From fe0f169bcbcbdd4d7c417f7c9280e31c1b7dd663 Mon Sep 17 00:00:00 2001 From: lbonaldo Date: Tue, 19 Nov 2024 14:36:55 -0500 Subject: [PATCH 1/4] Fix demand path in tutorial 8 --- docs/src/Tutorials/Tutorial_8_outputs.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/docs/src/Tutorials/Tutorial_8_outputs.md b/docs/src/Tutorials/Tutorial_8_outputs.md index b344c3aab..b19b17662 100644 --- a/docs/src/Tutorials/Tutorial_8_outputs.md +++ b/docs/src/Tutorials/Tutorial_8_outputs.md @@ -351,12 +351,9 @@ results = cd(readdir,joinpath(case,"results")) "time_weights.csv" "tlosses.csv" - - ### Power -The file `power.csv`, shown below, outputs the power in MW discharged by each node at each time step. Note that if TimeDomainReduction is in use the file will be shorter. The first row states which zone each node is part of, and the total power per year is located in the second row. After that, each row represents one time step of the series. - +The file `power.csv`, shown below, contains the power output in MW discharged by each node at each time step. Note that if `TimeDomainReduction` is enabled, the file will have fewer rows compared to the number of time steps in the `system/Demand_data.csv` file. In this case, the corresponding `Demand_data.csv` file that matches the time series in `power.csv` can be found in the `TDR_results` folder. The first row of `power.csv` indicates the zone each node belongs to, while the second row contains the total power per year. Each subsequent row represents one time step in the series. ```julia power = CSV.read(joinpath(case,"results/power.csv"),DataFrame,missingstring="NA") @@ -386,11 +383,16 @@ for i in range(2,4) power_plot = [power_plot; power_plot_temp] end -demands = CSV.read(joinpath(case,"system/Demand_data.csv"),DataFrame,missingstring="NA") +demands = CSV.read(joinpath(case,"TDR_results/Demand_data.csv"),DataFrame,missingstring="NA") demands_tot = demands[!,"Demand_MW_z1"]+demands[!,"Demand_MW_z2"]+demands[!,"Demand_MW_z3"] power_plot[!,"Demand_Total"] = repeat(demands_tot[tstart:tend],4); ``` +Note that since the `power.csv` file is generated by running GenX with `TimeDomainReduction: 1`, the demands time series must be taken from the `Demand_data.csv` file located in the `TDR_results` folder. + +GenX also has the ability to output the reconstructed version of power generation by setting `OutputFullTimeSeries: 1` in `genx_settings.yml`. In this case, a second version of the `power.csv` file will be created inside the `results/Full_TimeSeries` folder. To plot the reconstructed version against the demand, ensure you use the `Demand_data.csv` from the `settings` folder, not the one in the `TDR_results` folder. + +Finally, if `TimeDomainReduction: 0` is set, the `power.csv` file will contain the full time series of power generation, and the `Demand_data.csv` should be taken from the `settings` folder. ```julia power_plot |> From c07066e798530c67b4428ff82911a0722b7c3071 Mon Sep 17 00:00:00 2001 From: lbonaldo Date: Tue, 19 Nov 2024 14:37:10 -0500 Subject: [PATCH 2/4] Tutorial 8 cleanup --- docs/src/Tutorials/Tutorial_8_outputs.md | 165 ++++++++++++++++------- docs/src/Tutorials/files/t8_cap.svg | 2 +- 2 files changed, 114 insertions(+), 53 deletions(-) diff --git a/docs/src/Tutorials/Tutorial_8_outputs.md b/docs/src/Tutorials/Tutorial_8_outputs.md index b19b17662..05f6638fb 100644 --- a/docs/src/Tutorials/Tutorial_8_outputs.md +++ b/docs/src/Tutorials/Tutorial_8_outputs.md @@ -28,7 +28,6 @@ using StatsPlots case = joinpath("example_systems/1_three_zones"); ``` - ```julia include("example_systems/1_three_zones/Run.jl") ``` @@ -40,15 +39,12 @@ include("example_systems/1_three_zones/Run.jl") 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: ------------------------------------------------------- @@ -89,8 +85,7 @@ include("example_systems/1_three_zones/Run.jl") CSV Files Successfully Read In From /Users/mayamutic/Desktop/GenX-Tutorials/Tutorials/example_systems/1_three_zones Generating the Optimization Model - - Thermal.csv Successfully Read. + Thermal.csv Successfully Read. Vre.csv Successfully Read. Storage.csv Successfully Read. Resource_energy_share_requirement.csv Successfully Read. @@ -115,6 +110,7 @@ include("example_systems/1_three_zones/Run.jl") Minimum Capacity Requirement Module Time elapsed for model building is 5.887781667 + Solving Model Running HiGHS 1.6.0: Copyright (c) 2023 HiGHS under MIT licence terms Presolving model @@ -251,6 +247,7 @@ include("example_systems/1_three_zones/Run.jl") Objective value : 9.4121364078e+03 HiGHS run time : 107.89 LP solved for primal + Writing Output Time elapsed for writing costs is 0.8427745 @@ -312,17 +309,12 @@ include("example_systems/1_three_zones/Run.jl") Time elapsed for writing is 6.909353542 - Below are all 33 files output by running GenX: - ```julia results = cd(readdir,joinpath(case,"results")) ``` - - - 33-element Vector{String}: "CO2_prices_and_penalties.csv" "ChargingCost.csv" @@ -358,8 +350,38 @@ The file `power.csv`, shown below, contains the power output in MW discharged by ```julia power = CSV.read(joinpath(case,"results/power.csv"),DataFrame,missingstring="NA") ``` -``` @raw html -
1850×12 DataFrame
1825 rows omitted
RowResourceMA_natural_gas_combined_cycleCT_natural_gas_combined_cycleME_natural_gas_combined_cycleMA_solar_pvCT_onshore_windCT_solar_pvME_onshore_windMA_batteryCT_batteryME_batteryTotal
String15Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64
1Zone1.02.03.01.02.02.03.01.02.03.00.0
2AnnualSum1.04015e73.42459e68.94975e52.47213e72.90683e72.69884e72.625e75.06354e61.45833e74.90368e61.463e8
3t1-0.0-0.0-0.0-0.08510.78-0.05300.610.02537.45673.3417022.2
4t2-0.0-0.0-0.0-0.08420.78-0.06282.040.02537.450.017240.3
5t3-0.0-0.0-0.0-0.08367.78-0.02409.840.02537.451828.2415143.3
6t4-0.0-0.0-0.0-0.08353.78-0.02762.241591.462537.450.015244.9
7t5-0.0-0.0-0.0-0.07482.39-0.00.01617.462980.641384.6213465.1
8t6-0.0-0.0-0.0-0.02429.93-0.02797.241717.965535.370.012480.5
9t7-0.0-0.0-0.0-0.011868.8-0.01374.731320.78871.4431340.6716776.4
10t8-0.0-0.0-0.0-0.02656.93-0.00.02115.965535.371452.6211760.9
11t9-0.0-0.0-0.03061.280.03110.82982.24868.8175389.440.015412.6
12t10-0.0-0.0-0.06100.227597.995543.690.00.00.01521.1220763.0
13t11-0.0-0.0-0.08314.290.06341.983080.240.02458.820.020195.3
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
1839t1837-0.0-0.0-0.06712.182541.66736.37305.6081410.33763.7261427.8219897.6
1840t1838-0.0-0.0-0.06514.150.06847.243153.240.03464.220.019978.9
1841t1839-0.0-0.0-0.05582.073848.886280.20.0195.4222048.31571.1219526.0
1842t1840-0.0-0.0-0.03688.139349.984892.73490.611006.020.00.022427.4
1843t1841-0.0-0.0-0.0509.228124.991351.083653.061218.52507.81828.2419192.9
1844t1842-0.0-0.0-0.0-0.02918.2-0.06896.822194.615535.37256.86317801.9
1845t1843-0.0-0.0-0.0-0.06800.37-0.07324.661838.113950.1541.947219955.2
1846t1844-0.0-0.0-0.0-0.09505.82-0.05683.661744.782567.93838.07720340.3
1847t1845-0.0-0.0-0.0-0.03491.93-0.05128.561597.615535.371107.4916861.0
1848t1846-0.0-0.0-0.0-0.012135.6-0.05021.751341.111140.561125.920764.9
1849t1847-0.0-0.0-0.0-0.08875.71-0.03605.98974.612665.481783.7917905.6
1850t1848-0.0-0.0-0.0-0.013549.1-0.04098.0541.61205.311478.2719872.3
+ +``` +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 ``` @@ -404,11 +426,9 @@ power_plot |> ``` ![svg](./files/t8_cap.svg) - We can separate it by zone in the following plot: - ```julia Zone1 = [power[2,2] power[2,5] 0 power[2,9]] Zone2 = [power[2,3] power[2,7] power[2,6] power[2,10]] @@ -450,7 +470,6 @@ end ``` - ```julia Plots.heatmap(heat,yticks=0:4:24,xticks=([15:30:364;], ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sept","Oct","Nov","Dec"]), @@ -460,7 +479,6 @@ Plots.heatmap(heat,yticks=0:4:24,xticks=([15:30:364;], ![svg](./files/t8_heatmap.svg) - ### Cost and Revenue The basic cost of each power plant and the revenue it generates can be found in files `costs.csv`, `NetRevenue.csv`,and `EnergyRevenue.csv`. `NetRevenue.csv` breaks down each specific cost per node in each zone, which is useful to visualize what the cost is coming from. @@ -470,13 +488,21 @@ The basic cost of each power plant and the revenue it generates can be found in netrevenue = CSV.read(joinpath(case,"results/NetRevenue.csv"),DataFrame,missingstring="NA") ``` - - -``` @raw html -
10×28 DataFrame
RowregionResourcezoneClusterR_IDInv_cost_MWInv_cost_MWhInv_cost_charge_MWFixed_OM_cost_MWFixed_OM_cost_MWhFixed_OM_cost_charge_MWVar_OM_cost_outFuel_costVar_OM_cost_inStartCostCharge_costCO2SequestrationCostEnergyRevenueSubsidyRevenueOperatingReserveRevenueOperatingRegulationRevenueReserveMarginRevenueESRRevenueEmissionsCostRegSubsidyRevenueRevenueCostProfit
String3String31Int64Int64Int64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64
1MAMA_natural_gas_combined_cycle1115.54734e80.00.08.72561e70.00.03.69253e72.10416e80.03.84832e70.00.02.77103e90.00.00.00.00.01.84321e90.02.77103e92.77103e91.43051e-6
2CTCT_natural_gas_combined_cycle2121.42906e80.00.02.11911e70.00.01.22258e74.97792e70.07.75292e60.00.08.4423e80.00.00.00.00.06.10375e80.08.4423e88.4423e81.19209e-7
3MEME_natural_gas_combined_cycle3133.52336e70.00.08.77661e60.00.04.02739e62.26505e70.03.33663e60.00.02.19267e80.00.00.00.00.01.45243e80.02.19267e82.19267e80.0
4MAMA_solar_pv1141.27007e90.00.02.79327e80.00.00.00.00.00.00.00.01.5494e90.00.00.00.00.00.00.01.5494e91.5494e9-2.86102e-6
5CTCT_onshore_wind2151.40748e90.00.06.25617e80.00.02.90683e60.00.00.00.00.02.036e90.00.00.00.00.00.00.02.036e92.036e9-5.00679e-6
6CTCT_solar_pv2161.35108e90.00.02.97142e80.00.00.00.00.00.00.00.01.64822e90.00.00.00.00.00.00.01.64822e91.64822e99.53674e-7
7MEME_onshore_wind3171.03673e90.00.04.60821e80.00.02.625e60.00.00.00.00.01.50017e90.00.00.00.00.00.00.01.50017e91.50017e92.38419e-6
8MAMA_battery1084.29792e72.23673e80.01.07426e75.59033e70.07.59532e50.08.97367e50.01.3432e80.04.48833e80.00.00.00.00.00.00.04.48833e84.69275e8-2.0442e7
9CTCT_battery2091.08405e85.73615e80.02.70957e71.43365e80.02.1875e60.02.58447e60.05.24177e80.01.31941e90.00.00.00.00.00.00.01.31941e91.38143e9-6.20165e7
10MEME_battery30103.58043e71.03994e80.08.94925e62.59915e70.07.35552e50.08.69036e50.03.81057e70.02.03732e80.00.00.00.00.00.00.02.03732e82.14449e8-1.0717e7
``` - - +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 +``` ```julia xnames = netrevenue[!,2] @@ -493,8 +519,6 @@ StatsPlots.scatter!(xnames,netrevenue[!,"Revenue"],label="Revenue",color="black" ![svg](./files/t8_cost.svg) - - ### Emissions The file `emmissions.csv` gives the total CO2 emmissions per zone for each hour GenX runs. The first three rows give the marginal CO2 abatement cost in $/ton CO2. @@ -504,13 +528,38 @@ The file `emmissions.csv` gives the total CO2 emmissions per zone for each hour emm1 = CSV.read(joinpath(case,"results/emissions.csv"),DataFrame) ``` - - -``` @raw html -
1852×5 DataFrame
1827 rows omitted
RowZone123Total
String15Float64Float64Float64Float64
1CO2_Price_1444.9210.00.00.0
2CO2_Price_20.0468.6680.00.0
3CO2_Price_30.00.0240.860.0
4AnnualSum4.14279e61.30236e66.03017e56.04816e6
5t10.00.00.00.0
6t20.00.00.00.0
7t30.00.00.00.0
8t40.00.00.00.0
9t50.00.00.00.0
10t60.00.00.00.0
11t70.00.00.00.0
12t80.00.00.00.0
13t90.00.00.00.0
⋮⋮⋮⋮⋮⋮
1841t18370.00.00.00.0
1842t18380.00.00.00.0
1843t18390.00.00.00.0
1844t18400.00.00.00.0
1845t18410.00.00.00.0
1846t18420.00.00.00.0
1847t18430.00.00.00.0
1848t18440.00.00.00.0
1849t18450.00.00.00.0
1850t18460.00.00.00.0
1851t18470.00.00.00.0
1852t18480.00.00.00.0
``` - - +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 +``` ```julia # Pre-processing @@ -521,7 +570,6 @@ names_emm = ["Zone 1","Zone 2","Zone 3"] emm_tot = DataFrame([emm1[3:end,2] emm1[3:end,3] emm1[3:end,4]], ["Zone 1","Zone 2","Zone 3"]) - emm_plot = DataFrame([collect((tstart-3):(tend-3)) emm_tot[tstart:tend,1] repeat([names_emm[1]],(tend-tstart+1))], ["Hour","MW","Zone"]); @@ -532,7 +580,6 @@ end ``` - ```julia emm_plot |> @vlplot(mark={:line}, @@ -543,11 +590,8 @@ emm_plot |> ![svg](./files/t8_emm1.svg) - - Let's try changing the CO2 cap, as in Tutorial 7, and plotting the resulting emmissions. - ```julia genx_settings_TZ = YAML.load(open((joinpath(case,"settings/genx_settings.yml")))) genx_settings_TZ["CO2Cap"] = 0 @@ -557,7 +601,6 @@ include("example_systems/1_three_zones/Run.jl") # run outside of notebook ``` - Configuring Settings Time Series Data Already Clustered. Configuring Solver @@ -578,15 +621,12 @@ include("example_systems/1_three_zones/Run.jl") Total number of resources: 10 ------------------------------------------------------- - 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. - - Generators_variability.csv Successfully Read! Validating time basis Minimum_capacity_requirement.csv Successfully Read! @@ -609,6 +649,7 @@ include("example_systems/1_three_zones/Run.jl") Minimum Capacity Requirement Module Time elapsed for model building is 0.531860834 + Solving Model Running HiGHS 1.6.0: Copyright (c) 2023 HiGHS under MIT licence terms Presolving model @@ -742,6 +783,7 @@ include("example_systems/1_three_zones/Run.jl") Objective value : 5.5855435982e+03 HiGHS run time : 66.51 LP solved for primal + Writing Output Time elapsed for writing costs is 0.099885792 @@ -801,19 +843,42 @@ include("example_systems/1_three_zones/Run.jl") Time elapsed for writing is 0.530491792 - - ```julia emm2 = CSV.read(joinpath(case,"results_1/emissions.csv"),DataFrame) ``` - - -``` @raw html -
1849×5 DataFrame
1824 rows omitted
RowZone123Total
String15Float64Float64Float64Float64
1AnnualSum1.68155e71.41088e74310.213.09286e7
2t1997.1690.00.0997.169
3t2997.1690.00.0997.169
4t3997.1690.00.0997.169
5t4997.1690.00.0997.169
6t5997.1690.00.0997.169
7t6997.1690.00.0997.169
8t7997.1690.00.0997.169
9t8997.1690.00.0997.169
10t9997.1690.00.0997.169
11t101471.460.00.01471.46
12t11997.1690.00.0997.169
13t121115.810.00.01115.81
⋮⋮⋮⋮⋮⋮
1838t18372789.351012.990.03802.34
1839t18382835.211012.990.03848.2
1840t18392520.571012.990.03533.56
1841t18401496.47445.850.01942.32
1842t18412571.261012.990.03584.25
1843t18422835.211012.990.03848.2
1844t18432835.211012.990.03848.2
1845t18442625.42960.1840.03585.6
1846t18452506.32342.3910.02848.71
1847t18462277.59342.3910.02619.98
1848t18471960.08524.5260.02484.6
1849t18481566.77342.3910.01909.16
``` - - +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 +``` ```julia # Pre-processing @@ -824,7 +889,6 @@ names_emm = ["Zone 1","Zone 2","Zone 3"] emm_tot2 = DataFrame([emm2[3:end,2] emm2[3:end,3] emm2[3:end,4]], ["Zone 1","Zone 2","Zone 3"]) - emm_plot2 = DataFrame([collect((tstart-3):(tend-3)) emm_tot2[tstart:tend,1] repeat([names_emm[1]],(tend-tstart+1))], ["Hour","MW","Zone"]); @@ -860,12 +924,9 @@ Plots.plot(collect((tstart-3):(tend-3)),emm1sum[tstart:tend],size=(800,400),labe Plots.plot!(collect((tstart-3):(tend-3)),emm2sum[tstart:tend],label="No CO2 Cap",linewidth = 1.5) ``` ![svg](./files/t8_emm_comp.svg) - - Finally, set the CO2 Cap back to 2: - ```julia genx_settings_TZ["CO2Cap"] = 2 YAML.write_file((joinpath(case,"settings/genx_settings.yml")), genx_settings_TZ) diff --git a/docs/src/Tutorials/files/t8_cap.svg b/docs/src/Tutorials/files/t8_cap.svg index c40ef607c..a8747901c 100644 --- a/docs/src/Tutorials/files/t8_cap.svg +++ b/docs/src/Tutorials/files/t8_cap.svg @@ -1 +1 @@ -1224364860728496108120132144156168Time Step (hours)02,0004,0006,0008,00010,00012,00014,00016,00018,00020,00022,00024,00026,000Demand (MW)WindSolarNatural_GasBatteryDemandResource_TypeResource Capacity per Hour with Demand Curve, all Zones \ No newline at end of file +1224364860728496108120132144156168Time Step (hours)02,0004,0006,0008,00010,00012,00014,00016,00018,00020,00022,00024,00026,000Load (MW)WindSolarNatural_GasBatteryDemandResource_TypeResource Capacity per Hour with Load Demand Curve, all Zones \ No newline at end of file From c8b476f1dd4f9e4a51a59d1376edf0fc37541f66 Mon Sep 17 00:00:00 2001 From: lbonaldo Date: Tue, 3 Dec 2024 17:00:26 -0500 Subject: [PATCH 3/4] Fix table in html --- docs/src/Tutorials/Tutorial_8_outputs.md | 116 ++--------------------- 1 file changed, 8 insertions(+), 108 deletions(-) diff --git a/docs/src/Tutorials/Tutorial_8_outputs.md b/docs/src/Tutorials/Tutorial_8_outputs.md index 05f6638fb..e6b9f130f 100644 --- a/docs/src/Tutorials/Tutorial_8_outputs.md +++ b/docs/src/Tutorials/Tutorial_8_outputs.md @@ -351,37 +351,8 @@ The file `power.csv`, shown below, contains the power output in MW discharged by power = CSV.read(joinpath(case,"results/power.csv"),DataFrame,missingstring="NA") ``` -``` -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 +```@raw html +
1850×12 DataFrame
1825 rows omitted
RowResourceMA_natural_gas_combined_cycleCT_natural_gas_combined_cycleME_natural_gas_combined_cycleMA_solar_pvCT_onshore_windCT_solar_pvME_onshore_windMA_batteryCT_batteryME_batteryTotal
String15Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64
1Zone1.02.03.01.02.02.03.01.02.03.00.0
2AnnualSum1.04015e73.42459e68.94975e52.47213e72.90683e72.69884e72.625e75.06354e61.45833e74.90368e61.463e8
3t1-0.0-0.0-0.0-0.08510.78-0.05300.610.02537.45673.3417022.2
4t2-0.0-0.0-0.0-0.08420.78-0.06282.040.02537.450.017240.3
5t3-0.0-0.0-0.0-0.08367.78-0.02409.840.02537.451828.2415143.3
6t4-0.0-0.0-0.0-0.08353.78-0.02762.241591.462537.450.015244.9
7t5-0.0-0.0-0.0-0.07482.39-0.00.01617.462980.641384.6213465.1
8t6-0.0-0.0-0.0-0.02429.93-0.02797.241717.965535.370.012480.5
9t7-0.0-0.0-0.0-0.011868.8-0.01374.731320.78871.4431340.6716776.4
10t8-0.0-0.0-0.0-0.02656.93-0.00.02115.965535.371452.6211760.9
11t9-0.0-0.0-0.03061.280.03110.82982.24868.8175389.440.015412.6
12t10-0.0-0.0-0.06100.227597.995543.690.00.00.01521.1220763.0
13t11-0.0-0.0-0.08314.290.06341.983080.240.02458.820.020195.3
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
1839t1837-0.0-0.0-0.06712.182541.66736.37305.6081410.33763.7261427.8219897.6
1840t1838-0.0-0.0-0.06514.150.06847.243153.240.03464.220.019978.9
1841t1839-0.0-0.0-0.05582.073848.886280.20.0195.4222048.31571.1219526.0
1842t1840-0.0-0.0-0.03688.139349.984892.73490.611006.020.00.022427.4
1843t1841-0.0-0.0-0.0509.228124.991351.083653.061218.52507.81828.2419192.9
1844t1842-0.0-0.0-0.0-0.02918.2-0.06896.822194.615535.37256.86317801.9
1845t1843-0.0-0.0-0.0-0.06800.37-0.07324.661838.113950.1541.947219955.2
1846t1844-0.0-0.0-0.0-0.09505.82-0.05683.661744.782567.93838.07720340.3
1847t1845-0.0-0.0-0.0-0.03491.93-0.05128.561597.615535.371107.4916861.0
1848t1846-0.0-0.0-0.0-0.012135.6-0.05021.751341.111140.561125.920764.9
1849t1847-0.0-0.0-0.0-0.08875.71-0.03605.98974.612665.481783.7917905.6
1850t1848-0.0-0.0-0.0-0.013549.1-0.04098.0541.61205.311478.2719872.3
``` @@ -488,20 +459,8 @@ The basic cost of each power plant and the revenue it generates can be found in netrevenue = CSV.read(joinpath(case,"results/NetRevenue.csv"),DataFrame,missingstring="NA") ``` -``` -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 +``` @raw html +
10×28 DataFrame
RowregionResourcezoneClusterR_IDInv_cost_MWInv_cost_MWhInv_cost_charge_MWFixed_OM_cost_MWFixed_OM_cost_MWhFixed_OM_cost_charge_MWVar_OM_cost_outFuel_costVar_OM_cost_inStartCostCharge_costCO2SequestrationCostEnergyRevenueSubsidyRevenueOperatingReserveRevenueOperatingRegulationRevenueReserveMarginRevenueESRRevenueEmissionsCostRegSubsidyRevenueRevenueCostProfit
String3String31Int64Int64Int64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64
1MAMA_natural_gas_combined_cycle1115.54734e80.00.08.72561e70.00.03.69253e72.10416e80.03.84832e70.00.02.77103e90.00.00.00.00.01.84321e90.02.77103e92.77103e91.43051e-6
2CTCT_natural_gas_combined_cycle2121.42906e80.00.02.11911e70.00.01.22258e74.97792e70.07.75292e60.00.08.4423e80.00.00.00.00.06.10375e80.08.4423e88.4423e81.19209e-7
3MEME_natural_gas_combined_cycle3133.52336e70.00.08.77661e60.00.04.02739e62.26505e70.03.33663e60.00.02.19267e80.00.00.00.00.01.45243e80.02.19267e82.19267e80.0
4MAMA_solar_pv1141.27007e90.00.02.79327e80.00.00.00.00.00.00.00.01.5494e90.00.00.00.00.00.00.01.5494e91.5494e9-2.86102e-6
5CTCT_onshore_wind2151.40748e90.00.06.25617e80.00.02.90683e60.00.00.00.00.02.036e90.00.00.00.00.00.00.02.036e92.036e9-5.00679e-6
6CTCT_solar_pv2161.35108e90.00.02.97142e80.00.00.00.00.00.00.00.01.64822e90.00.00.00.00.00.00.01.64822e91.64822e99.53674e-7
7MEME_onshore_wind3171.03673e90.00.04.60821e80.00.02.625e60.00.00.00.00.01.50017e90.00.00.00.00.00.00.01.50017e91.50017e92.38419e-6
8MAMA_battery1084.29792e72.23673e80.01.07426e75.59033e70.07.59532e50.08.97367e50.01.3432e80.04.48833e80.00.00.00.00.00.00.04.48833e84.69275e8-2.0442e7
9CTCT_battery2091.08405e85.73615e80.02.70957e71.43365e80.02.1875e60.02.58447e60.05.24177e80.01.31941e90.00.00.00.00.00.00.01.31941e91.38143e9-6.20165e7
10MEME_battery30103.58043e71.03994e80.08.94925e62.59915e70.07.35552e50.08.69036e50.03.81057e70.02.03732e80.00.00.00.00.00.00.02.03732e82.14449e8-1.0717e7
``` ```julia @@ -528,37 +487,8 @@ The file `emmissions.csv` gives the total CO2 emmissions per zone for each hour emm1 = CSV.read(joinpath(case,"results/emissions.csv"),DataFrame) ``` -``` -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 +``` @raw html +
1852×5 DataFrame
1827 rows omitted
RowZone123Total
String15Float64Float64Float64Float64
1CO2_Price_1444.9210.00.00.0
2CO2_Price_20.0468.6680.00.0
3CO2_Price_30.00.0240.860.0
4AnnualSum4.14279e61.30236e66.03017e56.04816e6
5t10.00.00.00.0
6t20.00.00.00.0
7t30.00.00.00.0
8t40.00.00.00.0
9t50.00.00.00.0
10t60.00.00.00.0
11t70.00.00.00.0
12t80.00.00.00.0
13t90.00.00.00.0
⋮⋮⋮⋮⋮⋮
1841t18370.00.00.00.0
1842t18380.00.00.00.0
1843t18390.00.00.00.0
1844t18400.00.00.00.0
1845t18410.00.00.00.0
1846t18420.00.00.00.0
1847t18430.00.00.00.0
1848t18440.00.00.00.0
1849t18450.00.00.00.0
1850t18460.00.00.00.0
1851t18470.00.00.00.0
1852t18480.00.00.00.0
``` ```julia @@ -847,39 +777,9 @@ include("example_systems/1_three_zones/Run.jl") emm2 = CSV.read(joinpath(case,"results_1/emissions.csv"),DataFrame) ``` +``` @raw html +
1849×5 DataFrame
1824 rows omitted
RowZone123Total
String15Float64Float64Float64Float64
1AnnualSum1.68155e71.41088e74310.213.09286e7
2t1997.1690.00.0997.169
3t2997.1690.00.0997.169
4t3997.1690.00.0997.169
5t4997.1690.00.0997.169
6t5997.1690.00.0997.169
7t6997.1690.00.0997.169
8t7997.1690.00.0997.169
9t8997.1690.00.0997.169
10t9997.1690.00.0997.169
11t101471.460.00.01471.46
12t11997.1690.00.0997.169
13t121115.810.00.01115.81
⋮⋮⋮⋮⋮⋮
1838t18372789.351012.990.03802.34
1839t18382835.211012.990.03848.2
1840t18392520.571012.990.03533.56
1841t18401496.47445.850.01942.32
1842t18412571.261012.990.03584.25
1843t18422835.211012.990.03848.2
1844t18432835.211012.990.03848.2
1845t18442625.42960.1840.03585.6
1846t18452506.32342.3910.02848.71
1847t18462277.59342.3910.02619.98
1848t18471960.08524.5260.02484.6
1849t18481566.77342.3910.01909.16
``` -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 -``` - ```julia # Pre-processing tstart = 470 From 0d8013dc84bda5bafae4acebebf03f7805a6fd8a Mon Sep 17 00:00:00 2001 From: lbonaldo Date: Tue, 3 Dec 2024 17:04:34 -0500 Subject: [PATCH 4/4] Update dev version in Project.toml --- Project.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Project.toml b/Project.toml index 2bc710952..a6d2b1048 100644 --- a/Project.toml +++ b/Project.toml @@ -1,7 +1,7 @@ name = "GenX" uuid = "5d317b1e-30ec-4ed6-a8ce-8d2d88d7cfac" authors = ["Bonaldo, Luca", "Chakrabarti, Sambuddha", "Cheng, Fangwei", "Ding, Yifu", "Jenkins, Jesse D.", "Luo, Qian", "Macdonald, Ruaridh", "Mallapragada, Dharik", "Manocha, Aneesha", "Mantegna, Gabe ", "Morris, Jack", "Patankar, Neha", "Pecci, Filippo", "Schwartz, Aaron", "Schwartz, Jacob", "Schivley, Greg", "Sepulveda, Nestor", "Xu, Qingyu", "Zhou, Justin"] -version = "0.4.1-dev.15" +version = "0.4.1-dev.16" [deps] CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"