Double deepQ-learningcoordinated control of hybrid energy storage system in island micro-grid

It is difficult for a single energy storage to meet both power and energy requirements in the island micro-grid because of the randomness of wind and solar irradiation. A reasonable way is to use hybrid energy storage in the island micro-grid. For the energy management and optimization control of energy storage systems, there are various problems with traditional methods, such as the large computational complexity in dynamic programming. Q-learning has recently been applied to the optimal control of energy storage systems. Due to the limitations of the Q-learning algorithm in the state space, this article uses the Double deep Q-learning (DQN) algorithm to design the control strategy of energy storage systems. It is applied to an island Micro-grid system consisting of photovoltaic (PV), wind turbine, hydrogen storage (long-term energy storage devices), and battery (short-term energy storage devices). Transform the coordinated control of the hybrid energy storage system into a sequence decision problem. Due to the influence of renewable energy, load and other factors, different control strategies have different effects. DDQN algorithm combines the perception ability of deep learning with the decision-making ability of reinforcement learning which can realize real-time online decision control after training. Experimental results show that, the method of this article can be effectively processed for different weather scenarios and increase utilization of renewable energy.

Automated summary

This study focuses on using a hybrid energy storage system in island micro-grids to address energy demands, proposing the Double deep Q-learning algorithm for optimizing control strategies. Experimental results demonstrate the method's effectiveness in handling various weather conditions and increasing renewable energy utilization.