High-resolution siting and sizing of grid-scale energy storage for real-world transmission networks under high renewable penetration: a two-stage metaheuristic approach

Abstract

The penetration of renewable energy sources has introduced several challenges to the existing energy systems due to the inherent variability and intermittency of renewables required for grid infrastructure upgrades and the integration of energy storage solutions. One promising solution to address the intermittency of non-dispatchable renewable sources is using batteries that enhance grid stability and resilience while promoting the reliable integration of renewable energy. Optimal siting and sizing of grid-scale energy storage in transmission networks is computationally demanding, especially at a national scale. This study proposes a scalable framework for integrating batteries into national grids to identify high-resolution geospatial locations and optimal storage capacities that minimize the total net cost of electricity supply through strategic investment in battery installations. The approach employs a two-level metaheuristic optimisation with an ε-dominance parameter in the siting–sizing search. The ε parameter relaxes Pareto dominance to preserve solution diversity and convergence while reducing computational time, making the framework tractable for regional and national systems. The method is applied to the coal-dominated Czech Republic power system (50 buses), producing a high-resolution map of candidate battery capacities and locations that can support grid modernization and cost-effective expansion planning. While the framework is applicable to different storage technologies, this study focuses on LiFePO₄ batteries due to their widespread deployment and incorporates charge–discharge degradation to represent realistic operational impacts. The proposed approach demonstrates that optimizing battery storage capacities and locations can reduce total electricity production costs by up to 30% by 2030. Additionally, the selected Pareto-optimal battery portfolio leads to a 23% reduction in lignite power plant utilization, contributing substantially to the reduction of carbon dioxide emissions.