AI 中文总结
针对配电网停电恢复问题，传统方法忽视客户体验。本文提出新方法，纳入潜在故障概率，设计分区策略和维修人员调度算法，利用滚动时域优化算法，实现社会公平且客户敏感的恢复，仿真验证了其有效性。
AI 中文摘要
本文提出了一种用于配电网中人类感知和公平服务恢复的新方法，明确考虑停电持续时间的客户体验。该问题的复杂性源于停电事件固有的不可预测性和随机性。传统方法常将故障视为确定性来过度简化问题，忽视客户体验和停电模式的真正不确定性。相比之下，所提方法纳入潜在故障概率以指导客户感知和公平驱动的资源分配。为此设计了空间分布式、自适应且可扩展的分区策略来平衡所有故障位置的恢复时间，还提出了自适应分布式维修人员调度算法。框架利用滚动时域优化算法在随机停电情况下动态最小化总恢复时间。随机停电条件下在修改后的69节点配电网的仿真结果证明了该模型在实现社会公平和客户敏感恢复结果方面有效。
英文摘要
This paper proposes a novel methodology for human-aware and fair service restoration in power distribution networks, explicitly accounting for the customer experience of outage duration. The complexity of this problem stems from the inherently unpredictable and stochastic nature of power outage events. Traditional approaches often oversimplify the problem by treating failures as deterministic, overlooking the lived experiences of customers and the true uncertainty of outage patterns. In contrast, the proposed method incorporates the probability of potential failures to guide a customer-aware and fairness-driven resource allocation, ensuring that restoration is not only fast but also perceived as fair from the customer's perspective. To achieve this, a spatially distributed, adaptive, and scalable partitioning policy is designed to balance restoration time across all failure locations, promoting consistency and equity in the outage experience. Next, an adaptive and distributed repair crew dispatch algorithm is proposed to accelerate service restoration while ensuring that no customer segment is disproportionately affected. The framework leverages a Receding Horizon (RH) optimization algorithm to dynamically minimize total restoration time amid randomly occurring outages in both space and time. Simulation results on modified 69-bus distribution networks under stochastic outage conditions demonstrate the model's effectiveness in delivering socially fair and customer-sensitive restoration outcomes
