Multi-Criteria Algorithm for Optimal Site Selection of Floating Offshore Wind Farms and Maintenance Route Design Using TSP
Description
In this study, we propose an integrated framework that leverages GIS, multi-criteria decision-making, and route optimization techniques to identify the most promising deployment sites. Our algorithm systematically evaluates technical, environmental, logistical, and economic parameters—such as wind speed, water depth, marine restrictions, and port proximity—to determine areas with high energy potential. Experimental results from diverse Spanish coastal sectors reveal notable variability in wind power density and operational logistics. In certain regions, elevated wind power densities compensate for smaller installation areas, while expansive zones with moderate resources can yield comparable total output.
Files
Steps to reproduce
The experiment folder contains four subfolders: North, Northeast, South, and Southeast. Each subfolder includes two experiments, and each experiment comes with all the necessary files to rerun the test. These experiments must be executed in a Python environment using Jupyter Notebook. The files are as follows: 1. **MainProgram1.ipynb** – The primary file for the geographic selection of wind turbines. Running this file generates a CSV file with information on each wind turbine. 2. **SimuladorTSP.ipynb** – This file simulates the optimal route between the turbines and the nearest port. 3. **EnergiaEolicaPotencial.ipynb** – This file calculates the potential wind energy based on the CSV file produced in step 1. Each subfolder also contains the necessary auxiliary files: - **Geological files:** - *Geological faults - Pre-Quaternary - Faults.csv* - *World port list - world_port_list.csv* - *Marine protected areas - Areas_Marinas_Protegidas.shp* (and related files) - **Wind and fatigue files:** - *area_Norte1_capacity-factor_IEC1.geotif* - *area_Norte1_wind-speed_100m.geotif*
Institutions
- Colegio Universitario de Estudios Financieros