Data-driven resilience analysis of power grids

Qicong Sun; Yan Li; Jason Philhower

doi:10.1016/j.gloei.2021.03.010

Data-driven resilience analysis of power grids

Qicong Sun, Yan Li, Jason Philhower

Electrical Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

With the integration of renewable energy resources, the inertia of power systems significantly reduces, thereby making the system sensitive to operational disturbances. A disturbance-based method is presented herein to estimate inertia, uncovering the influence of renewables on system-resilient operations. The Gaussian process regression method is then used to predict the power system trajectory after disturbance. Extensive tests demonstrate the data-driven method mathematically estimates the inertia of the system as well as predicts the dynamics operations of power grids subject to disturbances. Numerical results also offer insights into the enhancement of system resilience by strategically designing the inertia of power systems.

Original language	English (US)
Pages (from-to)	104-114
Number of pages	11
Journal	Global Energy Interconnection
Volume	4
Issue number	1
DOIs	https://doi.org/10.1016/j.gloei.2021.03.010
State	Published - Feb 2021

All Science Journal Classification (ASJC) codes

Energy Engineering and Power Technology
Electrical and Electronic Engineering
Control and Systems Engineering
Automotive Engineering
Renewable Energy, Sustainability and the Environment

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.gloei.2021.03.010

Cite this

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title = "Data-driven resilience analysis of power grids",

abstract = "With the integration of renewable energy resources, the inertia of power systems significantly reduces, thereby making the system sensitive to operational disturbances. A disturbance-based method is presented herein to estimate inertia, uncovering the influence of renewables on system-resilient operations. The Gaussian process regression method is then used to predict the power system trajectory after disturbance. Extensive tests demonstrate the data-driven method mathematically estimates the inertia of the system as well as predicts the dynamics operations of power grids subject to disturbances. Numerical results also offer insights into the enhancement of system resilience by strategically designing the inertia of power systems.",

author = "Qicong Sun and Yan Li and Jason Philhower",

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year = "2021",

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doi = "10.1016/j.gloei.2021.03.010",

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AU - Sun, Qicong

AU - Li, Yan

AU - Philhower, Jason

PY - 2021/2

Y1 - 2021/2

N2 - With the integration of renewable energy resources, the inertia of power systems significantly reduces, thereby making the system sensitive to operational disturbances. A disturbance-based method is presented herein to estimate inertia, uncovering the influence of renewables on system-resilient operations. The Gaussian process regression method is then used to predict the power system trajectory after disturbance. Extensive tests demonstrate the data-driven method mathematically estimates the inertia of the system as well as predicts the dynamics operations of power grids subject to disturbances. Numerical results also offer insights into the enhancement of system resilience by strategically designing the inertia of power systems.

AB - With the integration of renewable energy resources, the inertia of power systems significantly reduces, thereby making the system sensitive to operational disturbances. A disturbance-based method is presented herein to estimate inertia, uncovering the influence of renewables on system-resilient operations. The Gaussian process regression method is then used to predict the power system trajectory after disturbance. Extensive tests demonstrate the data-driven method mathematically estimates the inertia of the system as well as predicts the dynamics operations of power grids subject to disturbances. Numerical results also offer insights into the enhancement of system resilience by strategically designing the inertia of power systems.

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JO - Global Energy Interconnection

JF - Global Energy Interconnection

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ER -

Data-driven resilience analysis of power grids

Abstract

All Science Journal Classification (ASJC) codes

UN SDGs

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