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Quantitative Metrics for Grid Resilience Evaluation and Optimization

delete2023-04-01
delete18
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OA
AI
Y
Yao, Yiyun
W
Weijia Liu *
R
Rishabh Jain
B
Badrul Chowdhury
J
Jianhui Wang
R
Robert W. Cox
DOI:10.1109/TSTE.2022.3230019delete
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摘要

摘要

En 中文
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. 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.
Keyword:
Resilience
Measurement
Power systems
Optimization
Load modeling
Microgrids
Renewable energy sources
Community microgrid
distributed energy resource (DER)
power system resilience
power system optimization
quantitative resilience metrics

期刊

IEEE Transactions on Energy Conversion 封面图
IEEE Transactions on Energy Conversion
IF:
5.4
论文数:
6.8K
被引数:
1.5W

机构

U
university of north carolina
学者数:
7.4W
论文数: 6.5W
被引数: 93
N
national renewable energy laboratory - usa
学者数:
3.8K
论文数: 2.8K
被引数: 10
U
united states department of energy (doe)
学者数:
11.3W
论文数: 9.6W
被引数: 246
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