Quantitative Metrics for Grid Resilience Evaluation and Optimization

Yao, Yiyun; Liu, Weijia; Jain, Rishabh; Chowdhury, Badrul; Wang, Jianhui; Cox, Robert

doi:10.1109/tste.2022.3230019

Title: Quantitative Metrics for Grid Resilience Evaluation and Optimization

Journal Article · Mon Dec 19 00:00:00 EST 2022 · IEEE Transactions on Sustainable Energy

DOI:https://doi.org/10.1109/tste.2022.3230019· OSTI ID:1967500

Yao, Yiyun ^[1];

^[1]; Jain, Rishabh ^[1];

^[2];

^[3]; Cox, Robert ^[2]

National Renewable Energy Laboratory (NREL), Golden, CO (United States)
University of North Carolina, Charlotte, NC (United States)
Southern Methodist University, Dallas, TX (United States)

Power system resilience has become a critical topic in recent years because of the increasing trend of extreme events and the growing integration of intermittent renewable energy sources. To enhance grid resilience against high-impact, low-frequency events, two questions should be answered: how to quantify the resilience of a given grid and how to incorporate the quantification into power system planning, operation, and restoration. Here this paper develops a new set of quantitative metrics with clear physical interpretation to comprehensively evaluate power system resilience. Using microgrids as an example, an event-based corrective scheduling (ECS) model and an online model predictive control (OMPC) model are developed to integrate the proposed quantitative resilience metrics into power system optimization models for resilience enhancement. The ECS model employs extreme event data to investigate the optimal restoration solution and to help microgrid operators prepare to respond to similar events. The OMPC model provides online decision-making support for operators to handle ongoing outages in the most resilient fashion. The effectiveness and superiority of the proposed quantitative resilience metrics and the resilience enhancement models are demonstrated through simulations and comparative studies on an IEEE test feeder and a real distribution feeder in Southern California.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: AC36-08GO28308; EE0009337

OSTI ID:: 1967500

Report Number(s):: NREL/JA-5D00-81285; MainId:82058; UUID:7f1678f2-f755-49e7-87f1-36badfc6c558; MainAdminID:69118

Journal Information:: IEEE Transactions on Sustainable Energy, Vol. 14, Issue 2; ISSN 1949-3029

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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