## 6 Conclusions and Future Works  
This research aims to develop the hybrid renewable hydrogen energy system, especially for a standalone microgrid with the applications of rural and islanding electrification. The problems involved in the system design process were clearly introduced, including optimal sizing, MPPT control, and energy management system. Firstly, according to the data collected from the Basco island in the Philippines, the optimal design of HRES was determined by the HOMER software which has the features of being cost-effective, reliable, and environmentally friendly. According to the analysis, the optimal configuration of power system includes 5483 kW of PV, 236 units of 10 kW of wind turbines, 20,948 kW of batteries (48V DC, 4 modules, 5237 strings), 500 kW of fuel cells, a 750 kW diesel generator, a 3000 kW electrolyzer, a 500 kg H-tank, and a 1575 kW converter with the energy cost of US$0.774/kWh based on a 1 US$/liter fuel cost. Moreover, from the analyzed results, the combination of the fuel cell system and the battery is one of the best options for the design of HRES, in which FC can be used as long term energy storage option and the battery can act as a short term energy storage medium. The system is not only practical and cost-effective but can also satisfy the load demand in the applied area. The same work can be considered for the other sites around the world, especially in remote areas, to efficiently increase the renewable energy use and reduce emissions. In regard to the recent successful applications of RL techniques in various fields, especially the areas of computer vision and robotics, this research aims to consider these theories for the MPPT control and energy management of HRES. According to the brief review and comparison between techniques for MPPT control and EMS, from conventional methods to the current AI ones, this paper can be a good reference for researchers in this field. This work introduces a new hybrid approach for MPPT control based on the combination of Q-learning and P&O, named as h-POQL. The proposed method was simulated in Simulink with various scenarios based on the change of weather conditions to test its efficiency and performance. It also shows better results in terms of power generation and speed. Additionally, it can define the optimal duty cycle in a specific control region by reducing the redundant states. Based on the optimal results learned by the Q-learning algorithm, the P&O can tune the reference input values of the duty cycle and track the maximum power point with faster speed and higher accuracy. Based on the ability to learn from experiences and optimally solving complex control problems with no prior knowledge of the environment or complex mathematical model needed, reinforcement learning is supposed to be the new and potential trend in the fields of energy conversion and management. In the future, optimal sizing based reinforcement learning will be studied and compared with the approach from HOMER software in order to obtain optimal results and be able to meet more required variables and constraints. Then the practical system will be installed at the applied site when all the design requirements can be met. In addition, we plan to study more RL algorithms so that it can deal with continuous state-space problems besides the proposed h-POQL method. Further experiments will be implemented to test and compare the performance of these methods. Finally, the DRL algorithm will be integrated with the multi-agent-based HRES for energy management. Many real tests will be carried out for validation besides the simulation results. Our goal is to implement the proposed system on an isolated micro-grid. Conceptualization, B.C.P. and Y.-C.L. ; methodology, Y.-C.L. ; software, B.C.P. ; validation, B.C.P. and Y.-C.L. ; formal analysis, B.C.P. ; investigation, B.C.P. ; resources, B.C.P. and Y.-C.L. ; data curation, B.C.P. ; writing--original draft preparation, B.C.P. ; writing--review and editing, Y.-C.L. ; visualization, B.C.P. and Y.-C.L.