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Application of Minimum Reward Risk Model in Reservoir Generation Scheduling

Author

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  • Ming Zhang
  • Fei Yang
  • Jing-Xiu Wu
  • Zi-Wu Fan
  • Ying-Ying Wang

Abstract

Discrete time Markov decision process is studied and the minimum reward risk model is established for the reservoir long-term generation optimization. Different form the commonly used optimization criterion of best expected reward in reservoir scheduling, the probability that the expected generation of the whole period not exceeding the predete reward target to be smallest is chosen as the optimizing target for this random process. For the hydropower tends to operate as peak-clipping mode in Market-based model to gain more profits, the function of electricity price and output can be founded by analyzing on the typical day load course of the electricity system. Compared with the generally used criteria of the largest expectation power generation model, this model is fitted for the decision-making in which the risk is needed to be limited to reflect the risk preference of the policy makers. Stochastic dynamic programming method is adopted to solve the model and the model is tested on the Three Gorges Hydropower Station. Copyright The Author(s) 2016

Suggested Citation

  • Ming Zhang & Fei Yang & Jing-Xiu Wu & Zi-Wu Fan & Ying-Ying Wang, 2016. "Application of Minimum Reward Risk Model in Reservoir Generation Scheduling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1345-1355, March.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:4:p:1345-1355
    DOI: 10.1007/s11269-015-1218-1
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    References listed on IDEAS

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    1. Mahsa Jahandideh-Tehrani & Omid Bozorg Haddad & Hugo Loáiciga, 2015. "Hydropower Reservoir Management Under Climate Change: The Karoon Reservoir System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(3), pages 749-770, February.
    2. Asmadi Ahmad & Ahmed El-Shafie & Siti Razali & Zawawi Mohamad, 2014. "Reservoir Optimization in Water Resources: a Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3391-3405, September.
    3. Xuemin Wang & Jianzhong Zhou & Shuo Ouyang & Chunlong Li, 2014. "Research on Joint Impoundment Dispatching Model for Cascade Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5527-5542, December.
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    Cited by:

    1. Wei Ge & Zongkun Li & Robert Y. Liang & Wei Li & Yingchun Cai, 2017. "Methodology for Establishing Risk Criteria for Dams in Developing Countries, Case Study of China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(13), pages 4063-4074, October.
    2. Jianzhong Zhou & Mengfei Xie & Zhongzhen He & Hui Qin & Liu Yuan, 2017. "Medium-Term Hydro Generation Scheduling (MTHGS) with Chance Constrained Model (CCM) and Dynamic Control Model (DCM)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(11), pages 3543-3555, September.
    3. 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.

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