gridops

gridops is an open-source and customizable grid operations model for three major U.S. interconnections (Western, Eastern, and ERCOT). Production cost modeling in gridops utilizes linear DC Optimal Power Flow (DC-OPF) approximation to optimize hourly unit commitment (UC) and economic dispatch (ED) processes over a user-configurable rolling horizon.

Purpose

gridops was created to:

• Address weather and water dynamics and associated vulnerabilities in U.S. bulk power systems.
• Link with a wide range of hydrometeorological datasets and energy system/machine learning models to explore future uncertainty in grid operations.
• Analyze current and future grid stress and reliability trends under a wide range of weather conditions, generation mixes, and demand growth scenarios.
• Create simple and user-tailored representations of the U.S. interconnections to find a balance between model accuracy and run time to answer research questions of interest.

Installation

pip install gridops

Quickstart

import gridops as go

# Instantiate the model
model = go.Model(
    region="west",              # U.S. Western Interconnection
    problem="linear",           # Linear programming (economic dispatch only)
    solver_name="appsi_highs",  # Using open-source HiGHS solver
    solver_params=None,         # Optional – using default solver parameters
)

# Run for 7 days with a 24-hour rolling horizon
model.run(
    config_file="example_config_file.yml",  # Path to the model configuration file
    n_days=7,                               # Running for 7 days
    horizon_hours=24,                       # Optional – using default 24-hour horizon
)

Configuration file

gridops needs a YAML file that maps each input CSV and NPY files to their file path and each output Parquet to its destination. Check out below for a minimal example (see gridops/data/example_config_file.yml for a full template):

generator_parameters_file: Input/data_genparams.csv
line_parameters_file: Input/line_param.csv
# … all other input entries …
flow_file: Output/flow.parquet
mwh_file: Output/mwh.parquet
# … all other output entries …
restart_file_directory: Restart/

Supported solvers

Users can pass any desired solver parameters via the solver_params:

model = go.Model(
    solver_name="appsi_highs",
    solver_params={"presolve": "on", "solver": "ipm", "time_limit": 3600},
    ...
)

License

BSD-2-Clause – See LICENSE.

Disclaimer

See DISCLAIMER.

Reference

If you use gridops, please cite the following article:

Akdemir, K. Z., Oikonomou, K., Kern, J. D., Voisin, N., Ssembatya, H., & Qian, J. (2024). An open-source framework for balancing computational speed and fidelity in production cost models. Environmental Research: Energy, 1(1), 015003. https://doi.org/10.1088/2753-3751/ad1751