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The transition from traditional, centralized electric grids to smart grids offers numerous opportunities for optimizing energy distribution and consumption. This paper presents a reinforcement learning-based approach for load scheduling in smart grids, aiming to reduce energy loss and enhance grid reliability. By leveraging consumer preferences, the proposed system schedules loads efficiently, thereby minimizing energy loss in transmission lines and reducing peak loads. Our results, tested on simulated grid environments of varying scales, demonstrate significant improvements in energy efficiency, suggesting that reinforcement learning can play a crucial role in the future of smart grid management.

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Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency

  • 会议论文

  • 发表日期：

    2025年

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  • Christoph Wittner,
  • Gabriele Kotsis,
  • Ismail Khalil

摘要

The transition from traditional, centralized electric grids to smart grids offers numerous opportunities for optimizing energy distribution and consumption. This paper presents a reinforcement learning-based approach for load scheduling in smart grids, aiming to reduce energy loss and enhance grid reliability. By leveraging consumer preferences, the proposed system schedules loads efficiently, thereby minimizing energy loss in transmission lines and reducing peak loads. Our results, tested on simulated grid environments of varying scales, demonstrate significant improvements in energy efficiency, suggesting that reinforcement learning can play a crucial role in the future of smart grid management.