epnlink 0.0.7

Python interface to EnergyPLAN - Advanced energy system analysis computer model

epnlink

Python interface to EnergyPLAN - Advanced energy system analysis computer model.

Description

This project provides python input-output parsers for the EnergyPLAN energy system modelling software, with data managed using pandas dataframes. It includes a descriptive key:value map of the GUI fields. It is intended for simulation automation using command line calls, and is confirmed to work correctly with EnergyPLAN 16.2. running under WINE.

Functionality

• Map GUI fields to configuration key:value pairs
• Read and write EnergyPLAN simulation input files
• Read and parse EnergyPLAN ascii output files

Installation

Install from pypi with pip install epnlink

Usage

Please refer to the docstrings in the code for detailed usage. While this library can be used to fully specify simulations, the recommended workflow is to build base simulations using the GUI and leverage this library for sensitivity testing or optimisation.

Quick-start example:

import epnlink as ep

# Create a template file
df_tmp = ep.utilities.get_ep_template()

# Initialise the template to zeros
df_tmp = ep.utilities.initialise_settings(df_tmp)

# Or, read an existing file that was saved by EnergyPLAN
fpath_raw = "/home/EnergyPLAN/input.txt"
df = ep.utilities.load_settings_file(fpath_raw, raw=True)

# Modify the capacity of renewable generation technology 1
df.loc['input_RES1_capacity', 'value'] = 42

# Save the modified file
fpath_mod = "/home/EnergyPLAN/input-mod.txt"
df_out = df.dropna(subset='value')
ep.utilities.save_settings_file(df, fpath_mod)

# Readback the modified file as a demonstration - notice that raw=False
df_mod = ep.utilities.load_settings_file(fpath_mod, raw=False)

# < RUN A SIMULATION VIA CLI - e.g., subprocess.run() >
# "energyPLAN.exe -i input-mod.txt -ascii results.ascii"

# Parse the simulation results to dict of dataframes
fpath_results = "/home/EnergyPLAN/results.ascii"
results = ep.results.read_ascii_file(fpath_results)

# Examine annual fuel balance dataframe
df_fb = results['annual_fuel_balance']

Working with EnergyPLAN input .txt files

EnergyPLAN input files are in .txt format. If the files are produced directly from EnergyPLAN, they will be encoded as cp1252. If they have been saved by this library they will use utf-8 encoding. EnergyPLAN can work with either format, but it defaults to cp1252.

Creating input files

Create a template input file with df = ep.utilities.get_ep_template() and initialise it with df = ep.utilities.initialise_settings(df). The template contains the descriptive mapping between GUI fields and parameter key:value pairs.

Reading input files

Load an existing EnergyPLAN input file with ep.utilities.load_settings_file(fpath, raw=True). If the file has been produced by this library, set raw=False. If in doubt - try each setting!

Writing input files

Save an input file for EnergyPLAN with ep.utilities.save_settings_file(df, fpath), where df contains the EnergyPLAN settings, and fpath is the absolute or relative path to save the file.

Working with EnergyPLAN output .ascii files

This library parses the .ascii files that EnergyPLAN outputs when called from the command line interface. When used from the GUI, EnergyPLAN can also output files to the screen as tabulated plaintext or to the clipboard for pasting into spreadsheet software - these workflows are not supported by epnlink.

Parsing .ascii results

Parse the results into a dict of dataframes with ep.results.read_ascii_file(fpath) containing:

Results key	Contents
annual_costs	Costs - total, variable, and components
annual_emissions	Emissions - SO2, PM2.5, NOx, CH4, N2O
annual_emissions_co2	CO2 emissions
annual_fuel_balance	Fuel balance across applications
annual_fuel_consumption	Fuel consumption – total and households
annual_investment_costs	Investment costs – Total, Annual, and Fixed O&M
annual_share_of_res	Share of renewable energy – PES, Electricity, Total production
application_annual_avg_max_min_mw	Application demand - Average, Max, and Min
application_annual_total_twh	Application demand – Total TWh
application_hourlies_mw	Application hourly demand
application_monthly_avg_mw	Application monthly averages
calculation_time	Simulation calculation time (invalid)
sim_parameters	Copy of the simulation input settings

Citation

If you find this library useful in your work, we would appreciate it if you cite the following paper:

C. Cameron, A. Foley, H. Lund, and S. McLoone, ‘A soft-linking method for responsive modelling of decarbonisation scenario costs’, International Journal of Sustainable Energy Planning and Management, 2024, https://doi.org/10.54337/ijsepm.8234

Bibtex:

@article{cameron_2024,
	title = {A soft-linking method for responsive modelling of decarbonisation scenario costs},
	doi = {10.54337/ijsepm.8234},
	author = {Cameron, Ché and Foley, Aoife and Lund, Henrik and McLoone, Seán},
}