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Article

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

Department of Aeronautics and Aeronautics, National Cheng Kung University, Tainan 701, Taiwan
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Appl. Sci. 2019, 9(19), 4001; https://doi.org/10.3390/app9194001
Received: 30 July 2019 / Revised: 17 September 2019 / Accepted: 19 September 2019 / Published: 24 September 2019
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. View Full-Text
Keywords: HRES; optimal sizing; MPPT control; EMS and reinforcement learning HRES; optimal sizing; MPPT control; EMS and reinforcement learning
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MDPI and ACS Style

Phan, B.C.; Lai, Y.-C. Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid. Appl. Sci. 2019, 9, 4001. https://doi.org/10.3390/app9194001

AMA Style

Phan BC, Lai Y-C. Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid. Applied Sciences. 2019; 9(19):4001. https://doi.org/10.3390/app9194001

Chicago/Turabian Style

Phan, Bao C., and Ying-Chih Lai. 2019. "Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid" Applied Sciences 9, no. 19: 4001. https://doi.org/10.3390/app9194001

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