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

2025-10-20

AI Summary

Optimal power flow (OPF) control in smart grid energy management suffers from low sample efficiency and prohibitively high simulation costs. To address this, we propose a surrogate modeling framework that integrates reinforcement learning (RL) with physics-informed neural networks (PINNs): PINNs serve as high-fidelity, differentiable surrogates of grid dynamics, replacing computationally expensive high-resolution simulators and enabling efficient, low-cost environment interactions for RL policy training. This approach significantly reduces reliance on real-world systems or detailed simulations while preserving physical consistency, thereby accelerating training and improving sample efficiency. Experiments demonstrate several-fold reduction in policy convergence time, substantial decrease in computational overhead, and control performance comparable to baseline methods. The key contribution lies in the first systematic integration of PINNs into an RL architecture for OPF, enabling physics-constrained, data-efficient policy learning.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Machine Learning: Reinforcement Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Social networks and social learning

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.

Problem

• Optimizing smart grid energy management with reinforcement learning
• Reducing sample inefficiency in reinforcement learning training
• Using physics-informed neural networks as surrogate simulators

Innovation

• Reinforcement Learning optimizes smart grid energy management
• Physics-informed Neural Networks create surrogate models
• Surrogate models accelerate Reinforcement Learning training process