Double deep Q‐learning coordinated control of hybrid energy storage system in island micro‐grid. (19th October 2020) - Yu, Yunjun; Cai, Zhenfen; Liu, Yichen
Summary: 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. Abstract : This paper constructs an island micro‐grid that includes photovoltaic, wind turbine, hydrogen storage system (long‐term energy storage), and battery storage (short‐term energy storage). The coordinated control of the hybrid energy storage system is transformed into a sequence decision problem, which is solved by the Double deep Q‐learning method.