论文标题
使用简单的深度强化学习方法探索网格拓扑结构
Exploring grid topology reconfiguration using a simple deep reinforcement learning approach
论文作者
论文摘要
系统操作员面临越来越挥发的操作条件。为了以具有成本效益的方式管理系统可靠性，控制室操作员正在根据AI和机器学习来转向计算机化的决策支持工具。具体而言，增强学习（RL）是一种培训向操作员进行网格控制动作的代理的有前途的技术。在本文中，使用RL提出了一种简单的基线方法，以代表一个可以在1周内操作IEEE 14总线测试用例的人工控制室操作员。该代理采取拓扑切换动作来控制网格上的电源流，并且仅在一个精心挑选的场景上接受训练。该代理的行为对不同的发电和需求时间序列进行了测试，证明了其在1000个场景中的965次成功操作网格的能力。在测试方案中分析了代理建议的拓扑的类型和可变性，证明了有效而多样的代理行为。
System operators are faced with increasingly volatile operating conditions. In order to manage system reliability in a cost-effective manner, control room operators are turning to computerised decision support tools based on AI and machine learning. Specifically, Reinforcement Learning (RL) is a promising technique to train agents that suggest grid control actions to operators. In this paper, a simple baseline approach is presented using RL to represent an artificial control room operator that can operate a IEEE 14-bus test case for a duration of 1 week. This agent takes topological switching actions to control power flows on the grid, and is trained on only a single well-chosen scenario. The behaviour of this agent is tested on different time-series of generation and demand, demonstrating its ability to operate the grid successfully in 965 out of 1000 scenarios. The type and variability of topologies suggested by the agent are analysed across the test scenarios, demonstrating efficient and diverse agent behaviour.
