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EGRET: Electrical Grid Research and Engineering Tools

repository·main·Indexed 41 days ago

https://github.com/grid-parity-exchange/egret

A Python-based optimization framework for electrical grid modeling built on Pyomo. EGRET supports solving Unit Commitment and Economic Dispatch problems, including DCOPF and ACOPF, and provides a library of problem formulations and approximations for high-level analysis and research.

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What's inside EGRET

  1. EGRET Overview

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    EGRET is a Python-based package for electrical grid optimization built on the Pyomo optimization modeling language. It is designed for both high-level analysis (serving as an engine for various optimization formulations) and rapid research into new formulations.

    Key Capabilities:

    • Unit-Commitment: Solving unit-commitment problems.
    • Economic Dispatch: Solving optimal power flow problems, including DCOPF (Direct Current Optimal Power Flow) and ACOPF (Alternating Current Optimal Power Flow).
    • Formulation Library: Access to various problem formulations and approximations.
    • Data Handling: Generic handling of data across different model formulations.
    • Declarative Modeling: Supports declarative model representation for easier formulation development.

  2. Test the EGRET installation

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    To verify that the unit commitment functionality is working correctly, run the following command from the models/tests sub-directory of the EGRET repository:

    Note on Solver Behavior:

    • If a commercial MIP solver (like Gurobi or CPLEX) is found by Pyomo, EGRET will run a full test suite including solving several MIPs to optimality.
    • If only open-source solvers are found, EGRET will run a limited test suite primarily focused on solving LP relaxations.
    pytest test_unit_commitment.py

  3. EGRET System Requirements

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    To run EGRET, ensure your environment meets the following specifications:

    Core Dependencies:

    • Python: version 3.7 or later.
    • Pyomo: version 6.4.0 or later.
    • Testing: pytest.

    Optimization Solvers: EGRET requires solvers configured via Pyomo. Requirements vary by model type:

    • MIP-based problems (e.g., Unit Commitment): Tested with Gurobi or CPLEX. It is also recommended to install the open-source CBC solver.
    • NLP problems: Tested with Ipopt (ideally with HSL linear solvers).

    Installing CBC (via Conda): On Linux and Mac platforms using Anaconda, you can install the CBC solver using:

    conda install -c conda-forge coincbc

  4. Install EGRET via pip

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    EGRET is installed as a Python package. To install it in editable mode, navigate to the main EGRET directory (the folder containing setup.py) in your terminal or Anaconda prompt and run the following command:

    Prerequisites:

    • A Python installation (Anaconda is recommended).
    • Pyomo installed.
    • Relevant optimization solvers installed (see Requirements).
    pip install -e .