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SSDP Model with Inflow Clustering for Hydropower System Operation

Author

Listed:
  • Xinyu Wu

    (Dalian University of Technology)

  • Shuai Yin

    (Dalian University of Technology)

  • Chuntian Cheng

    (Dalian University of Technology)

  • Zhiyong Chen

    (Dalian University of Technology)

  • Huaying Su

    (Electric Power Dispatch and Control Center of Guizhou Power Grid Co)

Abstract

Sampling stochastic dynamic programming (SSDP), which considers the uncertainty of streamflow, is a popular and useful method for solving release decisions of reservoirs. It is easy to implement the long-term operation for cascaded hydropower system with poor inflow prediction ability. Furthermore, SSDP describes the temporal and spatial structure of the stochastic streamflow processes implicitly through inflow scenarios instead of representing the multivariate distribution of inflow by conditional probabilities in stochastic dynamic programming (SDP). However, computation time of SSDP procedure will increase exponentially with the growth inflow scenarios. Thus, clustering algorithm is employed to reduce the number of inflow scenarios in order to improve the efficiency and operability of SSDP in practical applications. The calculation results of SSDP with inflow clustering are analyzed with different cluster numbers. The principle of how to find the least inflow scenarios to represent all inflow sequences has also been proposed. The least inflow scenarios and relevant probabilities found by clustering algorithm can approximate the empirical distribution of all streamflow scenarios used in this study without obviously decreasing the energy and exacerbating the shortage of firm power.

Suggested Citation

  • Xinyu Wu & Shuai Yin & Chuntian Cheng & Zhiyong Chen & Huaying Su, 2023. "SSDP Model with Inflow Clustering for Hydropower System Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1109-1123, February.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:3:d:10.1007_s11269-022-03417-5
    DOI: 10.1007/s11269-022-03417-5
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    References listed on IDEAS

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    1. Aadhityaa Mohanavelu & Bankaru-Swamy Soundharajan & Ozgur Kisi, 2022. "Modeling Multi-objective Pareto-optimal Reservoir Operation Policies Using State-of-the-art Modeling Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3107-3128, July.
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    3. Tongtiegang Zhao & Jianshi Zhao & Xiaohui Lei & Xu Wang & Bisheng Wu, 2017. "Improved Dynamic Programming for Reservoir Flood Control Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2047-2063, May.
    4. Didier Haguma & Robert Leconte, 2018. "Long-Term Planning of Water Systems in the Context of Climate Non-Stationarity with Deterministic and Stochastic Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1725-1739, March.
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