{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Uncertainty Analysis\n", "\n", "In this tutorial, we will perform an uncertainty analysis using the EnergyScope model. We will explore how varying certain parameters affects the energy system configuration and results. This is achieved by generating a sequence of parameter values using Sobol sequences, running multiple optimizations, and analyzing the outcomes.\n", "\n", "---\n", "## Import Necessary Libraries\n", "\n", "First, we import all the required libraries and modules:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:38:32.843015Z", "start_time": "2024-10-01T08:38:32.700968Z" } }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "from energyscope.energyscope import Energyscope\n", "from energyscope.models import infrastructure_ch_2050\n", "from energyscope.result import postprocessing\n", "from energyscope.datasets import gen_sobol_sequence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- **`pandas`**: For data manipulation and analysis.\n", "- **`numpy`**: For numerical operations.\n", "- **`seaborn`**: For statistical data visualization.\n", "- **`Energyscope`**: Main class for initializing and running the EnergyScope model.\n", "- **`infrastructure_ch_2050`**: Predefined model for different configurations.\n", "- **`postprocessing`**: Functions to process results after optimizations.\n", "- **`gen_sobol_sequence`**: Function to generate Sobol sequences for parameter sampling.\n", "\n", "---\n", "## Manual Parameter Definition\n", "\n", "### Define Parameters for Uncertainty Analysis\n", "\n", "We manually define a list of parameters whose uncertainty we want to analyze. Each parameter includes a name, a lower bound, and an upper bound:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:38:35.090951Z", "start_time": "2024-10-01T08:38:35.086997Z" } }, "outputs": [], "source": [ "varying_parameter = 'c_inv'\n", "parameters = [\n", " {'name': 'PV_LV', 'lower_bound': 0, 'upper_bound': 50},\n", " {'name': 'WIND', 'lower_bound': 0, 'upper_bound': 20},\n", " {'name': 'CCGT', 'lower_bound': 0, 'upper_bound': 10}\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "- **`PV_LV`**: Photovoltaic at Low Voltage level.\n", "- **`WIND`**: Wind energy capacity.\n", "- **`CCGT`**: Combined Cycle Gas Turbine capacity.\n", "- The bounds represent the range within which the parameter values will vary during the analysis.\n", "\n", "### Generate Sobol Sequence for Parameter Sampling\n", "\n", "We use the `gen_sobol_sequence` function to generate a Sobol sequence, which provides quasi-random samples of parameter values within the specified bounds:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:38:37.246912Z", "start_time": "2024-10-01T08:38:37.243093Z" } }, "outputs": [], "source": [ "seq, prob = gen_sobol_sequence(parameters=parameters, trajectories=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- **`trajectories=4`**: Specifies the number of trajectories or samples to generate.\n", "- **`seq`**: The generated sequence of parameter values.\n", "- **`prob`**: Metadata about the parameters and their sampling.\n", "\n", "### Display Number of Generated Samples\n", "\n", "We can display the number of generated samples to verify:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:38:40.243189Z", "start_time": "2024-10-01T08:38:40.237858Z" } }, "outputs": [ { "data": { "text/plain": [ "20" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(len(seq))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- This should output the total number of parameter sets generated.\n", "\n", "### Prepare Parameter DataFrame\n", "\n", "We prepare a DataFrame to hold the generated parameter sequences in a format suitable for the EnergyScope model:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:38:42.919266Z", "start_time": "2024-10-01T08:38:42.902353Z" } }, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "index0", "rawType": "object", "type": "string" }, { "name": "value0", "rawType": "float64", "type": "float" }, { "name": "value1", "rawType": "float64", "type": "float" }, { "name": "value2", "rawType": "float64", "type": "float" }, { "name": "value3", "rawType": "float64", "type": "float" }, { 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" ], "text/plain": [ " index0 value0 value1 value2 value3 value4 value5 value6 value7 \\\n", "0 PV_LV 4.6875 32.8125 4.6875 4.6875 32.8125 29.6875 7.8125 29.6875 \n", "1 WIND 9.3750 9.3750 5.6250 9.3750 5.6250 19.3750 19.3750 15.6250 \n", "2 CCGT 4.6875 4.6875 4.6875 9.6875 9.6875 9.6875 9.6875 9.6875 \n", "\n", " value8 ... value14 value15 value16 value17 value18 value19 param \\\n", "0 29.6875 ... 45.3125 17.1875 20.3125 17.1875 17.1875 20.3125 c_inv \n", "1 19.3750 ... 10.6250 14.3750 14.3750 0.6250 14.3750 0.6250 c_inv \n", "2 4.6875 ... 2.1875 7.1875 7.1875 7.1875 7.1875 7.1875 c_inv \n", "\n", " index1 index2 index3 \n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "\n", "[3 rows x 25 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(seq, columns=prob['names']).T\n", "df.columns = ['value' + str(x) for x in list(df.columns) if not str(x) == \"nan\"]\n", "df = df.reset_index(names=['index0'])\n", "df['param'] = varying_parameter\n", "\n", "df['index1'] = np.nan\n", "df['index2'] = np.nan\n", "df['index3'] = np.nan\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- **Transpose the DataFrame**: To match the expected format.\n", "- **Rename Columns**: Prefix columns with `'value'`.\n", "- **Reset Index**: To ensure the parameters are properly indexed.\n", "- **Add Parameter Type**: We specify that these values correspond to `'c_inv'` (investment costs).\n", "- **Add Empty Indices**: Additional indices are added for compatibility with the model's expectations.\n", "\n", "---\n", "## Define Solver Options\n", "\n", "We specify solver options to control the optimization process:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:38:45.180139Z", "start_time": "2024-10-01T08:38:45.177290Z" } }, "outputs": [], "source": [ "solver_options = {\n", " 'solver': 'gurobi',\n", " 'presolve_eps': 5e-9,\n", " 'presolve_fixeps': 7e-10,\n", " 'mipgap': 1e-10,\n", " 'display_eps': 1e-10,\n", " 'omit_zero_rows': 1,\n", " 'omit_zero_cols': 1,\n", " 'show_stats': 0,\n", " 'solver_msg': 0,\n", " 'cplex_options': 'integrality=5e-7',\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- **`'solver': 'gurobi'`**: Specifies that the Gurobi solver should be used.\n", "- Other options fine-tune the solver's precision and output behavior.\n", "\n", "---\n", "## Initialize and Run the Model with Parameter Variations\n", "\n", "### Initialize the EnergyScope Model\n", "\n", "We create an instance of the EnergyScope model using the infrastructure_ch_2050 dataset and the specified solver options:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:38:47.947377Z", "start_time": "2024-10-01T08:38:47.944659Z" } }, "outputs": [], "source": [ "es_infra_ch = Energyscope(model=infrastructure_ch_2050, solver_options=solver_options)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "### Run Multiple Optimizations with Varying Parameters\n", "\n", "We use the `calc_sequence` method to run the model multiple times, each time with a different set of parameters from the Sobol sequence:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:39:32.428310Z", "start_time": "2024-10-01T08:38:49.236644Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gurobi 12.0.2:Run 1 complete.\n", "Gurobi 12.0.2:Run 2 complete.\n", "Gurobi 12.0.2:Run 3 complete.\n", "Gurobi 12.0.2:Run 4 complete.\n", "Gurobi 12.0.2:Run 5 complete.\n", "Gurobi 12.0.2:Run 6 complete.\n", "Gurobi 12.0.2:Run 7 complete.\n", "Gurobi 12.0.2:Run 8 complete.\n", "Gurobi 12.0.2:Run 9 complete.\n", "Gurobi 12.0.2:Run 10 complete.\n", "Gurobi 12.0.2:Run 11 complete.\n", "Gurobi 12.0.2:Run 12 complete.\n", "Gurobi 12.0.2:Run 13 complete.\n", "Gurobi 12.0.2:Run 14 complete.\n", "Gurobi 12.0.2:Run 15 complete.\n", "Gurobi 12.0.2:Run 16 complete.\n", "Gurobi 12.0.2:Run 17 complete.\n", "Gurobi 12.0.2:Run 18 complete.\n", "Gurobi 12.0.2:Run 19 complete.\n", "Gurobi 12.0.2:Run 20 complete.\n" ] } ], "source": [ "results_ch_n = es_infra_ch.calc_sequence(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Post-Process and Analyze Results\n", "\n", "### Post-Process Results" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:39:32.569871Z", "start_time": "2024-10-01T08:39:32.435551Z" } }, "outputs": [], "source": [ "# Postcompute KPIs\n", "results_ch_n = postprocessing(results_ch_n, df_monthly=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- **`df_monthly=True`**: Indicates that monthly data should be included in the post-processing.\n", "\n", "### Extract Annual Results\n", "\n", "We extract the annual results DataFrame for further analysis:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:39:32.686447Z", "start_time": "2024-10-01T08:39:32.682250Z" } }, "outputs": [], "source": [ "df_annual = results_ch_n.postprocessing['df_annual']\n", "df_annual = df_annual.loc[:, ['F_Mult']]\n", "df_annual = df_annual.reset_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- **`F_Mult`**: Represents the flow multipliers or capacities of technologies.\n", "\n", "### Pivot the Results DataFrame\n", "\n", "We pivot the DataFrame to have technologies as columns and runs as rows:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:39:48.252306Z", "start_time": "2024-10-01T08:39:48.228274Z" } }, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "Run", "rawType": "int64", "type": "integer" }, 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"execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_annual = pd.pivot_table(df_annual, values='F_Mult', index=['Run'], columns='level_0')\n", "df_annual.sample(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- This format is suitable for statistical analysis and visualization.\n", "\n", "---\n", "## Visualize Technology Distribution\n", "\n", "### Plot Pairwise Relationships\n", "\n", "We use Seaborn's `pairplot` function to visualize pairwise relationships between the capacities of different technologies:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:39:58.141785Z", "start_time": "2024-10-01T08:39:50.793966Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cols = ['CCGT_CC', 'DEC_COGEN_GAS', 'DEC_HP_ELEC', 'GASIFICATION_H2', 'IND_BOILER_WASTE', 'IND_COGEN_WASTE', 'PV_LV', 'WIND']\n", "display(sns.pairplot(df_annual[cols].fillna(0), diag_kind=\"kde\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- **`cols`**: List of technologies to include in the plot.\n", "- **`fillna(0)`**: Replaces NaN values with zero for plotting purposes.\n", "- **`diag_kind=\"kde\"`**: Uses Kernel Density Estimation for the diagonal plots.\n", "\n", "---\n", "## Semi-Automated Sequence Creation\n", "\n", "In this section, we automate the generation of parameter sequences based on existing model parameters.\n", "\n", "### Initialize the Model\n", "\n", "We initialize the EnergyScope model again:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:40:02.144425Z", "start_time": "2024-10-01T08:40:02.138900Z" } }, "outputs": [], "source": [ "es_infra_ch = Energyscope(model=infrastructure_ch_2050)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "### Run the Base Model\n", "\n", "We perform an initial calculation to obtain baseline parameter values:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:40:06.126120Z", "start_time": "2024-10-01T08:40:03.663758Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gurobi 11.0.0:Gurobi 11.0.0: optimal solution; objective 8151.373244\n", "6079 simplex iteration(s)\n", "1 branching node(s)\n", "absmipgap=0.102405, relmipgap=1.25629e-05\n" ] } ], "source": [ "# Solve the model\n", "results_ch = es_infra_ch.calc()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "### Extract and Adjust Investment Costs\n", "\n", "We extract the investment costs (`c_inv`) from the model parameters and define new bounds for uncertainty analysis:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:40:06.137471Z", "start_time": "2024-10-01T08:40:06.131588Z" } }, "outputs": [], "source": [ "c_inv = results_ch.parameters['c_inv'].drop('Run', axis=1)\n", "c_inv['lower_bound'] = 0.5 * c_inv['c_inv']\n", "c_inv['upper_bound'] = 1.5 * c_inv['c_inv']\n", "c_inv = c_inv.reset_index(names=['name']).drop('c_inv', axis=1)\n", "c_inv = c_inv.loc[c_inv['lower_bound'] != 0, :]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- **Adjust Bounds**: We set the lower and upper bounds to ±50% of the original investment costs.\n", "- **Filter Out Zero Costs**: Remove parameters where the lower bound is zero.\n", "\n", "### Select Key Technologies\n", "\n", "Due to computational limitations, we select a subset of technologies to include in the uncertainty analysis:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:40:08.167868Z", "start_time": "2024-10-01T08:40:08.163958Z" } }, "outputs": [], "source": [ "selected_technologies = ['PV_LV', 'PV_MV', 'PV_HV', 'PV_EHV', 'DEC_HP_ELEC', 'WIND', 'GASIFICATION_H2', 'GASIFICATION_SNG']\n", "c_inv = c_inv.loc[c_inv['name'].isin(selected_technologies), :]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- **`selected_technologies`**: List of technologies chosen for analysis.\n", "\n", "### Convert Parameters to Dictionary Format\n", "\n", "We convert the DataFrame of parameters to a list of dictionaries required by the `gen_sobol_sequence` function:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:40:10.176273Z", "start_time": "2024-10-01T08:40:10.171810Z" } }, "outputs": [], "source": [ "c_inv = c_inv.to_dict(orient='records')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "### Generate Sobol Sequence for Selected Parameters\n", "\n", "We generate a Sobol sequence for the selected parameters:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:40:11.985815Z", "start_time": "2024-10-01T08:40:11.981229Z" } }, "outputs": [], "source": [ "seq, prob = gen_sobol_sequence(parameters=c_inv, trajectories=8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- **`trajectories=8`**: Generates more samples for a more detailed analysis.\n", "\n", "### Display Number of Generated Samples" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:40:13.760537Z", "start_time": "2024-10-01T08:40:13.756312Z" } }, "outputs": [ { "data": { "text/plain": [ "80" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(len(seq))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "### Prepare Parameter DataFrame\n", "\n", "We prepare the DataFrame for the new sequence:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:40:15.525313Z", "start_time": "2024-10-01T08:40:15.509758Z" } }, "outputs": [ { "data": { "text/html": [ "
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index0value0value1value2value3value4value5value6value7value8...value74value75value76value77value78value79paramindex1index2index3
0DEC_HP_ELEC311.985938509.029688311.985938311.985938311.985938311.985938311.985938311.985938311.985938...377.667188377.667188377.667188377.667188377.667188706.073438c_invNaNNaNNaN
1GASIFICATION_H21383.1812501383.181250936.9937501383.1812501383.1812501383.1812501383.1812501383.1812501383.181250...1918.6062501918.6062501918.6062501918.6062501918.6062501829.368750c_invNaNNaNNaN
2GASIFICATION_SNG2838.5440632838.5440632838.5440631739.7528122838.5440632838.5440632838.5440632838.5440632838.544063...1739.7528121739.7528121739.7528121739.7528121739.7528123571.071563c_invNaNNaNNaN
3PV_EHV1503.1250001503.1250001503.1250001503.1250001178.1250001503.1250001503.1250001503.1250001503.125000...1015.6250001340.6250001340.6250001340.6250001340.6250001015.625000c_invNaNNaNNaN
4PV_HV367.187500367.187500367.187500367.187500367.187500543.437500367.187500367.187500367.187500...425.937500249.687500425.937500425.937500425.937500249.687500c_invNaNNaNNaN
5PV_LV954.687500954.687500954.687500954.687500954.687500954.687500751.562500954.687500954.687500...710.937500710.937500832.812500710.937500710.937500832.812500c_invNaNNaNNaN
6PV_MV629.062500629.062500629.062500629.062500629.062500629.062500629.062500514.687500629.062500...400.312500400.312500400.312500590.937500400.312500590.937500c_invNaNNaNNaN
7WIND1969.4268751969.4268751969.4268751969.4268751969.4268751969.4268751969.4268751969.426875778.610625...1419.8193751419.8193751419.8193751419.819375961.813125961.813125c_invNaNNaNNaN
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" ], "text/plain": [ " index0 value0 value1 value2 value3 \\\n", "0 DEC_HP_ELEC 311.985938 509.029688 311.985938 311.985938 \n", "1 GASIFICATION_H2 1383.181250 1383.181250 936.993750 1383.181250 \n", "2 GASIFICATION_SNG 2838.544063 2838.544063 2838.544063 1739.752812 \n", "3 PV_EHV 1503.125000 1503.125000 1503.125000 1503.125000 \n", "4 PV_HV 367.187500 367.187500 367.187500 367.187500 \n", "5 PV_LV 954.687500 954.687500 954.687500 954.687500 \n", "6 PV_MV 629.062500 629.062500 629.062500 629.062500 \n", "7 WIND 1969.426875 1969.426875 1969.426875 1969.426875 \n", "\n", " value4 value5 value6 value7 value8 ... \\\n", "0 311.985938 311.985938 311.985938 311.985938 311.985938 ... \n", "1 1383.181250 1383.181250 1383.181250 1383.181250 1383.181250 ... \n", "2 2838.544063 2838.544063 2838.544063 2838.544063 2838.544063 ... \n", "3 1178.125000 1503.125000 1503.125000 1503.125000 1503.125000 ... \n", "4 367.187500 543.437500 367.187500 367.187500 367.187500 ... \n", "5 954.687500 954.687500 751.562500 954.687500 954.687500 ... \n", "6 629.062500 629.062500 629.062500 514.687500 629.062500 ... \n", "7 1969.426875 1969.426875 1969.426875 1969.426875 778.610625 ... \n", "\n", " value74 value75 value76 value77 value78 \\\n", "0 377.667188 377.667188 377.667188 377.667188 377.667188 \n", "1 1918.606250 1918.606250 1918.606250 1918.606250 1918.606250 \n", "2 1739.752812 1739.752812 1739.752812 1739.752812 1739.752812 \n", "3 1015.625000 1340.625000 1340.625000 1340.625000 1340.625000 \n", "4 425.937500 249.687500 425.937500 425.937500 425.937500 \n", "5 710.937500 710.937500 832.812500 710.937500 710.937500 \n", "6 400.312500 400.312500 400.312500 590.937500 400.312500 \n", "7 1419.819375 1419.819375 1419.819375 1419.819375 961.813125 \n", "\n", " value79 param index1 index2 index3 \n", "0 706.073438 c_inv NaN NaN NaN \n", "1 1829.368750 c_inv NaN NaN NaN \n", "2 3571.071563 c_inv NaN NaN NaN \n", "3 1015.625000 c_inv NaN NaN NaN \n", "4 249.687500 c_inv NaN NaN NaN \n", "5 832.812500 c_inv NaN NaN NaN \n", "6 590.937500 c_inv NaN NaN NaN \n", "7 961.813125 c_inv NaN NaN NaN \n", "\n", "[8 rows x 85 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(seq, columns=prob['names']).T\n", "df.columns = ['value' + str(x) for x in list(df.columns) if not str(x) == \"nan\"]\n", "df = df.reset_index(names=['index0'])\n", "df['param'] = 'c_inv'\n", "\n", "df['index1'] = np.nan\n", "df['index2'] = np.nan\n", "df['index3'] = np.nan\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Run Optimizations with New Parameter Sets\n", "\n", "### Define Solver Options\n", "\n", "We define the solver options again (same as before):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:40:19.418521Z", "start_time": "2024-10-01T08:40:19.413848Z" } }, "outputs": [], "source": [ "solver_options = {\n", " 'solver': 'gurobi',\n", " 'presolve_eps': 5e-9,\n", " 'presolve_fixeps': 7e-10,\n", " 'mipgap': 1e-10,\n", " 'display_eps': 1e-10,\n", " 'omit_zero_rows': 1,\n", " 'omit_zero_cols': 1,\n", " 'show_stats': 0,\n", " 'solver_msg': 0,\n", " 'cplex_options': 'integrality=5e-7',\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "### Initialize the Model with Solver Options" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:40:21.427993Z", "start_time": "2024-10-01T08:40:21.423017Z" } }, "outputs": [], "source": [ "es_infra_ch = Energyscope(model=infrastructure_ch_2050, solver_options=solver_options)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "### Run Multiple Optimizations\n", "\n", "We run the model multiple times with the new parameter sets:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:43:05.905313Z", "start_time": "2024-10-01T08:40:23.220711Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gurobi 11.0.0:Gurobi 11.0.0:1\n", "Gurobi 11.0.0:2\n", "Gurobi 11.0.0:3\n", "Gurobi 11.0.0:4\n", "Gurobi 11.0.0:5\n", "Gurobi 11.0.0:6\n", "Gurobi 11.0.0:7\n", "Gurobi 11.0.0:8\n", "Gurobi 11.0.0:9\n", "Gurobi 11.0.0:10\n", "Gurobi 11.0.0:11\n", "Gurobi 11.0.0:12\n", "Gurobi 11.0.0:13\n", "Gurobi 11.0.0:14\n", "Gurobi 11.0.0:15\n", "Gurobi 11.0.0:16\n", "Gurobi 11.0.0:17\n", "Gurobi 11.0.0:18\n", "Gurobi 11.0.0:19\n", "Gurobi 11.0.0:20\n", "Gurobi 11.0.0:21\n", "Gurobi 11.0.0:22\n", "Gurobi 11.0.0:23\n", "Gurobi 11.0.0:24\n", "Gurobi 11.0.0:25\n", "Gurobi 11.0.0:26\n", "Gurobi 11.0.0:27\n", "Gurobi 11.0.0:28\n", "Gurobi 11.0.0:29\n", "Gurobi 11.0.0:30\n", "Gurobi 11.0.0:31\n", "Gurobi 11.0.0:32\n", "Gurobi 11.0.0:33\n", "Gurobi 11.0.0:34\n", "Gurobi 11.0.0:35\n", "Gurobi 11.0.0:36\n", "Gurobi 11.0.0:37\n", "Gurobi 11.0.0:38\n", "Gurobi 11.0.0:39\n", "Gurobi 11.0.0:40\n", "Gurobi 11.0.0:41\n", "Gurobi 11.0.0:42\n", "Gurobi 11.0.0:43\n", "Gurobi 11.0.0:44\n", "Gurobi 11.0.0:45\n", "Gurobi 11.0.0:46\n", "Gurobi 11.0.0:47\n", "Gurobi 11.0.0:48\n", "Gurobi 11.0.0:49\n", "Gurobi 11.0.0:50\n", "Gurobi 11.0.0:51\n", "Gurobi 11.0.0:52\n", "Gurobi 11.0.0:53\n", "Gurobi 11.0.0:54\n", "Gurobi 11.0.0:55\n", "Gurobi 11.0.0:56\n", "Gurobi 11.0.0:57\n", "Gurobi 11.0.0:58\n", "Gurobi 11.0.0:59\n", "Gurobi 11.0.0:60\n", "Gurobi 11.0.0:61\n", "Gurobi 11.0.0:62\n", "Gurobi 11.0.0:63\n", "Gurobi 11.0.0:64\n", "Gurobi 11.0.0:65\n", "Gurobi 11.0.0:66\n", "Gurobi 11.0.0:67\n", "Gurobi 11.0.0:68\n", "Gurobi 11.0.0:69\n", "Gurobi 11.0.0:70\n", "Gurobi 11.0.0:71\n", "Gurobi 11.0.0:72\n", "Gurobi 11.0.0:73\n", "Gurobi 11.0.0:74\n", "Gurobi 11.0.0:75\n", "Gurobi 11.0.0:76\n", "Gurobi 11.0.0:77\n", "Gurobi 11.0.0:78\n", "Gurobi 11.0.0:79\n", "Gurobi 11.0.0:80\n" ] } ], "source": [ "results_ch_n = es_infra_ch.calc_sequence(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "---\n", "\n", "## Post-Process and Analyze Results\n", "\n", "### Post-Process Results" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:43:08.728421Z", "start_time": "2024-10-01T08:43:08.627614Z" } }, "outputs": [], "source": [ "# Postcompute KPIs\n", "results_ch_n = postprocessing(results_ch_n, df_monthly=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- **`df_monthly=False`**: We are focusing on annual data in this case.\n", "\n", "### Extract and Prepare Annual Results" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:43:10.620012Z", "start_time": "2024-10-01T08:43:10.595412Z" } }, "outputs": [ { "data": { "text/html": [ "
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TRAIN_NG TRAMWAY TRUCK TRUCK_CO2 TRUCK_EV \\\n", "Run ... \n", "32 0.0 ... 0.0 0.000000 0.0 0.0 0.611416 \n", "43 0.0 ... 0.0 2.390099 0.0 0.0 0.611416 \n", "35 0.0 ... 0.0 0.000000 0.0 0.0 0.611416 \n", "72 0.0 ... 0.0 2.390099 0.0 0.0 0.611416 \n", "29 0.0 ... 0.0 0.000000 0.0 0.0 0.611416 \n", "\n", "level_0 TRUCK_FC TRUCK_SNG UNMINEABLE_COAL_SEAM WIND WOOD_METHANOL \n", "Run \n", "32 0.0 0.0 0.0 4.258822 0.0 \n", "43 0.0 0.0 0.0 20.000000 0.0 \n", "35 0.0 0.0 0.0 4.258822 0.0 \n", "72 0.0 0.0 0.0 12.521969 0.0 \n", "29 0.0 0.0 0.0 20.000000 0.0 \n", "\n", "[5 rows x 232 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_annual = results_ch_n.postprocessing['df_annual']\n", "df_annual = df_annual.loc[:, ['F_Mult']]\n", "df_annual = df_annual.reset_index()\n", "\n", "df_annual = pd.pivot_table(df_annual, values='F_Mult', index=['Run'], columns='level_0')\n", "df_annual.sample(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "### Visualize Technology Distribution" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-10-01T08:43:21.147607Z", "start_time": "2024-10-01T08:43:13.698999Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cols = ['CCGT_CC', 'DEC_COGEN_GAS', 'DEC_HP_ELEC', 'GASIFICATION_H2', 'IND_BOILER_WASTE', 'IND_COGEN_WASTE', 'PV_LV', 'WIND']\n", "fig = sns.pairplot(df_annual[cols].fillna(0), diag_kind=\"kde\")\n", "fig.show(renderer=\"notebook\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "- We plot the distributions to understand how the uncertainty in investment costs affects the capacities of different technologies.\n", "\n", "---\n", "\n", "By following these steps, we perform an uncertainty analysis on the EnergyScope model by varying the investment costs of selected technologies. This approach allows us to understand the sensitivity of the energy system configuration to changes in key parameters.\n", "\n", "**Note**: Ensure that all the necessary data files and dependencies are correctly set up in your environment. The computational time may vary depending on the number of optimizations and the complexity of the model." ] } ], "metadata": { "kernelspec": { "display_name": "venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 2 }