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

• URL: http://arxiv.org/abs/2510.17380v1
• Date: Mon, 20 Oct 2025 10:17:42 GMT
• Title: Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks
• Authors: Julen Cestero, Carmine Delle Femine, Kenji S. Muro, Marco Quartulli, Marcello Restelli,
• Abstract summary: Reinforcement Learning (RL) is gaining prominence as a solution for addressing the challenges of Optimal Power Flow in smart grids.<n>We address this problem by substituting costly smart grid simulators with surrogate models built using Phisics-informed Neural Networks (PINNs)
• Score: 29.49941497527361
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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