微电网
强化学习
自动频率控制
频率偏差
控制器(灌溉)
控制理论(社会学)
计算机科学
理论(学习稳定性)
人工神经网络
电力系统
控制工程
状态空间
功率(物理)
控制(管理)
工程类
人工智能
机器学习
数学
电信
物理
量子力学
生物
农学
统计
作者
L. Xie,Yonghui Li,Peixiao Fan,Li Wan,Kanjun Zhang,Jun Yang
摘要
Abstract Load variation, distributed power output uncertainty and multi‐microgrids network complexity have brought great difficulties to the frequency stability of the whole microgrid. To address this problem, this paper uses a multi‐agent deep reinforcement learning(DRL) algorithm to design the controllers to control the frequency of the multi‐microgrids. Firstly, a load frequency control (LFC) model for multi‐microgrids was built. Secondly, based on the centralized training and decentralized execution (CTDE) multi‐agent reinforcement learning (RL) framework, the multi‐agent soft actor‐critic (MASAC) algorithm was designed and applied to the multi‐microgrids model. The state space and action space of multi‐agent were established according to the frequency deviation of every sub‐microgrid and the output of each distributed power source. The reward function was then established according to the frequency deviation. The appropriate neural network and training parameters were selected to generate the interconnected microgrid controllers through multiple training of pre‐learning. Finally, the simulation study shows that the MASAC controller proposed in this paper can quickly maintain frequency stability when the system is disturbed. Sensitivity analysis shows that the MASAC controller can effectively cope with the uncertainty of the system parameters.
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