Co-Optimized Expansion Planning to Enhance Electrical System Resilience in Puerto Rico

In September of 2017, the islands of Puerto Rico (PR) was devastated by a category 5 hurricane, Hurricane Maria. The island experienced complete blackout, and full restoration of the electrical system took nearly 11 months to complete. Therefore, it is of high interest to re-develop the infrastructu...

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Bibliographic Details
Published in2019 North American Power Symposium (NAPS) pp. 1 - 6
Main Authors Newlun, Cody J., Figueroa-Acevedo, Armando L., McCalley, James D., Kimber, Anne, O'Neill - Carrillo, Efrain
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
Subjects
Adaptation models
distributed generation
expansion planning
hurricane events
Hurricanes
Integrated circuit modeling
Investment
Load modeling
optimization
Planning
Resilience
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Summary:In September of 2017, the islands of Puerto Rico (PR) was devastated by a category 5 hurricane, Hurricane Maria. The island experienced complete blackout, and full restoration of the electrical system took nearly 11 months to complete. Therefore, it is of high interest to re-develop the infrastructure at the generation, transmission, and distribution levels so that it is hurricane-resilient. This paper describes the methodologies behind developing a more resilient electric infrastructure using a co-optimized expansion planning (CEP) software. First, a model of the PR electric power system was developed to perform longterm CEP studies. The CEP tool developed seeks the minimum total cost of the PR system in a 2018-2038 planning horizon while exploring various levels of expansion investment options. The CEP also models the system under extreme events (i.e. hurricanes) to allow for data-driven resilience enhancement decisions. Second, the paper summarizes infrastructure visions that contain resilience investment options; the visions differ in terms of invested amounts of distributed generation and centralized resource. Lastly, key findings from these visions are reported and the CEP model performance is discussed.
DOI:10.1109/NAPS46351.2019.9000265