基于深度Q学习的电热综合能源系统能量管理

能量管理是电热综合能源系统运行优化的重要组成部分.然而,系统中可再生能源出力的波动性以及用户负荷的随机性使得能量优化管理问题充满挑战.针对此问题,文章提出了一种计及可再生能源和负荷需求不确定性的综合能源系统能量管理方法.将电热综合能源系统的能量管理问题表述为转移概率未知的马尔科夫决策过程,定义了系统的状态空间、动作空间...

Energy Management Approach for Integrated Electricity-Heat Energy System Based on Deep Q-Learning Network

Energy management plays an important role in the operation optimization of integrated electricity-heat energy systems. However, the fluctuation of renewable energy power generation and the randomness of energy loads in the system make the energy management problem full of challenges. In order to solve this problem, this paper proposes an optimal energy management approach for integrated energy system considering the uncertainties of renewable energy and load demands. In this paper, the energy management problem of the system is expressed as a Markov decision process with unknown transition probability, and the state space, action space and reward function of the process are defined. In order to solve the Markov decision process, an optimal energy management approach based on deep Q-learning network is proposed. Simulation results show that the proposed method can adaptively respond to the random fluctuations of source and loads and realize the optimal energy management.