Deep Reinforcement Learning Based Approach for Optimal Power Flow of Microgrid with Grid Services Implementation
Deep Reinforcement Learning Based Approach for Optimal Power Flow of Microgrid with Grid Services Implementation
复制标题
基于深度强化学习的微电网优化潮流与电网服务实施的方法
DOI：
10.1109/itec53557.2022.9813862
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
M. Preindl
中科院分区：
文献类型：
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作者：
Jingping Nie;Yanchen Liu;Liwei Zhou;Xiaofan Jiang;M. Preindl
电动汽车（EV）已经迅速普及，并且基于逆变器的EV充电器的数量由于其高效率和提供电网服务的能力而迅速增加。电动汽车和其他分布式能源（DER）将成为微电网弹性和性能的关键部分。由于非线性和不确定性，优化EV接口微电网具有挑战性。在本文中，我们提出了一种基于深度强化学习（DRL）和双延迟深度确定性策略约束（TD3）的方法来优化微电网。所提出的方法可以用于优化不同的目标。以IEEE 30节点系统为基础，提出了一种稳定电力系统电压波动的方法。所提出的系统可以根据IEEE 1547标准中规定的要求提供用于无功功率控制的电网服务策略。这种无模型DRL方法可以适用于其他微电网系统。
Electric vehicles (EVs) have rapidly grown in popularity, and the number of inverter-based EV chargers increases promptly due to their high efficiency and capabilities of providing grid services. EV and other distributed energy resources (DER) would become a crucial part of the resilience and performance of the microgrid. Optimizing the EV-interfaced microgrid is challenging due to the non-linearity and uncertainty. In this paper, we propose a method based on deep reinforcement learning (DRL) with Twin Delayed Deep Deterministic Policy Gradients (TD3) to optimize the microgrid. The proposed method can be used to optimize different objectives. An example objective of stabilizing the voltage fluctuations in a power system modified from the IEEE 30-bus system is presented. The proposed system can provide grid service policies for reactive power control according to the requirements specified in the IEEE 1547 standard. This model-free DRL approach can be adapted to other microgrid systems.