Dynamic Distribution Factors

Abdullah Al-Digs; Sairaj V. Dhople; Yu Christine Chen

doi:10.1109/TPWRS.2019.2917691

Dynamic Distribution Factors

Abdullah Al-Digs, Sairaj V. Dhople, Yu Christine Chen

Electrical and Computer Engineering

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

5 Scopus citations

Abstract

This paper proposes an approach to obtain dynamic versions of static distribution factors, such as power-transfer, line-outage, and outage-transfer distribution factors. With the proposed dynamic distribution factors (DDFs), one can predict line flows over the post-contingency transient period with the same computational effort as obtaining static distribution factors. Our development centers on deriving closed-form expressions that approximate generator outputs through the post-contingency transient period with a reduced-order aggregate dynamical model to recover dynamic generator participation factors. The full suite of DDFs can then be derived by combining these dynamic generator participation factors with injection shift factors, i.e., static linear sensitivities of line active-power flows with respect to nodal active-power injections, computed at the pre-disturbance steady-state operating point. We illustrate the accuracy and computational benefits of the proposed DDFs via numerical case studies involving the New England test system.

Original language	English (US)
Article number	8718323
Pages (from-to)	4974-4983
Number of pages	10
Journal	IEEE Transactions on Power Systems
Volume	34
Issue number	6
DOIs	https://doi.org/10.1109/TPWRS.2019.2917691
State	Published - Nov 2019

Bibliographical note

Funding Information:
This work was supported by the Natural Sciences and Engineering Research Council of Canada, funding reference RGPIN-2016-04271 and PGSD3- 519078-2018. The work of S. V. Dhople was supported by the National Science Foundation underGrant 1453921.

Funding Information:
Manuscript received December 2, 2018; revised April 3, 2019; accepted May 11, 2019. Date of publication May 20, 2019; date of current version October 24, 2019. This work was supported by the Natural Sciences and Engineering Research Council of Canada, funding reference RGPIN-2016-04271 and PGSD3-519078-2018. The work of S. V. Dhople was supported by the National Science Foundation under Grant 1453921. Paper no. TPWRS-01815-2018. (Corresponding author: Yu Christine Chen.) A. Al-Digs and Y. C. Chen are with the Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada (e-mail: aldigs@ece.ubc.ca; chen@ece.ubc.ca).

Publisher Copyright:
© 1969-2012 IEEE.

Keywords

Contingency analysis
distribution factors
injection shift factors
line-outage distribution factors
outage-transfer distribution factors
participation factors
power-transfer distribution factors
reduced-order models

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10.1109/TPWRS.2019.2917691

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title = "Dynamic Distribution Factors",

abstract = "This paper proposes an approach to obtain dynamic versions of static distribution factors, such as power-transfer, line-outage, and outage-transfer distribution factors. With the proposed dynamic distribution factors (DDFs), one can predict line flows over the post-contingency transient period with the same computational effort as obtaining static distribution factors. Our development centers on deriving closed-form expressions that approximate generator outputs through the post-contingency transient period with a reduced-order aggregate dynamical model to recover dynamic generator participation factors. The full suite of DDFs can then be derived by combining these dynamic generator participation factors with injection shift factors, i.e., static linear sensitivities of line active-power flows with respect to nodal active-power injections, computed at the pre-disturbance steady-state operating point. We illustrate the accuracy and computational benefits of the proposed DDFs via numerical case studies involving the New England test system.",

keywords = "Contingency analysis, distribution factors, injection shift factors, line-outage distribution factors, outage-transfer distribution factors, participation factors, power-transfer distribution factors, reduced-order models",

author = "Abdullah Al-Digs and Dhople, {Sairaj V.} and Chen, {Yu Christine}",

note = "Funding Information: This work was supported by the Natural Sciences and Engineering Research Council of Canada, funding reference RGPIN-2016-04271 and PGSD3- 519078-2018. The work of S. V. Dhople was supported by the National Science Foundation underGrant 1453921. Funding Information: Manuscript received December 2, 2018; revised April 3, 2019; accepted May 11, 2019. Date of publication May 20, 2019; date of current version October 24, 2019. This work was supported by the Natural Sciences and Engineering Research Council of Canada, funding reference RGPIN-2016-04271 and PGSD3-519078-2018. The work of S. V. Dhople was supported by the National Science Foundation under Grant 1453921. Paper no. TPWRS-01815-2018. (Corresponding author: Yu Christine Chen.) A. Al-Digs and Y. C. Chen are with the Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada (e-mail: aldigs@ece.ubc.ca; chen@ece.ubc.ca). Publisher Copyright: {\textcopyright} 1969-2012 IEEE.",

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N2 - This paper proposes an approach to obtain dynamic versions of static distribution factors, such as power-transfer, line-outage, and outage-transfer distribution factors. With the proposed dynamic distribution factors (DDFs), one can predict line flows over the post-contingency transient period with the same computational effort as obtaining static distribution factors. Our development centers on deriving closed-form expressions that approximate generator outputs through the post-contingency transient period with a reduced-order aggregate dynamical model to recover dynamic generator participation factors. The full suite of DDFs can then be derived by combining these dynamic generator participation factors with injection shift factors, i.e., static linear sensitivities of line active-power flows with respect to nodal active-power injections, computed at the pre-disturbance steady-state operating point. We illustrate the accuracy and computational benefits of the proposed DDFs via numerical case studies involving the New England test system.

AB - This paper proposes an approach to obtain dynamic versions of static distribution factors, such as power-transfer, line-outage, and outage-transfer distribution factors. With the proposed dynamic distribution factors (DDFs), one can predict line flows over the post-contingency transient period with the same computational effort as obtaining static distribution factors. Our development centers on deriving closed-form expressions that approximate generator outputs through the post-contingency transient period with a reduced-order aggregate dynamical model to recover dynamic generator participation factors. The full suite of DDFs can then be derived by combining these dynamic generator participation factors with injection shift factors, i.e., static linear sensitivities of line active-power flows with respect to nodal active-power injections, computed at the pre-disturbance steady-state operating point. We illustrate the accuracy and computational benefits of the proposed DDFs via numerical case studies involving the New England test system.

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ER -

Dynamic Distribution Factors

Abstract

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Keywords

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