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Introduction

This repository is part of a broader research focused on electricity system modeling and analysis using a GAMS model. The primary focus of this project is on the management of net load variations in the electricity system. Most scripts herein have been used at some point (and in some version) for the following publications:

• Lehtveer et al. (2021), Actuating the European Energy System Transition: Indicators for Translating Energy Systems Modelling Results into Policy-Making
• Ullmark et al. (2021), Inclusion of frequency control constraints in energy system investment modeling
• Licentiate thesis by J. Ullmark (2022), Optimization modeling of frequency reserves and inertia in the transition to a climate-neutral electricity system
• Ullmark et al. (2023), Frequency reserves and inertia in the transition to future electricity systems
• Ullmark et al. (2024), Potential Revenue from Reserve Market Participation in Wind Power- and Solar Power-Dominated Electricity Grids: The Near-Term, Mid-Term, and Long-Term
• Ullmark et al., "Representing net load variability in electricity system capacity expansion models - Accounting for challenging weather-years", submitted for publication
• Doctoral thesis by J. Ullmark (2024)

Repository Contents

This repository includes several scripts primarily written in Python and Julia, that are used for various stages of data processing, model running, and visualization. The key scripts are:

Set Building

1. fingerprintmatching.jl
2. year_selection.py
3. profile_analysis.py
4. figure_CFD.py

These scripts are used for building representative sets of typical and particularly challenging net-load events. This is done by running the year_selection script first, followed by fingerprintmatching script (with a start-up parameter of max_time=1 min), and then year_selection script again. After these, fingerprintmatching is run again with a longer max_time option (60-120 mins).

Both year_selection.py and fingerprintmatching.jl prompt the user for a 'ref#' which refers to a sheet with installed capacity of each technology in each region from input/cap_ref.xlsx. The script year_selection.py loads functions from profile_analysis.py and figure_CFD.py, and executes them in a that generates .mat files for as well as figures for visual analysis. When ran for a 'ref#' that already has been fingerprintmatched, figure_CFD will create combination-specific figures and create files containing the best 25/50/100 combinations. fingerprintmatching.jl, in turn, can read these files and optimize for only the best 25/50/100 combinations by entering 'i50' (or one of the other numbers) after starting the script.

Data Processing

5. gdx_processing.py
6. gdx_processing_functions.py
7. get_from_gams_db.py

These scripts are used for reading the GAMS output in gdx format and turning it into .pickle files for further processing and analysis. This is done by setting up variables in, and running, gdx_processing.py. gdx_processing_functions.py loads variables/parameters/sets from the gdxes specified in gdx_processing.py and, possibly, calculates new values that will be included in the .pickle file output. To add new data to the .pickle files, add their collection in gdx_processing_functions.py.

Visualization Several other scripts in this repository are used for generating figures out of the model results stored in the .pickle files created by 5.

Dependencies

The Data Processing scripts use the gdx-reader package developed by Joel Goop and are run from an Anaconda environment exported to environment_pygams.yml. All other scripts are ran in an environment with a more recent version of Python, found in environment_py311.yml

To install these environments in the Anaconda command prompt, this command should do the trick:

conda env create -f environment[..].yml

Note that you may have to manually install the GAMS API package by running:

python "C:\GAMS\37\apifiles\Python\api_36\setup.py" install

If the environment packages provided do not work then you may find more success using the incomplete_pygams.yml file or following the instructions https://github.com/joelgoop/gdx-reader, then manually installing the missing packages using pip install package_name_1 in the Anaconda environment.

Input

Input for running these scripts is either provided in the /input folder or .gdx files generated from the Multinode GAMS model.

Contact

If you have questions or need further clarification, feel free to reach out to Jonathan Ullmark at jonathan[dot][email protected] (obfuscated to prevent spam).

License

This repository is licensed under the Creative Commons Attribution 4.0 International license (CC BY 4.0). You are free to share and adapt the material for any purpose, even commercially, as long as you give appropriate credit, provide a link to the license, and indicate if changes were made.