考虑电动汽车充放电的智慧社区强化学习能源优化调度策略

为提高智慧社区能源系统(smart community energy system, SCES)运行的经济性，引入了电动汽车作为储能设备，并在此基础上提出了一种基于强化学习的新型智慧社区能源系统运行优化调度策略。首先，基于各能源设备运行机理构建了新型智慧社区能源系统模型，该模型考虑了电动汽车作为储能设备入网运行对负荷和供需平衡的影响。其次，将电动汽车分为耗能组和储能组，分别作为用电负荷和储能设备参与系统运行。分析了新型智慧社区能源系统多种能源设备的能量耦合关系，进而建立了电动汽车及各能源设备的非线性约束条件，并重新设计了算法的状态空间、动作空间以及奖励函数。再次，运用基于深度双Q网络(double deep Q network, DDQN)的新型智慧社区能源优化调度策略解决能源系统运行优化问题。最后，以某社区为例，仿真验证了所提策略可有效提高智慧社区能源系统运行的经济性。

Energy optimal scheduling strategy based on reinforcement learning for smart community with electric vehicle

A new optimization and scheduling strategy based on reinforcement learning for smart community energy system (SCES) with electric vehicles (EVs) as energy storage devices is proposed to improve the economic efficiency. Firstly, a new SCES model is established based on the operation mechanism of EVs and other energy devices. The impact of EVs on load and supply-demand balance is taken into account when EVs are connected to the grid. Secondly, EVs are classified into energy consuming groups and energy storing groups, which participate in the system operation as electricity loads and energy storage devices respectively. The energy coupling relations of various energy devices in the new SCES are analyzed, and the nonlinear constraints of EVs and other energy devices are established accordingly. The state space, action space, and reward function of the algorithm are redefined to accommodate the EVs and other energy devices. Thirdly, the proposed novel SCES optimization strategy based on deep double Q-learning network (DDQN) is utilized to solve the energy system optimization problem. Finally, the application of this strategy verifies that the proposed strategy can effectively improve the economic efficiency of SCES.