simeasren.h2_techno_eco.LCOF_diff_all

Functions

calculate_all_LCOF_diff(data_sim_meas, location_name, ...)

Calculate the difference in Levelized Cost of Fuel (LCOF) between

Module Contents

simeasren.h2_techno_eco.LCOF_diff_all.calculate_all_LCOF_diff(data_sim_meas, location_name, H2_end_user_min_load, solver_name, technoeco_file_name='Techno_eco_data_NH3')

Calculate the difference in Levelized Cost of Fuel (LCOF) between measured and simulated data for a given location.

This function performs a techno-economic analysis by comparing the measured energy production profile with multiple simulated profiles for the specified location. For each dataset, it computes the LCOF using the solve_optiplant optimization model, stores detailed results and flow data as CSV files, and calculates the relative difference in LCOF between measured and simulated cases.

Parameters:

  • data_sim_meas (pandas.DataFrame) – DataFrame containing both simulated and measured time-series data for one or more locations. Each column name should include the location name, and the measured data column should contain “PV-MEAS”.

  • location_name (str) – Name of the location to analyze (used to filter columns in data_sim_meas).

  • H2_end_user_min_load (float) – Minimum hydrogen end-user load (fraction of nominal load) to be considered in the techno-economic model.

  • solver_name (str) – Name of the optimization solver to use (e.g., “PULP_CBC_CMD”, “GUROBI_CMD”, “CPLEX_CMD”).

  • technoeco_file_name (str, optional) – Base filename (without extension) of the techno-economic data CSV file. Defaults to “Techno_eco_data_NH3”.

Returns:

A list of dictionaries, each containing:

  • ”Location” (str): Name of the analyzed location.

  • ”Tool” (str): Identifier of the simulation tool or dataset.

  • ”LCOF Difference (%)” (float): Relative difference in LCOF between simulated and measured data.

Return type:

list of dict

Raises:

  • FileNotFoundError – If the specified techno-economic CSV file does not exist.

  • ValueError – If no measured data column (containing “PV-MEAS”) is found for the given location.

Notes

The function assumes that the solve_optiplant() function is available in the current environment and returns: (LCOF_value, technoeco_results_df, flow_results_df).

Intermediate results (system costs and hourly flow profiles) are saved automatically in the folders System size and costs and Hourly profiles

Examples

Import and run the function using a CSV with simulated and measured PV data:

>>> from simeasren import calculate_all_LCOF_diff, prepare_pv_data_for_plots
>>> data_sim_meas, _, _ = prepare_pv_data_for_plots("Utrecht", "2017")
>>> results = calculate_all_LCOF_diff(
...     data_sim_meas=data_sim_meas,
...     location_name="Utrecht",
...     H2_end_user_min_load=0.3,
...     solver_name="GUROBI_CMD",
...     technoeco_file_name="Techno_eco_data_NH3"
... )
>>> results[0]
{'Location': 'Utrecht', 'Tool': 'PG2-SARAH2', 'LCOF Difference (%)': -6.3}