A Mid-Term Scheduling Method for Cascade Hydropower Stations to Safeguard Against Continuous Extreme New Energy Fluctuations

Huaying Su, Yupeng Li (), Yan Zhang, Yujian Wang, Gang Li and Chuntian Cheng
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Huaying Su: Guizhou Electric Power Dispatching and Control Center, Guiyang 550000, China
Yupeng Li: Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Yan Zhang: Guizhou Electric Power Dispatching and Control Center, Guiyang 550000, China
Yujian Wang: Guizhou Electric Power Dispatching and Control Center, Guiyang 550000, China
Gang Li: Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Chuntian Cheng: Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China

Energies, 2025, vol. 18, issue 14, 1-17

Abstract: Continuous multi-day extremely low or high new energy outputs have posed significant challenges in relation to power supply and new energy accommodations. Conventional reservoir hydropower, with the advantage of controllability and the storage ability of reservoirs, can represent a reliable and low-carbon flexibility resource to safeguard against continuous extreme new energy fluctuations. This paper proposes a mid-term scheduling method for reservoir hydropower to enhance our ability to regulate continuous extreme new energy fluctuations. First, a data-driven scenario generation method is proposed to characterize the continuous extreme new energy output by combining kernel density estimation, Monte Carlo sampling, and the synchronized backward reduction method. Second, a two-stage stochastic hydropower–new energy complementary optimization scheduling model is constructed with the reservoir water level as the decision variable, ensuring that reservoirs have a sufficient water buffering capacity to free up transmission channels for continuous extremely high new energy outputs and sufficient water energy storage to compensate for continuous extremely low new energy outputs. Third, the mathematical model is transformed into a tractable mixed-integer linear programming (MILP) problem by using piecewise linear and triangular interpolation techniques on the solution, reducing the solution complexity. Finally, a case study of a hydropower–PV station in a river basin is conducted to demonstrate that the proposed model can effectively enhance hydropower’s regulation ability, to mitigate continuous extreme PV outputs, thereby improving power supply reliability in this hybrid renewable energy system.

Keywords: new energy; mid-term scheduling; stochastic optimization; scenario generation; extreme scenarios (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: 2025
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