Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks

TL;DR

This paper enhances smart grid energy management by integrating reinforcement learning with physics-informed neural network surrogate models, significantly reducing training time and improving efficiency.

Contribution

It introduces a novel approach combining RL with PINN-based surrogate models to address sample efficiency issues in smart grid optimization.

Findings

Reduced RL training time using surrogate models

Maintained optimality of energy management policies

Demonstrated effectiveness on smart grid scenarios

Abstract

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.