Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning
Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning
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DOI：
10.1016/j.apenergy.2020.116355
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发表时间：
2021-03
期刊：
影响因子：
11.2
通讯作者：
Nariman L. Dehghani;Ashkan B. Jeddi;A. Shafieezadeh
中科院分区：
文献类型：
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作者：
Nariman L. Dehghani;Ashkan B. Jeddi;A. Shafieezadeh
配电系统不断受到极端气候事件的挑战。能源部门对电力分配的架空基础设施的依赖使得电网管理必须向增强弹性的模式转变。电网加固策略是提高弹性的有效方法之一。然而，有限的预算和资源要求对硬化策略进行最佳规划。本文开发了一种基于深度强化学习（DRL）的规划框架，以使用硬化策略增强配电系统的长期弹性。弹性最大化问题被制定为一个马尔可夫决策过程，并通过一个新的排名策略，神经网络和强化学习的集成解决。相对于针对单一的未来灾害的弹性-现有方法中的一种常见方法-拟议的框架量化生命周期的弹性，考虑到系统生命周期中多个随机事件的可能性。这一发展是由一个时间的可靠性模型，捕捉随机飓风发生的逐渐恶化和危险的影响的复合效应。该框架适用于一个大型配电系统，超过7000杆。结果进行了比较，一个最佳的策略，由一个混合整数非线性规划模型解决使用分支和界限（BB），以及基于强度的战略，美国国家电气安全规范（NESC）。结果表明，拟议的框架显着提高了系统的长期弹性相比，NESC的战略超过30%的100年的规划视野。此外，DRL为基础的方法产生的问题，是计算上棘手的BB算法的最佳解决方案。
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.