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Hydropower Unit Commitment Using a Genetic Algorithm with Dynamic Programming

Author

Listed:
  • Shuangquan Liu

    (System Operation Department, Yunnan Power Grid Co., Ltd., 73# Tuodong Road, Kunming 650011, China)

  • Pengcheng Wang

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Zifan Xu

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Zhipeng Feng

    (Huaneng Lancang River Hydropower Inc., 1# Shijicheng Road, Kunming 650214, China)

  • Congtong Zhang

    (System Operation Department, Yunnan Power Grid Co., Ltd., 73# Tuodong Road, Kunming 650011, China)

  • Jinwen Wang

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China
    Institute of Water Resources and Hydropower, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Cheng Chen

    (Faculty of Electric Engineering, Kunming University of Science and Technology, 727# Jingming South Road, Kunming 650500, China)

Abstract

This study presents a genetic algorithm integrated with dynamic programming to address the challenges of the hydropower unit commitment problem, which is a nonlinear, nonconvex, and discrete optimization, involving the hourly scheduling of generators in a hydropower system to maximize benefits and meet various constraints. The introduction of a progressive generating discharge allocation enhances the performance of dynamic programming in fitness evaluations, allowing for the fulfillment of various constraints, such as unit start-up times, shutdown/operating durations, and output ranges, thereby reducing complexity and improving the efficiency of the genetic algorithm. The application of the genetic algorithm with dynamic programming and progressive generating discharge allocation at the Manwan Hydropower Plant in Yunnan Province, China, showcases increased flexibility in outflow allocation, reducing spillages by 79%, and expanding high-efficiency zones by 43%.

Suggested Citation

  • Shuangquan Liu & Pengcheng Wang & Zifan Xu & Zhipeng Feng & Congtong Zhang & Jinwen Wang & Cheng Chen, 2023. "Hydropower Unit Commitment Using a Genetic Algorithm with Dynamic Programming," Energies, MDPI, vol. 16(15), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5842-:d:1212153
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    References listed on IDEAS

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    1. Hu Hu & Kan Yang & Lang Liu & Lyuwen Su & Zhe Yang, 2019. "Short-Term Hydropower Generation Scheduling Using an Improved Cloud Adaptive Quantum-Inspired Binary Social Spider Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2357-2379, May.
    2. Alireza Amani & Hosein Alizadeh, 2021. "Solving Hydropower Unit Commitment Problem Using a Novel Sequential Mixed Integer Linear Programming Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1711-1729, April.
    3. Wang, Jinwen & Guo, Min & Liu, Yong, 2018. "Hydropower unit commitment with nonlinearity decoupled from mixed integer nonlinear problem," Energy, Elsevier, vol. 150(C), pages 839-846.
    4. Ming, Bo & Liu, Pan & Guo, Shenglian & Cheng, Lei & Zhou, Yanlai & Gao, Shida & Li, He, 2018. "Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1341-1352.
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