VY4E-OptModel 1.0.15

🚀 Features

• Modular formulation for multi-vector energy systems

• Compatible with deterministic, stochastic, and equilibrium approaches

• Flexible temporal structure: hours, days, representative periods

• Built on JuMP / Pyomo (depending on module choice)

• Interfaces with VY4E-data (datasets) and VY4E-examples (notebooks)

📂 Structure

• src/: Core source code for the optimization model.

• data/: Sample case studies.

• docs/: Documentation and formulation notes.

• tests/: Validation and regression tests.

📦 Prerequisites

• Python 3.12 or higher.

• A supported solver: Gurobi, CBC, or CPLEX. Make sure the solver is installed and accessible in your system’s PATH.

🚀 Installation

1. Clone the repository:

git clone https://github.com/VY4E-nexus/VY4E-OptModel.git
cd VY4E-OptModel

2. Create and activate a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required Python packages:

pip install -r requirements.txt

Usage

To run the optimization model, use the oM_Main.py script from the src directory.

python src/oM_Main.py --case <case_name> --solver <solver_name>

Command-line Arguments

• --dir: Directory containing the case data (defaults to the current directory).

• --case: Name of the case to run (e.g., Home1).

• --solver: Solver to use (e.g., gurobi, cbc, cplex).

• --date: Model run date in “YYYY-MM-DD HH:MM:SS” format.

• --rawresults: Save raw results (True/False).

• --plots: Generate plots (True/False).

🤝 Contributing

Contributions are welcome! If you want to contribute to VY4E-OptModel, please follow these steps:

1. Fork the repository.

2. Create a new branch for your feature or bug fix.

3. Make your changes and commit them with a clear message.

4. Push your changes to your fork.

5. Create a pull request to the main branch of this repository.

📄 License

This project is licensed under the terms of the GNU General Public License v3.0.