TemoaProject: Energy Storage Options for North Carolina

This zenodo archive includes the Temoa code and data used to produce Energy Storage Options for North Carolina. Temoa was used to evaluate the use of storage for bulk energy time shifting and peak capacity deferral, as described in Section 6.5.

The data files are split into the capacity expansion and operational versions of the data. To begin, the capacity expansion runs are conducted to estimate the 2030 electricity generation mix in North Carolina. The optimal installed capacity is then fixed in the operational model, and the operational model is run to determine how storage with a fixed installed capacity and duration affects the objective function value. The change in objective function value, corresponding to the change in total system cost, is used to assess the net benefit associated with using storage for bulk energy time shifting and peak capacity deferral. (See report for more details.)

The primary difference between the capacity expansion and operational versions of the model are the number of time slices. In the capacity expansion model, electricity supply and demand is balanced over a representative 24-hour period for each of the four seasons, for a total of 96 time slices per year. The operational model includes 8760 time slices per year, thereby representing every hour of the year, which allows us to better capture the value of storage.

In terms of workflow, the capacity expansion runs were conducted with an input sqlite database file. Here we include the raw text-based sql file. It can be converted to a binary sqlite database file with the freely available sqlite program: https://www.sqlite.org. The results from the capacity expansion runs were automatically stored in the output tables associated with the same sqlite database. To run the operational version, capacity results are extracted from the capacity expansion sqlite file, and added to an input DAT file. These input DAT files are text-based files located in the scenario-specific folders for the operational runs. (Note that Temoa can run with either a sqlite or DAT file specified as input.)

Each scenario folder for both the capacity expansion and operational runs also contain the config file used to run the model. Each model run was executed from the command line with the following syntax:

$ python temoa_model/ --config=temoa_model/<config_file_name>

Note that the specific instance of the Temoa source code we ran corresponds to git commit hash 06edf7b883cee44ebd1129803f2145687c46bea0.
We verified the model code works with Pyomo versions 5.3 and 5.5.