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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| Veröffentlicht in: | Proceedings (International Conference on Computer Engineering and Applications. Online) S. 656 - 659 |
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| Sprache: | Englisch |
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IEEE
25.04.2025
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| ISSN: | 2159-1288 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Duan, Junhong Niu, Wei Zhang, Hailong Song, Jun Tu, Chao |
| Author_xml | – sequence: 1 givenname: Junhong surname: Duan fullname: Duan, Junhong email: junhongduan@yeah.net organization: Gansu Electric Power Company of State Grid,Lanzhou,China – sequence: 2 givenname: Jun surname: Song fullname: Song, Jun email: cnsongjun@gs.sgcc.com organization: Gansu Electric Power Company of State Grid,Lanzhou,China – sequence: 3 givenname: Wei surname: Niu fullname: Niu, Wei email: cnniuw@gs.sgcc.com.cn organization: Gansu Electric Power Company of State Grid,Lanzhou,China – sequence: 4 givenname: Hailong surname: Zhang fullname: Zhang, Hailong email: 90002607@gs.sgcc.com.cn organization: Gansu Electric Power Company of State Grid,Lanzhou,China – sequence: 5 givenname: Chao surname: Tu fullname: Tu, Chao email: gansutuchao@126.com organization: Zhangye Power Supply Company of Gansu Electric Power Company of State Grid,Zhangye,China |
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| Snippet | This study proposes a virtual wind tower siting model using a Multi-Mayfly Optimization Algorithm (M-MOA) to address inefficiencies in renewable energy... |
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| SubjectTerms | 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 |
| Title | Virtual Wind Tower Siting Method Based on Multi-Objective Mayfly Optimization Algorithm |
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