An MILP model based on a processing strategy of complex multisource constraints for the short-term peak shaving operation of large-scale cascaded hydropower plants

Zhou Fang, Shengli Liao, Hongye Zhao, Chuntian Cheng, Benxi Liu, Huan Wang and Shushan Li

Renewable Energy, 2024, vol. 231, issue C

Abstract: Hydropower with flexible regulation plays an important role in short-term peak shaving operations. However, short-term peak shaving operation is a challenging problem due to the large scale, the nonconvex and nonlinear characteristics, and the complex multisource tasks. This study proposes a mixed integer linear programming (MILP) model, termed MILPoPSC, based on a processing strategy for complex multisource constraints, tailored for short-term peak shaving in large-scale cascaded hydropower plants. The MILP model, designed to incorporate multisource tasks by abstracting them into constraints, ensures that task requirements are met. A novel multisource constraint transformation method is introduced to derive constraint expressions related to power flow, facilitating the unification of constrained variables. Additionally, a classification and integration method based on restriction mode and set theory is proposed to improve solving efficiency by integrating constrains of the same type. The proposed method was applied to 7 hydropower cascade plants in the Wujiang River. The results showed that the linearization method and the processing strategy of complex multisource constraints can successfully reduce the complexity of the MILP model without affecting the solution quality. This indicates that MILPoPSC has good practical value for the short-term peak shaving operation of large-scale cascaded hydropower plants in China.

Keywords: Cascade hydropower plants; Short-term peak shaving operation; Mixed integer linear programming; Multisource constraints (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:231:y:2024:i:c:s0960148124010000

DOI: 10.1016/j.renene.2024.120932

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