基于具有动作自寻优能力的深度强化学习的智能发电控制

Smart generation control based on deep reinforcement learning with the ability of action self-optimization

新能源以及分布式能源大规模并网所带来的随机扰动问题,影响电网安全和经济运行.本文提出一种具有动作自寻优能力的多智能体深度强化学习算法,即DDRQN-AD算法.所提算法可有效获取电网最优协调控制,从而解决传统集中式自动发电控制难以解决的新能源以及分布式能源大规模并网所带来的强随机扰动,使新能源得到最大限度的开发利用.通过对两区域微电网负荷频率控制电力系统模型以及广东电网模型进行仿真,结果显示DDRQN-AD与已有的多种智能算法相比,具有更强的鲁棒性及学习能力,可减少碳排放,提高新能源利用率.

Random disturbance or noise caused by the large-scale integration of new energy and distributed energy affects the safe and economical operation of the interconnected power grids. This study aims to propose DDRQN-AD; which was based on action self-optimization. In comparison with the traditional centralized automatic generation control systems; DDRQN-AD identified the optimal strategies easily and addressed the stochastic disturbance caused by the extensive integration of new energy and distributed energy sources into interconnected power grids to maximize the utilization of new energy. Simulation results for a two-area microgrid load-frequency control power system model and the Guangdong Power Grid model showed that the proposed algorithm can reduce carbon emissions can enhance the utilization rates of new energy sources. Moreover; the robustness and learning ability of DDRQN-AD were stronger than those of the traditional smart methods.