-```
+
-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`.
+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`.
-```@raw html
-
-```
+
To open `genx_settings.yml` in Jupyter, use the function `YAML.load(open(...))` and navigate to file in the desired directory:
@@ -69,11 +65,9 @@ YAML.write_file("example_systems/1_three_zones/settings/genx_settings.yml", new_
The empty file will look like this:
-```@raw html
-
-```
+
-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:
+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:
```julia
diff --git a/docs/src/Tutorials/Tutorial_2_network_visualization.md b/docs/src/Tutorials/Tutorial_2_network_visualization.md
index 67624d285f..9c598962d7 100644
--- a/docs/src/Tutorials/Tutorial_2_network_visualization.md
+++ b/docs/src/Tutorials/Tutorial_2_network_visualization.md
@@ -24,10 +24,9 @@ network = CSV.read("example_systems/1_three_zones/system/Network.csv",DataFrame,
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, MA in this case is also used to refer to those states.
-Columns `Start_Zone` and `End_Zone` specify the network of the three regions. In this case, there are only two network lines, specified in the `Network_Lines` columns. The `Start_Zone` column indicates that the first node, MA, is the source of both lines as both rows have value 1. Rows `z1` and `z2` have values of 2 and 3 in `End_Zone`, which means both nodes CT and ME recieve energy from node MA. This is also indicated in the column `transmission_path_name'.
+Columns `Start_Zone` and `End_Zone` specify the network of the three regions. In this case, there are only two network lines, specified in the `Network_Lines` columns. The `Start_Zone` column indicates that the first node, MA, is the source of both lines as both rows have value 1. Rows `z1` and `z2` have values of 2 and 3 in `End_Zone`, which means both nodes CT and ME recieve energy from node MA. This is also indicated in the column `transmission path name'.
Below is a visualization of the network:
-```@raw html
-
-```
+
+
diff --git a/docs/src/Tutorials/Tutorial_3_K-means_time_domain_reduction.md b/docs/src/Tutorials/Tutorial_3_K-means_time_domain_reduction.md
index 4e9a49a6e6..5648c1eadc 100644
--- a/docs/src/Tutorials/Tutorial_3_K-means_time_domain_reduction.md
+++ b/docs/src/Tutorials/Tutorial_3_K-means_time_domain_reduction.md
@@ -3,19 +3,19 @@
[Interactive Notebook of the tutorial](https://github.com/GenXProject/GenX-Tutorials/blob/main/Tutorials/Tutorial_3_K-means_Time_Domain_Reduction.ipynb)
-A good tool to reduce computation time of GenX is to use [Time-domain reduction](@ref). 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. For more information on TDR in capacity expansion models, see [Mallapragada et al](https://www.sciencedirect.com/science/article/pii/S0360544218315238).
+A good tool to reduce computation time of GenX is to use [Time-domain reduction](@ref). 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. For more information on TDR in capacity expansion models, see [Mallapragada et al](https://www.sciencedirect.com/science/article/pii/S0360544218315238).
### Table of Contents
* [Time Domain Reduction](#TDR)
* [K-Means Clustering](#Kmeans)
* [Results of Time Domain Reduction](#TDRResults)
- * [Reconstruction](#Reconstruction)
+* [Reconstruction](#Reconstruction)
* [Extreme Periods](#ExtPeriods)
* [Objective Values and Representative Periods](#ObjVals)
### Time Domain Reduction
-To see how Time Domain Reduction works, let's look at the `Doad_data` in `example_systems/1_three_zones`:
+To see how Time Domain Reduction works, let's look at the `Demand_data` in `example_systems/1_three_zones`:
```julia
@@ -29,9 +29,6 @@ using Plots
using Clustering
using ScikitLearn
@sk_import datasets: (make_blobs)
-```
- WARNING: redefinition of constant make_blobs. This may fail, cause incorrect answers, or produce other errors.
- PyObject 52×3 DataFrame
27 rows omitted
| Row | Period_Index | Rep_Period | Rep_Period_Index |
|---|---|---|---|
| Int64 | Int64 | Int64 | |
| 1 | 1 | 4 | 1 |
| 2 | 2 | 4 | 1 |
| 3 | 3 | 4 | 1 |
| 4 | 4 | 4 | 1 |
| 5 | 5 | 8 | 2 |
| 6 | 6 | 8 | 2 |
| 7 | 7 | 8 | 2 |
| 8 | 8 | 8 | 2 |
| 9 | 9 | 8 | 2 |
| 10 | 10 | 12 | 3 |
| 11 | 11 | 12 | 3 |
| 12 | 12 | 12 | 3 |
| 13 | 13 | 12 | 3 |
| ⋮ | ⋮ | ⋮ | ⋮ |
| 41 | 41 | 20 | 5 |
| 42 | 42 | 20 | 5 |
| 43 | 43 | 23 | 6 |
| 44 | 44 | 17 | 4 |
| 45 | 45 | 48 | 10 |
| 46 | 46 | 48 | 10 |
| 47 | 47 | 48 | 10 |
| 48 | 48 | 48 | 10 |
| 49 | 49 | 49 | 11 |
| 50 | 50 | 49 | 11 |
| 51 | 51 | 8 | 2 |
| 52 | 52 | 8 | 2 |
-```
+
JuMP is a modeling language for Julia. It allows users to create models for optimization problems, define variables and constraints, and apply a variety of solvers for the model.
@@ -122,16 +120,30 @@ When `Run.jl` is called, the model for GenX is constructed in a similar way, but
The basic structure of the way `Run.jl` generates and solves the model is as follows:
-```@raw html
-
-```
+
The function `run_genx_case(case)` takes the "case" as its input. The case is all of the input files and settings found in the same folder as `Run.jl`. For example, in `example_systems/1_three_zones`, the case is:
-```@raw html
-
+```julia
+cd(readdir,"example_systems/1_three_zones")
```
+
+
+
+ 9-element Vector{String}:
+ ".DS_Store"
+ "README.md"
+ "Run.jl"
+ "TDR_results"
+ "policies"
+ "resources"
+ "results"
+ "settings"
+ "system"
+
+
+
`Run_genx_case` defines the __setup__, which are the settings in `genx_settings.yml`. From there, either `run_genx_case_simple(case, mysetup)` or`run_genx_case_multistage(case, mysetup)` is called. Both of these define the __inputs__ and __optimizer__. The optimizer is the solver as specified in `genx_settings.yml`, and the inputs are a variety of parameters specified by the settings and csv files found in the folder. Both of these functions then call `generate_model(mysetup, myinputs, OPTIMIZER)`, which is the main subject of this tutorial.
As in the above example, `generate_model` utilizes the JuMP functions `Model()`, `@expression`, `@variable`, and `@constraints` to form a model. This section goes through `generate_model` and explains how the expressions are formed to create the model.
@@ -260,7 +272,7 @@ typeof(OPTIMIZER)
MathOptInterface.OptimizerWithAttributes
```
-The "inputs" argument is generated by the function `load_inputs` from the case in `run_genx_case_simple` (or multistage). If TDR is set to 1 in the settings file, then `load_inputs` will draw some of the files from the `TDR_Results` folder. `TDR_Results` is produced when the case is run.
+The "inputs" argument is generated by the function `load_inputs` from the case in `run_genx_case_simple` (or multistage). If TDR is set to 1 in the settings file, then `load_inputs` will draw some of the files from the `TDR_results` folder. `TDR_results` is produced when the case is run.
```julia
diff --git a/docs/src/Tutorials/Tutorial_7_setup.md b/docs/src/Tutorials/Tutorial_7_setup.md
new file mode 100644
index 0000000000..0d93604c83
--- /dev/null
+++ b/docs/src/Tutorials/Tutorial_7_setup.md
@@ -0,0 +1,2872 @@
+# Tutorial 7: Policy Constraints
+
+[Interactive Notebook of the tutorial](https://github.com/GenXProject/GenX-Tutorials/blob/main/Tutorials/Tutorial_7_Setup.ipynb)
+
+As show in previous tutorials, the settings file can be changed to adapt to a number of different systems. In Tutorial 3, we discussed how the setting Time Domain Reduction can reduce the computation time of the solver. Other settings, however, directly impact the values of the solution itself. This tutorial goes over the policy attributes in the settings and shows how implementing certain policies affects the optimal solution in GenX. To demonstrate these effects, we will be using `example_systems/1_three_zones`.
+
+## Table of Contents
+* [No Policy Constraints](#NoPolicies)
+* [CO2 Cap](#CO2Cap)
+ * [Mass Cap](#MassCap)
+ * [Tolerance](#Tolerance)
+ * [CO2 Slack](#CO2Slack)
+ * [Load-based Cap](#LoadCap)
+ * [Generator-based Cap](#GenerateCap)
+* [Energy Share Requirement](#ESR)
+* [Capacity Reserve Margin](#CRM)
+* [Minimum Capacity Requirement](#MCR)
+* [All Together](#All)
+
+
+## No Policy Constraints
+
+
+```julia
+using JuMP
+using HiGHS
+using GenX
+using CSV
+using DataFrames
+using Plots
+using StatsPlots
+```
+
+
+```julia
+case = joinpath("example_systems/1_three_zones")
+
+genx_settings = GenX.get_settings_path(case, "genx_settings.yml");
+writeoutput_settings = GenX.get_settings_path(case, "output_settings.yml")
+setup = GenX.configure_settings(genx_settings,writeoutput_settings)
+```
+
+ Configuring Settings
+
+
+
+
+
+ Dict{Any, Any} with 33 entries:
+ "HydrogenHourlyMatching" => 0
+ "NetworkExpansion" => 1
+ "TimeDomainReductionFolder" => "TDR_results"
+ "WriteOutputs" => "full"
+ "SystemFolder" => "system"
+ "EnableJuMPStringNames" => 1
+ "Trans_Loss_Segments" => 1
+ "ModelingtoGenerateAlternativeSlack" => 0.1
+ "PoliciesFolder" => "policies"
+ "MultiStage" => 0
+ "ComputeConflicts" => 1
+ "OverwriteResults" => 0
+ "ModelingToGenerateAlternatives" => 0
+ "MaxCapReq" => 0
+ "MinCapReq" => 1
+ "CO2Cap" => 2
+ "WriteShadowPrices" => 1
+ "OperationalReserves" => 0
+ "ParameterScale" => 1
+ "EnergyShareRequirement" => 0
+ "PrintModel" => 0
+ "TimeDomainReduction" => 1
+ "DC_OPF" => 0
+ "CapacityReserveMargin" => 0
+ "MethodofMorris" => 0
+ ⋮ => ⋮
+
+
+
+The settings we'll focus on here are , `CO2Cap`, `EnergyShareRequirement`, `CapacityReserveMargin`, and `MinCapReq`. Each of these ensures that the environmental impact of the model is taken into account, and are therefore referred to as __policy settings__ . For more information on what each one does, see the documentation on [Emission mitigation policies].
+
+
+```julia
+println("MaxCapReq: ", setup["MaxCapReq"])
+println("MinCapReq: ", setup["MinCapReq"])
+println("CO2Cap: ", setup["CO2Cap"])
+println("EnergyShareRequirement: ", setup["EnergyShareRequirement"])
+println("CapacityReserveMargin: ", setup["CapacityReserveMargin"])
+```
+
+ MaxCapReq: 0
+ MinCapReq: 1
+ CO2Cap: 2
+ EnergyShareRequirement: 0
+ CapacityReserveMargin: 0
+
+
+`1_three_zones` uses `MinCapReq` and `CO2Cap`. For the purpose of this tutorial, we're going to set these back to zero to start.
+
+
+```julia
+setup["MinCapReq"] = 0
+setup["CO2Cap"] = 0;
+```
+
+Now, we'll generate and solve the model using these results:
+
+
+```julia
+## Delte Previous TDR Results
+if "TDR_results" in cd(readdir,case)
+ rm(joinpath(case,"TDR_results"), recursive=true)
+end
+
+### Create TDR_Results
+TDRpath = joinpath(case, setup["TimeDomainReductionFolder"])
+system_path = joinpath(case, setup["SystemFolder"])
+settings_path = GenX.get_settings_path(case)
+
+if setup["TimeDomainReduction"] == 1
+ GenX.prevent_doubled_timedomainreduction(system_path)
+ if !GenX.time_domain_reduced_files_exist(TDRpath)
+ println("Clustering Time Series Data (Grouped)...")
+ GenX.cluster_inputs(case, settings_path, setup)
+ else
+ println("Time Series Data Already Clustered.")
+ end
+end
+
+```
+
+ Clustering Time Series Data (Grouped)...
+ Reading Input CSV Files
+ Network.csv Successfully Read!
+ Demand (load) data Successfully Read!
+ Fuels_data.csv Successfully Read!
+
+
+ Thermal.csv Successfully Read.
+ Vre.csv Successfully Read.
+ Storage.csv Successfully Read.
+ Resource_energy_share_requirement.csv Successfully Read.
+ Resource_capacity_reserve_margin.csv Successfully Read.
+ Resource_minimum_capacity_requirement.csv Successfully Read.
+
+
+ Summary of resources loaded into the model:
+ -------------------------------------------------------
+ Resource type Number of resources
+ =======================================================
+ Thermal 3
+ VRE 4
+ Storage 3
+ =======================================================
+ Total number of resources: 10
+ -------------------------------------------------------
+ Generators_variability.csv Successfully Read!
+ Validating time basis
+ CSV Files Successfully Read In From example_systems/1_three_zones
+ Error: Geography Key 1 is invalid. Select `System' or `Zone'.
+
+
+
+
+
+ Dict{String, Any} with 9 entries:
+ "RMSE" => Dict("ME_NG"=>0.210014, "ME_onshore_wind_z3"=>0.310986, "D…
+ "OutputDF" => 1848×19 DataFrame
+ "ColToZoneMap" => Dict("Demand_MW_z3"=>3, "CT_battery_z2"=>2, "MA_natural_ga…
+ "ClusterObject" => KmeansResult{Matrix{Float64}, Float64, Int64}([-0.734116 2…
+ "TDRsetup" => Dict{Any, Any}("IterativelyAddPeriods"=>1, "ExtremePeriods…
+ "Assignments" => [1, 1, 1, 1, 2, 2, 2, 2, 2, 3 … 6, 9, 3, 10, 10, 10, 11,…
+ "InputDF" => 1680×52 DataFrame
+ "Weights" => [673.846, 1179.23, 842.308, 673.846, 1010.77, 1347.69, 134…
+ "Centers" => Any[4, 8, 12, 15, 20, 23, 27, 30, 44, 48, 49]
+
+
+
+
+```julia
+OPTIMIZER = GenX.configure_solver(settings_path,HiGHS.Optimizer);
+inputs = GenX.load_inputs(setup, case)
+```
+
+ Reading Input CSV Files
+ Network.csv Successfully Read!
+ Demand (load) data Successfully Read!
+ Fuels_data.csv Successfully Read!
+
+ Summary of resources loaded into the model:
+ -------------------------------------------------------
+ Resource type Number of resources
+ =======================================================
+ Thermal 3
+ VRE 4
+ Storage 3
+ =======================================================
+ Total number of resources: 10
+ -------------------------------------------------------
+ Generators_variability.csv Successfully Read!
+ Validating time basis
+ CSV Files Successfully Read In From example_systems/1_three_zones
+
+
+ Thermal.csv Successfully Read.
+ Vre.csv Successfully Read.
+ Storage.csv Successfully Read.
+ Resource_energy_share_requirement.csv Successfully Read.
+ Resource_capacity_reserve_margin.csv Successfully Read.
+ Resource_minimum_capacity_requirement.csv Successfully Read.
+
+
+
+
+ Dict{Any, Any} with 67 entries:
+ "Z" => 3
+ "LOSS_LINES" => [1, 2]
+ "STOR_HYDRO_SHORT_DURATION" => Int64[]
+ "RET_CAP_CHARGE" => Set{Int64}()
+ "pC_D_Curtail" => [50.0, 45.0, 27.5, 10.0]
+ "pTrans_Max_Possible" => [5.9, 4.0]
+ "pNet_Map" => [1.0 -1.0 0.0; 1.0 0.0 -1.0]
+ "omega" => 4.01099, 4.01099, 4.01099, 4.01099, 4.01099, …
+ "pMax_Line_Reinforcement" => [2.95, 2.0]
+ "RET_CAP_ENERGY" => Int64[]
+ "RESOURCES" => AbstractResource
+ "COMMIT" => [1, 2, 3]
+ "pMax_D_Curtail" => [1.0, 0.04, 0.024, 0.003]
+ "STOR_ALL" => [8, 9, 10]
+ "THERM_ALL" => [1, 2, 3]
+ "REP_PERIOD" => 11
+ "PWFU_Num_Segments" => 0
+ "STOR_LONG_DURATION" => Int64[]
+ "THERM_COMMIT_PWFU" => Int64[]
+ "STOR_SYMMETRIC" => [8, 9, 10]
+ "VRE" => [4, 5, 6, 7]
+ "RETRO" => Int64[]
+ "THERM_COMMIT" => [1, 2, 3]
+ "TRANS_LOSS_SEGS" => 1
+ "H" => 168
+ ⋮ => ⋮
+
+
+
+
+```julia
+EP = GenX.generate_model(setup,inputs,OPTIMIZER)
+```
+
+ Discharge Module
+ Non-served Energy Module
+ Investment Discharge Module
+ Unit Commitment Module
+ Fuel Module
+ CO2 Module
+ Investment Transmission Module
+ Transmission Module
+ Dispatchable Resources Module
+ Storage Resources Module
+ Storage Investment Module
+ Storage Core Resources Module
+ Storage Resources with Symmetric Charge/Discharge Capacity Module
+ Thermal (Unit Commitment) Resources Module
+
+
+
+
+
+ A JuMP Model
+ Minimization problem with:
+ Variables: 120136
+ Objective function type: AffExpr
+ `AffExpr`-in-`MathOptInterface.EqualTo{Float64}`: 35112 constraints
+ `AffExpr`-in-`MathOptInterface.GreaterThan{Float64}`: 20331 constraints
+ `AffExpr`-in-`MathOptInterface.LessThan{Float64}`: 97949 constraints
+ `VariableRef`-in-`MathOptInterface.EqualTo{Float64}`: 1 constraint
+ `VariableRef`-in-`MathOptInterface.GreaterThan{Float64}`: 116439 constraints
+ Model mode: AUTOMATIC
+ CachingOptimizer state: EMPTY_OPTIMIZER
+ Solver name: HiGHS
+ Names registered in the model: FuelCalculationCommit_single, cFuelCalculation_single, cMaxCap, cMaxCapEnergy, cMaxCapEnergyDuration, cMaxFlow_in, cMaxFlow_out, cMaxLineReinforcement, cMaxNSE, cMaxRetCommit, cMaxRetEnergy, cMaxRetNoCommit, cMinCap, cMinCapEnergy, cMinCapEnergyDuration, cNSEPerSeg, cPowerBalance, cSoCBalInterior, cSoCBalStart, cStartFuel_single, cTAuxLimit, cTAuxSum, cTLoss, eAvail_Trans_Cap, eCFix, eCFixEnergy, eCFuelOut, eCFuelStart, eCNSE, eCStart, eCVar_in, eCVar_out, eELOSS, eELOSSByZone, eEmissionsByPlant, eEmissionsByZone, eExistingCap, eExistingCapEnergy, eFuelConsumption, eFuelConsumptionYear, eFuelConsumption_single, eGenerationByThermAll, eGenerationByVRE, eGenerationByZone, eLosses_By_Zone, eNet_Export_Flows, eObj, ePlantCFuelOut, ePlantCFuelStart, ePlantFuel_generation, ePlantFuel_start, ePowerBalance, ePowerBalanceDisp, ePowerBalanceLossesByZone, ePowerBalanceNetExportFlows, ePowerBalanceNse, ePowerBalanceStor, ePowerBalanceThermCommit, eStartFuel, eTotalCFix, eTotalCFixEnergy, eTotalCFuelOut, eTotalCFuelStart, eTotalCNSE, eTotalCNSET, eTotalCNSETS, eTotalCNetworkExp, eTotalCStart, eTotalCStartT, eTotalCVarIn, eTotalCVarInT, eTotalCVarOut, eTotalCVarOutT, eTotalCap, eTotalCapEnergy, eTransMax, eZonalCFuelOut, eZonalCFuelStart, vCAP, vCAPENERGY, vCHARGE, vCOMMIT, vFLOW, vFuel, vNEW_TRANS_CAP, vNSE, vP, vRETCAP, vRETCAPENERGY, vS, vSHUT, vSTART, vStartFuel, vTAUX_NEG, vTAUX_POS, vTLOSS, vZERO
+
+
+
+
+```julia
+GenX.solve_model(EP,setup)
+```
+
+ Running HiGHS 1.6.0: Copyright (c) 2023 HiGHS under MIT licence terms
+ Presolving model
+ 118155 rows, 81204 cols, 422835 nonzeros
+ 110998 rows, 74047 cols, 423349 nonzeros
+ Presolve : Reductions: rows 110998(-42394); columns 74047(-46089); elements 423349(-47782)
+ Solving the presolved LP
+ IPX model has 110998 rows, 74047 columns and 423349 nonzeros
+ Input
+ Number of variables: 74047
+ Number of free variables: 3696
+ Number of constraints: 110998
+ Number of equality constraints: 16867
+ Number of matrix entries: 423349
+ Matrix range: [4e-07, 1e+01]
+ RHS range: [7e-01, 2e+01]
+ Objective range: [1e-04, 4e+02]
+ Bounds range: [2e-03, 2e+01]
+ Preprocessing
+ Dualized model: no
+ Number of dense columns: 15
+ Range of scaling factors: [5.00e-01, 1.00e+00]
+ IPX version 1.0
+ Interior Point Solve
+ Iter P.res D.res P.obj D.obj mu Time
+ 0 8.62e+00 3.81e+02 3.30336414e+06 -5.31617580e+06 3.30e+03 0s
+ 1 4.09e+00 1.06e+02 2.34353411e+05 -5.13796175e+06 1.43e+03 0s
+ 2 3.78e+00 7.03e+01 1.87013341e+05 -1.15199236e+07 1.34e+03 0s
+ 3 1.33e+00 4.12e+01 -3.76464137e+05 -1.37088411e+07 7.85e+02 1s
+ Constructing starting basis...
+ 4 4.13e-01 1.08e+01 2.66640168e+05 -8.48314805e+06 2.43e+02 3s
+ 5 1.12e-01 5.62e+00 3.71879810e+05 -5.58576107e+06 1.28e+02 4s
+ 6 7.53e-03 1.62e+00 2.30531116e+05 -1.92720962e+06 3.67e+01 5s
+ 7 9.27e-04 1.77e-01 1.30486918e+05 -3.83901614e+05 5.66e+00 6s
+ 8 1.14e-04 4.38e-02 5.27259057e+04 -1.00386376e+05 1.48e+00 7s
+ 9 1.52e-05 6.88e-03 2.76584248e+04 -2.19746140e+04 3.52e-01 8s
+ 10 5.20e-06 2.59e-03 1.39025442e+04 -7.44814138e+03 1.35e-01 8s
+ 11 2.36e-06 1.09e-03 1.02345396e+04 -1.80403130e+03 7.13e-02 10s
+ 12 1.03e-06 3.59e-04 7.72508848e+03 1.35005473e+03 3.59e-02 12s
+ 13 6.67e-07 1.53e-04 6.83171406e+03 2.57204744e+03 2.35e-02 15s
+ 14 5.06e-07 7.64e-05 6.41494456e+03 3.13597410e+03 1.79e-02 18s
+ 15 3.52e-07 4.64e-05 5.95636098e+03 3.44861286e+03 1.36e-02 21s
+ 16 1.69e-07 2.49e-05 5.33436713e+03 3.75261594e+03 8.58e-03 24s
+ 17 1.38e-07 2.05e-05 5.23488752e+03 3.81660239e+03 7.68e-03 26s
+ 18 1.21e-07 1.95e-05 5.23765885e+03 3.83998603e+03 7.57e-03 28s
+ 19 7.35e-08 1.60e-05 5.05272742e+03 3.91685812e+03 6.15e-03 30s
+ 20 6.04e-08 1.49e-05 5.02221768e+03 3.93278446e+03 5.90e-03 33s
+ 21 2.85e-08 1.13e-05 4.88298181e+03 4.01774654e+03 4.68e-03 35s
+ 22 2.29e-08 6.29e-06 4.83470832e+03 4.00260600e+03 4.48e-03 37s
+ 23 1.31e-08 3.63e-06 4.72358228e+03 4.16712858e+03 3.00e-03 38s
+ 24 9.10e-09 2.96e-06 4.70648899e+03 4.19085995e+03 2.77e-03 40s
+ 25 4.45e-09 1.89e-06 4.63836996e+03 4.26416898e+03 2.01e-03 41s
+ 26 3.49e-09 1.84e-06 4.63722238e+03 4.26628187e+03 1.99e-03 42s
+ 27 2.15e-09 1.23e-06 4.59679916e+03 4.32342389e+03 1.47e-03 43s
+ 28 1.87e-09 1.07e-06 4.59259771e+03 4.33476451e+03 1.39e-03 46s
+ 29 1.58e-09 1.05e-06 4.59058841e+03 4.33567604e+03 1.37e-03 48s
+ 30 1.18e-09 8.27e-07 4.58014487e+03 4.35414040e+03 1.21e-03 49s
+ 31 7.95e-10 4.22e-07 4.56632970e+03 4.39355672e+03 9.27e-04 51s
+ 32 4.34e-10 3.79e-07 4.55792749e+03 4.39835388e+03 8.56e-04 51s
+ 33 2.37e-10 2.16e-07 4.54724964e+03 4.42097386e+03 6.77e-04 52s
+ 34 2.10e-10 2.06e-07 4.54686537e+03 4.42339252e+03 6.62e-04 53s
+ 35 7.73e-11 1.26e-07 4.53250796e+03 4.44139125e+03 4.89e-04 54s
+ 36 1.55e-11 1.06e-07 4.52064756e+03 4.44667475e+03 3.97e-04 55s
+ 37 1.50e-11 1.03e-07 4.52062467e+03 4.44749533e+03 3.92e-04 55s
+ 38 9.79e-12 1.00e-07 4.52199331e+03 4.44806728e+03 3.96e-04 56s
+ 39 8.92e-12 6.93e-08 4.52041735e+03 4.46101031e+03 3.19e-04 56s
+ 40 7.30e-12 5.17e-08 4.52001352e+03 4.46367984e+03 3.02e-04 57s
+ 41 4.70e-12 3.05e-08 4.51328477e+03 4.47536516e+03 2.03e-04 57s
+ 42 3.83e-12 2.70e-08 4.51240381e+03 4.47626904e+03 1.94e-04 58s
+ 43 2.85e-12 2.63e-08 4.51136761e+03 4.47647929e+03 1.87e-04 59s
+ 44 2.10e-12 1.31e-08 4.50946086e+03 4.48203628e+03 1.47e-04 59s
+ 45 1.31e-12 4.15e-09 4.50665353e+03 4.48821996e+03 9.88e-05 60s
+ 46 9.09e-13 3.31e-09 4.50579733e+03 4.48871202e+03 9.15e-05 61s
+ 47 9.09e-13 3.26e-09 4.50576718e+03 4.48852664e+03 9.24e-05 61s
+ 48 7.53e-13 2.25e-09 4.50502226e+03 4.49018328e+03 7.95e-05 62s
+ 49 7.53e-13 2.21e-09 4.50492035e+03 4.48999997e+03 7.99e-05 62s
+ 50 5.83e-13 1.52e-09 4.50350728e+03 4.49199895e+03 6.17e-05 63s
+ 51 1.99e-13 1.32e-09 4.50092854e+03 4.49240806e+03 4.57e-05 63s
+ 52 1.78e-13 1.23e-09 4.50080001e+03 4.49253227e+03 4.43e-05 64s
+ 53 9.24e-14 7.11e-10 4.49993325e+03 4.49367022e+03 3.36e-05 64s
+ 54 7.11e-14 5.59e-10 4.49966942e+03 4.49408670e+03 2.99e-05 65s
+ 55 7.11e-14 5.54e-10 4.49963721e+03 4.49407202e+03 2.98e-05 65s
+ 56 7.11e-14 5.25e-10 4.49964358e+03 4.49413082e+03 2.95e-05 66s
+ 57 4.97e-14 3.86e-10 4.49901737e+03 4.49483758e+03 2.24e-05 66s
+ 58 4.97e-14 3.77e-10 4.49900976e+03 4.49485417e+03 2.23e-05 66s
+ 59 4.97e-14 3.61e-10 4.49894867e+03 4.49494477e+03 2.15e-05 67s
+ 60 2.84e-14 2.16e-10 4.49842279e+03 4.49567956e+03 1.47e-05 67s
+ 61 2.84e-14 1.25e-10 4.49813093e+03 4.49622420e+03 1.02e-05 67s
+ 62 2.13e-14 9.05e-11 4.49785493e+03 4.49641576e+03 7.71e-06 68s
+ 63 2.13e-14 2.46e-11 4.49758346e+03 4.49698685e+03 3.20e-06 68s
+ 64 2.13e-14 1.77e-11 4.49754878e+03 4.49704995e+03 2.67e-06 68s
+ 65 2.13e-14 3.18e-12 4.49747123e+03 4.49722789e+03 1.30e-06 69s
+ 66 2.13e-14 1.36e-12 4.49740334e+03 4.49723907e+03 8.80e-07 69s
+ 67 2.13e-14 1.36e-12 4.49734663e+03 4.49727135e+03 4.03e-07 70s
+ 68 2.13e-14 9.66e-13 4.49734376e+03 4.49727731e+03 3.56e-07 70s
+ 69 2.13e-14 5.68e-13 4.49732633e+03 4.49728530e+03 2.20e-07 70s
+ 70 2.13e-14 4.55e-13 4.49731802e+03 4.49730841e+03 5.15e-08 71s
+ 71 2.13e-14 4.83e-13 4.49731683e+03 4.49730985e+03 3.74e-08 71s
+ 72 2.13e-14 1.92e-12 4.49731281e+03 4.49731141e+03 7.53e-09 72s
+ 73 2.13e-14 5.35e-12 4.49731227e+03 4.49731210e+03 9.13e-10 72s
+ 74* 2.13e-14 2.73e-12 4.49731219e+03 4.49731218e+03 7.21e-11 72s
+ 75* 2.84e-14 7.65e-12 4.49731219e+03 4.49731218e+03 4.94e-12 73s
+ 76* 3.55e-14 4.81e-12 4.49731219e+03 4.49731219e+03 4.09e-13 73s
+ 77* 3.55e-14 7.19e-12 4.49731219e+03 4.49731219e+03 2.31e-15 73s
+ Running crossover as requested
+ Primal residual before push phase: 1.40e-09
+ Dual residual before push phase: 5.05e-10
+ Number of dual pushes required: 41031
+ Number of primal pushes required: 722
+ Summary
+ Runtime: 73.32s
+ Status interior point solve: optimal
+ Status crossover: optimal
+ objective value: 4.49731219e+03
+ interior solution primal residual (abs/rel): 5.48e-11 / 3.35e-12
+ interior solution dual residual (abs/rel): 7.19e-12 / 1.79e-14
+ interior solution objective gap (abs/rel): -5.22e-10 / -1.16e-13
+ basic solution primal infeasibility: 2.78e-17
+ basic solution dual infeasibility: 5.41e-16
+ Ipx: IPM optimal
+ Ipx: Crossover optimal
+ Solving the original LP from the solution after postsolve
+ Model status : Optimal
+ IPM iterations: 77
+ Crossover iterations: 4712
+ Objective value : 4.4973121850e+03
+ HiGHS run time : 73.55
+ LP solved for primal
+
+
+
+
+
+ (A JuMP Model
+ Minimization problem with:
+ Variables: 120136
+ Objective function type: AffExpr
+ `AffExpr`-in-`MathOptInterface.EqualTo{Float64}`: 35112 constraints
+ `AffExpr`-in-`MathOptInterface.GreaterThan{Float64}`: 20331 constraints
+ `AffExpr`-in-`MathOptInterface.LessThan{Float64}`: 97949 constraints
+ `VariableRef`-in-`MathOptInterface.EqualTo{Float64}`: 1 constraint
+ `VariableRef`-in-`MathOptInterface.GreaterThan{Float64}`: 116439 constraints
+ Model mode: AUTOMATIC
+ CachingOptimizer state: ATTACHED_OPTIMIZER
+ Solver name: HiGHS
+ Names registered in the model: FuelCalculationCommit_single, cFuelCalculation_single, cMaxCap, cMaxCapEnergy, cMaxCapEnergyDuration, cMaxFlow_in, cMaxFlow_out, cMaxLineReinforcement, cMaxNSE, cMaxRetCommit, cMaxRetEnergy, cMaxRetNoCommit, cMinCap, cMinCapEnergy, cMinCapEnergyDuration, cNSEPerSeg, cPowerBalance, cSoCBalInterior, cSoCBalStart, cStartFuel_single, cTAuxLimit, cTAuxSum, cTLoss, eAvail_Trans_Cap, eCFix, eCFixEnergy, eCFuelOut, eCFuelStart, eCNSE, eCStart, eCVar_in, eCVar_out, eELOSS, eELOSSByZone, eEmissionsByPlant, eEmissionsByZone, eExistingCap, eExistingCapEnergy, eFuelConsumption, eFuelConsumptionYear, eFuelConsumption_single, eGenerationByThermAll, eGenerationByVRE, eGenerationByZone, eLosses_By_Zone, eNet_Export_Flows, eObj, ePlantCFuelOut, ePlantCFuelStart, ePlantFuel_generation, ePlantFuel_start, ePowerBalance, ePowerBalanceDisp, ePowerBalanceLossesByZone, ePowerBalanceNetExportFlows, ePowerBalanceNse, ePowerBalanceStor, ePowerBalanceThermCommit, eStartFuel, eTotalCFix, eTotalCFixEnergy, eTotalCFuelOut, eTotalCFuelStart, eTotalCNSE, eTotalCNSET, eTotalCNSETS, eTotalCNetworkExp, eTotalCStart, eTotalCStartT, eTotalCVarIn, eTotalCVarInT, eTotalCVarOut, eTotalCVarOutT, eTotalCap, eTotalCapEnergy, eTransMax, eZonalCFuelOut, eZonalCFuelStart, vCAP, vCAPENERGY, vCHARGE, vCOMMIT, vFLOW, vFuel, vNEW_TRANS_CAP, vNSE, vP, vRETCAP, vRETCAPENERGY, vS, vSHUT, vSTART, vStartFuel, vTAUX_NEG, vTAUX_POS, vTLOSS, vZERO, 73.97517013549805)
+
+
+
+Using `value.()`, we can see what the total capacity is of the optimized model:
+
+
+```julia
+totCap_base = value.(EP[:eTotalCap])
+```
+
+
+
+
+ 10-element Vector{Float64}:
+ 10.41532872265646
+ 10.085613331810192
+ 0.0
+ 0.0
+ 0.0
+ 0.0
+ 2.026239619715743
+ 0.0
+ 0.0
+ 0.16552558225070782
+
+
+
+Each element corresponds to the MW value of the node in the grid. In `1_three_zones`, there are ten nodes, each of which are either natural gas, wind, solar, or battery plants. We can see which is which using `RESOURCE_NAMES` in the inputs dictionary:
+
+
+```julia
+RT = inputs["RESOURCE_NAMES"];
+DataFrame([RT totCap_base],["Resource","Total Capacity"])
+```
+
+
+
+```@raw html
+10×2 DataFrame
| Row | Resource | Total Capacity |
|---|---|---|
| Any | Any | |
| 1 | MA_natural_gas_combined_cycle | 10.4153 |
| 2 | CT_natural_gas_combined_cycle | 10.0856 |
| 3 | ME_natural_gas_combined_cycle | 0.0 |
| 4 | MA_solar_pv | 0.0 |
| 5 | CT_onshore_wind | 0.0 |
| 6 | CT_solar_pv | 0.0 |
| 7 | ME_onshore_wind | 2.02624 |
| 8 | MA_battery | 0.0 |
| 9 | CT_battery | 0.0 |
| 10 | ME_battery | 0.165526 |
10×2 DataFrame
| Row | Resource Type | Total Capacity |
|---|---|---|
| Any | Any | |
| 1 | MA_natural_gas_combined_cycle | 0.423658 |
| 2 | CT_natural_gas_combined_cycle | 0.629033 |
| 3 | ME_natural_gas_combined_cycle | 0.35463 |
| 4 | MA_solar_pv | 42.9756 |
| 5 | CT_onshore_wind | 0.0 |
| 6 | CT_solar_pv | 67.9858 |
| 7 | ME_onshore_wind | 7.80683 |
| 8 | MA_battery | 13.262 |
| 9 | CT_battery | 29.851 |
| 10 | ME_battery | 2.62375 |
3×11 DataFrame
| Row | Column1 | 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| String3 | String3 | Int64 | Int64 | Int64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | |
| 1 | MA | z1 | 1 | 0 | 0 | 0.05 | 0.0 | 0.0 | 0.018 | 0.0 | 0.0 |
| 2 | CT | z2 | 0 | 1 | 0 | 0.0 | 0.05 | 0.0 | 0.0 | 0.025 | 0.0 |
| 3 | ME | z3 | 0 | 0 | 1 | 0.0 | 0.0 | 0.05 | 0.0 | 0.0 | 0.025 |
3×2 DataFrame
| Row | CO2_Cap_Constraint | PriceCap |
|---|---|---|
| Any | Any | |
| 1 | CO_2_Cap_Zone_1 | 250 |
| 2 | CO_2_Cap_Zone_2 | 250 |
| 3 | CO_2_Cap_Zone_2 | 250 |
3×4 DataFrame
| Row | Column1 | Network_zones | ESR_1 | ESR_2 |
|---|---|---|---|---|
| String3 | String3 | Float64 | Float64 | |
| 1 | MA | z1 | 0.259 | 0.348 |
| 2 | CT | z2 | 0.44 | 0.44 |
| 3 | ME | z3 | 0.776 | 0.776 |
3×3 DataFrame
| Row | Column1 | Network_zones | CapRes_1 |
|---|---|---|---|
| String3 | String3 | Float64 | |
| 1 | MA | z1 | 0.156 |
| 2 | CT | z2 | 0.156 |
| 3 | ME | z3 | 0.156 |
3×3 DataFrame
| Row | MinCapReqConstraint | ConstraintDescription | Min_MW |
|---|---|---|---|
| Int64 | String15 | Int64 | |
| 1 | 1 | MA_PV | 5000 |
| 2 | 2 | CT_Wind | 10000 |
| 3 | 3 | All_Batteries | 6000 |
1850×12 DataFrame
1825 rows omitted
| Row | Resource | MA_natural_gas_combined_cycle | CT_natural_gas_combined_cycle | ME_natural_gas_combined_cycle | MA_solar_pv | CT_onshore_wind | CT_solar_pv | ME_onshore_wind | MA_battery | CT_battery | ME_battery | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| String15 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | |
| 1 | Zone | 1.0 | 2.0 | 3.0 | 1.0 | 2.0 | 2.0 | 3.0 | 1.0 | 2.0 | 3.0 | 0.0 |
| 2 | AnnualSum | 1.04015e7 | 3.42459e6 | 8.94975e5 | 2.47213e7 | 2.90683e7 | 2.69884e7 | 2.625e7 | 5.06354e6 | 1.45833e7 | 4.90368e6 | 1.463e8 |
| 3 | t1 | -0.0 | -0.0 | -0.0 | -0.0 | 8510.78 | -0.0 | 5300.61 | 0.0 | 2537.45 | 673.34 | 17022.2 |
| 4 | t2 | -0.0 | -0.0 | -0.0 | -0.0 | 8420.78 | -0.0 | 6282.04 | 0.0 | 2537.45 | 0.0 | 17240.3 |
| 5 | t3 | -0.0 | -0.0 | -0.0 | -0.0 | 8367.78 | -0.0 | 2409.84 | 0.0 | 2537.45 | 1828.24 | 15143.3 |
| 6 | t4 | -0.0 | -0.0 | -0.0 | -0.0 | 8353.78 | -0.0 | 2762.24 | 1591.46 | 2537.45 | 0.0 | 15244.9 |
| 7 | t5 | -0.0 | -0.0 | -0.0 | -0.0 | 7482.39 | -0.0 | 0.0 | 1617.46 | 2980.64 | 1384.62 | 13465.1 |
| 8 | t6 | -0.0 | -0.0 | -0.0 | -0.0 | 2429.93 | -0.0 | 2797.24 | 1717.96 | 5535.37 | 0.0 | 12480.5 |
| 9 | t7 | -0.0 | -0.0 | -0.0 | -0.0 | 11868.8 | -0.0 | 1374.73 | 1320.78 | 871.443 | 1340.67 | 16776.4 |
| 10 | t8 | -0.0 | -0.0 | -0.0 | -0.0 | 2656.93 | -0.0 | 0.0 | 2115.96 | 5535.37 | 1452.62 | 11760.9 |
| 11 | t9 | -0.0 | -0.0 | -0.0 | 3061.28 | 0.0 | 3110.8 | 2982.24 | 868.817 | 5389.44 | 0.0 | 15412.6 |
| 12 | t10 | -0.0 | -0.0 | -0.0 | 6100.22 | 7597.99 | 5543.69 | 0.0 | 0.0 | 0.0 | 1521.12 | 20763.0 |
| 13 | t11 | -0.0 | -0.0 | -0.0 | 8314.29 | 0.0 | 6341.98 | 3080.24 | 0.0 | 2458.82 | 0.0 | 20195.3 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| 1839 | t1837 | -0.0 | -0.0 | -0.0 | 6712.18 | 2541.6 | 6736.37 | 305.608 | 1410.33 | 763.726 | 1427.82 | 19897.6 |
| 1840 | t1838 | -0.0 | -0.0 | -0.0 | 6514.15 | 0.0 | 6847.24 | 3153.24 | 0.0 | 3464.22 | 0.0 | 19978.9 |
| 1841 | t1839 | -0.0 | -0.0 | -0.0 | 5582.07 | 3848.88 | 6280.2 | 0.0 | 195.422 | 2048.3 | 1571.12 | 19526.0 |
| 1842 | t1840 | -0.0 | -0.0 | -0.0 | 3688.13 | 9349.98 | 4892.7 | 3490.61 | 1006.02 | 0.0 | 0.0 | 22427.4 |
| 1843 | t1841 | -0.0 | -0.0 | -0.0 | 509.22 | 8124.99 | 1351.08 | 3653.06 | 1218.5 | 2507.8 | 1828.24 | 19192.9 |
| 1844 | t1842 | -0.0 | -0.0 | -0.0 | -0.0 | 2918.2 | -0.0 | 6896.82 | 2194.61 | 5535.37 | 256.863 | 17801.9 |
| 1845 | t1843 | -0.0 | -0.0 | -0.0 | -0.0 | 6800.37 | -0.0 | 7324.66 | 1838.11 | 3950.15 | 41.9472 | 19955.2 |
| 1846 | t1844 | -0.0 | -0.0 | -0.0 | -0.0 | 9505.82 | -0.0 | 5683.66 | 1744.78 | 2567.93 | 838.077 | 20340.3 |
| 1847 | t1845 | -0.0 | -0.0 | -0.0 | -0.0 | 3491.93 | -0.0 | 5128.56 | 1597.61 | 5535.37 | 1107.49 | 16861.0 |
| 1848 | t1846 | -0.0 | -0.0 | -0.0 | -0.0 | 12135.6 | -0.0 | 5021.75 | 1341.11 | 1140.56 | 1125.9 | 20764.9 |
| 1849 | t1847 | -0.0 | -0.0 | -0.0 | -0.0 | 8875.71 | -0.0 | 3605.98 | 974.61 | 2665.48 | 1783.79 | 17905.6 |
| 1850 | t1848 | -0.0 | -0.0 | -0.0 | -0.0 | 13549.1 | -0.0 | 4098.0 | 541.61 | 205.31 | 1478.27 | 19872.3 |
10×28 DataFrame
| Row | region | Resource | zone | Cluster | R_ID | Inv_cost_MW | Inv_cost_MWh | Inv_cost_charge_MW | Fixed_OM_cost_MW | Fixed_OM_cost_MWh | Fixed_OM_cost_charge_MW | Var_OM_cost_out | Fuel_cost | Var_OM_cost_in | StartCost | Charge_cost | CO2SequestrationCost | EnergyRevenue | SubsidyRevenue | OperatingReserveRevenue | OperatingRegulationRevenue | ReserveMarginRevenue | ESRRevenue | EmissionsCost | RegSubsidyRevenue | Revenue | Cost | Profit |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| String3 | String31 | Int64 | Int64 | Int64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | |
| 1 | MA | MA_natural_gas_combined_cycle | 1 | 1 | 1 | 5.54734e8 | 0.0 | 0.0 | 8.72561e7 | 0.0 | 0.0 | 3.69253e7 | 2.10416e8 | 0.0 | 3.84832e7 | 0.0 | 0.0 | 2.77103e9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.84321e9 | 0.0 | 2.77103e9 | 2.77103e9 | 1.43051e-6 |
| 2 | CT | CT_natural_gas_combined_cycle | 2 | 1 | 2 | 1.42906e8 | 0.0 | 0.0 | 2.11911e7 | 0.0 | 0.0 | 1.22258e7 | 4.97792e7 | 0.0 | 7.75292e6 | 0.0 | 0.0 | 8.4423e8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.10375e8 | 0.0 | 8.4423e8 | 8.4423e8 | 1.19209e-7 |
| 3 | ME | ME_natural_gas_combined_cycle | 3 | 1 | 3 | 3.52336e7 | 0.0 | 0.0 | 8.77661e6 | 0.0 | 0.0 | 4.02739e6 | 2.26505e7 | 0.0 | 3.33663e6 | 0.0 | 0.0 | 2.19267e8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.45243e8 | 0.0 | 2.19267e8 | 2.19267e8 | 0.0 |
| 4 | MA | MA_solar_pv | 1 | 1 | 4 | 1.27007e9 | 0.0 | 0.0 | 2.79327e8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.5494e9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.5494e9 | 1.5494e9 | -2.86102e-6 |
| 5 | CT | CT_onshore_wind | 2 | 1 | 5 | 1.40748e9 | 0.0 | 0.0 | 6.25617e8 | 0.0 | 0.0 | 2.90683e6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.036e9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.036e9 | 2.036e9 | -5.00679e-6 |
| 6 | CT | CT_solar_pv | 2 | 1 | 6 | 1.35108e9 | 0.0 | 0.0 | 2.97142e8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.64822e9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.64822e9 | 1.64822e9 | 9.53674e-7 |
| 7 | ME | ME_onshore_wind | 3 | 1 | 7 | 1.03673e9 | 0.0 | 0.0 | 4.60821e8 | 0.0 | 0.0 | 2.625e6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.50017e9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.50017e9 | 1.50017e9 | 2.38419e-6 |
| 8 | MA | MA_battery | 1 | 0 | 8 | 4.29792e7 | 2.23673e8 | 0.0 | 1.07426e7 | 5.59033e7 | 0.0 | 7.59532e5 | 0.0 | 8.97367e5 | 0.0 | 1.3432e8 | 0.0 | 4.48833e8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.48833e8 | 4.69275e8 | -2.0442e7 |
| 9 | CT | CT_battery | 2 | 0 | 9 | 1.08405e8 | 5.73615e8 | 0.0 | 2.70957e7 | 1.43365e8 | 0.0 | 2.1875e6 | 0.0 | 2.58447e6 | 0.0 | 5.24177e8 | 0.0 | 1.31941e9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.31941e9 | 1.38143e9 | -6.20165e7 |
| 10 | ME | ME_battery | 3 | 0 | 10 | 3.58043e7 | 1.03994e8 | 0.0 | 8.94925e6 | 2.59915e7 | 0.0 | 7.35552e5 | 0.0 | 8.69036e5 | 0.0 | 3.81057e7 | 0.0 | 2.03732e8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.03732e8 | 2.14449e8 | -1.0717e7 |
1852×5 DataFrame
1827 rows omitted
| Row | Zone | 1 | 2 | 3 | Total |
|---|---|---|---|---|---|
| String15 | Float64 | Float64 | Float64 | Float64 | |
| 1 | CO2_Price_1 | 444.921 | 0.0 | 0.0 | 0.0 |
| 2 | CO2_Price_2 | 0.0 | 468.668 | 0.0 | 0.0 |
| 3 | CO2_Price_3 | 0.0 | 0.0 | 240.86 | 0.0 |
| 4 | AnnualSum | 4.14279e6 | 1.30236e6 | 6.03017e5 | 6.04816e6 |
| 5 | t1 | 0.0 | 0.0 | 0.0 | 0.0 |
| 6 | t2 | 0.0 | 0.0 | 0.0 | 0.0 |
| 7 | t3 | 0.0 | 0.0 | 0.0 | 0.0 |
| 8 | t4 | 0.0 | 0.0 | 0.0 | 0.0 |
| 9 | t5 | 0.0 | 0.0 | 0.0 | 0.0 |
| 10 | t6 | 0.0 | 0.0 | 0.0 | 0.0 |
| 11 | t7 | 0.0 | 0.0 | 0.0 | 0.0 |
| 12 | t8 | 0.0 | 0.0 | 0.0 | 0.0 |
| 13 | t9 | 0.0 | 0.0 | 0.0 | 0.0 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| 1841 | t1837 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1842 | t1838 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1843 | t1839 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1844 | t1840 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1845 | t1841 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1846 | t1842 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1847 | t1843 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1848 | t1844 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1849 | t1845 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1850 | t1846 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1851 | t1847 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1852 | t1848 | 0.0 | 0.0 | 0.0 | 0.0 |
1849×5 DataFrame
1824 rows omitted
| Row | Zone | 1 | 2 | 3 | Total |
|---|---|---|---|---|---|
| String15 | Float64 | Float64 | Float64 | Float64 | |
| 1 | AnnualSum | 1.68155e7 | 1.41088e7 | 4310.21 | 3.09286e7 |
| 2 | t1 | 997.169 | 0.0 | 0.0 | 997.169 |
| 3 | t2 | 997.169 | 0.0 | 0.0 | 997.169 |
| 4 | t3 | 997.169 | 0.0 | 0.0 | 997.169 |
| 5 | t4 | 997.169 | 0.0 | 0.0 | 997.169 |
| 6 | t5 | 997.169 | 0.0 | 0.0 | 997.169 |
| 7 | t6 | 997.169 | 0.0 | 0.0 | 997.169 |
| 8 | t7 | 997.169 | 0.0 | 0.0 | 997.169 |
| 9 | t8 | 997.169 | 0.0 | 0.0 | 997.169 |
| 10 | t9 | 997.169 | 0.0 | 0.0 | 997.169 |
| 11 | t10 | 1471.46 | 0.0 | 0.0 | 1471.46 |
| 12 | t11 | 997.169 | 0.0 | 0.0 | 997.169 |
| 13 | t12 | 1115.81 | 0.0 | 0.0 | 1115.81 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| 1838 | t1837 | 2789.35 | 1012.99 | 0.0 | 3802.34 |
| 1839 | t1838 | 2835.21 | 1012.99 | 0.0 | 3848.2 |
| 1840 | t1839 | 2520.57 | 1012.99 | 0.0 | 3533.56 |
| 1841 | t1840 | 1496.47 | 445.85 | 0.0 | 1942.32 |
| 1842 | t1841 | 2571.26 | 1012.99 | 0.0 | 3584.25 |
| 1843 | t1842 | 2835.21 | 1012.99 | 0.0 | 3848.2 |
| 1844 | t1843 | 2835.21 | 1012.99 | 0.0 | 3848.2 |
| 1845 | t1844 | 2625.42 | 960.184 | 0.0 | 3585.6 |
| 1846 | t1845 | 2506.32 | 342.391 | 0.0 | 2848.71 |
| 1847 | t1846 | 2277.59 | 342.391 | 0.0 | 2619.98 |
| 1848 | t1847 | 1960.08 | 524.526 | 0.0 | 2484.6 |
| 1849 | t1848 | 1566.77 | 342.391 | 0.0 | 1909.16 |