Research output: Scientific - peer-review › Article

**Comparing the Effects of Failures in Power Grids under the AC and DC Power Flow Models.** / Cetinay, Hale; Soltan, Saleh; Kuipers, Fernando A.; Zussman, Gil; Van Mieghem, Piet.

Research output: Scientific - peer-review › Article

Cetinay, H, Soltan, S, Kuipers, FA, Zussman, G & Van Mieghem, P 2018, 'Comparing the Effects of Failures in Power Grids under the AC and DC Power Flow Models' *IEEE Transactions on Network Science and Engineering*, vol PP, no. 99, pp. 1-13. DOI: 10.1109/TNSE.2017.2763746

Cetinay, H., Soltan, S., Kuipers, F. A., Zussman, G., & Van Mieghem, P. (2018). Comparing the Effects of Failures in Power Grids under the AC and DC Power Flow Models. *IEEE Transactions on Network Science and Engineering*, *PP*(99), 1-13. DOI: 10.1109/TNSE.2017.2763746

Cetinay H, Soltan S, Kuipers FA, Zussman G, Van Mieghem P. Comparing the Effects of Failures in Power Grids under the AC and DC Power Flow Models. IEEE Transactions on Network Science and Engineering. 2018;PP(99):1-13. Available from, DOI: 10.1109/TNSE.2017.2763746

@article{6489c19cbeb246289816b872ad3a1e4a,

title = "Comparing the Effects of Failures in Power Grids under the AC and DC Power Flow Models",

abstract = "In this paper, we compare the effects of failures in power grids under the nonlinear AC and linearized DC power flow models. First, we numerically demonstrate that when there are no failures and the assumptions underlying the DC model are valid, the DC model approximates the AC model well in four considered test networks. Then, to evaluate the validity of the DC approximation upon failures, we numerically compare the effects of single line failures and the evolution of cascades under the AC and DC flow models using different metrics, such as yield (the ratio of the demand supplied at the end of the cascade to the initial demand). We demonstrate that the effects of a single line failure on the distribution of the flows on other lines are similar under the AC and DC models. However, the cascade simulations demonstrate that the assumptions underlying the DC model (e.g., ignoring power losses, reactive power flows, and voltage magnitude variations) can lead to inaccurate and overly optimistic cascade predictions. Particularly, in large networks the DC model tends to overestimate the yield. Hence, using the DC model for cascade prediction may result in a misrepresentation of the gravity of a cascade.",

keywords = "AC versus DC, cascading failures, contingency analysis, power flows, Power grids",

author = "Hale Cetinay and Saleh Soltan and Kuipers, {Fernando A.} and Gil Zussman and {Van Mieghem}, Piet",

note = "This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.",

year = "2018",

doi = "10.1109/TNSE.2017.2763746",

volume = "PP",

pages = "1--13",

journal = "IEEE Transactions on Network Science and Engineering",

issn = "2327-4697",

number = "99",

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TY - JOUR

T1 - Comparing the Effects of Failures in Power Grids under the AC and DC Power Flow Models

AU - Cetinay,Hale

AU - Soltan,Saleh

AU - Kuipers,Fernando A.

AU - Zussman,Gil

AU - Van Mieghem,Piet

N1 - This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

PY - 2018

Y1 - 2018

N2 - In this paper, we compare the effects of failures in power grids under the nonlinear AC and linearized DC power flow models. First, we numerically demonstrate that when there are no failures and the assumptions underlying the DC model are valid, the DC model approximates the AC model well in four considered test networks. Then, to evaluate the validity of the DC approximation upon failures, we numerically compare the effects of single line failures and the evolution of cascades under the AC and DC flow models using different metrics, such as yield (the ratio of the demand supplied at the end of the cascade to the initial demand). We demonstrate that the effects of a single line failure on the distribution of the flows on other lines are similar under the AC and DC models. However, the cascade simulations demonstrate that the assumptions underlying the DC model (e.g., ignoring power losses, reactive power flows, and voltage magnitude variations) can lead to inaccurate and overly optimistic cascade predictions. Particularly, in large networks the DC model tends to overestimate the yield. Hence, using the DC model for cascade prediction may result in a misrepresentation of the gravity of a cascade.

AB - In this paper, we compare the effects of failures in power grids under the nonlinear AC and linearized DC power flow models. First, we numerically demonstrate that when there are no failures and the assumptions underlying the DC model are valid, the DC model approximates the AC model well in four considered test networks. Then, to evaluate the validity of the DC approximation upon failures, we numerically compare the effects of single line failures and the evolution of cascades under the AC and DC flow models using different metrics, such as yield (the ratio of the demand supplied at the end of the cascade to the initial demand). We demonstrate that the effects of a single line failure on the distribution of the flows on other lines are similar under the AC and DC models. However, the cascade simulations demonstrate that the assumptions underlying the DC model (e.g., ignoring power losses, reactive power flows, and voltage magnitude variations) can lead to inaccurate and overly optimistic cascade predictions. Particularly, in large networks the DC model tends to overestimate the yield. Hence, using the DC model for cascade prediction may result in a misrepresentation of the gravity of a cascade.

KW - AC versus DC

KW - cascading failures

KW - contingency analysis

KW - power flows

KW - Power grids

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U2 - 10.1109/TNSE.2017.2763746

DO - 10.1109/TNSE.2017.2763746

M3 - Article

VL - PP

SP - 1

EP - 13

JO - IEEE Transactions on Network Science and Engineering

T2 - IEEE Transactions on Network Science and Engineering

JF - IEEE Transactions on Network Science and Engineering

SN - 2327-4697

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ER -

ID: 31754007