Long-Term Stochastic Co-Scheduling of Hydro–Wind–PV Systems Using Enhanced Evolutionary Multi-Objective Optimization

Bin Ji, Haiyang Huang, Yu Gao, Fangliang Zhu, Jie Gao, Chen Chen, Samson S. Yu and Zenghai Zhao ()
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Bin Ji: School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
Haiyang Huang: School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
Yu Gao: School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
Fangliang Zhu: China Water Resources and Hydropower Construction Engineering Consulting Co., Ltd., Beijing 100120, China
Jie Gao: China Renewable Energy Engineering Institute, Beijing 100120, China
Chen Chen: China Renewable Energy Engineering Institute, Beijing 100120, China
Samson S. Yu: School of Engineering, Deakin University, Geelong 3216, Australia
Zenghai Zhao: China Water Resources and Hydropower Construction Engineering Consulting Co., Ltd., Beijing 100120, China

Sustainability, 2025, vol. 17, issue 5, 1-34

Abstract: With the increasing presence of large-scale new energy sources, such as wind and photovoltaic (PV) systems, integrating traditional hydropower with wind and PV power into a hydro–wind–PV complementary system in economic dispatch can effectively mitigate wind and PV fluctuations. In this study, Markov chains and the Copula joint distribution function were adopted to quantize the spatiotemporal relationships among hydro, wind and PV, whereby runoff, wind, and PV output scenarios were generated to simulate their uncertainties. A dual-objective optimization model is proposed for the long-term hydro–wind–PV co-scheduling (LHWP-CS) problem. To solve the model, a well-tailored evolutionary multi-objective optimization method was developed, which combines multiple recombination operators and two different dominance rules for basic and elite populations. The proposed model and algorithm were tested on three annual reservoirs with large wind and PV farms in the Hongshui River Basin. The proposed algorithm demonstrates superior performance, with average improvements of 2.90% and 2.63% in total power generation, and 1.23% and 0.96% in minimum output expectation compared to BORG and NSGA-II, respectively. The results also infer that the number of scenarios is a key parameter in achieving a tradeoff between economics and risk.

Keywords: hydro–wind–PV co-scheduling; long-term stochastic scheduling; Markov chains; Copula joint distribution; multi-objective optimization (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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