Two-stage stochastic optimal operation model for hydropower station based on the approximate utility function of the carryover stage

Qiao-feng Tan, Xiao-hui Lei, Xin Wen, Guo-hua Fang, Xu Wang, Chao Wang, Yi Ji and Xian-feng Huang

Energy, 2019, vol. 183, issue C, 670-682

Abstract: Challenge remains to find the optimal carryover storage to balance the immediate and carryover utilities for long-term hydropower reservoir operation due to high uncertainties of long-term forecasts. Thus, this paper develops a two-stage stochastic optimal operation model to dynamically decide the optimal carryover storage. First, a successive iteration method based on periodic Markov characteristics of reservoir operation is proposed to obtain the approximate utility function of the carryover stage. Then, three two-stage stochastic optimal operation models based on different forecast accuracy (no forecasts, perfect forecasts, and uncertainty forecasts) are developed to guide the long-term hydropower reservoir operation. The applications shows that: 1) the back propagation neural network can approximate the utility function of the carryover stage with a high accuracy and avoid the need to predetermine the function type; 2) the approximate utility function of the carryover stage increases with the carryover storage and current inflow, and it changes gradually from a nearly linear surface to an approximate concave surface with the shift from the dry season to the flood season; 3) two-stage stochastic optimal operation models outperform the conventional operating rules and conventional optimization method in guiding the long-term hydropower operation.

Keywords: Hydropower reservoir operation; Carryover storage; Utility function of the carryover stage; Two-stage stochastic optimal operation model (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:183:y:2019:i:c:p:670-682

DOI: 10.1016/j.energy.2019.05.116

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