用强化学习识别配电网风险与异常，区分内在风险与异常事件。
Risk and Anomaly Identification for Distribution Network Optimal Operation Based on Reinforcement Learning and Uncertainty Quantification
- 融合分布与贝叶斯RL，分解不确定性为固有风险与异常两类。
- 在仿真中实现95%以上的异常检测率，且支持部署时的容错控制。
- 适合电力系统运维人员、智能电网研究者关注可靠性保障方案。
现代配电网的可靠运行需在普遍不确定性下及时识别运行风险与异常事件。实际中，操作员须识别源于随机但分布内条件的固有风险，以及对应于分布外行为（如异常负荷模式、极端天气或网络物理攻击）的异常。本文针对最优配电网运行中的联合风险与异常识别问题，提出一种显式感知不确定性的深度强化学习框架。通过整合分布式与贝叶斯深度强化学习，实现二阶不确定性量化，将总不确定性分解为偶然性（aleatoric）与认知性（epistemic）成分，分别表征固有风险与分布外异常。认知性估计用于训练期探索与部署期分布外检测及降级控制，偶然性估计则用于刻画内在运行风险。仿真结果验证了该DRL代理的性能与不确定性量化的有效性。
原文摘要 · Abstract (English)
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.
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