A novel multi-layer energy management system for enhanced grid services by a hybrid energy storage system

Background The increasing integration of renewable energy sources (RES) in power systems introduces operational challenges due to their intermittent and difficult-to-predict generation. Hybrid Energy Storage Systems (HESS) can mitigate these issues by providing flexibility and stability to microgrids. However, efficient coordination between storage systems, renewable generation, and the utility grid requires advanced Energy Management Systems (EMS). This work presents a hierarchical EMS developed within the HAVEN project to optimize energy flow, reduce operational costs, and extend battery lifetime in microgrid environments. Methods The proposed EMS follows a three-layer hierarchical architecture. The High-Level EMS, deployed in the cloud, uses artificial intelligence algorithms to forecast renewable generation, load demand, and battery Remaining Useful Life (RUL). The Medium-Level EMS performs optimal energy management within the microgrid through a two-layer control strategy that separates long-term planning and short-term adjustments. It determines optimal power setpoints for the grid and the HESS, which consists of two lithium-ion batteries with different characteristics (high-energy and high-power). The Low-Level EMS, embedded in power converters through edge devices, supervises individual energy storage systems, monitors system states, and ensures the execution of the setpoints while maintaining converter stability. The system performance is evaluated through simulation using realistic seasonal profiles of load demand, RES generation, and time-varying electricity tariffs. Results Simulation results demonstrate that the EMS effectively coordinates renewable generation, grid interaction, and battery operation under both summer and winter conditions. The high-power battery compensates short-term fluctuations between forecasted and real profiles, while the high-energy battery manages longer-term energy balancing. Annual extrapolation of the results shows a reduction of 31.7% in electricity costs and grid energy imports, as well as the complete elimination of renewable energy curtailment. Conclusions The proposed hierarchical EMS improves the operational efficiency and economic performance of microgrids integrating hybrid energy storage systems. Its modular multi-layer design enables scalable deployment and efficient coordination between forecasting, optimization, and real-time control. The approach demonstrates significant potential for supporting renewable integration, reducing energy costs, and improving battery lifetime in microgrid applications.