Preparatory Operation of Automated Distribution Systems for Resilience Enhancement of Critical Loads

This paper proposes a stochastic optimization model for preparatory operation of distributed energy resources that enhance the resilience of critical loads against extreme weather events. Adopting hurricanes as a case study of extreme weather events, a high-precision spatio-temporal hurricane impact...

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Bibliographic Details
Published in:IEEE transactions on power delivery Vol. 36; no. 4; pp. 2354 - 2362
Main Authors: Nguyen, Hieu Trung, Muhs, John, Parvania, Masood
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Automation
Circuit faults
Cost function
customer interruption cost
Customer services
Customers
Decisions
distributed energy resources
Distributed generation
Electrical loads
Energy sources
Energy storage
Hurricanes
Impact analysis
Interruption costs
Load modeling
Mixed integer
Optimization
Outage costs
Power distribution automation
Power flow
Resilience
Schedules
Service restoration
Situational awareness
stochastic mixed integer conic programming
Stochastic processes
Weather
Wind speed
ISSN:0885-8977, 1937-4208
Online Access:Get full text
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Summary:This paper proposes a stochastic optimization model for preparatory operation of distributed energy resources that enhance the resilience of critical loads against extreme weather events. Adopting hurricanes as a case study of extreme weather events, a high-precision spatio-temporal hurricane impact analysis model is first developed to enhance the situational awareness of automated distribution systems and generate a set of hurricane-induced outage scenarios. A stochastic mixed-integer conic programming model is then developed for co-optimizing the preparatory schedule of distributed energy storage and distributed generation ahead of hurricane along with post-event decisions of restoring services to customers, while minimizing the expected cost of energy not served as the index to measure the resilience. The proposed model prioritizes the service restoration of critical loads by integrating customer interruption cost function, which captures the customer type and outage costs as well as interruption duration in making restoration decisions. Additionally, a conic power flow model is adopted to accurately capture network operation constraints in pre- and post-outage scenarios. Numerical results conducted on the IEEE 33-bus system show that the proposed model can enhance the performance of automated distribution systems in responding to hurricane-induced outages by efficiently utilizing grid resources to improve the resilience of critical loads.
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ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2020.3030927