different components of the GenX. **SmallNewEngland/ThreeZones_MultiStage**, a toy multi-stage example with hourly resolution, contains zones representing Massachusetts, Connecticut, and Maine. The ten represented resources include only natural gas, solar PV, wind, and lithium-ion battery storage. + +To run the model, first navigate to the example directory at `GenX/Example_Systems/SmallNewEngland/ThreeZones_MultiStage`: + +`cd("Example_Systems/SmallNewEngland/ThreeZones_MultiStage")` + +Next, ensure that your settings in `GenX_settings.yml` are correct. The default settings use the solver Gurobi (`Solver: Gurobi`), time domain reduced input data (`TimeDomainReduction: 1`). + +The `multi_stage_settings.yml` file contains settings parameters specific to multi-stage modeling. This example is configured for three model periods (`NumPeriods: 3`) of 10 years in length each (`PeriodLength: 10`). + +Multi-period modeling in GenX requires a separate set of model inputs for each period to be modeled, which are located in the directories `Inputs/Inputs_p$`, where `$` is the number of the model period. Although separate model periods can have different costs and policy parameters, the resources names and types, specified in each `Generators_data.csv` must be identical across model periods. In addition, multi-stage modeling with a single zone requires an additional input file, `Generators_data_multi_stage.csv`, also located in the `Inputs/` directory, which contains fields related to resource lifetimes, capital recovery periods, and endogenous retirements. + +A rate-based carbon cap becomes more stringent across the three model periods and for each zone, declining from of 1,000 gCO2 per kWh in the first period, 500 gCO2 per kWh in the second period, and 50 gCO2 per kWh in the third period, as specified in the `CO2_cap.csv` input files in `Inputs/Inputs_p1`, `Inputs/Inputs_p2`, and `Inputs/Inputs_p3` respectively. + +Once the settings are confirmed, run the model with the `Run_multi_stage.jl` script in the example directory: + +`include("Run_multi_stage.jl")` + +Once the model has completed, results will write to the `Results/` directory. \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Run.jl b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/genx_settings.yml new file mode 100644 index 0000000000..8e708f18dc --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/genx_settings.yml @@ -0,0 +1,22 @@ +#MacOrWindows: Mac # Set to either "Mac" (also works for Linux) or "Windows" to ensure use of proper file directory separator "\" or "/ +OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 1 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 0 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 0 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 2 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 0 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: HiGHS # Available solvers: Gurobi, CPLEX, CLP, Cbc +ParameterScale: 1 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 0 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 2 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 1 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 +MultiStage: 1 # 0 = Single-stage GenX, 1 = Multi-stage GenX diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..6593f70370 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/gurobi_settings.yml @@ -0,0 +1,16 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 4 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: -1 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. +OutputFlag: 0 # Controls Gurobi output.s diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/highs_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/highs_settings.yml new file mode 100644 index 0000000000..7257ed4da6 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/highs_settings.yml @@ -0,0 +1,12 @@ +# HiGHS Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Primal feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +Optimal_Tol: 1.0e-05 # Dual feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +TimeLimit: 1.0e23 # Time limit # [type: double, advanced: false, range: [0, inf], default: inf] +Pre_Solve: choose # Presolve option: "off", "choose" or "on" # [type: string, advanced: false, default: "choose"] +Method: ipm #HiGHS-specific solver settings # Solver option: "simplex", "choose" or "ipm" # [type: string, advanced: false, default: "choose"] + +# run the crossover routine for ipx +# [type: string, advanced: "on", range: {"off", "on"}, default: "off"] +run_crossover: "on" + diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/multi_stage_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/multi_stage_settings.yml new file mode 100644 index 0000000000..f6819d8c1a --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/multi_stage_settings.yml @@ -0,0 +1,5 @@ +NumStages: 3 # Number of model investment planning stages +StageLengths: [10,10,10] # Length of each model stage (years) +WACC: 0.045 # Weighted average cost of capital +ConvergenceTolerance: 0.01 # Relative optimality gap used for convergence +Myopic: 0 # If using multi-stage GenX, 1 = myopic, 0 = dual dynamic programming diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/time_domain_reduction_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/time_domain_reduction_settings.yml new file mode 100644 index 0000000000..4d228415e8 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_MultiStage/Settings/time_domain_reduction_settings.yml @@ -0,0 +1,150 @@ +##### +# +# TIME DOMAIN REDUCTION SETTINGS +# +# Set parameters here that organize how your full timeseries +# data will be divided into representative period clusters. +# Ensure that time_domain_reduction is set to 1 in GenX_settings.yml +# before running. Run within GenX or use PreCluster.jl to test and +# examine representative period output before proceeding. +# Specify your data input directory as inpath within Run_test.jl +# or PreCluster.jl. +# +##### + + # - TimestepsPerRepPeriod + # Typically 168 timesteps (e.g., hours) per period, this designates + # the length of each representative period. +TimestepsPerRepPeriod: 168 + + # - ClusterMethod + # Either 'kmeans' or 'kmedoids', this designates the method used to cluster + # periods and determine each point's representative period. +ClusterMethod: 'kmeans' + + # - ScalingMethod + # Either 'N' or 'S', this designates directs the module to normalize ([0,1]) + # or standardize (mean 0, variance 1) the input data. +ScalingMethod: "S" + + # - MaxPeriods + # The maximum number of periods - both clustered periods and extreme periods - + # that may be used to represent the input data. If IterativelyAddPeriods is on and the + # error threshold is never met, this will be the total number of periods. +MaxPeriods: 11 + + # - MinPeriods + # The minimum number of periods used to represent the input data. If using + # UseExtremePeriods, this must be at least the number of extreme periods requests. If + # IterativelyAddPeriods if off, this will be the total number of periods. +MinPeriods: 11 + + # - IterativelyAddPeriods + # Either 1 (yes) or 0 (no), this designates whether or not to add periods + # until the error threshold between input data and represented data is met or the maximum + # number of periods is reached. +IterativelyAddPeriods: 0 + + # - IterateMethod + # Either 'cluster' or 'extreme', this designates whether to add clusters to + # the kmeans/kmedoids method or to set aside the worst-fitting periods as a new extreme periods. + # The default option is 'cluster'. +IterateMethod: "cluster" + + # - Threshold + # Iterative period addition will end if the period farthest (Euclidean Distance) + # from its representative period is within this percentage of the total possible error (for normalization) + # or ~95% of the total possible error (for standardization). E.g., for a threshold of 0.01, + # every period must be within 1% of the spread of possible error before the clustering + # iterations will terminate (or until the max number of periods is reached). +Threshold: 0.05 + + # - nReps + # The number of times to repeat each kmeans/kmedoids clustering at the same setting. +nReps: 100 + + # - LoadWeight + # Default 1, this is an optional multiplier on load columns in order to prioritize + # better fits for load profiles over resource capacity factor profiles. +LoadWeight: 1 + + # - WeightTotal + # Default 8760, the sum to which the relative weights of representative periods will be scaled. +WeightTotal: 8760 + + # - ClusterFuelPrices + # Either 1 (yes) or 0 (no), this indicates whether or not to use the fuel price + # time series in Fuels_data.csv in the clustering process. If 0, this function will still write + # Fuels_data_clustered.csv with reshaped fuel prices based on the number and size of the + # representative weeks, assuming a constant time series of fuel prices with length equal to the + # number of timesteps in the raw input data. +ClusterFuelPrices: 1 + + # - MultiStageConcatenate + # (Only considered if MultiStage = 1 in genx_settings.yml) + # If 1, this designates that the model should time domain reduce the input data + # of all model stages together. Else if 0, the model will time domain reduce each + # stage separately +MultiStageConcatenate: 0 + + # - UseExtremePeriods + # Either 1 (yes) or 0 (no), this designates whether or not to include + # outliers (by performance or load/resource extreme) as their own representative periods. + # This setting automatically includes the periods with maximum load, minimum solar cf and + # minimum wind cf as extreme periods. +UseExtremePeriods: 1 + +# - ExtremePeriods +# Use this to define which periods to be included among the final representative periods +# as "Extreme Periods". +# Select by profile type: load ("Load"), solar PV capacity factors ("PV"), and wind capacity factors ("Wind"). +# Select whether to examine these profiles by zone ("Zone") or across the whole system ("System"). +# Select whether to look for absolute max/min at the timestep level ("Absolute") +# or max/min sum across the period ("Integral"). +# Select whether you want the maximum ("Max") or minimum ("Min") (of the prior type) for each profile type. +ExtremePeriods: + Load: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 1 + Min: 0 + Integral: + Max: 0 + Min: 0 + PV: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + Wind: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/CO2_cap.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/CO2_cap.csv new file mode 100644 index 0000000000..b2fb70e8a6 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/CO2_cap.csv @@ -0,0 +1,4 @@ +,Network_zones,CO_2_Cap_Zone_1,CO_2_Cap_Zone_2,CO_2_Cap_Zone_3,CO_2_Max_tons_MWh_1,CO_2_Max_tons_MWh_2,CO_2_Max_tons_MWh_3,CO_2_Max_Mtons_1,CO_2_Max_Mtons_2,CO_2_Max_Mtons_3 +MA,z1,1,0,0,0.05,0,0,0.18,0,0 +CT,z2,0,1,0,0,0.05,0,0,0.25,0 +ME,z3,0,0,1,0,0,0.05,0,0,0.25 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/CO2_cap_slack.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/CO2_cap_slack.csv new file mode 100644 index 0000000000..d957ee00b0 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/CO2_cap_slack.csv @@ -0,0 +1,4 @@ +CO2_Cap_Constraint,PriceCap +CO_2_Cap_Zone_1,9999 +CO_2_Cap_Zone_2,9999 +CO_2_Cap_Zone_3,9999 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Capacity_reserve_margin.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Capacity_reserve_margin.csv new file mode 100644 index 0000000000..55077ad095 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Capacity_reserve_margin.csv @@ -0,0 +1,4 @@ +,Network_zones,CapRes_1 +MA,z1,0.156 +CT,z2,0.156 +ME,z3,0.156 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Capacity_reserve_margin_slack.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Capacity_reserve_margin_slack.csv new file mode 100644 index 0000000000..3f75423dfa --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Capacity_reserve_margin_slack.csv @@ -0,0 +1,4 @@ +CRM_Constraint,PriceCap +CapRes_1,9999 +CapRes_2,9999 +CapRes_3,9999 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Energy_share_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Energy_share_requirement.csv new file mode 100644 index 0000000000..3d49badae7 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Energy_share_requirement.csv @@ -0,0 +1,4 @@ +,Network_zones,ESR_1,ESR_2 +MA,z1,0.259,0.348 +CT,z2,0.44,0.44 +ME,z3,0.776,0.776 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Energy_share_requirement_slack.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Energy_share_requirement_slack.csv new file mode 100644 index 0000000000..ac552d6fe1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Energy_share_requirement_slack.csv @@ -0,0 +1,3 @@ +ESR_Constraint,PriceCap +ESR_1,9999 +ESR_2,9999 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Fuels_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Fuels_data.csv new file mode 100644 index 0000000000..78227c2a5b --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Fuels_data.csv @@ -0,0 +1,8762 @@ +Time_Index,CT_NG,ME_NG,MA_NG,None +0,0.05306,0.05306,0.05306,0 +1,5.45,5.45,5.28,0 +2,5.45,5.45,5.28,0 +3,5.45,5.45,5.28,0 +4,5.45,5.45,5.28,0 +5,5.45,5.45,5.28,0 +6,5.45,5.45,5.28,0 +7,5.45,5.45,5.28,0 +8,5.45,5.45,5.28,0 +9,5.45,5.45,5.28,0 +10,5.45,5.45,5.28,0 +11,5.45,5.45,5.28,0 +12,5.45,5.45,5.28,0 +13,5.45,5.45,5.28,0 +14,5.45,5.45,5.28,0 +15,5.45,5.45,5.28,0 +16,5.45,5.45,5.28,0 +17,5.45,5.45,5.28,0 +18,5.45,5.45,5.28,0 +19,5.45,5.45,5.28,0 +20,5.45,5.45,5.28,0 +21,5.45,5.45,5.28,0 +22,5.45,5.45,5.28,0 +23,5.45,5.45,5.28,0 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a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Generators_variability.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Generators_variability.csv new file mode 100644 index 0000000000..32860a8860 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Generators_variability.csv @@ -0,0 +1,8761 @@ +Time_Index,MA_natural_gas_combined_cycle,MA_solar_pv,CT_natural_gas_combined_cycle,CT_onshore_wind,CT_solar_pv,ME_natural_gas_combined_cycle,ME_onshore_wind,MA_battery,CT_battery,ME_battery +1,1,0,1,0.569944978,0,1,0.920104027,1,1,1 +2,1,0,1,0.623258889,0,1,0.882233679,1,1,1 +3,1,0,1,0.694188416,0,1,0.89507395,1,1,1 +4,1,0,1,0.647399664,0,1,0.87304908,1,1,1 +5,1,0,1,0.381773531,0,1,0.831012249,1,1,1 +6,1,0,1,0.244582877,0,1,0.776541591,1,1,1 +7,1,0,1,0.293396771,0,1,0.704862773,1,1,1 +8,1,0,1,0.37860319,0,1,0.64796108,1,1,1 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+8760,1,0,1,0.652051687,0,1,0.908408403,1,1,1 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Load_data.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Load_data.csv new file mode 100644 index 0000000000..cbb4f36bc8 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Load_data.csv @@ -0,0 +1,8761 @@ +Voll,Demand_Segment,Cost_of_Demand_Curtailment_per_MW,Max_Demand_Curtailment,$/MWh,Rep_Periods,Timesteps_per_Rep_Period,Sub_Weights,Time_Index,Load_MW_z1,Load_MW_z2,Load_MW_z3 +50000,1,1,1,2000,1,8760,8760,1,7850,2242,1070 +,2,0.9,0.04,1800,,,,2,7424,2120,1012 +,3,0.55,0.024,1100,,,,3,7107,2029,969 +,4,0.2,0.003,400,,,,4,6947,1984,947 +,,,,,,,,5,6922,1977,944 +,,,,,,,,6,7045,2012,960 +,,,,,,,,7,7307,2087,996 +,,,,,,,,8,7544,2154,1029 +,,,,,,,,9,7946,2269,1083 +,,,,,,,,10,8340,2382,1137 +,,,,,,,,11,8578,2449,1169 +,,,,,,,,12,8666,2474,1181 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+,,,,,,,,8748,10907,3115,1487 +,,,,,,,,8749,10730,3064,1463 +,,,,,,,,8750,10550,3013,1439 +,,,,,,,,8751,10438,2981,1423 +,,,,,,,,8752,10469,2990,1427 +,,,,,,,,8753,11228,3206,1531 +,,,,,,,,8754,11908,3401,1624 +,,,,,,,,8755,11562,3302,1576 +,,,,,,,,8756,9923,3797,1339 +,,,,,,,,8757,9461,3621,1277 +,,,,,,,,8758,9018,3452,1217 +,,,,,,,,8759,8551,3281,1154 +,,,,,,,,8760,8089,3106,1092 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Maximum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Maximum_capacity_requirement.csv new file mode 100644 index 0000000000..18c9ba6719 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Maximum_capacity_requirement.csv @@ -0,0 +1,2 @@ +MaxCapReqConstraint,ConstraintDescription,Max_MW,PriceCap +1,MA_CT_Gas,4000,9999 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Minimum_capacity_requirement.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Minimum_capacity_requirement.csv new file mode 100644 index 0000000000..cb3722b42b --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Minimum_capacity_requirement.csv @@ -0,0 +1,4 @@ +MinCapReqConstraint,ConstraintDescription,Min_MW,PriceCap +1,MA_PV,5000,9999 +2,CT_Wind,10000,9999 +3,All_Batteries,6000,9999 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Network.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Network.csv new file mode 100644 index 0000000000..5cca655c66 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Network.csv @@ -0,0 +1,4 @@ +,Network_zones,Network_Lines,z1,z2,z3,Line_Max_Flow_MW,transmission_path_name,distance_mile,Line_Loss_Percentage,Line_Max_Reinforcement_MW,Line_Reinforcement_Cost_per_MWyr,DerateCapRes_1,CapRes_1,CapRes_Excl_1 +MA,z1,1,1,-1,0,2950,MA_to_CT,123.0584,0.012305837,2950,12060,0.95,0,0 +CT,z2,2,1,0,-1,2000,MA_to_ME,196.5385,0.019653847,2000,19261,0.95,0,0 +ME,z3,,,,,,,,,,,,, \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/README.md b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/README.md new file mode 100644 index 0000000000..4189de4fe1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/README.md @@ -0,0 +1,15 @@ +# Small New England: Three Zones, Slack Variables Example + +**SmallNewEngland** is set of a simplified versions of the more detailed example system RealSystemExample. It is condensed for easy comprehension and quick testing of different components of the GenX. **SmallNewEngland/ThreeZones_Slack_Variables_Example**, a one-year example with hourly resolution, contains zones representing Massachusetts, Connecticut, and Maine. The ten represented resources include only natural gas, solar PV, wind, and lithium-ion battery storage. It additionally contains example input files establishing slack variables for policy constraints (e.g. the Capacity Reserve Margin, CO2 Cap, etc.). These slack variables allow the relevant constraints to be violated at the cost of a specified objective function penalty, which can be used to either identify problematic constraints without causing infeasibilities in GenX, or to set price caps beyond which policies are no longer enforced. These slack variables will only be created if the relevant input data (Capacity_reserve_margin_slack.csv, CO2_cap_slack.csv, Energy_share_requirement_slack.csv, or the 'PriceCap' column in 'Minimum_capacity_requirement.csv') are present. If any of these inputs are not present, GenX will isntantiate the relevant policy as a hard constraint, which will throw an infeasibility if violated. + +To run the model, first navigate to the example directory at `GenX/Example_Systems/SmallNewEngland/ThreeZones`: + +`cd("Example_Systems/SmallNewEngland/ThreeZones_Slack_Variables_Example")` + +Next, ensure that your settings in `GenX_settings.yml` are correct. The default settings use the solver Gurobi (`Solver: Gurobi`), time domain reduced input data (`TimeDomainReduction: 1`). Other optional policies include minimum capacity requirements, a capacity reserve margin, and more. A rate-based carbon cap of 50 gCO2 per kWh is specified in the `CO2_cap.csv` input file. + +Once the settings are confirmed, run the model with the `Run.jl` script in the example directory: + +`include("Run.jl")` + +Once the model has completed, results will write to the `Results` directory. \ No newline at end of file diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Reserves.csv b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Reserves.csv new file mode 100644 index 0000000000..b96bce1284 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Reserves.csv @@ -0,0 +1,2 @@ +Reg_Req_Percent_Load,Reg_Req_Percent_VRE,Rsv_Req_Percent_Load,Rsv_Req_Percent_VRE,Unmet_Rsv_Penalty_Dollar_per_MW,Dynamic_Contingency,Static_Contingency_MW +0.01,0.0032,0.033,0.0795,1000,0,0 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Run.jl b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Run.jl new file mode 100644 index 0000000000..b44ca23ec1 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Run.jl @@ -0,0 +1,3 @@ +using GenX + +run_genx_case!(dirname(@__FILE__)) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/cbc_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/cbc_settings.yml new file mode 100644 index 0000000000..92c6fa892f --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/cbc_settings.yml @@ -0,0 +1,11 @@ +# CBC Solver Parameters +# Common solver settings +TimeLimit: 110000 # Solution timeout limit. For example, set_optimizer_attribute(model, "seconds", 60.0). + +#CBC-specific solver settings +logLevel: 1 # Set to 1 to enable solution output. For example, set_optimizer_attribute(model, "logLevel", 1). +maxSolutions: -1 # Terminate after this many feasible solutions have been found. For example, set_optimizer_attribute(model, "maxSolutions", 1). +maxNodes: 2000 # Terminate after this many branch-and-bound nodes have been evaluated. For example, set_optimizer_attribute(model, "maxNodes", 1). +allowableGap: 1 # Terminate after optimality gap is less than this value (on an absolute scale). For example, set_optimizer_attribute(model, "allowableGap", 0.05). +ratioGap: 0.01 # Terminate after optimality gap is smaller than this relative fraction. For example, set_optimizer_attribute(model, "allowableGap", 0.05). +threads: 2 # Set the number of threads to use for parallel branch & bound. For example, set_optimizer_attribute(model, "threads", 2) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/clp_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/clp_settings.yml new file mode 100644 index 0000000000..c4c003d08e --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/clp_settings.yml @@ -0,0 +1,14 @@ +# Clp Solver parameters https://github.com/jump-dev/Clp.jl +# Common solver settings +Feasib_Tol: 1e-5 # Primal/Dual feasibility tolerance +TimeLimit: -1.0 # Terminate after this many seconds have passed. A negative value means no time limit +Pre_Solve: 0 # Set to 1 to disable presolve +Method: 5 # Solution method: dual simplex (0), primal simplex (1), sprint (2), barrier with crossover (3), barrier without crossover (4), automatic (5) + +#Clp-specific solver settings +DualObjectiveLimit: 1e308 # When using dual simplex (where the objective is monotonically changing), terminate when the objective exceeds this limit +MaximumIterations: 2147483647 # Terminate after performing this number of simplex iterations +LogLevel: 1 # Set to 1, 2, 3, or 4 for increasing output. Set to 0 to disable output +InfeasibleReturn: 0 # Set to 1 to return as soon as the problem is found to be infeasible (by default, an infeasibility proof is computed as well) +Scaling: 3 # 0 -off, 1 equilibrium, 2 geometric, 3 auto, 4 dynamic(later) +Perturbation: 100 # switch on perturbation (50), automatic (100), don't try perturbing (102) diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/cplex_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/cplex_settings.yml new file mode 100644 index 0000000000..8d37873eef --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/cplex_settings.yml @@ -0,0 +1,10 @@ +# CPLEX Solver Parameters +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +Pre_Solve: 1 # Controls presolve level. +TimeLimit: 110000 # Limits total time solver. +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +Method: 2 # Algorithm used to solve continuous models (including MIP root relaxation). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +SolutionType: 2 # Solution type for LP or QP. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/genx_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/genx_settings.yml new file mode 100644 index 0000000000..1ca3b92e72 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/genx_settings.yml @@ -0,0 +1,22 @@ +OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results +PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active +NetworkExpansion: 1 # Transmission network expansionl; 0 = not active; 1 = active systemwide +Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic +Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide +EnergyShareRequirement: 0 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide +CapacityReserveMargin: 0 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide +CO2Cap: 2 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint +StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost) +MinCapReq: 0 # Activate minimum technology carveout constraints; 0 = not active; 1 = active +MaxCapReq: 0 # Activate maximum technology carveout constraints; 0 = not active; 1 = active +Solver: Gurobi # Available solvers: Gurobi, CPLEX, CLP, SCIP +ParameterScale: 1 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide +WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active +UCommit: 2 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering +TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again. +TimeDomainReduction: 1 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered) +ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output +ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value +ModelingToGenerateAlternativeIterations: 3 # Number of MGA iterations with maximization and minimization objective +MultiStage: 0 # Multi-stage modeling; 0 if single-stage; 1 if multi-stage. +MethodofMorris: 0 #Flag for turning on the Method of Morris analysis diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/gurobi_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/gurobi_settings.yml new file mode 100644 index 0000000000..fd4d9b8a83 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/gurobi_settings.yml @@ -0,0 +1,15 @@ +# Gurobi Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Constraint (primal) feasibility tolerances. +Optimal_Tol: 1e-5 # Dual feasibility tolerances. +TimeLimit: 110000 # Limits total time solver. +Pre_Solve: 1 # Controls presolve level. +Method: 4 # Algorithm used to solve continuous models (including MIP root relaxation). + +#Gurobi-specific solver settings +MIPGap: 1e-3 # Relative (p.u. of optimal) mixed integer optimality tolerance for MIP problems (ignored otherwise). +BarConvTol: 1.0e-08 # Barrier convergence tolerance (determines when barrier terminates). +NumericFocus: 0 # Numerical precision emphasis. +Crossover: -1 # Barrier crossver strategy. +PreDual: 0 # Decides whether presolve should pass the primal or dual linear programming problem to the LP optimization algorithm. +AggFill: 10 # Allowed fill during presolve aggregation. diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/highs_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/highs_settings.yml new file mode 100644 index 0000000000..573ad402a3 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/highs_settings.yml @@ -0,0 +1,15 @@ +# HiGHS Solver Parameters +# Common solver settings +Feasib_Tol: 1.0e-05 # Primal feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +Optimal_Tol: 1.0e-05 # Dual feasibility tolerance # [type: double, advanced: false, range: [1e-10, inf], default: 1e-07] +TimeLimit: 1.0e23 # Time limit # [type: double, advanced: false, range: [0, inf], default: inf] +Pre_Solve: choose # Presolve option: "off", "choose" or "on" # [type: string, advanced: false, default: "choose"] +Method: ipm #HiGHS-specific solver settings # Solver option: "simplex", "choose" or "ipm" # [type: string, advanced: false, default: "choose"] + +#HiGHS-specific solver settings + +# Run the crossover routine for IPX +# [type: string, advanced: "on", range: {"off", "on"}, default: "off"] +run_crossover: "off" + + diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/scip_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/scip_settings.yml new file mode 100644 index 0000000000..2779d54826 --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/scip_settings.yml @@ -0,0 +1,5 @@ +# SCIP Solver Parameters + +#SCIP-specific solver settings +Dispverblevel: 0 +limitsgap: 0.05 diff --git a/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/time_domain_reduction_settings.yml b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/time_domain_reduction_settings.yml new file mode 100644 index 0000000000..a1db56f2be --- /dev/null +++ b/Tutorials/Example_Systems_Tutorials/SmallNewEngland/ThreeZones_Slack_Variables_Example/Settings/time_domain_reduction_settings.yml @@ -0,0 +1,151 @@ +##### +# +# TIME DOMAIN REDUCTION SETTINGS +# +# Set parameters here that organize how your full timeseries +# data will be divided into representative period clusters. +# Ensure that time_domain_reduction is set to 1 in GenX_settings.yml +# before running. Run within GenX or use PreCluster.jl to test and +# examine representative period output before proceeding. +# Specify your data input directory as inpath within Run_test.jl +# or PreCluster.jl. +# +##### + + # - TimestepsPerRepPeriod + # Typically 168 timesteps (e.g., hours) per period, this designates + # the length of each representative period. +TimestepsPerRepPeriod: 168 + + # - ClusterMethod + # Either 'kmeans' or 'kmedoids', this designates the method used to cluster + # periods and determine each point's representative period. +ClusterMethod: 'kmeans' + + # - ScalingMethod + # Either 'N' or 'S', this designates directs the module to normalize ([0,1]) + # or standardize (mean 0, variance 1) the input data. +ScalingMethod: "S" + + # - MaxPeriods + # The maximum number of periods - both clustered periods and extreme periods - + # that may be used to represent the input data. If IterativelyAddPeriods is on and the + # error threshold is never met, this will be the total number of periods. +MaxPeriods: 11 + + # - MinPeriods + # The minimum number of periods used to represent the input data. If using + # UseExtremePeriods, this must be at least the number of extreme periods requests. If + # IterativelyAddPeriods if off, this will be the total number of periods. +MinPeriods: 8 + + # - IterativelyAddPeriods + # Either 'yes' or 'no', this designates whether or not to add periods + # until the error threshold between input data and represented data is met or the maximum + # number of periods is reached. +IterativelyAddPeriods: 1 + + # - IterateMethod + # Either 'cluster' or 'extreme', this designates whether to add clusters to + # the kmeans/kmedoids method or to set aside the worst-fitting periods as a new extreme periods. + # The default option is 'cluster'. +IterateMethod: "cluster" + + # - Threshold + # Iterative period addition will end if the period farthest (Euclidean Distance) + # from its representative period is within this percentage of the total possible error (for normalization) + # or ~95% of the total possible error (for standardization). E.g., for a threshold of 0.01, + # every period must be within 1% of the spread of possible error before the clustering + # iterations will terminate (or until the max number of periods is reached). +Threshold: 0.05 + + # - nReps + # The number of times to repeat each kmeans/kmedoids clustering at the same setting. +nReps: 100 + + # - LoadWeight + # Default 1, this is an optional multiplier on load columns in order to prioritize + # better fits for load profiles over resource capacity factor profiles. +LoadWeight: 1 + + # - WeightTotal + # Default 8760, the sum to which the relative weights of representative periods will be scaled. +WeightTotal: 8760 + + # - ClusterFuelPrices + # Either 1 (yes) or 0 (no), this indicates whether or not to use the fuel price + # time series in Fuels_data.csv in the clustering process. If 0, this function will still write + # Fuels_data_clustered.csv with reshaped fuel prices based on the number and size of the + # representative weeks, assuming a constant time series of fuel prices with length equal to the + # number of timesteps in the raw input data. +ClusterFuelPrices: 1 + + # - UseExtremePeriods + # Either 'yes' or 'no', this designates whether or not to include + # outliers (by performance or load/resource extreme) as their own representative periods. + # This setting automatically includes the periods with maximum load, minimum solar cf and + # minimum wind cf as extreme periods. +UseExtremePeriods: 1 + + # - MultiStageConcatenate + # (Only considered if MultiStage = 1 in genx_settings.yml) + # If 1, this designates that the model should time domain reduce the input data + # of all model stages together. Else if 0, the model will time domain reduce each + # stage separately +MultiStageConcatenate: 0 + +# STILL IN DEVELOPMENT - Currently just uses integral max load, integral min PV and wind. +# - ExtremePeriods +# Use this to define which periods to be included among the final representative periods +# as "Extreme Periods". +# Select by profile type: load ("Load"), solar PV capacity factors ("PV"), and wind capacity factors ("Wind"). +# Select whether to examine these profiles by zone ("Zone") or across the whole system ("System"). +# Select whether to look for absolute max/min at the timestep level ("Absolute") +# or max/min sum across the period ("Integral"). +# Select whether you want the maximum ("Max") or minimum ("Min") (of the prior type) for each profile type. +ExtremePeriods: + Load: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + System: + Absolute: + Max: 1 + Min: 0 + Integral: + Max: 0 + Min: 0 + PV: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 + Wind: + Zone: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 1 + System: + Absolute: + Max: 0 + Min: 0 + Integral: + Max: 0 + Min: 0 diff --git a/Tutorials/Tutorial_0_Introduction.ipynb b/Tutorials/Tutorial_0_Introduction.ipynb new file mode 100644 index 0000000000..58137acefd --- /dev/null +++ b/Tutorials/Tutorial_0_Introduction.ipynb @@ -0,0 +1,115 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f8b3c984", + "metadata": {}, + "source": [ + "# Tutorial 0: Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "5fe615ef", + "metadata": {}, + "source": [ + "Welcome to the GenX tutorials! In the following tutorials, we outline some important features of GenX and how to run them." + ] + }, + { + "cell_type": "markdown", + "id": "283cfb5a", + "metadata": {}, + "source": [ + "Running these notebooks follows similar steps to those in the GenX documentation here. In this tutorial, we go through setting up GenX on Jupyter and navigating to the `Tutorials` file from there." + ] + }, + { + "cell_type": "markdown", + "id": "4984f841", + "metadata": {}, + "source": [ + "First, as in the documentation, open a terminal window on your computer and navigate to GenX. Then, navigate to Julia and open a project using the command `julia --project=.`.\n" + ] + }, + { + "cell_type": "markdown", + "id": "906b7b33", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "14bd5e78", + "metadata": {}, + "source": [ + "If you don't already have the package IJulia, add it here using `import(Pkg); Pkg.add(\"IJulia\")`:" + ] + }, + { + "cell_type": "markdown", + "id": "50fe200d", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "0e1b2998", + "metadata": {}, + "source": [ + "After IJulia is added, open a Jupyter notebook with `using IJulia; notebook()`:" + ] + }, + { + "cell_type": "markdown", + "id": "af32af4d", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "762ecdbb", + "metadata": {}, + "source": [ + "This will open a notebook in your default browser. From there, navigate through the GenX directory to the `Tutorials` folder and you're good to go!" + ] + }, + { + "cell_type": "markdown", + "id": "97b2b84b", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "c6ada492", + "metadata": {}, + "source": [ + "**For a more detailed explanation of IJulia, Jupyter, and downloading packages, see here.**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.2", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Tutorials/Tutorial_1_Configuring_Settings.ipynb b/Tutorials/Tutorial_1_Configuring_Settings.ipynb new file mode 100644 index 0000000000..712b2339d6 --- /dev/null +++ b/Tutorials/Tutorial_1_Configuring_Settings.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "78a5ccd4-f602-4381-aadc-d468cff03ba3", + "metadata": {}, + "source": [ + "# Tutorial 1: Configuring Settings" + ] + }, + { + "cell_type": "markdown", + "id": "c78bfdbd", + "metadata": {}, + "source": [ + "GenX is easy to customize to fit a variety of problems. In this tutorial, we show which settings are available to change, what their defaults are, and how to change them in your code." + ] + }, + { + "cell_type": "markdown", + "id": "d6814a53", + "metadata": {}, + "source": [ + "## What settings are there?" + ] + }, + { + "cell_type": "markdown", + "id": "69655945", + "metadata": {}, + "source": [ + "There are 21 settings available to edit in GenX, found in the file `genx_settings.yml`. These settings are described here in the documentation. The file is located in the `Settings` folder in the working directory. To change the location of the file, edit the `settings_path` variable in `Run.jl` within your directory." + ] + }, + { + "cell_type": "markdown", + "id": "6e11cc81", + "metadata": {}, + "source": [ + "Most settings are set as either 0 or 1, which correspond to whether or not to include a specifc feature. For example, to use `TimeDomainReduction`, you would set its parameter to 0 within `genx_settings.yml`. If you would like to run GenX without it, you would set its parameter to 1." + ] + }, + { + "cell_type": "markdown", + "id": "4c88b3b9", + "metadata": {}, + "source": [ + "Other settings, such as `CO2Cap`, have more options corresponding to integers, while some settings such as `ModelingtoGenerateAlternativeSlack` take a numerical input directly (in this case, the slack value). Two settings, `Solver` and `TimeDomainReductionFolder` take in text as input. To learn more about different solvers, read here. For `TimeDomainReductionFolder`, specify the name of the directory you wish to see the results in. For a more comprehensive description of the input options, see the documentation linked above." + ] + }, + { + "cell_type": "markdown", + "id": "bbd18f76", + "metadata": {}, + "source": [ + "Below is the settings file for `SmallNewEngland/OneZone`:" + ] + }, + { + "cell_type": "markdown", + "id": "57ee59c3", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "c37c756e", + "metadata": {}, + "source": [ + "All `genx_settings.yml` files in `Example_Systems` specify most parameters. When configuring your own settings, however, it is not necessary to input all parameters as defaults are specified for each one in `configure_settings.jl`." + ] + }, + { + "cell_type": "markdown", + "id": "16ec73eb", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "527e600d", + "metadata": {}, + "source": [ + "To open `genx_settings.yml` in Jupyter, use the function `YAML.load(open(...))` and navigate to file in the desired directory:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "47fb7911", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Parsers ─────────── v2.8.1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m ArrayInterface ──── v7.7.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m DataStructures ──── v0.18.16\n", + 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To see why use `status --outdated -m`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mArrayInterface\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mArrayInterface → ArrayInterfaceStaticArraysCoreExt\u001b[39m\n", + "\u001b[32m ✓ \u001b[39mDataStructures\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mMacroTools\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mCommonSubexpressions\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mSortingAlgorithms\u001b[39m\n", + "\u001b[32m ✓ \u001b[39mStatsBase\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mForwardDiff\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mStaticArrays\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mAdapt → AdaptStaticArraysExt\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mStaticArrays → StaticArraysStatisticsExt\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mForwardDiff → ForwardDiffStaticArraysExt\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mGPUArraysCore\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mArrayInterface → ArrayInterfaceGPUArraysCoreExt\u001b[39m\n", + "\u001b[32m ✓ \u001b[39mRecursiveArrayTools\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mNearestNeighbors\u001b[39m\n", + "\u001b[32m ✓ \u001b[39mClustering\n", + "\u001b[33m ✓ \u001b[39m\u001b[90mParsers\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mInlineStrings\u001b[39m\n", + "\u001b[33m ✓ \u001b[39m\u001b[90mJSON\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mWeakRefStrings\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mBenchmarkTools\u001b[39m\n", + "\u001b[32m ✓ \u001b[39mCSV\n", + "\u001b[32m ✓ \u001b[39mDataFrames\n", + "\u001b[32m ✓ \u001b[39mMathOptInterface\n", + "\u001b[32m ✓ \u001b[39mCbc\n", + "\u001b[32m ✓ \u001b[39mClp\n", + "\u001b[32m ✓ \u001b[39mHiGHS\n", + "\u001b[32m ✓ \u001b[39mJuMP\n", + "\u001b[32m ✓ \u001b[39mGenX\n", + " 30 dependencies successfully precompiled in 77 seconds. 66 already precompiled.\n", + " \u001b[33m2\u001b[39m dependencies precompiled but different versions are currently loaded. Restart julia to access the new versions\n" + ] + } + ], + "source": [ + "import(Pkg); Pkg.add(\"YAML\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "751b0e46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dict{Any, Any} with 21 entries:\n", + " \"NetworkExpansion\" => 0\n", + " \"TimeDomainReductionFolder\" => \"TDR_Results\"\n", + " \"ModelingToGenerateAlternativeIterations\" => 3\n", + " \"ParameterScale\" => 1\n", + " \"EnergyShareRequirement\" => 1\n", + " \"PrintModel\" => 0\n", + " \"TimeDomainReduction\" => 1\n", + " \"Trans_Loss_Segments\" => 1\n", + " \"CapacityReserveMargin\" => 1\n", + " \"ModelingtoGenerateAlternativeSlack\" => 0.1\n", + " \"Solver\" => \"HiGHS\"\n", + " \"MethodofMorris\" => 0\n", + " \"Reserves\" => 0\n", + " \"StorageLosses\" => 1\n", + " \"OverwriteResults\" => 0\n", + " \"UCommit\" => 2\n", + " \"ModelingToGenerateAlternatives\" => 0\n", + " \"MaxCapReq\" => 1\n", + " \"MinCapReq\" => 1\n", + " \"CO2Cap\" => 2\n", + " \"WriteShadowPrices\" => 1" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "using YAML\n", + "genx_settings_SNE = YAML.load(open(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml\"))" + ] + }, + { + "cell_type": "markdown", + "id": "dcdd15fd", + "metadata": {}, + "source": [ + "Since all settings have defaults, you only need to specify the settings you would like to change. In fact, you can leave your settings file completely blank and it will still run! Let's try editing `genx_settings` in `SmallNewEngland/OneZone` to contain no parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f1ea1703", + "metadata": {}, + "outputs": [], + "source": [ + "new_params = Dict() # Empty dictionary\n", + "YAML.write_file(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml\", new_params)" + ] + }, + { + "cell_type": "markdown", + "id": "fbc6ce5d", + "metadata": {}, + "source": [ + "The empty file will look like this:" + ] + }, + { + "cell_type": "markdown", + "id": "e24980d7", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "67a336a9", + "metadata": {}, + "source": [ + "Now, we run GenX and output the file `capacity.csv` from the `Results` folder. To do this, we use the function `include`, which takes a .jl file and runs it in jupyter notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "236c0180", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Configuring Settings\n", + "Time Series Data Already Clustered.\n", + "Configuring Solver\n", + "Loading Inputs\n", + "Reading Input CSV Files\n", + "Network.csv Successfully Read!\n", + "Load_data.csv Successfully Read!\n", + "Fuels_data.csv Successfully Read!\n", + "Generators_data.csv Successfully Read!\n", + "Generators_variability.csv Successfully Read!\n", + "Validating time basis\n", + "Capacity_reserve_margin.csv Successfully Read!\n", + "Minimum_capacity_requirement.csv Successfully Read!\n", + "Maximum_capacity_requirement.csv Successfully Read!\n", + "Energy_share_requirement.csv Successfully Read!\n", + "CO2_cap.csv Successfully Read!\n", + "CSV Files Successfully Read In From /Users/mayamutic/Desktop/GenX/Example_Systems/SmallNewEngland/OneZone\n", + "Generating the Optimization Model\n", + "Discharge Module\n", + "Non-served Energy Module\n", + "Investment Discharge Module\n", + "Unit Commitment Module\n", + "Emissions Module (for CO2 Policy modularization\n", + "Dispatchable Resources Module\n", + "Storage Resources Module\n", + "Storage Investment Module\n", + "Storage Core Resources Module\n", + "Storage Resources with Symmetric Charge/Discharge Capacity Module\n", + "Thermal (Unit Commitment) Resources Module\n", + "C02 Policies Module\n", + "Energy Share Requirement Policies Module\n", + "Capacity Reserve Margin Policies Module\n", + "Minimum Capacity Requirement Module\n", + "Maximum Capacity Requirement Module\n", + "Time elapsed for model building is\n", + "4.274147083\n", + "Solving Model\n", + "Running HiGHS 1.6.0: Copyright (c) 2023 HiGHS under MIT licence terms\n", + "Presolving model\n", + "36012 rows, 17529 cols, 136567 nonzeros\n", + "34549 rows, 16070 cols, 136337 nonzeros\n", + "Presolve : Reductions: rows 34549(-6123); columns 16070(-2422); elements 136337(-29881)\n", + "Solving the presolved LP\n", + "Using EKK dual simplex solver - serial\n", + " Iteration Objective Infeasibilities num(sum)\n", + " 0 -1.9058295039e-01 Ph1: 12(61.9533); Du: 1(0.190583) 0s\n", + " 16420 9.7160052378e+03 Pr: 0(0); Du: 0(3.13359e-06) 4s\n", + "Using EKK primal simplex solver\n", + " Iteration Objective Infeasibilities num(sum)\n", + " 16420 9.7160052378e+03 Pr: 0(0); Du: 1(0.000200549) 4s\n", + " 16421 9.7160052378e+03 Pr: 0(0); Du: 0(6.09353e-15) 4s\n", + "Solving the original LP from the solution after postsolve\n", + "Model status : Optimal\n", + "Simplex iterations: 16421\n", + "Objective value : 9.7160052378e+03\n", + "HiGHS run time : 4.25\n", + "LP solved for primal\n", + "Writing Output\n", + "Time elapsed for writing costs is\n", + "0.659734\n", + "Time elapsed for writing storage is\n", + "0.078838791\n", + "Time elapsed for writing nse is\n", + "0.317711209\n", + "Time elapsed for writing power balance is\n", + "0.178140584\n", + "Time elapsed for writing emissions is\n", + "0.30562725\n", + "Time elapsed for writing reliability is\n", + "0.141396375\n", + "Time elapsed for writing storage duals is\n", + "0.326884042\n", + "Time elapsed for writing commitment is\n", + "0.028738458\n", + "Time elapsed for writing startup is\n", + "0.028648041\n", + "Time elapsed for writing shutdown is\n", + "0.024390583\n", + "Time elapsed for writing time weights is\n", + "0.048884292\n", + "Time elapsed for writing reserve margin is\n", + "0.124729875\n", + "Time elapsed for writing capacity value is\n", + "1.293372\n", + "Time elapsed for writing net revenue is\n", + "0.369404208\n", + "Wrote outputs to /Users/mayamutic/Desktop/GenX/Example_Systems/SmallNewEngland/OneZone/Results_2\n", + "Time elapsed for writing is\n", + "7.758552166\n" + ] + } + ], + "source": [ + "include(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Run.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b8ea6030", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling CSV [336ed68f-0bac-5ca0-87d4-7b16caf5d00b]\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling DataFrames [a93c6f00-e57d-5684-b7b6-d8193f3e46c0]\n" + ] + }, + { + "ename": "LoadError", + "evalue": "SystemError: opening file \"../Example_Systems/SmallNewEngland/OneZone/Results/capacity.csv\": No such file or directory", + "output_type": "error", + "traceback": [ + "SystemError: opening file \"../Example_Systems/SmallNewEngland/OneZone/Results/capacity.csv\": No such file or directory", + "", + "Stacktrace:", + " [1] systemerror(p::String, errno::Int32; extrainfo::Nothing)", + " @ Base ./error.jl:176", + " [2] #systemerror#82", + " @ ./error.jl:175 [inlined]", + " [3] systemerror", + " @ ./error.jl:175 [inlined]", + " [4] open(fname::String; lock::Bool, read::Nothing, write::Nothing, create::Nothing, truncate::Nothing, append::Nothing)", + " @ Base ./iostream.jl:293", + " [5] open(fname::String)", + " @ Base ./iostream.jl:275", + " [6] top-level scope", + " @ In[8]:3" + ] + } + ], + "source": [ + "using CSV\n", + "using DataFrames\n", + "results = CSV.read(open(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Results/capacity.csv\"),DataFrame)" + ] + }, + { + "cell_type": "markdown", + "id": "75d31b5a", + "metadata": {}, + "source": [ + "As you can see, this runs without a problem! To try with your own parameters, edit the `new_params` dictionary with whatever parameters you'd like to try and run the cells again." + ] + }, + { + "cell_type": "markdown", + "id": "f4f9679f", + "metadata": {}, + "source": [ + "Finally, let's rewite `genx_settings.yml` to put the original settings in the example back: NOTE: Put OneZone in Tutorials folder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41b5d0c7", + "metadata": {}, + "outputs": [], + "source": [ + "YAML.write_file(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml\", genx_settings_SNE)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.2", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Tutorials/Tutorial_2_Network_Visualization.ipynb b/Tutorials/Tutorial_2_Network_Visualization.ipynb new file mode 100644 index 0000000000..d814592144 --- /dev/null +++ b/Tutorials/Tutorial_2_Network_Visualization.ipynb @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3ce667ff", + "metadata": {}, + "source": [ + "# Tutorial 2: Network Visualization" + ] + }, + { + "cell_type": "markdown", + "id": "71b0f263", + "metadata": {}, + "source": [ + "To run GenX, there are five mandatory input files: `Fuels_data.csv`, `Network.csv`, `Load_data.csv`, `Generators_variability.csv`, and `Generators_data.csv`. Detailed descriptions of these files can be found here in the documentation under \"Inputs\". This tutorial helps visualize the file `Network.csv` using the example system `SmallNewEngland/ThreeZones`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fa98763b", + "metadata": {}, + "outputs": [], + "source": [ + "using Plots\n", + "using PlotlyJS\n", + "using CSV\n", + "using DataFrames" + ] + }, + { + "cell_type": "markdown", + "id": "d5f37531", + "metadata": {}, + "source": [ + "The input file `Network.csv` contains the nodes of your network, how they connect to each other, and some important features about them. Below is the network file for `SmallNewEngland/ThreeZones`:" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "id": "bcaa924c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
3×15 DataFrame
RowColumn1Network_zonesNetwork_Linesz1z2z3Line_Max_Flow_MWtransmission_path_namedistance_mileLine_Loss_PercentageLine_Max_Reinforcement_MWLine_Reinforcement_Cost_per_MWyrDerateCapRes_1CapRes_1CapRes_Excl_1
String3String3String3String3String3String3String7String15String15String15String7String7String7String3Int64?
1MAz111-102950MA_to_CT123.05840.0123058372950120600.9500
2CTz2210-12000MA_to_ME196.53850.0196538472000192610.9500
3MEz3missing
" + ], + "text/latex": [ + "\\begin{tabular}{r|cccccccc}\n", + "\t& Column1 & Network\\_zones & Network\\_Lines & z1 & z2 & z3 & Line\\_Max\\_Flow\\_MW & \\\\\n", + "\t\\hline\n", + "\t& String3 & String3 & String3 & String3 & String3 & String3 & String7 & \\\\\n", + "\t\\hline\n", + "\t1 & MA & z1 & 1 & 1 & -1 & 0 & 2950 & $\\dots$ \\\\\n", + "\t2 & CT & z2 & 2 & 1 & 0 & -1 & 2000 & $\\dots$ \\\\\n", + "\t3 & ME & z3 & & & & & & $\\dots$ \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m3×15 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m Column1 \u001b[0m\u001b[1m Network_zones \u001b[0m\u001b[1m Network_Lines \u001b[0m\u001b[1m z1 \u001b[0m\u001b[1m z2 \u001b[0m\u001b[1m z3 \u001b[0m\u001b[1m Line_\u001b[0m ⋯\n", + " │\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m Strin\u001b[0m ⋯\n", + "─────┼──────────────────────────────────────────────────────────────────────────\n", + " 1 │ MA z1 1 1 -1 0 2950 ⋯\n", + " 2 │ CT z2 2 1 0 -1 2000\n", + " 3 │ ME z3\n", + "\u001b[36m 9 columns omitted\u001b[0m" + ] + }, + "execution_count": 241, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "network = CSV.read(\"Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Network.csv\",DataFrame,missingstring=\"NA\")" + ] + }, + { + "cell_type": "markdown", + "id": "b9aafe7b", + "metadata": {}, + "source": [ + "Here, MA, CT, and ME are the abbreviations for states Massachusetts, Connecticut, and Maine. However, since the US region of New England contains other states as well, ME in this case is also used to refer to those states." + ] + }, + { + "cell_type": "markdown", + "id": "ca18429c", + "metadata": {}, + "source": [ + "Columns `z1, z2, z3` specify an adjacency matrix forming the network of the three regions. In this case, there are only two network lines, specified in the `Network_Lines` columns. The `z1` column indicates that the first node, MA, is the source of both lines as both rows have value 1. Columns `z2` and `z3` both have values of -1, which means both nodes CT and ME recieve energy from node MA:" + ] + }, + { + "cell_type": "markdown", + "id": "6a52eb00", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d89f8c96", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "@webio": { + "lastCommId": "22b36260144444988f1a70d85ef0b17a", + "lastKernelId": "0d15a65c-9b8a-4242-bdb1-4d39948d79a9" + }, + "kernelspec": { + "display_name": "Julia 1.9.2", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Tutorials/Tutorial_3_K-means_Time_Domain_Reduction.ipynb b/Tutorials/Tutorial_3_K-means_Time_Domain_Reduction.ipynb new file mode 100644 index 0000000000..cf59fac6f4 --- /dev/null +++ b/Tutorials/Tutorial_3_K-means_Time_Domain_Reduction.ipynb @@ -0,0 +1,158764 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7054e9e0", + "metadata": {}, + "source": [ + "# Tutorial 3: K-Means and Time Domain Reduction" + ] + }, + { + "cell_type": "markdown", + "id": "944dff9e", + "metadata": {}, + "source": [ + "A good tool to reduce computation time of GenX is to use Time Domain Reduction (TDR). Time Domain Reduction is a method that selects a smaller set of time steps from the data in a way that reduces computation time while still capturing the main information of the model. In this tutorial, we go over how TDR works in GenX and how it uses K-means clustering to choose the optimal time steps." + ] + }, + { + "cell_type": "markdown", + "id": "9cb4e61f", + "metadata": {}, + "source": [ + "### Time Domain Reduction" + ] + }, + { + "cell_type": "markdown", + "id": "4456f70f", + "metadata": {}, + "source": [ + "To see how Time Domain Reduction works, let's look at the `Load_data` in `Example_Systems_Tutorials/SmallNewEngland/OneZone/`:" + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "id": "5f8730d4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m registry at `~/.julia/registries/General.toml`\n", + "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m TickTock ─ v1.3.0\n", + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/Desktop/GenX/Project.toml`\n", + " \u001b[90m[9ff05d80] \u001b[39m\u001b[92m+ TickTock v1.3.0\u001b[39m\n", + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/Desktop/GenX/Manifest.toml`\n", + " \u001b[90m[9ff05d80] \u001b[39m\u001b[92m+ TickTock v1.3.0\u001b[39m\n", + "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n", + "\u001b[32m ✓ \u001b[39mTickTock\n", + "\u001b[33m ✓ \u001b[39mGenX\n", + " 2 dependencies successfully precompiled in 7 seconds. 128 already precompiled.\n", + " \u001b[33m1\u001b[39m dependency precompiled but a different version is currently loaded. Restart julia to access the new version\n" + ] + } + ], + "source": [ + "# First, load all packages needed\n", + "using DataFrames\n", + "using CSV\n", + "using VegaLite\n", + "using YAML\n", + "using TickTock\n", + "using PlotlyJS\n", + "using Plots\n", + "using Clustering\n", + "using ScikitLearn\n", + "@sk_import datasets: (make_blobs)" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "id": "328f601e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RowVollDemand_SegmentCost_of_Demand_Curtailment_per_MWMax_Demand_CurtailmentRep_PeriodsTimesteps_per_Rep_PeriodSub_WeightsTime_IndexLoad_MW_z1
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150000111187608760111162
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" + ], + "text/latex": [ + "\\begin{tabular}{r|ccccc}\n", + "\t& Voll & Demand\\_Segment & Cost\\_of\\_Demand\\_Curtailment\\_per\\_MW & Max\\_Demand\\_Curtailment & \\\\\n", + "\t\\hline\n", + "\t& String7 & String3 & String3 & String3 & \\\\\n", + "\t\\hline\n", + "\t1 & 50000 & 1 & 1 & 1 & $\\dots$ \\\\\n", + "\t2 & & & & & $\\dots$ \\\\\n", + "\t3 & & & & & $\\dots$ \\\\\n", + "\t4 & & & & & $\\dots$ \\\\\n", + "\t5 & & & & & $\\dots$ \\\\\n", + "\t6 & & & & & $\\dots$ \\\\\n", + "\t7 & & & & & $\\dots$ \\\\\n", + "\t8 & & & & & $\\dots$ \\\\\n", + "\t9 & & & & & $\\dots$ \\\\\n", + "\t10 & & & & & $\\dots$ \\\\\n", + "\t11 & & & & & $\\dots$ \\\\\n", + "\t12 & & & & & $\\dots$ \\\\\n", + "\t13 & & & & & $\\dots$ \\\\\n", + "\t14 & & & & & $\\dots$ \\\\\n", + "\t15 & & & & & $\\dots$ \\\\\n", + "\t16 & & & & & $\\dots$ \\\\\n", + "\t17 & & & & & $\\dots$ \\\\\n", + "\t18 & & & & & $\\dots$ \\\\\n", + "\t19 & & & & & $\\dots$ \\\\\n", + "\t20 & & & & & $\\dots$ \\\\\n", + "\t21 & & & & & $\\dots$ \\\\\n", + "\t22 & & & & & $\\dots$ \\\\\n", + "\t23 & & & & & $\\dots$ \\\\\n", + "\t24 & & & & & $\\dots$ \\\\\n", + "\t25 & & & & & $\\dots$ \\\\\n", + "\t26 & & & & & $\\dots$ \\\\\n", + "\t27 & & & & & $\\dots$ \\\\\n", + "\t28 & & & & & $\\dots$ \\\\\n", + "\t29 & & & & & $\\dots$ \\\\\n", + "\t30 & & & & & $\\dots$ \\\\\n", + "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m8760×9 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m Voll \u001b[0m\u001b[1m Demand_Segment \u001b[0m\u001b[1m Cost_of_Demand_Curtailment_per_MW \u001b[0m\u001b[1m Max_Demand\u001b[0m ⋯\n", + " │\u001b[90m String7 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m ⋯\n", + "──────┼─────────────────────────────────────────────────────────────────────────\n", + " 1 │ 50000 1 1 1 ⋯\n", + " 2 │\n", + " 3 │\n", + " 4 │\n", + " 5 │ ⋯\n", + " 6 │\n", + " 7 │\n", + " 8 │\n", + " 9 │ ⋯\n", + " 10 │\n", + " 11 │\n", + " ⋮ │ ⋮ ⋮ ⋮ ⋱\n", + " 8751 │\n", + " 8752 │ ⋯\n", + " 8753 │\n", + " 8754 │\n", + " 8755 │\n", + " 8756 │ ⋯\n", + " 8757 │\n", + " 8758 │\n", + " 8759 │\n", + " 8760 │ ⋯\n", + "\u001b[36m 6 columns and 8739 rows omitted\u001b[0m" + ] + }, + "execution_count": 259, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loads = CSV.read(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Load_data.csv\",DataFrame,missingstring=\"NA\")" + ] + }, + { + "cell_type": "markdown", + "id": "62d2def4", + "metadata": {}, + "source": [ + "The most columns to note in this file are `Rep_Periods`, `TimeSteps_Per_Rep_Period`, `Time_Index`, and `Load_MW_z1`. This file shows the number of time steps used in GenX before applying TDR, i.e. every hour in a year, totaling 8,760 hours. This means that there is only one representative period, as seen in `Rep_Periods`." + ] + }, + { + "cell_type": "markdown", + "id": "2fc22b2f", + "metadata": {}, + "source": [ + "Below shows the load per timestep in megawatts:" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "id": "6b684248", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.vega.v5+json": { + "$schema": "https://vega.github.io/schema/vega/v5.json", + "axes": [ + { + "aria": false, + "domain": false, + "grid": true, + "gridScale": "y", + "labels": false, + "maxExtent": 0, + "minExtent": 0, + "orient": "bottom", + "scale": "x", + "tickCount": { + "signal": "ceil(width/40)" + }, + "ticks": false, + "zindex": 0 + }, + { + "aria": false, + "domain": false, + "grid": true, + "gridScale": "x", + "labels": false, + "maxExtent": 0, + "minExtent": 0, + "orient": "left", + "scale": "y", + "tickCount": { + "signal": "ceil(height/40)" + }, + "ticks": false, + "zindex": 0 + }, + { + "grid": false, 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", + "image/svg+xml": [ + "05001,0001,5002,0002,5003,0003,5004,0004,5005,0005,5006,0006,5007,0007,5008,0008,5009,000Time_Index02,0004,0006,0008,00010,00012,00014,00016,00018,00020,00022,00024,000Load_MW_z1MW Load per hour, No TDR" + ], + "text/plain": [ + "@vlplot(\n", + " title=\"MW Load per hour, No TDR\",\n", + " width=600,\n", + " height=400,\n", + " linewidth=0.01,\n", + " mark=\"line\",\n", + " encoding={\n", + " x={\n", + " field=\"Time_Index\"\n", + " },\n", + " y={\n", + " field=\"Load_MW_z1\"\n", + " }\n", + " },\n", + " data={\n", + " values=...\n", + " }\n", + ")" + ] + }, + "execution_count": 260, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loads |>\n", + "@vlplot(:line, \n", + " x=:Time_Index, y=:Load_MW_z1, title=\"MW Load per hour, No TDR\",\n", + " width=600,height=400,linewidth=.01)" + ] + }, + { + "cell_type": "markdown", + "id": "86afb434", + "metadata": {}, + "source": [ + "As in Tutorial 1, we can open the `genx_settings.yml` file for `SmallNewEngland/OneZone` to see how `TimeDomainReduction` is set. If it's set to 1, this means TDR is being used." + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "id": "ebb8fde0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dict{Any, Any} with 21 entries:\n", + " \"NetworkExpansion\" => 0\n", + " \"TimeDomainReductionFolder\" => \"TDR_Results\"\n", + " \"ModelingToGenerateAlternativeIterations\" => 3\n", + " \"ParameterScale\" => 1\n", + " \"EnergyShareRequirement\" => 1\n", + " \"PrintModel\" => 0\n", + " \"TimeDomainReduction\" => 1\n", + " \"Trans_Loss_Segments\" => 1\n", + " \"CapacityReserveMargin\" => 1\n", + " \"ModelingtoGenerateAlternativeSlack\" => 0.1\n", + " \"Solver\" => \"HiGHS\"\n", + " \"MethodofMorris\" => 0\n", + " \"Reserves\" => 0\n", + " \"StorageLosses\" => 1\n", + " \"OverwriteResults\" => 0\n", + " \"UCommit\" => 2\n", + " \"ModelingToGenerateAlternatives\" => 0\n", + " \"MaxCapReq\" => 1\n", + " \"MinCapReq\" => 1\n", + " \"CO2Cap\" => 2\n", + " \"WriteShadowPrices\" => 1" + ] + }, + "execution_count": 303, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genx_settings_SNE = YAML.load(open(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml\"))" + ] + }, + { + "cell_type": "markdown", + "id": "1ad130ab", + "metadata": {}, + "source": [ + "To visualize how TDR decreases computation time, let's start by running `SmallNewEngland/OneZone` without TDR. We'll use the package `TickTock` to track the runtime." + ] + }, + { + "cell_type": "markdown", + "id": "a2c0a168", + "metadata": {}, + "source": [ + "To run GenX without TDR, we start by editing the settings to set `TimeDomainReduction` to 0:" + ] + }, + { + "cell_type": "code", + "execution_count": 304, + "id": "d6893321", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dict{Any, Any} with 21 entries:\n", + " \"NetworkExpansion\" => 0\n", + " \"TimeDomainReductionFolder\" => \"TDR_Results\"\n", + " \"ModelingToGenerateAlternativeIterations\" => 3\n", + " \"ParameterScale\" => 1\n", + " \"EnergyShareRequirement\" => 1\n", + " \"PrintModel\" => 0\n", + " \"TimeDomainReduction\" => 0\n", + " \"Trans_Loss_Segments\" => 1\n", + " \"CapacityReserveMargin\" => 1\n", + " \"ModelingtoGenerateAlternativeSlack\" => 0.1\n", + " \"Solver\" => \"HiGHS\"\n", + " \"MethodofMorris\" => 0\n", + " \"Reserves\" => 0\n", + " \"StorageLosses\" => 1\n", + " \"OverwriteResults\" => 0\n", + " \"UCommit\" => 2\n", + " \"ModelingToGenerateAlternatives\" => 0\n", + " \"MaxCapReq\" => 1\n", + " \"MinCapReq\" => 1\n", + " \"CO2Cap\" => 2\n", + " \"WriteShadowPrices\" => 1" + ] + }, + "execution_count": 304, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genx_settings_SNE[\"TimeDomainReduction\"] = 0\n", + "genx_settings_SNE ## Output settings" + ] + }, + { + "cell_type": "markdown", + "id": "8706d925", + "metadata": {}, + "source": [ + "Then we write the edited settings to the file path:" + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "id": "7768ac6a", + "metadata": {}, + "outputs": [], + "source": [ + "YAML.write_file(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml\", genx_settings_SNE)" + ] + }, + { + "cell_type": "markdown", + "id": "1865ad33", + "metadata": {}, + "source": [ + "And run it using `include`. (Note: this process will take a few minutes):" + ] + }, + { + "cell_type": "code", + "execution_count": 306, + "id": "137e9c95", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Configuring Settings\n", + "Configuring Solver\n", + "Loading Inputs\n", + "Reading Input CSV Files\n", + "Network.csv Successfully Read!\n", + "Load_data.csv Successfully Read!\n", + "Fuels_data.csv Successfully Read!\n", + "Generators_data.csv Successfully Read!\n", + "Generators_variability.csv Successfully Read!\n", + "Validating time basis\n", + "Capacity_reserve_margin.csv Successfully Read!\n", + "Minimum_capacity_requirement.csv Successfully Read!\n", + "Maximum_capacity_requirement.csv Successfully Read!\n", + "Energy_share_requirement.csv Successfully Read!\n", + "CO2_cap.csv Successfully Read!\n", + "CSV Files Successfully Read In From /Users/mayamutic/Desktop/GenX/Example_Systems/SmallNewEngland/OneZone\n", + "Generating the Optimization Model\n", + "Discharge Module\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m started timer at: 2024-01-14T18:50:47.180\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Non-served Energy Module\n", + "Investment Discharge Module\n", + "Unit Commitment Module\n", + "Emissions Module (for CO2 Policy modularization\n", + "Dispatchable Resources Module\n", + "Storage Resources Module\n", + "Storage Investment Module\n", + "Storage Core Resources Module\n", + "Storage Resources with Symmetric Charge/Discharge Capacity Module\n", + "Thermal (Unit Commitment) Resources Module\n", + "C02 Policies Module\n", + "Energy Share Requirement Policies Module\n", + "Capacity Reserve Margin Policies Module\n", + "Minimum Capacity Requirement Module\n", + "Maximum Capacity Requirement Module\n", + "Time elapsed for model building is\n", + "0.555159583\n", + "Solving Model\n", + "Running HiGHS 1.6.0: Copyright (c) 2023 HiGHS under MIT licence terms\n", + "Presolving model\n", + "170620 rows, 83017 cols, 646999 nonzeros\n", + "161830 rows, 74231 cols, 653419 nonzeros\n", + "Presolve : Reductions: rows 161830(-30906); columns 74231(-13381); elements 653419(-134175)\n", + "Solving the presolved LP\n", + "Using EKK dual simplex solver - serial\n", + " Iteration Objective Infeasibilities num(sum)\n", + " 0 -4.2035774355e+00 Ph1: 204(998.471); Du: 25(4.20358) 0s\n", + " 12911 3.5764275943e+03 Pr: 11955(9388.12); Du: 0(0.00728457) 5s\n", + " 27245 4.4018819620e+03 Pr: 15248(18133.3); Du: 0(0.0164783) 10s\n", + " 33480 4.6172015768e+03 Pr: 19961(178631); Du: 0(0.00123592) 16s\n", + " 41671 6.5841177886e+03 Pr: 19497(1.94677e+06); Du: 0(0.00303983) 21s\n", + " 45369 7.2446943919e+03 Pr: 47955(198575); Du: 0(0.00363254) 26s\n", + " 48032 7.4871889951e+03 Pr: 41159(54384.3); Du: 0(0.00323346) 31s\n", + " 51113 7.7263507185e+03 Pr: 31599(1.02636e+07); Du: 0(0.00359186) 37s\n", + " 54158 7.9912685513e+03 Pr: 16427(56826.4); Du: 0(0.00373523) 43s\n", + " 57341 8.2279131748e+03 Pr: 25016(70269.9); Du: 0(0.00401519) 49s\n", + " 60295 8.4411702548e+03 Pr: 22461(18551); Du: 0(0.00387185) 54s\n", + " 63129 8.6669193880e+03 Pr: 46660(9.78873e+06); Du: 0(0.0038402) 60s\n", + " 65751 8.8788576826e+03 Pr: 27101(119570); Du: 0(0.00396439) 65s\n", + " 68330 9.0772379783e+03 Pr: 15287(17459.6); Du: 0(0.0041059) 71s\n", + " 70852 9.2285381286e+03 Pr: 19466(50196.1); Du: 0(0.00439293) 76s\n", + " 73853 9.3631903691e+03 Pr: 38866(92264.6); Du: 0(0.00418896) 82s\n", + " 76708 9.5539040461e+03 Pr: 16172(14127.5); Du: 0(0.0045444) 87s\n", + " 79303 9.7553740900e+03 Pr: 26742(56495.4); Du: 0(0.0042862) 93s\n", + " 81856 9.9044159014e+03 Pr: 10467(3884.72); Du: 0(0.00444058) 98s\n", + " 84716 1.0129741603e+04 Pr: 62822(3.23315e+06); Du: 0(0.00483053) 104s\n", + " 87705 1.0220537743e+04 Pr: 31318(150590); Du: 0(0.00477644) 110s\n", + " 90348 1.0279428078e+04 Pr: 10918(5037.23); Du: 0(0.00476901) 115s\n", + " 93645 1.0357677034e+04 Pr: 12137(29845.6); Du: 0(0.00488115) 121s\n", + " 96623 1.0433817391e+04 Pr: 7282(4202.53); Du: 0(0.00443161) 126s\n", + " 99565 1.0485702387e+04 Pr: 25971(1.39873e+07); Du: 0(0.00416339) 132s\n", + " 102156 1.0527662238e+04 Pr: 21719(228836); Du: 0(0.0040159) 137s\n", + " 105159 1.0568969436e+04 Pr: 28146(190800); Du: 0(0.00408142) 143s\n", + " 107981 1.0591229947e+04 Pr: 8709(6382.63); Du: 0(0.00420473) 149s\n", + " 108857 1.0592942277e+04 Pr: 0(0); Du: 0(6.25e-06) 151s\n", + " 108857 1.0592942277e+04 Pr: 0(0); Du: 0(6.25e-06) 151s\n", + "Using EKK primal simplex solver\n", + " Iteration Objective Infeasibilities num(sum)\n", + " 108857 1.0592942277e+04 Pr: 0(0); Du: 1(0.0001) 151s\n", + " 108858 1.0592942277e+04 Pr: 0(0); Du: 0(2.99577e-15) 151s\n", + "Solving the original LP from the solution after postsolve\n", + "Model status : Optimal\n", + "Simplex iterations: 108858\n", + "Objective value : 1.0592942277e+04\n", + "HiGHS run time : 151.71\n", + "LP solved for primal\n", + "Writing Output\n", + "Time elapsed for writing costs is\n", + "0.09726075\n", + "Time elapsed for writing storage is\n", + "0.021158625\n", + "Time elapsed for writing nse is\n", + "0.057492916\n", + "Time elapsed for writing power balance is\n", + "0.054992458\n", + "Time elapsed for writing emissions is\n", + "0.10993575\n", + "Time elapsed for writing reliability is\n", + "0.014614333\n", + "Time elapsed for writing storage duals is\n", + "0.02906075\n", + "Time elapsed for writing commitment is\n", + "0.02163175\n", + "Time elapsed for writing startup is\n", + "0.066653583\n", + "Time elapsed for writing shutdown is\n", + "0.065220833\n", + "Time elapsed for writing time weights is\n", + "0.002030875\n", + "Time elapsed for writing reserve margin is\n", + "0.008165709\n", + "Time elapsed for writing capacity value is\n", + "0.025880458\n", + "Time elapsed for writing net revenue is\n", + "0.030976584\n", + "Wrote outputs to /Users/mayamutic/Desktop/GenX/Example_Systems/SmallNewEngland/OneZone/Results_6\n", + "Time elapsed for writing is\n", + "1.055933958\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m 153.662797875s: 2 minutes, 33 seconds, 662 milliseconds\n" + ] + } + ], + "source": [ + "tick()\n", + "include(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Run.jl\")\n", + "tock()" + ] + }, + { + "cell_type": "markdown", + "id": "bd41ae1e", + "metadata": {}, + "source": [ + "This took a little while to run, and would take even longer for larger systems. Let's see how we can get the run time down using Time Domain Reduction. The next sections go over how K-means clustering is used to perform TDR, and how to interpret the resulting files in GenX." + ] + }, + { + "cell_type": "markdown", + "id": "da668a37", + "metadata": {}, + "source": [ + "### K-means clustering" + ] + }, + { + "cell_type": "markdown", + "id": "379831dc", + "metadata": {}, + "source": [ + "Let's go over how TDR works. To perform TDR, GenX uses K-means clustering. K-means is an optimization method that clusters data into several groups based on their proximity to \"centers\" determined by the algorithm. Clustering data is " + ] + }, + { + "cell_type": "markdown", + "id": "d1505608", + "metadata": {}, + "source": [ + "To be more specific, k-means finds a set number of groups such that the variance between the distance of each point in the group to the mean of the group is minimized. \n", + "\n", + "$$\n", + "\\begin{align*}\n", + "\\mathop{\\arg \\min}\\limits_{\\mathbf{S}} & \\sum_{i = 1}^k \\sum_{x \\in S_i} ||x - \\mu_i||^2 \\\\\n", + "\\end{align*}\n", + "$$\n", + "\n", + "Where $\\mathbf{S} = \\{S_1, ... , S_k\\}$ are the clusters, and $\\mu_i$ is the mean of each cluster i.e. the mean of the distances between each point in the cluster. By taking the argmin over $\\mathbf{S}$, the points $x$ are clustered into groups where their distance to the center is the smallest." + ] + }, + { + "cell_type": "markdown", + "id": "8f6a69ea", + "metadata": {}, + "source": [ + "GenX uses the package `Clustering.jl`, with documentation here. As an example, using the package `ScikitLearn.jl`, let's generate data that can cluster easily." + ] + }, + { + "cell_type": "code", + "execution_count": 457, + "id": "69582aa0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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", + "image/svg+xml": [ + "-50510-10-50510Before K-means Clustering" + ], + "text/html": [ + "
\n", + " \n" + ] + }, + "execution_count": 458, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plotlyjs()\n", + "Plots.scatter(b[!,\"x1\"],b[!,\"x2\"],legend=false,title=\"Before K-means Clustering\")" + ] + }, + { + "cell_type": "markdown", + "id": "f82515f7", + "metadata": {}, + "source": [ + "Now we use the function `kmeans`, which is also used in `src/time_domain_reduction` in GenX." + ] + }, + { + "cell_type": "code", + "execution_count": 460, + "id": "ad777278", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KmeansResult{Matrix{Float64}, Float64, Int64}([-0.5983234957845184 7.533257370256171 … 7.527880189472178 -5.971220015686362; -1.2278405241483499 9.450101620955774 … 0.6882845988483258 -9.984381244149827], [1, 5, 1, 1, 4, 2, 1, 1, 1, 4 … 1, 1, 4, 2, 1, 5, 1, 4, 4, 1], [1.3255250419151228, 4.997847745986178, 8.091409099122394, 0.03714790494307074, 3.2626391548434412, 1.9889458740610166, 5.599991619086529, 5.546511424821204, 5.6808996899043525, 0.1286842679680973 … 2.501523413471922, 1.5778169684187557, 3.852127483939782, 0.5750996178271066, 10.710858491505114, 1.8568275420579425, 4.101412120977988, 1.5756217190165671, 0.005038328827794203, 5.487009130161922], [40, 20, 6, 20, 14], [40, 20, 6, 20, 14], 284.066131030526, 4, true)" + ] + }, + "execution_count": 460, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "R = kmeans(transpose(Matrix(b)),5)" + ] + }, + { + "cell_type": "markdown", + "id": "993370fe", + "metadata": {}, + "source": [ + "`kmeans` returns three outputs: assignments, centers, and counts. Assignments shows to which cluster each points belongs, centers shows where the center coordinates of each cluster are, and counts shows how many points belong to each cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 470, + "id": "c8f956cc", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 5, 1, 1, 4, 2, 1, 1, 1, 4, 2, 2, 4, 1, 4, 1, 3, 4, 4, 1, 2, 2, 1, 5, 4, 2, 2, 1, 2, 4, 3, 1, 5, 5, 1, 1, 4, 1, 1, 2, 5, 1, 5, 3, 5, 1, 2, 1, 5, 2, 5, 4, 4, 1, 2, 2, 1, 1, 5, 3, 2, 1, 1, 5, 4, 1, 2, 4, 1, 5, 2, 2, 4, 1, 4, 3, 2, 4, 1, 1, 1, 5, 1, 2, 1, 1, 1, 1, 4, 3, 1, 1, 4, 2, 1, 5, 1, 4, 4, 1]\n" + ] + } + ], + "source": [ + "println(R.assignments)" + ] + }, + { + "cell_type": "code", + "execution_count": 463, + "id": "c7b9ab46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2×5 Matrix{Float64}:\n", + " -0.598323 7.53326 -3.98791 7.52788 -5.97122\n", + " -1.22784 9.4501 -8.69267 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", + "image/svg+xml": [ + "-3036-12.5-10.0-7.5-5.0-2.50.02.5After K-means Clustering" + ], + "text/html": [ + "
\n", + " \n" + ] + }, + "execution_count": 453, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plotlyjs()\n", + "Plots.scatter(b[!,\"x1\"],b[!,\"x2\"],legend=false,marker_z=R.assignments,c=:lightrainbow,title=\"After K-means Clustering\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3b7ee69", + "metadata": {}, + "outputs": [], + "source": [ + "# Ask F: how are representative sets selected?" + ] + }, + { + "cell_type": "markdown", + "id": "b8b957e2", + "metadata": {}, + "source": [ + "### Results of Time Domain Reduction" + ] + }, + { + "cell_type": "markdown", + "id": "e2e1b15c", + "metadata": {}, + "source": [ + "To visualize the results of TDR, we'll set TDR = 1 back in the `genx_settings.yml` file in `Example_Systems_Tutorials/SmallNewEngland/OneZone/`:" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "id": "cbc4c84f", + "metadata": {}, + "outputs": [], + "source": [ + "genx_settings_SNE[\"TimeDomainReduction\"] = 1;" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "id": "5ffa9e4a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dict{Any, Any} with 21 entries:\n", + " \"NetworkExpansion\" => 0\n", + " \"TimeDomainReductionFolder\" => \"TDR_Results\"\n", + " \"ModelingToGenerateAlternativeIterations\" => 3\n", + " \"ParameterScale\" => 1\n", + " \"EnergyShareRequirement\" => 1\n", + " \"PrintModel\" => 0\n", + " \"TimeDomainReduction\" => 1\n", + " \"Trans_Loss_Segments\" => 1\n", + " \"CapacityReserveMargin\" => 1\n", + " \"ModelingtoGenerateAlternativeSlack\" => 0.1\n", + " \"Solver\" => \"HiGHS\"\n", + " \"MethodofMorris\" => 0\n", + " \"Reserves\" => 0\n", + " \"StorageLosses\" => 1\n", + " \"OverwriteResults\" => 0\n", + " \"UCommit\" => 2\n", + " \"ModelingToGenerateAlternatives\" => 0\n", + " \"MaxCapReq\" => 1\n", + " \"MinCapReq\" => 1\n", + " \"CO2Cap\" => 2\n", + " \"WriteShadowPrices\" => 1" + ] + }, + "execution_count": 296, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genx_settings_SNE" + ] + }, + { + "cell_type": "code", + "execution_count": 475, + "id": "8a61066a", + "metadata": {}, + "outputs": [], + "source": [ + "YAML.write_file(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml\", genx_settings_SNE)" + ] + }, + { + "cell_type": "markdown", + "id": "035bd2cb", + "metadata": {}, + "source": [ + "And run GenX again with TDR:" + ] + }, + { + "cell_type": "code", + "execution_count": 473, + "id": "19c3a604", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m started timer at: 2024-01-15T19:59:06.005\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Configuring Settings\n", + "Configuring Solver\n", + "Loading Inputs\n", + "Reading Input CSV Files\n", + "Network.csv Successfully Read!\n", + "Load_data.csv Successfully Read!\n", + "Fuels_data.csv Successfully Read!\n", + "Generators_data.csv Successfully Read!\n", + "Generators_variability.csv Successfully Read!\n", + "Validating time basis\n", + "CSV Files Successfully Read In From /Users/mayamutic/Desktop/GenX/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone\n", + "Generating the Optimization Model\n", + "Discharge Module\n", + "Non-served Energy Module\n", + "Investment Discharge Module\n", + "Emissions Module (for CO2 Policy modularization\n", + "Dispatchable Resources Module\n", + "Storage Resources Module\n", + "Storage Investment Module\n", + "Storage Core Resources Module\n", + "Storage Resources with Symmetric Charge/Discharge Capacity Module\n", + "Thermal (No Unit Commitment) Resources Module\n", + "Time elapsed for model building is\n", + "1.478717916\n", + "Solving Model\n", + "Running HiGHS 1.6.0: Copyright (c) 2023 HiGHS under MIT licence terms\n", + "Presolving model\n", + "109294 rows, 56737 cols, 292840 nonzeros\n", + "104401 rows, 51844 cols, 296648 nonzeros\n", + "Presolve : Reductions: rows 104401(-27006); columns 51844(-9487); elements 296648(-105909)\n", + "Solving the presolved LP\n", + "Using EKK dual simplex solver - serial\n", + " Iteration Objective Infeasibilities num(sum)\n", + " 0 -4.2724711023e+02 Ph1: 141(124.391); Du: 25(427.247) 0s\n", + " 13227 2.8278685533e+09 Pr: 9996(2.28476e+07); Du: 0(1.41046e-05) 5s\n", + " 63706 5.1701547992e+09 Pr: 0(0); Du: 0(9.6273e-13) 8s\n", + "Solving the original LP from the solution after postsolve\n", + "Model status : Optimal\n", + "Simplex iterations: 63706\n", + "Objective value : 5.1701547992e+09\n", + "HiGHS run time : 8.48\n", + "LP solved for primal\n", + "Writing Output\n", + "Time elapsed for writing costs is\n", + "0.089931958\n", + "Time elapsed for writing storage is\n", + "0.024121417\n", + "Time elapsed for writing nse is\n", + "0.072039042\n", + "Time elapsed for writing power balance is\n", + "0.085300792\n", + "Time elapsed for writing emissions is\n", + "0.111281875\n", + "Time elapsed for writing reliability is\n", + "0.032329834\n", + "Time elapsed for writing storage duals is\n", + "0.025279625\n", + "Time elapsed for writing time weights is\n", + "0.00560975\n", + "Time elapsed for writing net revenue is\n", + "0.028684708\n", + "Wrote outputs to /Users/mayamutic/Desktop/GenX/Tutorials/Example_Systems_Tutorials/SmallNewEngland/OneZone/Results\n", + "Time elapsed for writing is\n", + "0.979934208\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m 16.213814459s: 16 seconds, 213 milliseconds\n" + ] + } + ], + "source": [ + "tick()\n", + "include(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Run.jl\")\n", + "tock()" + ] + }, + { + "cell_type": "markdown", + "id": "7a098f2a", + "metadata": {}, + "source": [ + "Csv files with the results of TDR are generated automatically in a folder called `TDR_results` found within the same folder containing the input csv files, in this case `Example_Systems/SmallNewEngland/OneZone`. The csv files in this folder show the files used in `Run.jl` that have been pared down from the initial input files." + ] + }, + { + "cell_type": "markdown", + "id": "ab5f572f", + "metadata": {}, + "source": [ + "As an example, consider the input file `Fuels_data.csv`:" + ] + }, + { + "cell_type": "code", + "execution_count": 472, + "id": "fb178f63", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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The number of hours per representative period is set in `time_domain_reduction_settings.yml`. Also specified in the file is the minimum and maximum number of clusters we would like to have (in this case 6 and 11). The k-means algorithm will then select the number of clusters that should be sufficient to capture the GenX model in fewer time steps (in this case 11)." + ] + }, + { + "cell_type": "markdown", + "id": "db916072", + "metadata": {}, + "source": [ + "Below, we create arrays out of the representative weeks and plot them on the same plot used in the beginning of this tutorial:" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "id": "d6c7df93", + "metadata": {}, + "outputs": [], + "source": [ + "# To do: explain how to find periods using weights\n", + "\n", + "week1 = [repeat([\"Week 1\"],168) loads[1:168,end-1] loads[1:168,end]]\n", + "week2 = [repeat([\"Week 2\"],168) loads[1009:1176,end-1] loads[1009:1176,end]];\n", + "week3 = [repeat([\"Week 3\"],168) loads[1849:2016,end-1] loads[1849:2016,end]];\n", + "week4 = [repeat([\"Week 4\"],168) loads[2353:2520,end-1] loads[2353:2520,end]];\n", + "week5 = [repeat([\"Week 5\"],168) loads[3865:4032,end-1] loads[3865:4032,end]];\n", + "week6 = [repeat([\"Week 6\"],168) loads[4537:4704,end-1] loads[4537:4704,end]];\n", + "week7 = [repeat([\"Week 7\"],168) loads[4705:4872,end-1] loads[4705:4872,end]];\n", + "week8 = [repeat([\"Week 8\"],168) loads[7057:7224,end-1] loads[7057:7224,end]];\n", + "week9 = [repeat([\"Week 9\"],168) loads[7516:7683,end-1] loads[7516:7683,end]];\n", + "week10 = [repeat([\"Week 10\"],168) loads[7897:8064,end-1] loads[7897:8064,end]];\n", + "week11 = [repeat([\"Week 11\"],168) loads[8401:8568,end-1] loads[8401:8568,end]];" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "id": "5dd13d8a", + "metadata": {}, + "outputs": [], + "source": [ + "loads_plot = [repeat([\"Total\"],8760) loads[!,end-1] loads[!,end]];" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "id": "a7dd236a", + "metadata": {}, + "outputs": [], + "source": [ + "loads_with_TDR = [loads_TP; week1; week2; week3; week4; week5; week6; week7; week8; week9; week10; week11];" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "id": "8f46b99f", + "metadata": {}, + "outputs": [], + "source": [ + "loads_with_TDR = DataFrame(loads_with_TDR ,[\"week\",\"hour\", \"MW\"])\n", + "loads_with_TDR[!,:hour] = convert.(Int64,loads_with_TDR[!,:hour]);\n", + "loads_with_TDR[!,:MW] = convert.(Float64,loads_with_TDR[!,:MW]);" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "id": "8d389d51", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.vega.v5+json": { + "$schema": "https://vega.github.io/schema/vega/v5.json", + "axes": [ + { + "aria": false, + "domain": false, + "grid": true, + "gridScale": "y", + "labels": false, + "maxExtent": 0, + "minExtent": 0, + "orient": "bottom", + "scale": "x", + "tickCount": { + "signal": "ceil(width/40)" + }, + "ticks": false, + "zindex": 0 + }, + { + "aria": false, + "domain": false, + "grid": true, + "gridScale": "x", + "labels": false, + "maxExtent": 0, + "minExtent": 0, + "orient": "left", + "scale": "y", + 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", + "image/svg+xml": [ + "05001,0001,5002,0002,5003,0003,5004,0004,5005,0005,5006,0006,5007,0007,5008,0008,5009,000Time Step (hours)02,0004,0006,0008,00010,00012,00014,00016,00018,00020,00022,00024,000Load (MW)TotalWeek 1Week 2Week 3Week 4Week 5Week 6Week 7Week 8Week 9Week 10Week 11weekMW Load per hour with TDR Representative Weeks" + ], + "text/plain": [ + "@vlplot(\n", + " title=\"MW Load per hour with TDR Representative Weeks\",\n", + " width=845,\n", + " height=400,\n", + " mark={\n", + " type=\"line\"\n", + " },\n", + " encoding={\n", + " x={\n", + " title=\"Time Step (hours)\",\n", + " labels=\"week:n\",\n", + " field=\"hour\"\n", + " },\n", + " y={\n", + " title=\"Load (MW)\",\n", + " field=\"MW\"\n", + " },\n", + " color={\n", + " scale={\n", + " scheme=\"paired\"\n", + " },\n", + " sort=\"decsending\",\n", + " field=\"week\",\n", + " type=\"nominal\"\n", + " }\n", + " },\n", + " data={\n", + " values=...\n", + " }\n", + ")" + ] + }, + "execution_count": 254, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loads_with_TDR |>\n", + "@vlplot( \n", + " x={:hour,title=\"Time Step (hours)\",labels=\"week:n\"}, y={:MW,title=\"Load (MW)\"}, \n", + " color={\"week:n\", scale={scheme=\"paired\"},sort=\"decsending\"}, title=\"MW Load per hour with TDR Representative Weeks\",\n", + " width=845,height=400,mark={\n", + " :line\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3638c66a", + "metadata": {}, + "outputs": [], + "source": [ + "## For F: Is there a way to compare results to see similarities?" + ] + } + ], + "metadata": { + "@webio": { + "lastCommId": "f7fffbebf5b748078dd261c3af65bacc", + "lastKernelId": "1e8374ca-71e7-44d8-88e7-e43f681fa41c" + }, + "kernelspec": { + "display_name": "Julia 1.9.2", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.2" + } + }, + "nbformat": 4, + 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