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Cloud-Based Multidimensional Parallel Dynamic Programming Algorithm for a Cascade Hydropower System

Author

Listed:
  • Yufei Ma

    (Hohai University)

  • Ping-an Zhong

    (Hohai University)

  • Bin Xu

    (Hohai University
    Nanjing Hydraulic Research Institute)

  • Feilin Zhu

    (Hohai University)

  • Jieyu Li

    (Hohai University)

  • Han Wang

    (Hohai University)

  • Qingwen Lu

    (Hohai University)

Abstract

The joint optimal operation of cascade reservoir system can greatly improve the utilization of water resources. However, the complex high-dimensional and non-linear features and calculated costs often hinder the refined operation and management of reservoirs. Recently, the local parallel computing has become an effective way to alleviate the "curse of dimensionality". Current local parallel computing has hardware limitations, which is difficult to adapt to large-scale computing. This study proposes a novel parallel dynamic programming algorithm based on Spark (PDPoS) via cloud computing. The simulation experiments are carried out for a comparative analysis of the solution efficiency, influence factors and stability of cloud computing. The results are as follows: (1) The efficiency of the cloud-based PDPoS is related to some factors; the number of CPU cores is the main influencing factor, followed by the operator, and the architecture has the least influence. (2) The runtime variance of cloud computing is 2.03, indicating cloud computing has high stability. (3) Under the same configuration (i.e., CPU and memory), the runtime of cloud computing is 41.5% ~ 110.3% longer than that of physical machines. However, cloud computing has rich resources, good scalability, and good portability of online operations, which is an attractive alternative for optimal operation of large-scale reservoir system.

Suggested Citation

  • Yufei Ma & Ping-an Zhong & Bin Xu & Feilin Zhu & Jieyu Li & Han Wang & Qingwen Lu, 2021. "Cloud-Based Multidimensional Parallel Dynamic Programming Algorithm for a Cascade Hydropower System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2705-2721, July.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:9:d:10.1007_s11269-021-02859-7
    DOI: 10.1007/s11269-021-02859-7
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    References listed on IDEAS

    as
    1. Bo Ming & Pan Liu & Tao Bai & Rouxin Tang & Maoyuan Feng, 2017. "Improving Optimization Efficiency for Reservoir Operation Using a Search Space Reduction Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1173-1190, March.
    2. Yufei Ma & Ping-an Zhong & Bin Xu & Feilin Zhu & Yao Xiao & Qingwen Lu, 2020. "Multidimensional Parallel Dynamic Programming Algorithm Based on Spark for Large-Scale Hydropower Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3427-3444, September.
    3. Wang Zhang & Pan Liu & Xizhen Chen & Li Wang & Xueshan Ai & Maoyuan Feng & Dedi Liu & Yuanyuan Liu, 2016. "Optimal Operation of Multi-reservoir Systems Considering Time-lags of Flood Routing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 523-540, January.
    4. Tang, Wenzhe & Li, Zhuoyu & Qiang, Maoshan & Wang, Shuli & Lu, Youmei, 2013. "Risk management of hydropower development in China," Energy, Elsevier, vol. 60(C), pages 316-324.
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    6. Wang Zhang & Pan Liu & Xizhen Chen & Li Wang & Xueshan Ai & Maoyuan Feng & Dedi Liu & Yuanyuan Liu, 2016. "Optimal Operation of Multi-reservoir Systems Considering Time-lags of Flood Routing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 523-540, January.
    7. Xu, Bin & Zhu, Feilin & Zhong, Ping-an & Chen, Juan & Liu, Weifeng & Ma, Yufei & Guo, Le & Deng, Xiaoliang, 2019. "Identifying long-term effects of using hydropower to complement wind power uncertainty through stochastic programming," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
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    Cited by:

    1. Fang, Zhou & Liao, Shengli & Cheng, Chuntian & Zhao, Hongye & Liu, Benxi & Su, Huaying, 2023. "Parallel improved DPSA algorithm for medium-term optimal scheduling of large-scale cascade hydropower plants," Renewable Energy, Elsevier, vol. 210(C), pages 134-147.
    2. Shengli Liao & Huan Wang & Benxi Liu & Xiangyu Ma & Binbin Zhou & Huaying Su, 2023. "Runoff Forecast Model Based on an EEMD-ANN and Meteorological Factors Using a Multicore Parallel Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1539-1555, March.
    3. Liao, Shengli & Liu, Huan & Liu, Benxi & Liu, Tian & Li, Chonghao & Su, Huaying, 2023. "Solution framework for short-term cascade hydropower system optimization operations based on the load decomposition strategy," Energy, Elsevier, vol. 277(C).

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