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Article

Study on the Optimal Operation of a Hydropower Plant Group Based on the Stochastic Dynamic Programming with Consideration for Runoff Uncertainty

1
School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China
2
State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an 710048, China
3
College of Resources and Environmental Engineering, Mianyang Normal University, Mianyang 621000, China
4
Shaanxi Province Institute of Water Resources and Electric Power Investigation and Design, Xi’an 710005, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Marco Franchini
Water 2022, 14(2), 220; https://doi.org/10.3390/w14020220
Received: 14 December 2021 / Revised: 7 January 2022 / Accepted: 10 January 2022 / Published: 12 January 2022
(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)
Hydropower plant operation reorganizes the temporal and spatial distribution of water resources to promote the comprehensive utilization of water resources in the basin. However, a lot of uncertainties were brought to light concerning cascade hydropower plant operation with the introduction of the stochastic process of incoming runoff. Therefore, it is of guiding significance for the practice operation to investigate the stochastic operation of cascade hydropower plants while considering runoff uncertainty. The runoff simulation model was constructed by taking the cascade hydropower plants in the lower reaches of the Lancang River as the research object, and combining their data with the copula joint function and Gibbs method, and a Markov chain was adopted to construct the transfer matrix of runoff between adjacent months. With consideration for the uncertainty of inflow runoff, the stochastic optimal operation model of cascade hydropower plants was constructed and solved by the SDP algorithm. The results showed that 71.12% of the simulated monthly inflow of 5000 groups in the Nuozhadu hydropower plant drop into the reasonable range. Due to the insufficiency of measured runoff, there were too many 0 values in the derived transfer probability, but after the simulated runoff series were introduced, the results significantly improved. Taking the transfer probability matrix of simulated runoff as the input of the stochastic optimal operation model of the cascade hydropower plants, the operation diagram containing the future-period incoming water information was obtained, which could directly provide a reference for the optimal operation of the Nuozhadu hydropower plant. In addition, taking the incoming runoff process in a normal year as the standard, the annual mean power generation based on stochastic dynamic programming was similar to that based on dynamic programming (respectively 305.97 × 108 kWh and 306.91 × 108 kWh), which proved that the operation diagram constructed in this study was reasonable. View Full-Text
Keywords: reservoir operation; copula function; Gibbs; SDP; Lancang River reservoir operation; copula function; Gibbs; SDP; Lancang River
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MDPI and ACS Style

Zhang, H.; Zhang, L.; Chang, J.; Li, Y.; Long, R.; Xing, Z. Study on the Optimal Operation of a Hydropower Plant Group Based on the Stochastic Dynamic Programming with Consideration for Runoff Uncertainty. Water 2022, 14, 220. https://doi.org/10.3390/w14020220

AMA Style

Zhang H, Zhang L, Chang J, Li Y, Long R, Xing Z. Study on the Optimal Operation of a Hydropower Plant Group Based on the Stochastic Dynamic Programming with Consideration for Runoff Uncertainty. Water. 2022; 14(2):220. https://doi.org/10.3390/w14020220

Chicago/Turabian Style

Zhang, Hongxue, Lianpeng Zhang, Jianxia Chang, Yunyun Li, Ruihao Long, and Zhenxiang Xing. 2022. "Study on the Optimal Operation of a Hydropower Plant Group Based on the Stochastic Dynamic Programming with Consideration for Runoff Uncertainty" Water 14, no. 2: 220. https://doi.org/10.3390/w14020220

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