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
Hlavní autoři: Hu, Zhuang, Qian, Yimin, Zheng, Jian, Chen, Qiao, Wang, Yi, Li, Jiahao, Ding, Kai, Li, Xiaoping
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 15.04.2025
Témata:
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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Shrnutí: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.
ISSN:2996-2951
DOI:10.1109/ACPEE64358.2025.11041560