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Effect of the quality of streamflow forecasts on the operation of cascade hydropower stations using stochastic optimization models

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  • Liu, Yuan
  • Ji, Changming
  • Wang, Yi
  • Zhang, Yanke
  • Jiang, Zhiqiang
  • Ma, Qiumei
  • Hou, Xiaoning

Abstract

Determining the economic value of streamflow forecasts is essential to judging the operation of cascade hydropower systems and investing in improved forecasting systems. Previous analyses of the streamflow forecast value are mainly based on deterministic optimization strategies. This paper investigates the impact of long-term (10-day-ahead) streamflow forecasts on the operation of a cascade hydropower system using stochastic dynamic programming (SDP) and Bayesian stochastic dynamic programming (BSDP). Synthetic streamflow forecasts with different bias, variance, and precision are generated by the generalized maintenance of variance extension approach. A case study is performed to evaluate the performance of these strategies in terms of cumulative annual power revenue (CAPR) and system reliability (SR). The results show that, even when using the forecast with the largest uncertainty and bias, the stochastic optimization strategies increase at least 6.63 × 108 CNY in CAPR and 33.89% in SR compared with a reference strategy that uses no forecast information. The SDP performs best with forecast systems that have a negative bias and high accuracy. Compared with the SDP, BSDP increases at least 1.80 CNY in CAPR and 0.28% in SR and is better able to handle forecast uncertainty, and is insensitive to forecast bias.

Suggested Citation

  • Liu, Yuan & Ji, Changming & Wang, Yi & Zhang, Yanke & Jiang, Zhiqiang & Ma, Qiumei & Hou, Xiaoning, 2023. "Effect of the quality of streamflow forecasts on the operation of cascade hydropower stations using stochastic optimization models," Energy, Elsevier, vol. 273(C).
    • Handle: RePEc:eee:energy:v:273:y:2023:i:c:s0360544223006928
    DOI: 10.1016/j.energy.2023.127298
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    References listed on IDEAS

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