Mid-Term Optimal Scheduling of Low-Head Cascaded Hydropower Stations Considering Inflow Unevenness

Shuo Huang, Xinyu Wu (), Yiyang Wu and Zheng Zhang
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Shuo Huang: Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Xinyu Wu: Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Yiyang Wu: Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Zheng Zhang: China Yangtze Power Co., Ltd., Yichang 443000, China

Energies, 2023, vol. 16, issue 17, 1-13

Abstract: China has a vast scale of hydropower, and the small hydropower stations account for a large proportion. In flood season, the excessive inflow keeps these stations at a high reservoir level, leading to a worse condition of hindered power output and a great error in the calculation of power generation. Therefore, this paper proposes a mid-term optimal scheduling model for low-head cascaded hydropower stations considering inflow unevenness, in which the power output is controlled by the expected power output curve and daily inflow–maximum power output curve. A case study of nine hydropower stations on the Guangxi power grid shows that, regardless of considering the fitted curve or not, there are different degrees of error between the planned and actual situations. However, the error and power generation are decreased when considering the fitted curve, which reflects the impact of hindered power output. Meanwhile, according to the comparison, the weekly plan is more in line with the real condition when using this model to solve the problem. The results indicate that this model improves the accuracy of power output calculation for low-head hydropower stations with uneven inflow, playing a key role in the process of scheduling.

Keywords: inflow unevenness; low-head hydropower stations; hindered power output; mid-term optimal scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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