{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Choose datasets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Import Necessary Libraries and Modules\n",
"\n",
"First, we import the essential libraries and modules required for this tutorial:\n",
"\n",
"- **`pandas` as `pd`**: A powerful data manipulation library.\n",
"- **`Energyscope` from `energyscope.energyscope`**: The main class to initialize and run the EnergyScope model.\n",
"- **`infrastructure_qc_2020` from `energyscope.models`**: Predefined models for different regions or configurations.\n",
"- **`postprocessing` from `energyscope.result`**: Functions and classes for handling and processing results.\n",
"- **`plot_sankey` from `energyscope.plots`**: Functions to create Sankey diagrams for visualizing energy flows.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from energyscope.energyscope import Energyscope\n",
"from energyscope.models import infrastructure_qc_2020\n",
"from energyscope.result import postprocessing\n",
"from energyscope.plots import plot_sankey"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"---\n",
"## Define Solver Options\n",
"\n",
"The solver options determine how the optimization solver behaves during the calculation. In this section, we demonstrate two methods to specify these options:\n",
"### Directly in the Code \n",
"You can define the solver options directly within your script by creating a dictionary.\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"solver_options = {\n",
" 'solver': 'cplex',\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': 1,\n",
" 'solver_msg': 0,\n",
" 'cplex_options': 'integrality=5e-7'\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
" - **Note**: These options are specific to the CPLEX solver and control aspects like presolve tolerances, MIP gap, and output display.\n",
"### Loading from a CSV File \n",
"Alternatively, you can load the solver options from a CSV file, which can be convenient for managing configurations externally.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"solver_options = pd.read_csv('tutorial_input/solver_options_infrastructure_qc_2020.csv')\n",
"solver_options = dict(zip(solver_options['Option'], solver_options['Value'])) # Reformat dict"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
" - Here, we read the CSV file into a Pandas DataFrame and then convert it into a dictionary that can be used by the model.\n",
"\n",
"After loading the solver options, you can inspect them to ensure they have been set correctly:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'solver': 'cplex',\n",
" 'presolve_eps': '5.00E-09',\n",
" 'presolve_fixeps': '7.00E-10',\n",
" 'mipgap': '1.00E-10',\n",
" 'display_eps': '1.00E-10',\n",
" 'omit_zero_rows': '1',\n",
" 'omit_zero_cols': '1',\n",
" 'show_stats': '1',\n",
" 'solver_msg': '0',\n",
" 'cplex_options': 'integrality=5e-7'}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"solver_options"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Initialize and Run the Model with Specific Dataset and Solver Options\n",
"\n",
"In this section, we initialize the EnergyScope model using the **`infrastructure_qc_2020`** dataset and apply the custom solver options defined earlier.\n",
"### Initialize the Model \n",
"Create an instance of the `Energyscope` class with the `infrastructure_qc_2020` model and the specified solver options."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"es_infra_qc = Energyscope(model=infrastructure_qc_2020, solver_options=solver_options)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
" - **Model**: The `infrastructure_qc_2020` model represents the energy system of Quebec with monthly resolution.\n",
" - **Solver Options**: The `solver_options` dictionary configures the solver's behavior during optimization.\n",
"### Run the Optimization \n",
"Execute the calculation to solve the optimization problem."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Presolve eliminates 14467 constraints and 10653 variables.\n",
"Adjusted problem:\n",
"15812 variables:\n",
"\t814 binary variables\n",
"\t14998 linear variables\n",
"21545 constraints, all linear; 108336 nonzeros\n",
"\t9834 equality constraints\n",
"\t11711 inequality constraints\n",
"1 linear objective; 1 nonzero.\n",
"\n",
"Setting $presolve_fixeps < 3.39e-10 or >= 5.5e-09\n",
"could change presolve results.\n",
"\n",
"CPLEX 22.1.1.0: integrality=5e-7\n",
"\n",
"\"option abs_boundtol 5.229594535194337e-12;\"\n",
"will change deduced dual values.\n",
"\n"
]
}
],
"source": [
"results_qc = es_infra_qc.calc()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
" - This step runs the solver (e.g., CPLEX) with the specified options and computes the optimal energy system configuration.\n",
" - **Output**: You may see console outputs indicating the solver's progress and any warnings or errors.\n",
"### Post-Process the Results \n",
"After obtaining the raw results, we apply post-processing to compute Key Performance Indicators (KPIs) and prepare the data for visualization."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"results_qc = postprocessing(results_qc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
" - The `postprocessing` function enriches the results with additional analysis, making them ready for further interpretation and plotting.\n",
"\n",
"---\n",
"## Visualize Results with a Sankey Diagram\n",
"\n",
"A Sankey diagram is an effective tool for visualizing energy flows within the system, showing how energy sources are converted and consumed.\n",
"### Generate the Sankey Diagram \n",
"Use the `plot_sankey` function with the processed results to create the diagram."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"fig = plot_sankey(results_qc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
" - This function processes the results and generates a Sankey diagram object.\n",
"### Display the Diagram \n",
"Render the Sankey diagram within the notebook for immediate visualization."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig.show(renderer=\"notebook\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
" - The diagram will display in the output cell, providing a visual representation of the energy flows in the optimized system.\n",
"\n",
" - **Optional**: You can save the diagram as an HTML file or an image for external use by uncommenting the following lines:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# fig.write_html(\"tutorial_output/sankey_infrastructure_qc_2020.html\")\n",
"# fig.write_image('tutorial_output/sankey_infrastructure_qc_2020.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"---\n",
"\n",
"By following these steps, you have:\n",
"\n",
"- **Imported** the necessary libraries and modules.\n",
"- **Defined** solver options either directly or by loading from a CSV file.\n",
"- **Initialized** and **ran** the EnergyScope model using the `infrastructure_qc_2020` dataset with custom solver configurations.\n",
"- **Post-processed** the results to compute KPIs and prepare for visualization.\n",
"- **Visualized** the energy flows using a Sankey diagram.\n",
"\n",
"This tutorial demonstrates how to customize solver settings and work with different datasets, enabling you to perform more advanced analyses tailored to specific regions or scenarios."
]
}
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