Quickstart

1. Set up the environment

git clone https://github.com/DeltaE/storage-in-OSeMOSYS.git
cd storage-in-OSeMOSYS

make setup                       # creates the conda env and installs dependencies
conda activate storage_osemosys
make test-env                    # checks Python packages and the GLPK solver

Full details and manual setup: Environment Setup Guide.

2. Choose a scenario

Edit config/config.yaml:

scenario_name: k4h1WND        # label used for result files
resource: WND                 # WND | SOL | HYD
n_clusters: 4                 # number of representative days k
hour_grouping: 1              # hours per intra-day block h (1, 2, 4, 6, 8 or 12)
random_state: 0               # k-means seed used in the paper

data_8760:
  CF: CapacityFactor_Wind.csv # match the resource: _Wind, _Solar or _Hydro

All parameters are described in Configuration Guide.

3. Run

python workflow/main.py      # or: make run-main

The pipeline clusters the hourly data into representative days, writes the inputs for each storage formulation, solves each model with GLPK and collects the results. The steps are explained in Workflow Guide.

4. Look at the results

Results are written to results/. See Outputs for the file layout, or Reproducibility guide to regenerate the published study.