Branching Dueling Q-Network-Based Online Scheduling of a Microgrid With Distributed Energy Storage Systems
Branching Dueling Q-Network-Based Online Scheduling of a Microgrid With Distributed Energy Storage Systems
复制标题
DOI：
10.1109/tsg.2021.3103405
复制
发表时间：
2021-05
影响因子：
9.6
通讯作者：
H. Shuai;F. Li;Héctor Pulgar-Painemal;Yaosuo Xue
中科院分区：
文献类型：
--
作者：
H. Shuai;F. Li;Héctor Pulgar-Painemal;Yaosuo Xue
本文研究了一种基于分支决斗q网络（BDQ）的分布式电池储能系统（BESSs）微电网在不确定条件下的在线运行策略。提出的基于深度强化学习（DRL）的微电网在线优化策略可以实现神经网络输出数量随分布式bess数量的线性增加，克服了多个bess充放电决策带来的维数困扰。数值仿真验证了该方法的有效性。
This letter investigates a Branching Dueling Q-Network (BDQ) based online operation strategy for a microgrid with distributed battery energy storage systems (BESSs) operating under uncertainties. The developed deep reinforcement learning (DRL) based microgrid online optimization strategy can achieve a linear increase in the number of neural network outputs with the number of distributed BESSs, which overcomes the curse of dimensionality caused by the charge and discharge decisions of multiple BESSs. Numerical simulations validate the effectiveness of the proposed method.