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Long-term multi-objective power generation operation for cascade reservoirs and risk decision making under stochastic uncertainties

Author

Listed:
  • Yang, Zhe
  • Yang, Kan
  • Wang, Yufeng
  • Su, Lyuwen
  • Hu, Hu

Abstract

The long-term multi-objective power generation operation (LTMOPGO) inherently exists multiple uncertainties coming from streamflow forecasting and decision-making process. With help of multi-criteria decision making (MCDM), reservoir scheduling solutions are evaluated and ranked. However, conventional MCDM provide decision makers (DMs) with deterministic rank sequence ignoring uncertainty effects which may lead to unignorable risk of decision error. Furthermore, algorithm improvements are greatly emphasized on multi-objective reservoir operation, while uncertainty analysis and decision risk are ignored to some degree. To this end, we establish framework for solving LTMOPGO and MCDM under multiple uncertainties, including criteria values (CVs) and criteria weights (CWs). First, reservoir operation solutions with uncertain information are acquired by improved multi-objective particle swarm optimization (IMOPSO) and LHS-Monte Carlo simulation. Then, stochastic multi-criteria acceptability analysis (SMAA) model coupling with grey correlation analysis (GCA) and TOPSIS is developed to assist stochastic decision making. Finally, we conduct simulation experiments for cascade reservoirs in Qingjiang river and disclose effect of uncertain factors on LTMOPGO and MCDM with probabilistic rank sequence and risk information. Comparison analysis indicates feasibility and efficiency of novel SMAA-GCA&TOPSIS model compared with SMAA-2. Overall, novel framework proposed are effective ways for DMs to make highly reliable and risk-informed decisions under stochastic environment.

Suggested Citation

  • Yang, Zhe & Yang, Kan & Wang, Yufeng & Su, Lyuwen & Hu, Hu, 2021. "Long-term multi-objective power generation operation for cascade reservoirs and risk decision making under stochastic uncertainties," Renewable Energy, Elsevier, vol. 164(C), pages 313-330.
    • Handle: RePEc:eee:renene:v:164:y:2021:i:c:p:313-330
    DOI: 10.1016/j.renene.2020.08.106
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    References listed on IDEAS

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    Cited by:

    1. Rashid, Muhammad Usman & Abid, Irfan & Latif, Abid, 2022. "Optimization of hydropower and related benefits through Cascade Reservoirs for sustainable economic growth," Renewable Energy, Elsevier, vol. 185(C), pages 241-254.
    2. Zhang, Shuo & Kang, Yan & Gao, Xuan & Chen, Peiru & Cheng, Xiao & Song, Songbai & Li, Lingjie, 2023. "Optimal reservoir operation and risk analysis of agriculture water supply considering encounter uncertainty of precipitation in irrigation area and runoff from upstream," Agricultural Water Management, Elsevier, vol. 277(C).
    3. Lei, Kaixuan & Chang, Jianxia & Long, Ruihao & Wang, Yimin & Zhang, Hongxue, 2022. "Cascade hydropower station risk operation under the condition of inflow uncertainty," Energy, Elsevier, vol. 244(PA).
    4. Xie, Yuzuo & Guo, Shenglian & Zhong, Sirui & He, Zhipeng & Liu, Pan & Zhou, Yanlai, 2024. "Optimal allocation of flood prevention storage and dynamic operation of water levels to increase cascade reservoir hydropower generation," Renewable Energy, Elsevier, vol. 228(C).
    5. Yueqiu Wu & Liping Wang & Yi Wang & Yanke Zhang & Jiajie Wu & Qiumei Ma & Xiaoqing Liang & Bin He, 2021. "Risk Analysis for Short-Term Operation of the Power Generation in Cascade Reservoirs Considering Multivariate Reservoir Inflow Forecast Errors," Sustainability, MDPI, vol. 13(7), pages 1-16, March.

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