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DOE OSTI · 1601133

Machine learning based energy management system for grid disaster mitigation

Maharjan, Lizon·Ditsworth, Mark·Niraula, Manish·Caicedo Narvaez, Carlos·Fahimi, Babak
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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.

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Maharjan, Lizon, Ditsworth, Mark, Niraula, Manish, Caicedo Narvaez, Carlos, Fahimi, Babak. 2018-12-21. Machine learning based energy management system for grid disaster mitigation. https://doi.org/10.1049/iet-stg.2018.0043

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