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 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.