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arXiv · 2509.03666

AutoGrid AI: Deep Reinforcement Learning Framework for Autonomous Microgrid Management

Kenny Guo·Nicholas Eckhert·Krish Chhajer·Luthira Abeykoon·Lorne Schell
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Abstract

We present a deep reinforcement learning-based framework for autonomous microgrid management. tailored for remote communities. Using deep reinforcement learning and time-series forecasting models, we optimize microgrid energy dispatch strategies to minimize costs and maximize the utilization of renewable energy sources such as solar and wind. Our approach integrates the transformer architecture for forecasting of renewable generation and a proximal-policy optimization (PPO) agent to make decisions in a simulated environment. Our experimental results demonstrate significant improvements in both energy efficiency and operational resilience when compared to traditional rule-based methods. This work contributes to advancing smart-grid technologies in pursuit of zero-carbon energy systems. We finally provide an open-source framework for simulating several microgrid environments.

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Kenny Guo, Nicholas Eckhert, Krish Chhajer, Luthira Abeykoon, Lorne Schell. 2025-09-03. AutoGrid AI: Deep Reinforcement Learning Framework for Autonomous Microgrid Management. https://arxiv.org/abs/2509.03666

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