## 5 Discussions  
This paper provides the assessment of hybrid renewable hydrogen energy system development, especially for the practical application of rural and islanded electrification. Most remote areas are currently powered by diesel generators that could significantly pollute our environment. With the development of new technologies, the cost of renewable energy will probably decrease allowing HRESs to be implemented for sustainable development. Optimal sizing of the system helps to define the optimal configuration that can ensure the power supply with the lowest cost, while the MPPT control and EMS are essential to maximize the harvested power and to control the power flow among the various components in the system. Based on the successful applications of reinforcement learning in various fields, the system could be a possible solution to the problems involved in the hybrid renewable energy system design. In recent times, various methodologies have been applied to size the system components so as to minimize the cost, ensure the reliability and reducing the emissions, the HOMER methodology is one of the most popular of these methodologies. A detailed process for optimally using HOMER was clearly indicated with the case study in Basco Island. As mentioned above, the major drawbacks of battery are a short lifetime and recycling problems, so the focus on the development of hydrogen systems combined with renewable resources should be significantly considered as alternatives to fossil fuel and nuclear power. Moreover, the analytical techniques or tools are necessary for solving the optimization problem in system sizing based on the design criterions and constraints. Huge research has been carried out based on various tools and techniques. AI techniques are able to completely search the workspace and to define the global optimal solution, but sometimes they also inefficiently solve certain difficulties when increasing the number of variables. For overcoming the limitation of sizing problems, ML and RL techniques, as well as the hybrid methodology, should be focused on. The main objectives of an MPPT controller are to deal with the problems involved in the fluctuation and intermittency of RE sources due to the change of weather conditions while EMS is used to optimize operations, ensure the system reliability, and provide power flow control in both standalone and grid-connected microgrids. In this study, the proposed h-POQL method was developed for MPPT control of the PV source. Based on the simulated results, the proposed method can efficiently track the maximum power under various changes to the weather conditions. In addition, it shows better results in terms on speed and accuracy when the h-POQL is compared against the P&O method. The Q-learning controllers have been trained offline for different desired targets, such temperature and irradiation, and then we transferred the training models to the P&O controller to increase the efficiency  
Figure 22: Comparison between the h-POQL and the P&O methods. of energy conversion. In contrast, the approach in reference [18] adopted Q-learning as an on-policy algorithm. Due to the different approaches between two studies, the comparison with the method in reference [18] was not carried out. However, based on the simulation results, the proposed h-POQL has clearly shown faster response based on the change of weather conditions, with less than a second compared to more than two seconds [18], meaning h-POQL could be more efficient. This is because the controller in the previous paper needs to spend time on the online learning. In future work, the real experiment will be set up for testing the h-POQL algorithm, and the comparison between these two methods will be conducted. Following the assessments of EMS and MPPT control conducted in this study, there is a trend in the application of ML and RL algorithms in this field.