Bayesian Stochastic Dynamic Programming for Hydropower Generation Operation Based on Copula Functions

Qiao-feng Tan, Guo-hua Fang, Xin Wen (), Xiao-hui Lei, Xu Wang, Chao Wang and Yi Ji
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Qiao-feng Tan: Hohai University
Guo-hua Fang: Hohai University
Xin Wen: Hohai University
Xiao-hui Lei: China Institute of Water Resources and Hydropower Research
Xu Wang: China Institute of Water Resources and Hydropower Research
Chao Wang: China Institute of Water Resources and Hydropower Research
Yi Ji: Northeast Agricultural University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2020, vol. 34, issue 5, No 2, 1589-1607

Abstract: Abstract Bayesian stochastic dynamic programming (BSDP) has been widely used in hydropower generation operation, as natural inflow and forecast uncertainties can be easily determined by transition probabilities. In this study, we propose a theoretical estimation method (TEM) based on copula functions to calculate the transition probability under conditions of limited historical inflow samples. The explicit expression of the conditional probability is derived using copula functions and then used to calculate prior and likelihood probabilities, and the prior probability can be revised to the posterior probability once new forecast information is available by Bayesian formulation. The performance of BSDP models in seven forecast scenarios and two extreme conditions considering no or perfect forecast information is evaluated and compared. The case study in the Ertan hydropower station in China shows that (1) TEM can avoid the shortcomings of empirical estimation method (EMM) in calculating the transition probability, so that the prior and likelihood probability matrices can be distributed more uniformly with less zeros, and the problem that the posterior probability cannot be calculated can be avoided; (2) there is a positive correlation between operating benefit and forecast accuracy; and (3) the operating policy considering reliable forecast information can improve hydropower generation. However, an incorrect decision may be made in the case of low forecast accuracy.

Keywords: Bayesian stochastic dynamic programming (BSDP); Copula function; Hydropower generation operation; Uncertainty (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s11269-019-02449-8

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