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arXiv 2102.00077eess.SYcs.LGcs.SY
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基于结构驱动分层深度强化学习的可扩展电压控制

Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning

Pacific Northwest National Laboratory(太平洋西北国家实验室)

Sayak Mukherjee, Renke Huang, Qiuhua Huang, Thanh Long Vu, Tianzhixi Yin

摘要:

本文提出了一种新的分层深度强化学习(DRL)的设计用于电网电压控制。DRL代理被训练用于控制动作的快速和自适应选择,使得在扰动之后可以满足电压恢复标准。现有的电压控制技术受到操作速度、不同位置之间的最佳协调和可扩展性的问题的困扰。我们利用区域明智的分工结构的电力系统,提出了一个分层的日间行车灯设计,可以扩展到更大的网格模型。我们采用了一种增强的增广随机搜索算法,是专为电压控制问题的两级架构。我们训练区域分散的RL代理计算较低级别的政策,为个别地区,并同时训练一个更高级别的DRL代理,使用更新的较低级别的政策,以有效地协调控制行动所采取的较低级别的代理。IEEE基准39节点模型的3个区域的数值实验表明,所提出的分层方法的优点和各种复杂性。

英文摘要:

This paper presents a novel hierarchical deep reinforcement learning (DRL) based design for the voltage control of power grids. DRL agents are trained for fast, and adaptive selection of control actions such that the voltage recovery criterion can be met following disturbances. Existing voltage control techniques suffer from the issues of speed of operation, optimal coordination between different locations, and scalability. We exploit the area-wise division structure of the power system to propose a hierarchical DRL design that can be scaled to the larger grid models. We employ an enhanced augmented random search algorithm that is tailored for the voltage control problem in a two-level architecture. We train area-wise decentralized RL agents to compute lower-level policies for the individual areas, and concurrently train a higher-level DRL agent that uses the updates of the lower-level policies to efficiently coordinate the control actions taken by the lower-level agents. Numerical experiments on the IEEE benchmark 39-bus model with 3 areas demonstrate the advantages and various intricacies of the proposed hierarchical approach.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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