A Risk-Based Framework for Power System Modeling to Improve Resilience to Extreme Events
The extent of the damage to Puerto Rico 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 supply chain interruptions to the inaccessibil...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9927237/ |
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| Summary: | The extent of the damage to Puerto Rico 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 supply chain interruptions 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 along with probabilistic methods and analytical metrics to better describe risk. |
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| ISSN: | 2687-7910 |