TY - JOUR
T1 - A Bayesian Approach to the Reliability Analysis of Renewables-Dominated Islanded DC Microgrids
AU - Eajal, Abdelsalam A.
AU - El-Awady, Ahmed
AU - El-Saadany, Ehab F.
AU - Ponnambalam, Kumaraswamy
AU - Al-Durra, Ahmed
AU - Al-Sumaiti, Ameena S.
AU - Salama, Magdy M.A.
N1 - 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:
© 1969-2012 IEEE.
PY - 2021/9
Y1 - 2021/9
N2 - 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.
AB - 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.
KW - Bayesian network
KW - DC microgrid
KW - distributed generator
KW - droop control
KW - islanding
KW - reliability
KW - renewable power
UR - http://www.scopus.com/inward/record.url?scp=85100789679&partnerID=8YFLogxK
U2 - 10.1109/TPWRS.2021.3056314
DO - 10.1109/TPWRS.2021.3056314
M3 - Article
AN - SCOPUS:85100789679
SN - 0885-8950
VL - 36
SP - 4296
EP - 4309
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 5
M1 - 9345988
ER -
