Multi-objective Optimization of Co-located Wave-Wind Farm Layouts

Supporting Codes for the Paper Published in Elsevier's Renewable Energy Journal

This repository contains the open-source codes and tools developed for the study:
"Multi-objective optimization of co-located wave-wind farm layouts supported by genetic algorithms and numerical models."

The research introduces a novel methodology for optimizing Wave Energy Converter (WEC) layouts using genetic algorithms and hydrodynamic numerical models. This repository provides all the necessary scripts, data, and instructions to replicate the study and apply the optimization framework to your projects.

---

🚀 Key Features

• Genetic Algorithms: Framework for optimizing WEC positioning, balancing exploration and computational cost.
• Continuous Domain Optimization: Flexible WEC layouts, improving upon traditional grid-based methodologies.
• K-means Clustering: Efficient sea state classification while preserving 90% of incoming wave energy.
• Performance Metrics: Integrated evaluation of absorbed wave power and wave height reduction.

---

📄 Highlights from the Study

• 87% increase in absorbed wave power compared to non-optimized layouts.
• 46% reduction in wave height, showcasing improved coastal protection.
• Open-source framework for advancing renewable energy farm optimization.

For more details, read the full paper: [Insert Link to Paper]

---

📁 Repository Structure

├── data/                # Example datasets and input files
├── scripts/             # Python scripts for optimization and analysis
│   ├── optimization/    # Genetic algorithm implementation
└── README.md            # Repository documentation