Optimal Scheduling of a Hydropower–Wind–Solar Multi-Objective System Based on an Improved Strength Pareto Algorithm

Haodong Huang, Qin Shen (), Wan Liu, Ying Peng, Shuli Zhu, Rungang Bao and Li Mo ()
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Haodong Huang: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Qin Shen: Hubei Qingjiang Hydropower Development Co., Ltd., Yichang 443000, China
Wan Liu: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Ying Peng: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Shuli Zhu: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Rungang Bao: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Li Mo: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Sustainability, 2025, vol. 17, issue 15, 1-26

Abstract: Under the current context of the large-scale integration of wind and solar power, the coupling of hydropower with wind and solar energy brings significant impacts on grid stability. To fully leverage the regulatory capacity of hydropower, this paper develops a multi-objective optimization scheduling model for hydropower, wind, and solar that balances generation-side power generation benefit and grid-side peak-regulation requirements, with the latter quantified by the mean square error of the residual load. To efficiently solve this model, Latin hypercube initialization, hybrid distance framework, and adaptive mutation mechanism are introduced into the Strength Pareto Evolutionary Algorithm II (SPEAII), yielding an improved algorithm named LHS-Mutate Strength Pareto Evolutionary Algorithm II (LMSPEAII). Its efficiency is validated on benchmark test functions and a reservoir model. Typical extreme scenarios—months with strong wind and solar in the dry season and months with weak wind and solar in the flood season—are selected to derive scheduling strategies and to further verify the effectiveness of the proposed model and algorithm. Finally, K-medoids clustering is applied to the Pareto front solutions; from the perspective of representative solutions, this reveals the evolutionary trends of different objective trade-off schemes and overall distribution characteristics, providing deeper insight into the solution set’s distribution features.

Keywords: hydro–wind–solar; generation–grid coordination; algorithm improvement; reservoir operation and dispatching; cluster analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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