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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Published in:Asia Conference on Power and Electrical Engineering (Online) pp. 303 - 307
Main Authors: Hu, Zhuang, Qian, Yimin, Zheng, Jian, Chen, Qiao, Wang, Yi, Li, Jiahao, Ding, Kai, Li, Xiaoping
Format: Conference Proceeding
Language:English
Published: IEEE 15.04.2025
Subjects:
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
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.
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
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  givenname: Xiaoping
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  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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StartPage 303
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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