An energy management strategy for integrated electricity-thermal energy systems using the DQN-CE algorithm - ZHU Jiejie; PI Zhiyong; CHEN Daicai; TAN Hong (Zhejiang dianli)
To address the uncertainty and intermittency of renewable energy output in integrated electricity-thermal energy systems， a reinforcement learning method for energy management is proposed， aiming to minimize the operating costs of the system. First， an energy management model for the integrated electricity-thermal energy system is established. Next， the energy management process of the system， which includes renewable energy， is transformed into a Markov decision process （MDP）. The DQN-CE （Deep Q-Network with cross-entropy） algorithm， integrating NoisyNet and a self-attention mechanism， is then used to train the agent through interactive learning. Finally， case study analysis shows that the agent trained using the proposed method can respond in real time to the uncertainties of renewable energy and manage the energy of the integrated electricity-thermal energy system with renewable sources online.