Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid

Featured Application This study has demonstrated an efficient maximum power point tracking method based on reinforcement learning to improve the renewable energy conversion. The theory can also be applied for the problems of the optimal sizing and energy management systems to develop the cost-efficient and environmentally friendly microgrids, especially for rural and islanding electrification. Abstract Due to the rising cost of fossil fuels and environmental pollution, renewable energy (RE) resources are currently being used as alternatives. To reduce the high dependence of RE resources on the change of weather conditions, a hybrid renewable energy system (HRES) is introduced in this research, especially for an isolated microgrid. In HRES, solar and wind energies are the primary energy resources while the battery and fuel cells (FCs) are considered as the storage systems that supply energy in case of insufficiency. Moreover, a diesel generator is adopted as a back-up system to fulfill the load demand in the event of a power shortage. This study focuses on the development of HRES with the combination of battery and hydrogen FCs. Three major parts were considered including optimal sizing, maximum power point tracking (MPPT) control, and the energy management system (EMS). Recent developments and achievements in the fields of machine learning (ML) and reinforcement learning (RL) have led to new challenges and opportunities for HRES development. Firstly, the optimal sizing of the hybrid renewable hydrogen energy system was defined based on the Hybrid Optimization Model for Multiple Energy Resources (HOMER) software for the case study in an island in the Philippines. According to the assessment of EMS and MPPT control of HRES, it can be concluded that RL is one of the most emerging optimal control solutions. Finally, a hybrid perturbation and observation (P&O) and Q-learning (h-POQL) MPPT was proposed for a photovoltaic (PV) system. It was conducted and validated through the simulation in MATLAB/Simulink. The results show that it showed better performance in comparison to the P&O method.