论文标题
培训有效的强化学习剂，用于实时电网操作和控制
On Training Effective Reinforcement Learning Agents for Real-time Power Grid Operation and Control
论文作者
论文摘要
快速有效地协调控制行动仍然是一项巨大的挑战，影响了当今大规模电网的安全和经济运作。本文提出了一种基于人工智能（AI）的新方法，以实现多目标实时电网控制以实现现实世界实施。采用最先进的非政策外增强学习（RL）算法，软演员 - 批判性（SAC），以培训具有多线程离线培训和定期在线培训的AI代理，以调节电压和传输损失而不违反线的热约束。开发并部署了一个软件原型，并部署在SGCC江苏电力公司的控制中心，该公司每5分钟与能源管理系统（EMS）进行交互。在实时环境中使用实际电网快照的大量数值研究验证了所提出的方法的有效性。训练有素的SAC代理可以学会在调节电压轮廓和减少传输损失方面提供有效的次要控制措施。
Deriving fast and effectively coordinated control actions remains a grand challenge affecting the secure and economic operation of today's large-scale power grid. This paper presents a novel artificial intelligence (AI) based methodology to achieve multi-objective real-time power grid control for real-world implementation. State-of-the-art off-policy reinforcement learning (RL) algorithm, soft actor-critic (SAC) is adopted to train AI agents with multi-thread offline training and periodic online training for regulating voltages and transmission losses without violating thermal constraints of lines. A software prototype was developed and deployed in the control center of SGCC Jiangsu Electric Power Company that interacts with their Energy Management System (EMS) every 5 minutes. Massive numerical studies using actual power grid snapshots in the real-time environment verify the effectiveness of the proposed approach. Well-trained SAC agents can learn to provide effective and subsecond control actions in regulating voltage profiles and reducing transmission losses.
