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The development of hybrid renewable energy systems (HRESs) can be the most feasible solution for a stable, environment-friendly, and cost-effective power generation, especially in rural and island territories. In this studied HRES, solar and wind energy are used as the major resources. Moreover, the electrolyzed hydrogen is utilized to store energy for the operation of a fuel cell. In case of insufficiency, battery and fuel cell are storage systems that supply energy, while a diesel generator adds a backup system to meet the load demand under bad weather conditions. An isolated HRES energy management system (EMS) based on a Deep Q Network (DQN) is introduced to ensure the reliable and efficient operation of the system. A DQN can deal with the problem of continuous state spaces and manage the dynamic behavior of hybrid systems without exact mathematical models. Following the power consumption data from Basco island of the Philippines, HOMER software is used to calculate the capacity of each component in the proposed power plant. In MATLAB/Simulink, the plant and its DQN-based EMS are simulated. Under different load profile scenarios, the proposed method is compared to the convectional dispatch (CD) control for a validation. Based on the outstanding performances with fewer fuel consumption, DQN is a very powerful and potential method for energy management.

Near-ground wind is not stable and turbulent due to the ground effect and heat ventilation. Small wind generation system design for public utilities needs to catch the small wind to rotate the turbine blades. Ground wind gust contains horizontal and vertical ventilation. A complex airfoil wind turbine is proposed in this paper for a special shape design to combine the horizontal and vertical blades which can deal with the different directions of the wind. Based on Bernoulli’s Law, these wind turbine blades are designed and assembled in the same axis shaft. Using 3D printing and metal machining, a small experiment system is constructed to meet the system demand and integrated to verify the complex design. Simulations and experiments are presented to demonstrate the complex turbine in near-ground operation. This design fits for small power generation in public utility applications such as street lights and emergency power in a remote shelter cabin. Systematic design and fabrication are completed for preliminary tests. The proposed complex airfoil turbine enhances small wind system performance and improves 15 ~ 20% efficiency.

On the issues of global environment protection, the renewable energy systems have been widely considered. The photovoltaic (PV) system converts solar power into electricity and significantly reduces the consumption of fossil fuels from environment pollution. Besides introducing new materials for the solar cells to improve the energy conversion efficiency, the maximum power point tracking (MPPT) algorithms have been developed to ensure the efficient operation of PV systems at the maximum power point (MPP) under various weather conditions. The integration of reinforcement learning and deep learning, named deep reinforcement learning (DRL), is proposed in this paper as a future tool to deal with the optimization control problems. Following the success of deep reinforcement learning (DRL) in several fields, the deep Q network (DQN) and deep deterministic policy gradient (DDPG) are proposed to harvest the MPP in PV systems, especially under a partial shading condition (PSC). Different from the reinforcement learning (RL)-based method, which is only operated with discrete state and action spaces, the methods adopted in this paper are used to deal with continuous state spaces. In this study, DQN solves the problem with discrete action spaces, while DDPG handles the continuous action spaces. The proposed methods are simulated in MATLAB/Simulink for feasibility analysis. Further tests under various input conditions with comparisons to the classical Perturb and observe (P&O) MPPT method are carried out for validation. Based on the simulation results in this study, the performance of the proposed methods is outstanding and efficient, showing its potential for further applications.

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.