el1xr_opt 1.0.13

An open-source optimization model for the design and operation of hybrid renewable energy systems with automatic solver setup

Electricity for Low-carbon Integration and eXchange of Resources (EL1XR)

el1xr_opt is the core optimisation engine of the EL1XR-dev ecosystem. It provides a powerful and flexible modelling framework for designing and analysing integrated, zero-carbon energy systems, with support for electricity, heat, hydrogen, and energy storage technologies.

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🚀 Features

• Documentation via ReadTheDocs.

• Modular formulation for multi-vector energy systems

• Compatible with deterministic, stochastic, and equilibrium approaches

• Flexible temporal structure: hours, days, representative periods

• Built on Pyomo

• Interfaces with EL1XR-data (datasets) and EL1XR-examples (notebooks)

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📂 Structure

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

• data/: Sample case studies.

• docs/: Documentation and formulation notes.

• tests/: Validation and regression tests.

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📦 Prerequisites

• Python 3.11 or higher.

• A supported solver: HiGHS, Gurobi, CBC, or CPLEX. The recommended solvers can be installed automatically using the command below.

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🚀 Installation

There are two ways to install el1xr_opt:

Option 1: Install from PyPI (Recommended)

1. Install the package from PyPI:

pip install el1xr_opt

2. Install the required solvers:

el1xr-install-solvers

Option 2: Install from Source (for Developers)

If you want to work with the latest development version or contribute to the project, you can install it from the source:

1. Clone the repository:

git clone https://github.com/EL1XR-dev/el1xr_opt.git
cd el1xr_opt

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 package in editable mode, which also installs the necessary dependencies:

pip install -e .

4. Install the required solvers:

el1xr-install-solvers

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⚡ Quick Example

Run the included Home1 example case with the following command from the root directory:

el1xr-run --case Home1 --solver highs

This will run the optimisation and save the results in the src/el1xr_opt/Home1/Results directory.

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Usage

To run the optimisation model, use the el1xr-run command. If you run the script without arguments, it will prompt you for them interactively. Moreover, the model can be executed with explicit information as follows:

python -m el1xr_opt --dir <folder_parent_case> --case <case_folder_name> --solver  <solver_name> --date <date_string> --rawresults <'Yes'-or-'No'> --plots <'Yes'-or-'No'>

For example:

python -m el1xr_opt --dir data --case Home1 --solver highs --date "2025-09-30 20:26:00" --rawresults No --plots No

Command-line Arguments

• --dir: Directory containing the case data. For the sample cases, this would be src/el1xr_opt.

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

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

• --date: Model run date in “YYYY-MM-DD HH:MM:SS” format. Defaults to the current time.

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

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

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🤝 Contributing

Contributions are welcome! If you want to contribute to el1xr_opt, 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.

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📄 License

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