Advanced Energy Management System for Solar-Plus-Storage Systems

A major challenge in the energy transition is the effective integration of renewable energy sources into the electrical grid, while optimizing power flow and reducing costs through advanced energy management system (EMS) controllers. Although recent advancements have improved EMS controllers, significant gaps remain in their validation under dynamic conditions, such as fluctuating loads and storage constraints. This study addresses these challenges by developing an optimized controller using linear programming, that minimizes costs and optimizes power flow among the grid, photovoltaic (PV) modules and battery storage. The proposed model utilizes historical load data from the University of Cyprus nanogrid, which features a 40 kWp PV system and a 50-kWh battery, and is benchmarked against a reference rule-based controller across various prediction horizons and operational scenarios. The results demonstrated that the advanced controller consistently reduces grid dependence and operational expenses, with the day ahead prediction horizon yielding mean daily savings of €6.90/kWh. Furthermore, in scenarios with sudden drops in PV output (such as during curtailment events), the advanced controller achieved cost savings of up to €24.04/kWh compared to the rule-based alternative, demonstrating its superior performance.