Abstract
Electric distribution utilities have an obligation to inform the public and government regulators about when they expect to complete service restoration after a major storm. In this study, we explore methods for calculating the estimated time of restoration (ETR) from weather impacts, defined as the time it will take for 99.5% of customers to be restored. Actual data from Storm Irene (2011), the October Nor’easter (2011) and Hurricane Sandy (2012) within the Eversource Energy-Connecticut service territory were used to calibrate and test the methods; data used included predicted outages, the peak number of customers affected, a ratio of how many outages a restoration crew can repair per day, and the count of crews working per day. Data known before a storm strikes (such as predicted outages and available crews) can be used to calculate ETR and support pre-storm allocation of crews and resources, while data available immediately after the storm passes (such as customers affected) can be used as motivation for securing or releasing crews to complete the restoration in a timely manner. Used together, the methods presented in this paper will help utilities provide a reasonable, data-driven ETR without relying solely on qualitative past experiences or instinct.
Acknowledgements
We gratefully acknowledge the support of Eversource and the Eversource Energy Center at the University of Connecticut, which provided funding and data for this research.
References
Abrams, R., and B. Lawsky. 2013. Moreland Commission on Utility Storm Preparation and Response: Final Report. Available online at https://www.moreland.ny.gov/sites/default/files/MACfinalreportjune22.pdf. Accessed September 15, 2017.Suche in Google Scholar
Ancona, J. J. 1995. “Framework for Power System Restoration Following a Major Power Failure.” IEEE Transactions on Power Systems 10 (3): 1480–1485.10.1109/59.466500Suche in Google Scholar
Brown, R. E., S. Gupta, R. D. Christie, and S. S. Venkata, R. 1997. “Fletcher. Distribution System Reliability Assessment: Momentary Interruptions and Storms.” IEEE Transactions on Power Delivery 12 (4):1569–1574.10.1109/61.634177Suche in Google Scholar
Campbell, R. J. 2013. Weather-Related Power Outages and Electric System Resiliency. Electricity Reliability in the United States: Select Research, 127–147.Suche in Google Scholar
Cao, C., and Y. Bai. 2014. “Quantitative Analysis of VIIRS DNB Nightlight Point Source for Light Power Estimation and Stability Monitoring.” Remote Sensing 6: 11915–11935.10.3390/rs61211915Suche in Google Scholar
Caron, M. A., A. H. House, J. W. Betkowski, J. Buckingham, and K. Braffman. 2013. PURA Investigation into the Performance of Connecticut’s Electric Distribution Companies and Gas Companies in Restoring Service Following Storm Sandy. Report No.: Docket 12-11-07.Suche in Google Scholar
Castillo, A. 2014. “Risk Analysis and Management in Power Outage and Restoration: A Literature Survey.” Electric Power Systems Research 107: 9–15.10.1016/j.epsr.2013.09.002Suche in Google Scholar
Cole, T., D. W. Wanik, A. Molthan, M. Roman, and E. Griffin. 2017. “Synergistic Use of Nighttime Satellite Data, Electric Utility Infrastructure, and Ambient Population to Improve Power Outage Detections in Urban Areas.” Remote Sensing 9 (3): 286.10.3390/rs9030286Suche in Google Scholar
Connecticut Light and Power. 2014. Transmission and Distribution Reliability Performance Report. Regulatory Compliance. Berlin, CT: Connecticut Light & Power.Suche in Google Scholar
Davidson, R. A., H. Liu, K. Sarpong, P. Sparks, and D. V. Rosowsky. 2003. “Electric Power Distribution System Performance in Carolina Hurricanes.” Natural Hazards Review 4 (1): 36–45.10.1061/(ASCE)1527-6988(2003)4:1(36)Suche in Google Scholar
Edison Electric Institute. 2014. Understanding the Electric Power Industry’s Response and Restoration Process. Washington D.C.: Edison Electric Institute; May 2014. Report No.: MA_101.Suche in Google Scholar
Guikema, S., S. R. Han, and S. Quiring. 2008. Estimating Power Outages During Hurricanes Using Semi-Parametric Statistical Methods. 2008 Structures Congress – Structures Congress 2008: Crossing the Borders; 24 April 2008 through 26 April 2008; Vancouver, BC.10.1061/41016(314)189Suche in Google Scholar
Guikema, S. D., R. Nateghi, S. M. Quiring, A. Staid, A. C. Reilly, and M. Gao. 2014a. “Predicting Hurricane Power Outages to Support Storm Response Planning.” IEEE Access 2: 1364–1373.10.1109/ACCESS.2014.2365716Suche in Google Scholar
Guikema, S. D., R. Nateghi, and S. M. Quiring. 2014b. Storm Power Outage Prediction Modeling. European Safety and Reliability Conference, ESREL 2013, Amsterdam: shers; 29 September 2013 through 2 October 2013.Suche in Google Scholar
He, J., D. W. Wanik, B. M. Hartman, E. N. Anagnostou, M. Astitha, and M. E. B. Frediani. 2016. “Nonparametric Tree-Based Predictive Modeling of Storm Outages on an Electric Distribution Network.” Risk Analysis 37 (3): 441–458.10.1111/risa.12652Suche in Google Scholar
Liu, H., R. A. Davidson, and T. V. Apanasovich. 2007. “Statistical Forecasting of Electric Power Restoration Times in Hurricanes and Ice Storms.” IEEE Transactions on Power Systems 22 (4): 2270–2279.10.1109/TPWRS.2007.907587Suche in Google Scholar
McGee, J., J. Skiff, P. Carozza, T. Edelstein, L. Hoffman, S. Jackson, R. McGrath, F. McGroarty, and C. Osten. 2012. Report of the Two Storm Panel. State of Connecticut.Suche in Google Scholar
Mensah, A. F., and L. Duenas-Osorio. 2014. Outage Predictions of Electric Power Systems Under Hurricane Winds by Bayesian Networks. 2014 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2014; 7 July 2014 through 10 July 2014; Institute of Electrical and Electronics Engineers Inc.10.1109/PMAPS.2014.6960677Suche in Google Scholar
Nateghi, R., S. D. Guikema, and S. M. Quiring. 2011. “Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes.” Risk Analysis 31 (12): 1897–1906.10.1111/j.1539-6924.2011.01618.xSuche in Google Scholar
Nateghi, R., S. Guikema, and S. M. Quiring. 2014a. “Power Outage Estimation for Tropical Cyclones: Improved Accuracy with Simpler Models.” Risk Analysis 34 (6): 1069–1078.10.1111/risa.12131Suche in Google Scholar
Nateghi, R., S. D. Guikema, and S. M. Quiring. 2014b. “Forecasting Hurricane-Induced Power Outage Durations.” Natural Hazards 74 (3): 1795–1811.10.1007/s11069-014-1270-9Suche in Google Scholar
Ouyang, M., and L. Dueñas-Osorio. 2014. “Multi-Dimensional Hurricane Resilience Assessment of Electric Power Systems.” Structural Safety 48: 15–24.10.1016/j.strusafe.2014.01.001Suche in Google Scholar
Reed, D. A. 2008. “Electric Utility Distribution Analysis for Extreme Winds.” Journal of Wind Engineering and Industrial Aerodynamics 96 (1): 123–140.10.1016/j.jweia.2007.04.002Suche in Google Scholar
Sullivan, M. J. 1996. “Interruption Costs, Customer Satisfaction and Expectations for Service Reliability.” IEEE Transactions on Power Systems 11 (2): 989–995.10.1109/59.496185Suche in Google Scholar
Wanik, D. W., E. N. Anagnostou, B. M. Hartman, M. E. B. Frediani, and M. Astitha. 2015. “Storm outage modeling for an electric distribution network in Northeastern USA.” Natural Hazards 79 (2): 1359–1384.10.1007/s11069-015-1908-2Suche in Google Scholar
Wanik, D. W., J. R. Parent, E. N. Anagnostou, and B. M. Hartman. 2017a. “Using Vegetation Management and LiDAR-Derived Tree Height Data to Improve Outage Predictions for Electric Utilities.” Electric Power Systems Research 146: 236–245.10.1016/j.epsr.2017.01.039Suche in Google Scholar
Wanik, D. W., E. N. Anagnostou, M. Astitha, B. M. Hartman, G. M. Lackmann, J. Yang, D. Cerrai, J. He, and M. E. B. Frediani. 2017b. “A Case Study on Power Outage Impacts from Future Hurricane Sandy Scenarios.” Journal of Applied Meteorology and Climatology. Early Online Release. Available from https://doi.org/10.1175/JAMC-D-16-0408.1. Published online: 2 October 2017.10.1175/JAMC-D-16-0408.1Suche in Google Scholar
Winkler, J., L. Dueñas-Osorio, R. Stein, and D. Subramanian. 2010. “Performance Assessment of Topologically Diverse Power Systems Subjected to Hurricane Events.” Reliability Engineering and System Safety 95 (4): 323–336.10.1016/j.ress.2009.11.002Suche in Google Scholar
©2018 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Research Articles
- Social Media and Crisis Communications: A Survey of Local Governments in Florida
- Behavior Analysis of Illegal Fishing in the Gulf of Mexico
- Estimated Time of Restoration (ETR) Guidance for Electric Distribution Networks
- Opinion
- A Theory of Homeland Security
- Book Review
- Emergency & Disaster Preparedness for Health Professionals: Second Edition
Artikel in diesem Heft
- Research Articles
- Social Media and Crisis Communications: A Survey of Local Governments in Florida
- Behavior Analysis of Illegal Fishing in the Gulf of Mexico
- Estimated Time of Restoration (ETR) Guidance for Electric Distribution Networks
- Opinion
- A Theory of Homeland Security
- Book Review
- Emergency & Disaster Preparedness for Health Professionals: Second Edition