期刊
JOURNAL OF STRUCTURAL ENGINEERING
卷 142, 期 8, 页码 -出版社
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)ST.1943-541X.0001423
关键词
Resilience; Restoration; Performance assessment; Power outages; Bayesian networks; Electric power systems; Hurricanes; Safety and reliability
资金
- Department of Civil and Environmental Engineering at Rice University
- Office of Public Safety and Homeland Security of the City of Houston [2008-CP-T8-0023]
- National Science Foundation (NSF) [CMMI 1436845]
- U.S. Department of Defense through its Army Research Office via MURI grant [W911NF-13-1-0340]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1436845] Funding Source: National Science Foundation
Ensuring electric power system resilience against natural and anthropogenic hazards is vital for public health, economy, security, and well-being across modern societies. This paper presents a resilience assessment framework that focuses on computationally efficient algorithms for quantifying the response of electric power systems to hurricane events, while demonstrating its applicability to large real systems by spanning hazards, structural and system responses, and restoration processes. The study models the transmission system responses as a Bayesian network to represent probabilistic dependencies in an intuitive and tractable manner. It propagates hurricane-induced failures throughout the system to service customers while efficiently considering the physics and constraints of power flow. The framework computes customer outages in distributed 1-km2 blocks connected by radial distribution feeders across the entire system, and simulates system restoration according to resource mobilization practices and a range of prioritization sequences. An illustration example using the electric power grid of Harris County, Texas, under Hurricane Ike in 2008 shows that the framework yields system responses that are in good agreement with actual outages reported after the event. It also shows that the framework requires only a fraction (2.3%) of the computation time needed by a previous outage assessment model. The example also illustrates how a resilience-informed restoration strategy can significantly improve the system-level functionality, particularly in the aftermath of hurricane events. The framework provides an efficient tool for exploring a wide range of what-if scenarios in large real systems, and supports practical use by citizens and city officials for rapid recovery and high resilience attainment.
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