The inherent variability and unpredictability of renewable energy output pose significant challenges to power grid stability. Pumped Storage Power Stations (PSPS) play a pivotal role in mitigating these challenges, enhancing the grid’s reliability and operational efficiency. This study proposes an advanced linear analytical method based on Mixed-Integer Linear Programming (MILP) to optimize the short-term scheduling of PSPSs. The goal is to simultaneously maximize the reduction in equivalent load fluctuations and improve power generation benefits. The model linearizes the objective functions, constraints, and decision variables, applying MILP to efficiently derive optimal dispatch solutions. Using the Heimifeng (HMF) PSPS in Hunan Province as a case study, data from four representative daily load scenarios in 2023 are employed to optimize both power output and pumping processes. The results highlight the following nonlinear, competitive relationship between load fluctuation improvements and power generation benefits: as power benefits increase the rate of improvement in load fluctuations tends to decrease. The optimal solutions demonstrate significant outcomes, with improvements exceeding 11. 5% in equivalent load fluctuations across all scenarios and daily power benefits surpassing 41, 100, reaching a peak of 55, 700. This study introduces a robust linear analytical framework capable of simultaneously enhancing power benefits and stabilizing load fluctuations, thereby offering valuable technical support for decision-makers.
Huang et al. (Fri,) studied this question.