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A Statistical Framework for Evaluating the Effectiveness of Vegetation Management in Reducing Power Outages Caused during Storms in Distribution Networks

Author

Listed:
  • William O. Taylor

    (Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
    Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA)

  • Peter L. Watson

    (Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
    Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA)

  • Diego Cerrai

    (Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
    Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA)

  • Emmanouil Anagnostou

    (Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
    Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA)

Abstract

This paper develops a statistical framework to analyze the effectiveness of vegetation management at reducing power outages during storms of varying severity levels. The framework was applied on the Eversource Energy distribution grid in Connecticut, USA based on 173 rain and wind events from 2005–2020, including Hurricane Irene, Hurricane Sandy, and Tropical Storm Isaias. The data were binned by storm severity (high/low) and vegetation management levels, where a maximum applicable length of vegetation management for each circuit was determined, and the data were divided into four bins based on the actual length of vegetation management performed divided by the maximum applicable value (0–25%, 25–50%, 50–75%, and 75–100%). Then, weather and overhead line length normalized outage statistics were taken for each group. The statistics were used to determine the effectiveness of vegetation management and its dependence on storm severity. The results demonstrate a higher reduction in damages for lower-severity storms, with a reduction in normalized outages between 45.8% and 63.8%. For high-severity events, there is a large increase in effectiveness between the highest level of vegetation management and the two lower levels, with 75–100% vegetation management leading to a 37.3% reduction in trouble spots. Yet, when evaluating system reliability, it is important to look at all storms combined, and the results of this study provide useful information on total annual trouble spots and allow for analysis of how various vegetation management scenarios would impact trouble spots in the electric grid. This framework can also be used to better understand how more rigorous vegetation management standards (applying ETT) help reduce outages at an individual event level. In future work, a similar framework may be used to evaluate other resilience improvements.

Suggested Citation

  • William O. Taylor & Peter L. Watson & Diego Cerrai & Emmanouil Anagnostou, 2022. "A Statistical Framework for Evaluating the Effectiveness of Vegetation Management in Reducing Power Outages Caused during Storms in Distribution Networks," Sustainability, MDPI, vol. 14(2), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:904-:d:724242
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    Cited by:

    1. Hughes, William & Zhang, Wei & Cerrai, Diego & Bagtzoglou, Amvrossios & Wanik, David & Anagnostou, Emmanouil, 2022. "A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Hughes, William & Watson, Peter L. & Cerrai, Diego & Zhang, Xinxuan & Bagtzoglou, Amvrossios & Zhang, Wei & Anagnostou, Emmanouil, 2024. "Assessing grid hardening strategies to improve power system performance during storms using a hybrid mechanistic-machine learning outage prediction model," Reliability Engineering and System Safety, Elsevier, vol. 248(C).

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