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Title: A Risk-Based Framework for Power System Modeling to Improve Resilience to Extreme Events

Journal Article · · IEEE Open Access Journal of Power and Energy
ORCiD logo [1];  [2];  [3]; ORCiD logo [1];  [4];  [5]; ORCiD logo [5]; ORCiD logo [2];  [5];  [6]; ORCiD logo [5]
  1. Department of Electricity Infrastructure, Pacific Northwest National Laboratory, Richland, WA, USA
  2. Department of Electricity Security, Pacific Northwest National Laboratory, Richland, WA, USA
  3. Pacific Northwest National Laboratory, Energy and Efficiency Division, Seattle, WA, USA
  4. Apex Clean Energy, Charlottesville, VA, USA
  5. Pacific Northwest National Laboratory, Richland, WA, USA
  6. Department of Energy and Environment, Pacific Northwest National Laboratory, Richland, WA, USA

The extent of the damage to Puerto Rico that resulted from Hurricane Maria in September 2017 led to outages in electricity service that persisted for months. Power system operators attempting to restore critical facilities faced challenges on almost every front from interruptions to necessary supply chains to the inaccessibility of key assets. After a disaster of this magnitude, it is critical, but challenging, to prioritize how limited resources are directed toward rebuilding and fortifying the electric power system. To inform these decisions, the U.S. Department of Energy funded efforts investigating methodologies to identify critical vulnerabilities to the Puerto Rican power system and to provide data-driven recommendations on how to harden and operate the system for greater resilience. This work presents the Risk-based Contingency Analysis Tool (RCAT), a framework developed as a part of that resilience initiative. The framework can qualitatively and quantitatively describe the most critical system vulnerabilities with an understanding of both likelihood of occurrence and impact. It evaluates the effectiveness of candidate remediation strategies in reducing overall risk to the system from future hurricane events. This paper will describe RCAT, with an emphasis on how different modeling capabilities have been integrated and the introduction of probabilistic methods and analytical metrics to better describe risk.

Research Organization:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
DEAC0576RL01830; AC05-76RL01830
OSTI ID:
1896934
Alternate ID(s):
OSTI ID: 1908896; OSTI ID: 1925238
Report Number(s):
PNNL-SA-166412; 9927237
Journal Information:
IEEE Open Access Journal of Power and Energy, Journal Name: IEEE Open Access Journal of Power and Energy Vol. 10; ISSN 2687-7910
Publisher:
Institute of Electrical and Electronics EngineersCopyright Statement
Country of Publication:
United States
Language:
English