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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Bin Ji & Haiyang Huang & Yu Gao & Fangliang Zhu & Jie Gao & Chen Chen & Samson S. Yu & Zenghai Zhao, 2025. "Long-Term Stochastic Co-Scheduling of Hydro–Wind–PV Systems Using Enhanced Evolutionary Multi-Objective Optimization," Sustainability, MDPI, vol. 17(5), pages 1-34, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:5:p:2181-:d:1604192
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    References listed on IDEAS

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