Using data envelopment analysis to evaluate the performance of post-hurricane electric power restoration activities. (August 2016)

• Record Type: Journal Article
• Authors: Reilly, Allison C.; Davidson, Rachel A.; Nozick, Linda K.; Chen, Thomas; Guikema, Seth D.
• Is Part Of: Reliability engineering & system safety. Volume 152(2016:Aug.)
• Page Start: 197
• Page End: 204
• Publication Date: 2016-08
• Subjects: BBC Banker, Charnes and Cooper Model. Also referred to as the Technical Efficiency Model -- CCR Charnes, Cooper, and Rhodes Model. Also referred to as the Scale and Technical Efficiency Model -- DEA Data Envelopment Analysis -- DMU(s) Decision Making Unit(s); Hurricane -- Electric-power system restoration -- Data envelopment analysis; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2016.03.007
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: Post-hurricane restoration of electric power is attracting increasing scrutiny as customers' tolerance for even short power interruptions decreases. At the peak, 8.5 million customers were without power after Hurricane Sandy and over 1 million customers were without power more than a week after the storm made landfall. Currently, restoration processes are typically evaluated on a case-by-case basis by a regional public service commission or similar body and lack systematic comparisons to other restoration experiences. This paper introduces a framework using data envelopment analysis to help evaluate post-hurricane restorations through comparison with the experiences of other companies in similar storms. The method accounts for the variable severity of the hurricanes themselves, so that companies are not penalized for outages that are long only because the hurricane that caused them was particularly severe. The analysis is illustrated through an application comparing 27 recent post-hurricane restoration experiences across 13 different electric power companies in the United States. The results of the study show some consistency in performance among individual utilities after the hurricanes they experience. The method could be applied to other types of infrastructure systems and other extreme events as well. Highlights: A Data Envelopment Analysis (DEA) framework is developed to compare post- hurricane power-outage restoration performance. Hurricane severity is considered, so that utilities are not penalized for long outages caused by severe storms. A case study using real data compares 27 recent post-hurricane restoration experiences. The results of the study show utilities tend to perform consistently after the hurricanes they experience.