Abstract
Seawater pumped energy storage (SPES) offers a promising solution to the intermittency of offshore wind and photovoltaic power in China's coastal regions. However, a targeted planning approach is lacking. We present a novel multi-stage, data-driven potential model that leverages the unique advantages of coastal depression topography to improve reservoir depth estimation and more accurately quantify energy storage potential. First, technically feasible locations are identified based on environmental and regulatory constraints. Next, costreduction criteria are applied to screen these candidate sites. Finally, a multi-objective optimization framework simultaneously minimizes total construction cost, number of sites, and spatial distribution disparities to select an optimal combination of sites. The results identify approximately 20,000 potential coastal sites with a combined storage capacity of 4379 GWh. Among them, 87 sites exhibit average construction costs of 949/kW, approaching those of conventional freshwater pumped storage systems. Considering the low excavation costs of depressions, the actual construction costs may be lower than the estimated values. Under projected 2050 electricity-storage requirements, only 15 well-distributed site combinations with a total installed capacity of 19.2 GW can satisfy national demand while remaining cost-competitive with alternative storage technologies. This study provides a scalable, data-driven framework to support coordinated planning of SPES infrastructure alongside renewable energy development.