Virtual Wind Tower Siting Method Based on Multi-Objective Mayfly Optimization Algorithm - Duan, Junhong; Song, Jun; Niu, Wei; Zhang, Hailong; Tu, Chao (Proceedings (International Conference on Computer Engineering and Applications. Online), 2025)
This study proposes a virtual wind tower siting model using a Multi-Mayfly Optimization Algorithm (M-MOA) to address inefficiencies in renewable energy planning. By integrating wind speed, geographic constraints (urban areas, roads, landscapes), and turbine layout, candidate sites are screened. The M-MOA optimizes locations through power generation, revenue, and cost objectives. Experiments show the algorithm achieves higher annual profit than benchmark methods (GA, SA, MOA) while avoiding environmentally sensitive zones. Key innovations include population interaction mechanisms and terrain-adaptive cost Results validate the method's effectiveness for sustainable wind energy development.