A Reinforced Learning Approach to Dispatch Distributed Generators for Enhanced Resilience
A Reinforced Learning Approach to Dispatch Distributed Generators for Enhanced Resilience
复制标题
DOI：
10.1109/pesgm48719.2022.9916963
复制
发表时间：
2022-07
期刊：
影响因子：
--
通讯作者：
Michael Abdelmalak;M. Kamruzzaman;Sean Morash;A. Snyder;M. Benidris
中科院分区：
文献类型：
--
作者：
Michael Abdelmalak;M. Kamruzzaman;Sean Morash;A. Snyder;M. Benidris
提出了一种基于强化学习的分布式电源调度方法，以提高配电网对飓风的抗灾能力。现有的弹性增强方法依赖于求解计算量大、耗时长的大规模优化问题，而这些问题不适合实时应用。本文提出了一种基于软Actor Critic算法的多智能体框架，用于分布式发电机组的调度，以提高系统的抗灾能力。该方法提供了一种快速控制算法，可以确定分布式发电机的大小和位置，以减少飓风期间的负荷削减量。该问题被描述为一个由系统状态、动作空间和奖励方案组成的马尔可夫决策过程。系统状态表示系统拓扑和特征，对这些特征采取操作并计算奖励值。使用飓风脆弱性模型产生的多次线路中断，使用迭代马尔可夫决策过程来训练所提出的软Actor Critic算法。每当存在孤岛电网和负荷削减时，经过训练的网络就会调度分布式发电机。该方法在IEEE33节点配电网馈线系统上得到了验证。结果表明，该算法能够确定分布式电源的最优大小和位置，从而提高系统的抗灾能力。
This paper proposes a reinforced learning-based approach for dispatching distributed generators to enhance operational resilience of electric distribution systems against hurricanes. Existing resilience enhancement approaches rely on solving large-scale optimization problems that are computationally expensive and time demanding, which are not suitable for real-time applications. In this paper, a multi-agent framework is developed using a Soft Actor Critic algorithm to dispatch distributed generators for resilience enhancement. The proposed approach provides a fast-acting control algorithm that determines the size and the location of distributed generators to reduce the amount of load curtailment during hurricanes. The problem is formulated as a Markov decision process that consists of system states, an action space, and a reward scheme. A system state represents the system topology and characteristics upon which an action is taken and a reward value is calculated. An iterative Markov decision process is used to train the proposed Soft Actor Critic algorithm using multiple line outages generated from a hurricane fragility model. The trained network dispatches distributed generators whenever there are islanded grids and load curtailments. The proposed method is demonstrated on the IEEE 33-node distribution feeder system. The results show the capability of the proposed algorithm to determine optimal sizes and locations of distributed generators for resilience enhancement.