Distributionally robust coordinated day-ahead scheduling of Cascade pumped hydro energy storage system and DC transmission

Abstract

Large-scale wind and solar power integration introduces significant operational uncertainty to power systems. To enhance the system's economic efficiency and reliability, this paper investigates the coordinated day-ahead scheduling of a multi-energy power system incorporating a cascade pumped hydro energy storage (CPHES) system and DC transmission. We propose a joint optimization model that minimizes the total system operating cost and renewable energy curtailment penalty, explicitly considering the flexible regulation capabilities of CPHES, DC transmission power losses, and various operational constraints. To effectively manage the uncertainty associated with wind and solar power forecasts, we develop a novel two-stage distributionally robust optimization (DRO) scheduling method based on moment information. This method constructs a moment-based ambiguity set, incorporating mean, variance, and skewness information, to effectively capture the uncertainty. Leveraging linearization techniques, duality theory, linear decision rules, and matrix transformations, the original problem is reformulated into a tractable mixed-integer linear programming (MILP) model. Case studies based on a modified IEEE 73-bus system and a real large-scale hydro-thermal power system in Brazil demonstrate that the proposed method effectively reduces system operating costs, improves wind and solar power accommodation, and enhances the system's resilience to output uncertainties.