Multi-scale offshore wind farm site selection decision framework based on GIS, MCDM and meta-heuristic algorithm

Offshore wind energy has emerged as a significant driving force to establish a sustainable energy system. Nevertheless, the field of wind farm site selection faces challenges regarding the lack of coherent scale of site selection and incomplete consideration of methodologies. To address these issues, this study proposes a multiscale framework and methodology, which combines Geographic Information System (GIS), Multi-Criteria Decision Making (MCDM), and meta-heuristic algorithms. The goal of this study is to determine the optimal scheme, its location and boundary, by integrating large, small, and micro-scale site selection. Within this framework, a combined weighting method based on Group Intuitionistic Fuzzy Analytic Hierarchy Process (GIAHP) and the Method based on the Removal Effects of Criteria (MEREC) is proposed to calculate the criteria weights. Additionally, the improved ORESTE method based on Cumulative Prospect Theory (CPT) is employed to rank the alternatives. Finally, the improved Variable Neighborhood Search (VNS) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms are implemented to design the offshore wind farm and determine its location and boundary. The proposed framework is then applied in the Shandong sea area in China. The results, obtained through sensitivity analysis and comparative analysis, can serve as a foundation for decision makers.