Hybrid energy storage planning in renewable-rich microgrids

The stable and economical operation of renewable-rich microgrids poses unprecedented challenges for the future. Effective energy storage planning is critical for addressing the inherent volatility of renewable energy. In this context, we propose a two-stage robust planning model for hybrid energy storage systems including thermal and battery energy. Our model demonstrates that an appropriately sized battery energy storage system can provide reserve power for grid-forming converters, thereby mitigating the derating of renewable energy output and enhancing the overall economic efficiency of the energy system. We construct the uncertain set using a novel data-based methodology and develop an unnested column-and-constraint generation (C&CG) approach to achieve accurate solutions. This approach enhances computational efficiency by circumventing the complexities associated with nested structures. Case studies indicate that the average solution time for the unnested C&CG algorithm is reduced by over 20% compared to traditional nested C&CG methods.