Resilient Preventive Dispatch Considering Load Priority and Interdependence of Critical Infrastructures

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
IET Smart Grid Pub Date : 2025-05-11 DOI:10.1049/stg2.70012
Ying Yang, Yuting Mou, Beibei Wang
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引用次数: 0

Abstract

As extreme weather events become increasingly frequent and societal development places greater emphasis on a stable electricity supply, there is an increasing focus on the resilience of electrical distribution systems (EDSs). This is especially crucial in preventing cascading failures among interdependent critical infrastructures, such as medical, water, and telecommunication systems. Proactively preparing to cope with disruptive events causing a high risk of power outage is essential for improving the resilience of EDSs. A performance index, human well-being loss (HWL), is proposed to assess the disruption to people's quality of life during extreme weather events, considering the interdependence among critical infrastructures. Based on the proposed index, a novel resilience enhancement framework is proposed, which takes into account expected generation costs, economic losses due to power curtailment and HWL. Subsequently, a two-stage stochastic model for preventive disaster is presented, which optimises power rationing strategies and dispatch schedules. Through a series of case studies, the authors illustrate that the proposed framework can effectively reduce costs, alleviate abnormal operational hours of critical infrastructures and achieve a balance between economic, safety, and social benefits.

Abstract Image

考虑负荷优先级和关键基础设施相互依赖的弹性预防调度
随着极端天气事件变得越来越频繁,社会发展更加强调稳定的电力供应,人们越来越关注配电系统(EDSs)的弹性。这对于防止相互依赖的关键基础设施(如医疗、水和电信系统)之间的级联故障尤其重要。积极准备应对造成高停电风险的破坏性事件对于提高电力系统的恢复能力至关重要。考虑到关键基础设施之间的相互依存关系,提出了一个绩效指数,即人类福祉损失(HWL),以评估极端天气事件对人们生活质量的破坏。在此基础上,提出了一种考虑预期发电成本、限电经济损失和HWL的弹性增强框架。在此基础上,提出了一种预防性灾害的两阶段随机模型,该模型可以优化电力分配策略和调度计划。通过一系列的案例研究表明,该框架可以有效地降低成本,缓解关键基础设施的非正常运行时间,实现经济效益、安全和社会效益的平衡。
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来源期刊
IET Smart Grid
IET Smart Grid Computer Science-Computer Networks and Communications
CiteScore
6.70
自引率
4.30%
发文量
41
审稿时长
29 weeks
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