Power distribution systems are continually challenged by extreme climatic events. The reliance of the energy sector on overhead infrastructures for electricity distribution has necessitated a paradigm shift in grid management toward resilience enhancement. Grid hardening strategies are among effective methods for improving resilience. Limited budget and resources, however, demand for optimal planning for hardening strategies. This paper develops a planning framework based on Deep Reinforcement Learning (DRL) to enhance the long-term resilience of distribution systems using hardening strategies. The resilience maximization problem is formulated as a Markov decision process and solved via integration of a novel ranking strategy, neural networks, and reinforcement learning. As opposed to targeting resilience against a single future hazard – a common approach in existing methods – the proposed framework quantifies life-cycle resilience considering the possibility of multiple stochastic events over a system’s life. This development is facilitated by a temporal reliability model that captures the compounding effects of gradual deterioration and hazard effects for stochastic hurricane occurrences. The framework is applied to a large-scale power distribution system with over 7000 poles. Results are compared to an optimal strategy by a mixed-integer nonlinear programming model solved using Branch and Bound (BB), as well as the strength-based strategy by U.S. National Electric Safety Code (NESC). Results indicate that the proposed framework significantly enhances the long-term resilience of the system compared to the NESC strategy by over 30% for a 100-year planning horizon. Furthermore, the DRL-based approach yields optimal solutions for problems that are computationally intractable for the BB algorithm.

配电系统一直受到极端气候事件的挑战。能源部门对架空基础设施进行配电的依赖已使电网管理模式向着增强弹性的方向转变。网格硬化策略是提高弹性的有效方法之一。但是，有限的预算和资源要求针对硬化策略进行最佳规划。本文开发了基于深度强化学习（DRL）的计划框架，以使用强化策略来增强配电系统的长期弹性。弹性最大化问题被表述为马尔可夫决策过程，并通过集成新型排名策略，神经网络和强化学习来解决。与针对单个未来危害的复原力（现有方法中的通用方法）相反，该框架考虑了系统生命周期内多个随机事件的可能性，量化了生命周期的复原力。时间可靠性模型促进了这一发展，该模型捕获了随机飓风发生的逐渐恶化和危害效应的复合效应。该框架适用于具有7000多个极点的大型配电系统。通过使用分支定界（BB）求解的混合整数非线性规划模型将结果与最佳策略进行比较，并通过美国国家电气安全法规（NESC）将基于强度的策略与最佳策略进行比较。结果表明，与NESC策略相比，在100年的规划期内，该框架大大提高了系统的长期弹性。此外，基于DRL的方法可为BB算法在计算上难以解决的问题提供最佳解决方案。