Control-Oriented Model of an Optimally Designed Hybrid Storage System for a Standalone Microgrid

The uncertainty in wind speed and load demand fluctuations has made the deployment of renewable energy (RE) challenging. However, in islanded microgrids, the implementation of hybrid energy storage systems (HESS) can improve the reliability of power supply and enable further utilization of surplus energy. In this study, we configure an energy management system (EMS) for an optimally constructed system that includes wind turbines (WTs), an electric storage system (i.e., lithium-ion battery), a hydrogen storage system (i.e., PEM type), and diesel generator (DG), based on model predictive control (MPC), to meet specified technical and economic benchmarks for a standalone microgrid (SMG). MPC is intended to maintain the state of charge (SOC) and level of hydrogen (LOH) within their technical limits to prevent degradation and extend the lifetimes of HESS by minimizing the objective function. Additionally, disturbances due to wind power and load demand variations are captured to determine the optimal instantaneous powers of hydrogen and DG sources exposed to certain weighting factors within opted constraints. To perform the simulations, MPC toolbox in MATLAB Simulink environment is used. We consider four cases under various weather conditions to validate the robustness of MPC on the designed EMS for the SMG with shared system element powers for 24 hours using real data of wind velocity and load demand. From the simulation results, we observe minimum and maximum SOC of 20% and 88.52%, and LOH of 10% and 90.06% for the entire studied periods respectively. The results of the designed EMS show that MPC has ensured the HRES bounds to prevent degradation, overcome power interruptions due to weather intermittencies, and reduce grid-integration establishment charges. Moreover, the utilization of the entire renewable energy produced by wind turbines is achieved. Likewise, the load demand is met completely by using this technique and excellent performance of MPC under uncertainty is achieved.