Optimal restoration of electricity distribution networks under rolling time windows and prediction of restoration time

滚动时间窗下配电网优化恢复及恢复时间预测

• 批准号: 2139837
• 负责人: Daniel Kirschen
• 金额: $ 38万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2139837&HistoricalAwards=false
• 关键词: Optimal; restoration; electricity; distribution; networks

This NSF project aims to develop transformative techniques to optimize the scheduling of crews to repair and restore electricity distribution networks following a large windstorm, an ice storm, or another type of natural disaster. Due to the nature of these networks and the need for crews to travel to different locations to perform repairs before power can be restored to customers, this optimization is particularly complex. However, it can be formulated as a budget-constrained, profit maximizing multiple traveling salesman problem on an edge and node weighted graph. This variant of the classical traveling salesman problem has not been studied before. The primary intellectual merits of the project include developing a formulation of the optimal repair and restoration process that is not only mathematically rigorous but also incorporates many practical constraints that must be considered for the resulting schedule to be feasible, developing algorithms to implement this formulation that are flexible and computationally lightweight and thus support easy re-optimization as additional information becomes available, and shedding light on novel variants of the multiple traveling salesman problem, which will be of independent interest to researchers in other disciplines such as computer science and operations research.Severe weather events can cause extensive damage to electricity distribution networks, depriving thousands of customers of light, heat, and power. Such disruptions in the supply of electricity have serious social and economic impacts and can have grave consequences for the health or life of vulnerable populations. It is thus imperative for electric utilities to repair their network and re-store power as soon as possible. This project thus has the following broader impacts: (1) it will provide these utilities with tools to help them optimally dispatch their repair crews, reduce the time required to restore power, and hence minimize the harm done to the affected communities, (2) it will also help utilities provide their customers with more accurate estimates of when they might expect power to be restored, thus helping them decide whether to stay put or seek shelter elsewhere.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在开发变革性技术，以优化人员的调度，以在大风暴，冰风暴或其他类型的自然灾害后修复和恢复配电网络。由于这些网络的性质以及在恢复客户供电之前需要工作人员前往不同地点进行维修，这种优化特别复杂。然而，它可以被公式化为边和节点加权图上的带约束的、利润最大化的多个旅行商问题。经典旅行商问题的这种变体以前没有被研究过。该项目的主要智力价值包括开发最佳修复和恢复过程的公式，该公式不仅在数学上严格，而且还包含许多实际约束，这些约束必须被考虑以使所产生的时间表可行，开发算法来实现灵活且计算量小的公式，从而在更多信息可用时支持轻松的重新优化，并揭示了多个旅行商问题的新变种，这将是计算机科学和运筹学等其他学科的研究人员的独立兴趣。恶劣的天气事件可能会对配电网络造成广泛的破坏，使成千上万的客户失去光，热和电力。这种电力供应中断会产生严重的社会和经济影响，并可能对弱势群体的健康或生命造成严重后果。因此，电力公司必须尽快修复其网络并重新储存电力。因此，该项目具有以下广泛影响：（1）它将为这些公用事业公司提供工具，以帮助他们最佳地派遣他们的维修人员，减少恢复电力所需的时间，并因此最小化对受影响社区造成的伤害，（2）它还将帮助公用事业公司向他们的客户提供他们可能期望电力恢复的时间的更准确的估计，这一奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。