Virtual Wind Tower Siting Method Based on Multi-Objective Mayfly Optimization Algorithm

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...

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Bibliographic Details
Published in:Proceedings (International Conference on Computer Engineering and Applications. Online) pp. 656 - 659
Main Authors: Duan, Junhong, Song, Jun, Niu, Wei, Zhang, Hailong, Tu, Chao
Format: Conference Proceeding
Language:English
Published: IEEE 25.04.2025
Subjects:
Costs
Genetic algorithms
Loss measurement
Multi-Mayfly optimization algorithm
Optimization
Planning
Poles and towers
Renewable energy
Renewable energy sources
Site selection
Urban areas
Wind energy
Wind speed
Wind tower
ISSN:2159-1288
Online Access:Get full text
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Summary: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.
ISSN:2159-1288
DOI:10.1109/ICCEA65460.2025.11103183