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Benefit and Risk Balance Optimization for Stochastic Hydropower Scheduling

Author

Listed:
  • Liu Yuan

    (Huazhong University of Science and Technology)

  • Jianzhong Zhou

    (Huazhong University of Science and Technology)

  • Chunlong Li

    (Huazhong University of Science and Technology)

  • Mengfei Xie

    (Huazhong University of Science and Technology)

  • Li Mo

    (Huazhong University of Science and Technology)

Abstract

Limited by inflow forecasting methods, the forecasting results are so unreliable that we have to take their uncertainty and risk into account when incorporating stochastic inflow into reservoir operation. Especially in the electricity market, punishment often happens when the hydropower station does not perform as planned. Therefore, focusing on the risk of power generation, a benefit and risk balance optimization model (BRM) which takes stochastic inflow as the major risk factor is proposed for stochastic hydropower scheduling. The mean-variance theory is firstly introduced into the optimal dispatching of hydropower station, and a variational risk coefficient is employed to give service to managers’ subjective preferences. Then, the multi-period stochastic inflow is simulated by multi-layer scenario tree. Moreover, a specific scenario reduction and reconstruction method is put forward to reduce branches and computing time accordingly. Finally, the proposed model is applied to the Three Gorges Reservoir (TGR) in China for constructing a weekly generation scheduling in falling stage. Compared to deterministic dynamic programming (DDP) and stochastic dynamic programming (SDP), BRM achieves more satisfactory performance. Moreover, the tradeoffs for risk-averse decision makers are discussed, and an efficient curve about benefit and risk is formed to help make decision.

Suggested Citation

  • Liu Yuan & Jianzhong Zhou & Chunlong Li & Mengfei Xie & Li Mo, 2016. "Benefit and Risk Balance Optimization for Stochastic Hydropower Scheduling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3347-3361, August.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:10:d:10.1007_s11269-016-1354-2
    DOI: 10.1007/s11269-016-1354-2
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    References listed on IDEAS

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    3. He, Zhongzheng & Zhou, Jianzhong & Xie, Mengfei & Jia, Benjun & Bao, Zhengfeng & Qin, Hui & Zhang, Hairong, 2019. "Study on guaranteed output constraints in the long term joint optimal scheduling for the hydropower station group," Energy, Elsevier, vol. 185(C), pages 1210-1224.
    4. Jacopo Napolitano & Giovanni M. Sechi & Paola Zuddas, 2016. "Scenario Optimisation of Pumping Schedules in a Complex Water Supply System Considering a Cost–Risk Balancing Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5231-5246, November.
    5. Gaudard, Ludovic & Avanzi, Francesco & De Michele, Carlo, 2018. "Seasonal aspects of the energy-water nexus: The case of a run-of-the-river hydropower plant," Applied Energy, Elsevier, vol. 210(C), pages 604-612.
    6. Liu Yuan & Jianzhong Zhou & Zijun Mai & Yuanzheng Li, 2017. "Random Fuzzy Optimization Model for Short-Term Hydropower Scheduling Considering Uncertainty of Power Load," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(9), pages 2713-2728, July.
    7. He, Zhongzheng & Zhou, Jianzhong & Qin, Hui & Jia, Benjun & He, Feifei & Liu, Guangbiao & Feng, Kuaile, 2020. "A fast water level optimal control method based on two stage analysis for long term power generation scheduling of hydropower station," Energy, Elsevier, vol. 210(C).
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