Run EPM with Python - Summary

First, navigate to the epm directory.

Example: Specify the input folder, enable sensitivity analysis, and use 4 CPU cores for parallel execution.

python epm.py --folder_input data_test_region --sensitivity --cpu 4

Other command-line options allow for further customization of the simulation run, such as specifying scenarios, enabling Monte Carlo analysis, and more.

General Configuration

Argument	Type	Default	Description	Example Usage
--config	string	input/config.csv	Path to the global configuration CSV file	--config input/my_config.csv
--folder_input	string	data_test	Folder containing input data files	--folder_input data_test_region

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Execution Control

Argument	Type	Default	Description	Example Usage
--simple	list[str]	[‘DiscreteCap’, ‘y’]	List of simplified parameters. DiscreteCap remove discrete constraint, yonly runs for first and last year.	--simple DiscreteCap y
--cpu	integer	1	Number of CPU cores to use	--cpu 4

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Postprocessing Options

Argument	Type	Default	Description	Example Usage
--postprocess	string	None	Only run postprocessing on specified output folder	--postprocess simulations_run_2024-10-01
--plot_selected_scenarios	list[str]	"all"	Select scenarios to plot	--plot_selected_scenarios baseline
--no_plot_dispatch	flag	False	Disable automatic dispatch plots	--no_plot_dispatch
--graphs_folder	string	img	Folder to save postprocessing graphs	--graphs_folder figures
--reduced_output	flag	False	Enable reduced output mode	--reduced_output

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Scenario & Sensitivity Analysis

Argument	Type	Default	Description	Example Usage
--scenarios	string	None	CSV file defining multiple scenarios	--scenarios input/scenarios.csv
--selected_scenarios	list[str]	None	List of scenario names to run	--selected_scenarios baseline HighDemand
--sensitivity	flag	False	Enable sensitivity analysis	--sensitivity
--project_assessment	list[str]	None	Project(s) to exclude in counterfactual analysis	--project_assessment SolarProject

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Monte Carlo Settings

Argument	Type	Default	Description	Example Usage
--montecarlo	flag	False	Enable Monte Carlo uncertainty analysis	--montecarlo
--montecarlo_samples	integer	10	Number of samples to generate	--montecarlo_samples 20
--uncertainties	string	None	CSV file defining uncertainty parameters	--uncertainties input/uncertainties.csv