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Title: Machine learning based energy management system for grid disaster mitigation

Abstract

The recent increase in infiltration of distributed resources has challenged the traditional operation of power systems. Simultaneously, devastating effects of recent natural disasters have questioned the resilience of power infrastructure for an electricity dependent community. In this study, a solution has been presented in the form of a resilient smart grid network which utilizes Distributed Energy Resources (DERs) and Machine Learning (ML) algorithms to improve the power availability during disastrous events. In addition to power electronics with load categorization features, the presented system utilizes ML tools to use the information from neighbouring units and external sources to make complicated logical decisions directed towards provide power to critical loads at all times. Furthermore, provided model encourages consideration of ML tools as a part of smart grid design process together with power electronics and controls, not as an additional feature.

Authors:
 [1];  [1];  [1];  [1];  [1]
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  1. Univ. of Texas at Dallas, Richardson, TX (United States)
Publication Date:
Research Org.:
Univ. of Central Florida, Orlando, FL (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1601133
Grant/Contract Number:  
EE0007327
Resource Type:
Accepted Manuscript
Journal Name:
IET Smart Grid
Additional Journal Information:
Journal Volume: 2; Journal Issue: 2; Journal ID: ISSN 2515-2947
Publisher:
The Institution of Engineering and Technology
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION

Citation Formats

Maharjan, Lizon, Ditsworth, Mark, Niraula, Manish, Caicedo Narvaez, Carlos, and Fahimi, Babak. Machine learning based energy management system for grid disaster mitigation. United States: N. p., 2018. Web. doi:10.1049/iet-stg.2018.0043.
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Maharjan, Lizon, Ditsworth, Mark, Niraula, Manish, Caicedo Narvaez, Carlos, & Fahimi, Babak. Machine learning based energy management system for grid disaster mitigation. United States. https://doi.org/10.1049/iet-stg.2018.0043
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Maharjan, Lizon, Ditsworth, Mark, Niraula, Manish, Caicedo Narvaez, Carlos, and Fahimi, Babak. Fri . "Machine learning based energy management system for grid disaster mitigation". United States. https://doi.org/10.1049/iet-stg.2018.0043. https://www.osti.gov/servlets/purl/1601133.
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@article{osti_1601133,
title = {Machine learning based energy management system for grid disaster mitigation},
author = {Maharjan, Lizon and Ditsworth, Mark and Niraula, Manish and Caicedo Narvaez, Carlos and Fahimi, Babak},
abstractNote = {The recent increase in infiltration of distributed resources has challenged the traditional operation of power systems. Simultaneously, devastating effects of recent natural disasters have questioned the resilience of power infrastructure for an electricity dependent community. In this study, a solution has been presented in the form of a resilient smart grid network which utilizes Distributed Energy Resources (DERs) and Machine Learning (ML) algorithms to improve the power availability during disastrous events. In addition to power electronics with load categorization features, the presented system utilizes ML tools to use the information from neighbouring units and external sources to make complicated logical decisions directed towards provide power to critical loads at all times. Furthermore, provided model encourages consideration of ML tools as a part of smart grid design process together with power electronics and controls, not as an additional feature.},
doi = {10.1049/iet-stg.2018.0043},
journal = {IET Smart Grid},
number = 2,
volume = 2,
place = {United States},
year = {Fri Dec 21 00:00:00 EST 2018},
month = {Fri Dec 21 00:00:00 EST 2018}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1049/iet-stg.2018.0043
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