用物理神经网络构建电网仿真代理模型，提升强化学习训练效率
Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks
- 用物理信息神经网络构建电网仿真代理模型替代昂贵模拟器
- 训练收敛时间缩短至原环境的极小比例，实现高效策略优化
- 适合电力系统优化与强化学习交叉研究者参考
智能电网的能量管理优化面临现实系统复杂性和组件间耦合关系的挑战。强化学习（RL）在解决智能电网最优潮流问题中日益受到关注，但其需反复迭代采样以获得最优策略，依赖高成本的仿真环境，导致样本效率低下。本文提出使用物理信息神经网络（PINNs）构建电网仿真代理模型，替代原有高成本仿真器，显著减少策略训练时间，使强化学习在极短时间内达到收敛，有效提升优化效率。
原文摘要 · Abstract (English)
Optimizing the energy management within a smart grids scenario presents significant challenges, primarily due to the complexity of real-world systems and the intricate interactions among various components. Reinforcement Learning (RL) is gaining prominence as a solution for addressing the challenges of Optimal Power Flow in smart grids. However, RL needs to iterate compulsively throughout a given environment to obtain the optimal policy. This means obtaining samples from a, most likely, costly simulator, which can lead to a sample efficiency problem. In this work, we address this problem by substituting costly smart grid simulators with surrogate models built using Phisics-informed Neural Networks (PINNs), optimizing the RL policy training process by arriving to convergent results in a fraction of the time employed by the original environment.
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