mlsanityData to 14 Oct 2026

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management

arXiv 2508.08132 ↗First posted 11 Aug 2025eess.SY cs.SYcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Renewable energy integration into microgrids has become a key approach to addressing global energy issues such as climate change and resource scarcity. However, the variability of renewable sources and the rising occurrence of High Impact Low Probability (HILP) events require innovative strategies for reliable and resilient energy management. This study introduces a practical approach to managing microgrid resilience through Explainable Deep Reinforcement Learning (XDRL). It combines the Proximal Policy Optimization (PPO) algorithm for decision-making with the Local Interpretable Model-agnostic Explanations (LIME) method to improve the transparency of the actor network's decisions. A case study in Ongole, India, examines a microgrid with wind, solar, and battery components to validate the proposed approach. The microgrid is simulated under extreme weather conditions during the Layla cyclone. LIME is used to analyse scenarios, showing the impact of key factors such as renewable generation, state of charge, and load prioritization on decision-making. The results demonstrate a Resilience Index (RI) of 0.9736 and an estimated battery lifespan of 15.11 years. LIME analysis reveals the rationale behind the agent's actions in idle, charging, and discharging modes, with renewable generation identified as the most influential feature. This study shows the effectiveness of integrating advanced DRL algorithms with interpretable AI techniques to achieve reliable and transparent energy management in microgrids.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 13 references that are in the map. First posted on arXiv 11 Aug 2025.

Builds on

4 of 13 references.

References · 4

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  4. 2024Strategies for Resilience and Battery Life Extension in the Face of Communication Losses for Isolated Microgridscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

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  5. 2025Real-Time Energy Management Strategies for Community Microgrids ↗
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  7. 2025A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization
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