mlsanityData to 14 Oct 2026

Risk and Anomaly Identification for Distribution Network Optimal Operation Based on Reinforcement Learning and Uncertainty Quantification

arXiv 2609.03308 ↗First posted 3 Sep 2026cs.LG cs.MA cs.SYcited by 0

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

Abstract (arXiv)

Reliable operation of modern distribution networks requires timely identification of operational risks and anomalous events under pervasive uncertainty. In practice, operators must identify risks that are inherent in stochastic yet in-distribution conditions, and anomalies that correspond to out-of-distribution behaviors such as unusual load patterns, extreme weather or cyber-physical attacks. This paper addresses this joint risk and anomaly identification problem for optimal distribution network operation and proposes a deep reinforcement learning framework that is explicitly uncertainty aware. We integrate distributional and Bayesian deep reinforcement learning to realize a second- order uncertainty quantification scheme that decomposes total uncertainty into aleatoric and epistemic components, which are respectively used to characterize inherent risk and out-of- distribution anomalies. The resulting epistemic estimates drive both exploration during training and out-of-distribution detec- tion with fallback control during deployment, whereas aleatoric estimates are used to characterize intrinsic operational risk. Simulation results demonstrate the performance of our DRL agent and the effectiveness of the uncertainty quantification.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 20 of its 48 references that are in the map. First posted on arXiv 3 Sep 2026.

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20 of 48 references.

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  1. 2024Uncertainty-Aware Transient Stability-Constrained Preventive Redispatch: A Distributional Reinforcement Learning Approach14 Feb

Other references · 19

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  10. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  11. 2018Implicit Quantile Networks for Distributional Reinforcement Learningcited by 369
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7 more
  1. 2017pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systemscited by 85
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  6. 2020Gaussian Process Learning-based Probabilistic Optimal Power Flowcited by 6
  7. 2021Distributionally Robust Joint Chance-Constrained Optimization for Networked Microgrids Considering Contingencies and Renewable Uncertaintycited by 2

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