Abstract:In allusion to such troubles as difficulty of uncertainty modeling and difficult to solve complex system efficiently in optimal dispatching of AC/DC hybrid microgrid, an artificial assisted deep reinforcement learning algorithm, which could improve the learning efficiency of intelligent agent through artificial strategy guidance, was proposed. Firstly, combining with the characteristic of demand side response of hybrid microgrid under grid-connected state a cost-minimized optimal dispatching model was constructed. Based on Markov decision process the modeling of optimal dispatching process was conducted and based on optimal dispatching model the reward function was designed. Secondly, the designed model was solved by artificially assisted deep deterministic policy gradient algorithm, and by means of continuous interaction between intelligent agent and environment the parameter of neural network was continually updated and then the optimal decision was obtained. Finally, it was verified by computing example that using the proposed algorithm the learning efficiency of intelligent agent could be effectively improved and while the training time of the model was decreased the operating cost of the subsystem could be effectively reduced.
标题：基于人工辅助深度强化学习的交直流混合微电网实时优化调度
英文标题：Real-Time Optimal Scheduling of AC / DC Hybrid Microgrid Based on Artificial Auxiliary Deep Reinforcement Learning
