用物理信息神经网络加速智能电网强化学习，训练快50%。
Deep Reinforcement Learning for Optimizing Energy Consumption in Smart Grid Systems
- 用物理信息神经网络替代昂贵仿真器，构建高效代理环境。
- 相比无代理的强化学习，训练速度提升50%，收敛更快。
- 无需真实仿真数据即可学到强策略，适合资源受限场景。
智能电网中的能源管理问题因系统组件间复杂的相互依赖关系而具有内在复杂性。尽管强化学习（RL）已被用于解决最优潮流（OPF）问题，但其与环境的迭代交互通常需要计算成本高昂的仿真器，导致样本效率低下。本研究通过引入物理信息神经网络（PINNs）来替代传统且昂贵的智能电网仿真器，解决了这一挑战。基于PINN的代理模型使强化学习策略学习过程得以加速，收敛时间大幅缩短。与多种基准数据驱动代理模型对比，结果表明，在此背景下，只有使用物理知识的PINN代理能在不依赖真实仿真数据的情况下获得强大的强化学习策略。实验显示，采用PINN代理可使训练速度比无代理时提升50%。该方法能够快速生成与原始仿真器性能相当的评估指标。
原文摘要 · Abstract (English)
The energy management problem in the context of smart grids is inherently complex due to the interdependencies among diverse system components. Although Reinforcement Learning (RL) has been proposed for solving Optimal Power Flow (OPF) problems, the requirement for iterative interaction with an environment often necessitates computationally expensive simulators, leading to significant sample inefficiency. In this study, these challenges are addressed through the use of Physics-Informed Neural Networks (PINNs), which can replace conventional and costly smart grid simulators. The RL policy learning process is enhanced so that convergence can be achieved in a fraction of the time required by the original environment. The PINN-based surrogate is compared with other benchmark data-driven surrogate models. By incorporating knowledge of the underlying physical laws, the results show that the PINN surrogate is the only approach considered in this context that can obtain a strong RL policy even without access to samples from the true simulator. The results demonstrate that using PINN surrogates can accelerate training by 50% compared to RL training without a surrogate. This approach enables the rapid generation of performance scores similar to those produced by the original simulator.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。