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Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System

Author

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  • Xinyu Wu

    (Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China
    Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China)

  • Rui Guo

    (Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Xilong Cheng

    (Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Chuntian Cheng

    (Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China
    Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China)

Abstract

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

Suggested Citation

  • Xinyu Wu & Rui Guo & Xilong Cheng & Chuntian Cheng, 2021. "Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System," Energies, MDPI, vol. 14(3), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:625-:d:487412
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    References listed on IDEAS

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    Cited by:

    1. Ali Al-Maktoumi & Mohammad Mahdi Rajabi & Slim Zekri & Chefi Triki, 2021. "A Probabilistic Multiperiod Simulation–Optimization Approach for Dynamic Coastal Aquifer Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3447-3462, September.
    2. Ailing Xu & Li Mo & Qi Wang, 2022. "Research on Operation Mode of the Yalong River Cascade Reservoirs Based on Improved Stochastic Fractal Search Algorithm," Energies, MDPI, vol. 15(20), pages 1-19, October.

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