An efficient distributionally robust framework for multi-period energy storage planning in renewable-dominated power systems under normal and hurricane conditions

This paper develops an efficient multi-period energy storage system planning (MPEP) framework for renewable-dominated power systems under both normal and severe weather conditions, aiming to enhance investment efficiency and operational security. First, a distributionally robust optimization (DRO)-based MPEP model is formulated that captures the long-term evolution of installed renewable capacity and load demand across the planning horizon, as well as different hurricane tracks and intensities, thereby reducing operational risks and overall costs. Second, to address the modeling difficulties arising from the inherent non-smooth and discontinuous characteristics of wind generation within the DRO-MPEP framework, a year-indexed, wind-speed-driven Wasserstein ambiguity set is constructed, which explicitly captures turbine cut-in and cut-out behavior and yields physically consistent distance metrics. Meanwhile, a moment-based ambiguity set is constructed for characterizing transmission line fault uncertainty during hurricanes. Furthermore, an efficient DRO solution framework is proposed to deal with the large-scale MPEP model, incorporating two acceleration techniques—topology-based scenario pre-screening and low-impact scenario filtering—to control scenario scale and reduce computational burden. Finally, numerical experiments benchmarked against previous models and algorithms demonstrate the effectiveness and superiority of the proposed method.