A Bayesian Approach to the Reliability Analysis of Renewables-Dominated Islanded DC Microgrids

Abdelsalam A. Eajal; Ahmed El-Awady; Ehab F. El-Saadany; Kumaraswamy Ponnambalam; Ahmed Al-Durra; Ameena S. Al-Sumaiti; Magdy M.A. Salama

doi:10.1109/TPWRS.2021.3056314

A Bayesian Approach to the Reliability Analysis of Renewables-Dominated Islanded DC Microgrids

Abdelsalam A. Eajal
, Ahmed El-Awady
, Ehab F. El-Saadany
, Kumaraswamy Ponnambalam
, Ahmed Al-Durra
, Ameena S. Al-Sumaiti
, Magdy M.A. Salama

Electrical Engineering

University of Waterloo
Ain Shams University

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

32 Scopus citations

Abstract

The DC microgrid (DC MG) concept enables the hosting of DC-type renewable energy resources. However, their intermittent nature means that a high penetration of renewables can jeopardize supply adequacy and voltage provision during islanding. The work presented in this paper was therefore directed at developing a probabilistic graphical approach based on Bayesian networks (BNs) for the reliability analysis of renewables-dominated DC MGs. The proposed BN model incorporates a family of novel reliability indices for quantifying the impact of a high penetration of renewables on MG reliability, including loss of renewable power supply, rise in voltage, and reversal of power flow. The model is supported by a newly formulated fast and accurate linearized power flow algorithm for probability calculations. The accuracy of the BN model has been verified against a Monte-Carlo simulation (MCS). The effective application of the new BN model for reasoning and impact assessment reveals that a high penetration of renewables affects reliability indices differently. Case study results suggest that the proposed BN model shows promise as a valuable tool for the reliability analysis of renewables-dominated MGs that feature islanding capability.

Original language	British English
Article number	9345988
Pages (from-to)	4296-4309
Number of pages	14
Journal	IEEE Transactions on Power Systems
Volume	36
Issue number	5
DOIs	https://doi.org/10.1109/TPWRS.2021.3056314
State	Published - Sep 2021

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Bayesian network
DC microgrid
distributed generator
droop control
islanding
reliability
renewable power

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title = "A Bayesian Approach to the Reliability Analysis of Renewables-Dominated Islanded DC Microgrids",

abstract = "The DC microgrid (DC MG) concept enables the hosting of DC-type renewable energy resources. However, their intermittent nature means that a high penetration of renewables can jeopardize supply adequacy and voltage provision during islanding. The work presented in this paper was therefore directed at developing a probabilistic graphical approach based on Bayesian networks (BNs) for the reliability analysis of renewables-dominated DC MGs. The proposed BN model incorporates a family of novel reliability indices for quantifying the impact of a high penetration of renewables on MG reliability, including loss of renewable power supply, rise in voltage, and reversal of power flow. The model is supported by a newly formulated fast and accurate linearized power flow algorithm for probability calculations. The accuracy of the BN model has been verified against a Monte-Carlo simulation (MCS). The effective application of the new BN model for reasoning and impact assessment reveals that a high penetration of renewables affects reliability indices differently. Case study results suggest that the proposed BN model shows promise as a valuable tool for the reliability analysis of renewables-dominated MGs that feature islanding capability.",

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note = "Funding Information: Manuscript received July 6, 2020; revised October 23, 2020 and December 3, 2020; accepted January 24, 2021. Date of publication February 3, 2021; date of current version August 19, 2021. This work was supported by the Advanced Power and Energy Center, RCII-006-2018, Khalifa University, Abu Dhabi, UAE. Paper no. TPWRS-01120-2020. (Corresponding author: Abdelsalam A. Eajal.) Abdelsalam A. Eajal and Magdy M. A. Salama are with the Department of Electrical, and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada (e-mail: [email protected]; [email protected]). Publisher Copyright: {\textcopyright} 1969-2012 IEEE.",

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AU - Al-Durra, Ahmed

AU - Al-Sumaiti, Ameena S.

AU - Salama, Magdy M.A.

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KW - renewable power

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A Bayesian Approach to the Reliability Analysis of Renewables-Dominated Islanded DC Microgrids

Abstract

UN SDGs

Keywords

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