A Novel Data-Driven Tool Based on Non-Linear Optimization for Offshore Wind Farm Siting

Abstract

One preliminary key step for developing an offshore wind farm is identifying favorable sites. The process of sitting involves multiple requirements and constraints, and therefore, its feasible implementation requires either approximating assumptions or an optimization method that is capable of handling non-linear relationships and heterogeneous factors. A new optimization method is proposed to address this problem that efficiently and accurately combines essential technical criteria, such as wind speed, water depth, and distance from shore, to identify favorable areas for offshore wind farm development through a user-friendly data-driven tool. Appropriate ranks and weighting factors are carefully selected to obtain realistic results. The proposed methodology is applied in the central Aegean Sea, which has a high offshore wind energy potential. The application of the proposed optimization method reveals large areas suitable for developing floating wind energy structures. The algorithm matches the accuracy of the exhaustive search method. It, therefore, produces the optimum outcome, however, at a lower computational expense demonstrating the proposed method’s potential for larger spatial-scale analysis and use as a decision support tool.

Keywords

• sequential Monte Carlo method
• simulation
• K-means clustering
• floating installations
• site selection
• Aegean Sea