Energy management with control parameter optimization for a PV/FC/battery islanded DC microgrid

Abstract The integration of renewable energy resources (RERs) and the need for reliable and efficient power distribution in emerging power systems has driven growing interest in microgrids. This has led to challenges like uneven load distribution, overloading, less usage of power, and unstable operations that may lead to damage to power electronic devices connected. The existing metaheuristic-based control mechanisms struggle to manage fluctuating sources and demands effectively, often leading to suboptimal energy management. Therefore, with such constraints, this research work proposes a method utilizing Sparrow Search Optimization (SSA) and optimizes the control parameters for more effective EMS functionality. To support efficient power management and balancing of generation versus load demands, the proposed system integrates dispatchable distributed generators such as fuel cells (FC) and battery storage systems (BSS) with non-dispatchable distributed generators such as solar photovoltaic (PV) source. The method uses SSA optimization of controller parameter estimation ensuring better stability and reliability, compared to conventional PI and other metaheuristic methods. The intended system was tested under various operating scenarios with the use of MATLAB Simulink, to validate the efficacy of this system in the regulation of renewable energy fluctuations and optimization of battery performance. The performance of the developed system was compared with systems optimized through Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Harris Hawk Optimization (HHA). Simulation results show that the system optimized using the SSA method results in better efficiency with faster execution time than other optimization-based systems.