Computational method for calculating effective load carrying capability (ELCC) of a solar or wind plant in the U.S. Developed by the University of Michigan Advancing Sustainable Systems through low-impact Energy Technologies (ASSET) Lab.

1. Download this repository via commmand line-

   git clone https://github.com/ijbd/elcc.git

2. Run ELCC calculation from bin via command line

   python elcc_driver.py

3. Change parameters with key-value pairs

   python elcc_driver.py year 2016 region CISO

4. See a full list of parameters in elcc_driver.py

5. For multi-worded parameters use underscores

   python elcc_driver.py conventional_efor .1

6. Changing the root directory will create a unique folder based on the arguments and redirect output to a file

   python elcc_driver.py root_directory testing/ region CISO

7. To add a balancing authority for a simulation. Use Tyler Ruggles' cleaned EIA-860 data from GitHub. Place it in the demand folder with the capitalized abbreviation for that balancing authority

8. To use, ARC-TS launcher to calculate ELCC values synchronously, refer to 9.

9. This manual is incomplete, but I'm happy to help if you're having trouble with anything! Email me at ijbd@umich.edu

elcc_impl.py: Contains the actual ELCC calculation, including file I/O, state sampling Monte Carlo simulation, storage operation, etc.

elcc_driver.py: Contains and fills dictionaries of parameters needed for the elcc calculation. At bare minimum to run an ELCC calculation, this is the script that must be run.

elcc.py: Poorly-named. Used for running a single ELCC calculations with SLURM. For calculations with many iterations (>1000), this is necessary as greatlakes will halt jobs not running on SLURM after a few hours. argv[1] should be a valid output directory.

elcc_single_job.sbat: Contains the SLURM resource allocations associated with the elcc.py

elcc_map_base_cases.py: For generating ELCC maps. The current file is set up for our particular study, so a fair bit of adjustment needs to be done. CAUTION: This script will start off potentially thousands of SLURM jobs at one time incurring a not insignificant charge!

elcc_batch_job.sbat: Contains the SLURM resource allocations associated with the elcc.py. The difference between elcc_batch_job.sbat and elcc_single_job.sbat are the number of tasks per node. Which can be optimized to minimize the charge incurred by SLURM.

extract_results.py: Used for extracting all ELCC values for a batch job (i.e. map). In the future, should automatically be run, but for now it's manual.

NOTE: Running maps of ELCCs is a little messy, but there are a few really important steps that need to be followed. If you need to run an ELCC map with SLURM. Please reach out, and we should schedule a meeting so I can explain it better.

[1] The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Ronald Gelaro, et al., 2017, J. Clim., doi: 10.1175/JCLI-D-16-0758.1

[2] U.S. Energy Information Administration Form EIA-860 (2019)

[3] Tyler Ruggles, & David Farnham. (2019). EIA Cleaned Hourly Electricity Demand Data (Version v1.0_23Oct2019) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3517197

[4] William F. Holmgren, Clifford W. Hansen, and Mark A. Mikofski. “pvlib python: a python package for modeling solar energy systems.” Journal of Open Source Software, 3(29), 884, (2018). https://doi.org/10.21105/joss.00884

[5] Blair, Nate, Nicholas DiOrio, Janine Freeman, Paul Gilman, Steven Janzou, Ty Neises, and Michael Wagner. 2018. System Advisor Model (SAM) General Description (Version 2017.9.5). Golden, CO: National Renewable Energy Laboratory. NREL/ TP-6A20-70414. https://www.nrel.gov/docs/fy18osti/70414.pdf

[6] de Chalendar, Jacques A., John Taggart, and Sally M. Benson. "Tracking emissions in the US electricity system." Proceedings of the National Academy of Sciences 116.51 (2019): 25497-25502.

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