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[2602.18531] Deep Reinforcement Learning for Optimizing Energy Consumption in Smart Grid Systems
Summary
This paper explores the use of Deep Reinforcement Learning (RL) combined with Physics-Informed Neural Networks (PINNs) to optimize energy consumption in smart grid systems, addressing challenges related to sample inefficiency in traditional RL approaches.
Why It Matters
As energy management becomes increasingly complex in smart grids, innovative methods like combining RL with PINNs can significantly enhance efficiency and reduce computational costs. This research provides a promising solution for optimizing energy consumption, which is critical for sustainable energy systems.
Key Takeaways
- Deep Reinforcement Learning can optimize energy consumption in smart grids.
- Physics-Informed Neural Networks can replace costly simulators, improving efficiency.
- Training time can be reduced by 50% using PINN surrogates compared to traditional methods.
- The approach allows for strong RL policy development without needing samples from the true simulator.
- This research contributes to advancing smart grid technology and energy management.
Computer Science > Machine Learning arXiv:2602.18531 (cs) [Submitted on 20 Feb 2026] Title:Deep Reinforcement Learning for Optimizing Energy Consumption in Smart Grid Systems Authors:Abeer Alsheikhi, Amirfarhad Farhadi, Azadeh Zamanifar View a PDF of the paper titled Deep Reinforcement Learning for Optimizing Energy Consumption in Smart Grid Systems, by Abeer Alsheikhi and 2 other authors View PDF Abstract: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 ...
