Postdisaster Routing of Movable Energy Resources for Enhanced Distribution System Resilience: A Deep Reinforcement Learning-Based Approach
Postdisaster Routing of Movable Energy Resources for Enhanced Distribution System Resilience: A Deep Reinforcement Learning-Based Approach
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DOI：
10.1109/mias.2023.3325046
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发表时间：
2023-11-01
影响因子：
0.8
通讯作者：
Ben-Idris，Mohammed
中科院分区：
文献类型：
--
作者：
Gautam，Mukesh;Bhusal，Narayan;Ben-Idris，Mohammed
在极端事件发生后没有其他能源可用时，部署可移动能源（MER）可以成为恢复关键负荷以增强电力系统恢复能力的有效策略。由于极端事件后MER的最佳位置取决于系统运行状态（例如，每个节点的负载、系统分支的开/关状态等），因此当系统运行状态发生变化时，现有的分析和基于群体的方法必须重复整个分析和计算。相反，如果基于深度强化学习（DRL）的算法在广泛的场景中得到充分的训练，那么无论系统状态如何变化，它们都可以快速找到最佳或接近最佳的位置。提出了一种基于深度 Q 学习的方法来优化 MER 部署，以增强电力系统的弹性。如果有的话，MER 还可用于补充其他类型的资源。所提出的方法在极端事件发生后分两个阶段进行。在第一阶段，配电网络被表示为图，然后使用 Kruskal 的生成林搜索算法 (KSFSA) 使用联络开关重新配置网络。为了最大化关键负载恢复，在第二阶段选择 MER 的最佳或接近最佳位置。对 33 节点分布式系统和改进的 IEEE 123 节点系统的案例研究证明了所提出的 MER 灾后路由方法的有效性。
The deployment of movable energy resources (MERs) can be an effective strategy to restore critical loads to enhance power system resilience when no other energy sources are available after the occurrence of an extreme event. Since the optimal locations of MERs following an extreme event are dependent on system operating states (e.g., the loads at each node, on/off status of system branches, and so on), existing analytical and population-based approaches must repeat the entire analysis and calculation when the system operating states change. On the contrary, if deep reinforcement learning (DRL)-based algorithms are sufficiently trained with a wide range of scenarios, they can quickly find optimal or near-optimal locations irrespective of changes in system states. A deep Q-learning-based approach is proposed for optimal MER deployment to enhance power system resilience. MERs can be also utilized to complement other types of resources, if available. The proposed approach operates in two stages after the occurrence of extreme events. In the first stage, the distribution network is represented as a graph, and the network is then reconfigured using tie switches by using Kruskal’s spanning forest search algorithm (KSFSA). To maximize critical load recovery, the optimal or near-optimal locations of MERs are chosen in the second stage. Case studies on a 33-node distribution system and a modified IEEE 123-node system demonstrate the effectiveness of the proposed approach for postdisaster routing of MERs.