Improved Multi-Objective Whale Optimization for Day-Ahead Scheduling of Wind-Photovoltaic-Water-Storage Multi-energy Complements
This study proposes an improved whale optimization algorithm. The algorithm has four improvement strategies: chaotic mapping, nonlinear convergence factor, adaptive crossover strategy and reverse learning strategy. The improved augmented whale algorithm and fast elite genetic algorithm are also comb...
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| Vydáno v: | Asia Conference on Power and Electrical Engineering (Online) s. 303 - 307 |
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| Hlavní autoři: | , , , , , , , |
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| Jazyk: | angličtina |
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IEEE
15.04.2025
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| ISSN: | 2996-2951 |
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| Abstract | This study proposes an improved whale optimization algorithm. The algorithm has four improvement strategies: chaotic mapping, nonlinear convergence factor, adaptive crossover strategy and reverse learning strategy. The improved augmented whale algorithm and fast elite genetic algorithm are also combined to construct a multi-objective whale optimization algorithm. Using this algorithm, a day-ahead scheduling model of a multi-energy complementary system containing wind-water pumping and storage, and the optimal day-ahead scheduling plan for a typical day-ahead multi-energy complementary system of the first type is derived with the two optimization objectives of maximizing the economic return of the multi-energy complementary system and minimizing the volatility of the renewable energy output as the two optimization objectives. From the results, it is shown that the improved multi-objective whale optimization algorithm proposed in this paper can more excellently improve the economic returns of the multi-energy complementary system and significantly reduce the volatility of the renewable energy sources at the time of grid connection. |
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| AbstractList | This study proposes an improved whale optimization algorithm. The algorithm has four improvement strategies: chaotic mapping, nonlinear convergence factor, adaptive crossover strategy and reverse learning strategy. The improved augmented whale algorithm and fast elite genetic algorithm are also combined to construct a multi-objective whale optimization algorithm. Using this algorithm, a day-ahead scheduling model of a multi-energy complementary system containing wind-water pumping and storage, and the optimal day-ahead scheduling plan for a typical day-ahead multi-energy complementary system of the first type is derived with the two optimization objectives of maximizing the economic return of the multi-energy complementary system and minimizing the volatility of the renewable energy output as the two optimization objectives. From the results, it is shown that the improved multi-objective whale optimization algorithm proposed in this paper can more excellently improve the economic returns of the multi-energy complementary system and significantly reduce the volatility of the renewable energy sources at the time of grid connection. |
| Author | Chen, Qiao Wang, Yi Li, Jiahao Ding, Kai Li, Xiaoping Qian, Yimin Hu, Zhuang Zheng, Jian |
| Author_xml | – sequence: 1 givenname: Zhuang surname: Hu fullname: Hu, Zhuang email: 1913221010@qq.com organization: Power Grid Technology Center, Electric Power Research Institute of State Grid Hubei Electric Power Co., Ltd,Wuhan,China – sequence: 2 givenname: Yimin surname: Qian fullname: Qian, Yimin email: 61893790@qq.com organization: Power Grid Technology Center, Electric Power Research Institute of State Grid Hubei Electric Power Co., Ltd,Wuhan,China – sequence: 3 givenname: Jian surname: Zheng fullname: Zheng, Jian email: 1516481040@qq.com organization: Power Grid Technology Center, Electric Power Research Institute of State Grid Hubei Electric Power Co., Ltd,Wuhan,China – sequence: 4 givenname: Qiao surname: Chen fullname: Chen, Qiao email: cq198812@163.com organization: Power Grid Technology Center, Electric Power Research Institute of State Grid Hubei Electric Power Co., Ltd,Wuhan,China – sequence: 5 givenname: Yi surname: Wang fullname: Wang, Yi email: yi.wang@bath.edu organization: Power Grid Technology Center, Electric Power Research Institute of State Grid Hubei Electric Power Co., Ltd,Wuhan,China – sequence: 6 givenname: Jiahao surname: Li fullname: Li, Jiahao email: Ljh17673627590@163.com organization: Power Grid Technology Center, Electric Power Research Institute of State Grid Hubei Electric Power Co., Ltd,Wuhan,China – sequence: 7 givenname: Kai surname: Ding fullname: Ding, Kai email: dingkay@sina.com organization: Power Grid Technology Center, Electric Power Research Institute of State Grid Hubei Electric Power Co., Ltd,Wuhan,China – sequence: 8 givenname: Xiaoping surname: Li fullname: Li, Xiaoping email: lixp@hb.sgcc.com.cn organization: Power Grid Technology Center, Electric Power Research Institute of State Grid Hubei Electric Power Co., Ltd,Wuhan,China |
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| Snippet | This study proposes an improved whale optimization algorithm. The algorithm has four improvement strategies: chaotic mapping, nonlinear convergence factor,... |
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| SubjectTerms | Chaotic mapping Convergence Day ahead dispatching Dispatching Economics Electrical engineering formatting Genetic algorithms Multi objective optimization Optimization Renewable energy sources Scheduling Whale Optimization Algorithm Whale optimization algorithms |
| Title | Improved Multi-Objective Whale Optimization for Day-Ahead Scheduling of Wind-Photovoltaic-Water-Storage Multi-energy Complements |
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