With the increasing penetration of renewable energy in power systems, the effective utilization of pumped storage power plant (PSP) clusters for peak shaving has become an important issue in system operation. In this study, an intraday scheduling model for PSP clusters is formulated to minimize the variance of the system net load, while accounting for operational constraints, including power balance, unit operation, and reservoir energy evolution. The resulting model is a mixed-integer nonlinear programming (MINLP) problem, which is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Case studies are conducted on an improved IEEE 39-bus system under both conventional scenarios and extreme renewable energy conditions. The results show that, under a unified peak-shaving objective, PSP clusters exhibit a stable structure of role differentiation even in conventional operating conditions. As the system peak-shaving pressure increases, this differentiation is progressively reinforced along existing functional roles, shifting from renewable energy absorption to peak-period generation support. It tends to converge under high operational stress due to the coupling between load and renewable variability. Further analysis indicates that when capacity differences among PSPs are eliminated, the differentiation structure is significantly weakened, suggesting that physical capability differences constitute an important foundation for the formation of role differentiation.
Li et al. (Tue,) studied this question.