optiwindnet 0.1.2

Tool for designing and optimizing the electrical cable network of offshore wind farms

OptiWindNet

Tool for designing and optimizing the electrical cable network of offshore wind farms.

Documentation: optiwindnet.readthedocs.io
Quickstart Download the Jupyter notebooks used in the documentation. Report an issue (at GitHub mirror) API Reference How to Cite

Installation

pip install optiwindnet

or

conda install optiwindnet

Detailed instructions in Installation.

Requirements

Python version 3.11 or 3.12 is recommended to run OptiWindNet. Python 3.13+ may cause issues with the optiwindnet.db module, but all other features work fine. The last version to support Python 3.10 was v0.1.0.

The use of a Python virtual environment is recommended. OptiWindNet's dependencies (except for MILP solvers, see docs) will be installed automatically when using pip install optiwindnet or conda install optiwindnet.

One may optionally pre-install the dependencies in a python environment by using either:

• requirements.txt: pip install -r requirements.txt
• environment.yml: conda env create -f environment.yml (name: optiwindnet_env)

Paper

The methodology implemented in OptiWindNet is described in the peer-reviewed scientific article:

• Mauricio Souza de Alencar, Tuhfe Göçmen, Nicolaos A. Cutululis, Flexible cable routing framework for wind farm collection system optimization, European Journal of Operational Research, 2025, ISSN 0377-2217, https://doi.org/10.1016/j.ejor.2025.07.069.

@article{
    SOUZADEALENCAR2025,
    title = {Flexible cable routing framework for wind farm collection system optimization},
    journal = {European Journal of Operational Research},
    year = {2025},
    issn = {0377-2217},
    doi = {https://doi.org/10.1016/j.ejor.2025.07.069},
    url = {https://www.sciencedirect.com/science/article/pii/S0377221725005946},
    author = {Mauricio {Souza de Alencar} and Tuhfe Göçmen and Nicolaos A. Cutululis},
    keywords = {Combinatorial optimization, Network design, Collection system, Wind farm},
}

Acknowledgements

The heuristics implemented in this repository (release 0.0.1) are presented and analyzed in the MSc thesis Optimization heuristics for offshore wind power plant collection systems design (DTU Wind - Technical University of Denmark, July 4, 2022)

The meta-heuristic used is vidalt/HGS-CVRP: Modern implementation of the hybrid genetic search (HGS) algorithm specialized to the capacitated vehicle routing problem (CVRP). This code also includes an additional neighborhood called SWAP*. via its Python bindings chkwon/PyHygese: A Python wrapper for the Hybrid Genetic Search algorithm for Capacitated Vehicle Routing Problems (HGS-CVRP).

The cable routing relies on a navigation mesh generated by the library artem-ogre/CDT: Constrained Delaunay Triangulation (C++) via its Python bindings - artem-ogre/PythonCDT: Constrained Delaunay Triangulation (Python).