Measurement-Based Sparsity-Promoting Optimal Control of Line Flows

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

doi:10.1109/TPWRS.2018.2808819

Measurement-Based Sparsity-Promoting Optimal Control of Line Flows

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

Electrical and Computer Engineering

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

2 Scopus citations

Abstract

This paper proposes an optimal strategy for regulating active-power flows in electric power systems based on sparsity-promoting linear-quadratic-Gaussian (LQG) control. The proposed method relies on the mapping of nodal active- and reactive-power injections to line flows, which are obtained via a measurement-based approach. Building on this, we outline a combined sparsity-promoting linear-quadratic regulator and Kalman-filter design. The optimal controller sparsity is identified using the alternating direction method of multipliers, which strikes a balance between feedback controller sparsity and the closed-loop dynamic performance. With this, we optimally dispatch generators and controllable loads to achieve desired line flows while ensuring zero steady-state frequency offset. We demonstrate the utility of the proposed LQG controller via a representative congestion-management application deployed on the New England 10-machine 39-bus test system.

Original language	English (US)
Article number	8299563
Pages (from-to)	5628-5638
Number of pages	11
Journal	IEEE Transactions on Power Systems
Volume	33
Issue number	5
DOIs	https://doi.org/10.1109/TPWRS.2018.2808819
State	Published - Sep 2018

Bibliographical note

Funding Information:
Manuscript received September 20, 2017; revised January 15, 2018; accepted February 5, 2018. Date of publication February 21, 2018; date of current version August 22, 2018. This work was supported by the Natural Sciences and Engineering Research Council of Canada under Grant RGPIN-2016-04271 and Grant 514710-17. The work of S. V. Dhople was supported in part by the National Science Foundation under the CAREER Award 1453921. Paper no. TPWRS-01455-2017. (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 1Z2, Canada (e-mail: aldigs@ece.ubc.ca; chen@ece.ubc.ca).

Keywords

Alternating direction method of multipliers (ADMM)
injection shift factors
line-flow control
linear-quadratic-Gaussian control
optimization
sparsity-promoting control

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title = "Measurement-Based Sparsity-Promoting Optimal Control of Line Flows",

abstract = "This paper proposes an optimal strategy for regulating active-power flows in electric power systems based on sparsity-promoting linear-quadratic-Gaussian (LQG) control. The proposed method relies on the mapping of nodal active- and reactive-power injections to line flows, which are obtained via a measurement-based approach. Building on this, we outline a combined sparsity-promoting linear-quadratic regulator and Kalman-filter design. The optimal controller sparsity is identified using the alternating direction method of multipliers, which strikes a balance between feedback controller sparsity and the closed-loop dynamic performance. With this, we optimally dispatch generators and controllable loads to achieve desired line flows while ensuring zero steady-state frequency offset. We demonstrate the utility of the proposed LQG controller via a representative congestion-management application deployed on the New England 10-machine 39-bus test system.",

keywords = "Alternating direction method of multipliers (ADMM), injection shift factors, line-flow control, linear-quadratic-Gaussian control, optimization, sparsity-promoting control",

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

note = "Funding Information: Manuscript received September 20, 2017; revised January 15, 2018; accepted February 5, 2018. Date of publication February 21, 2018; date of current version August 22, 2018. This work was supported by the Natural Sciences and Engineering Research Council of Canada under Grant RGPIN-2016-04271 and Grant 514710-17. The work of S. V. Dhople was supported in part by the National Science Foundation under the CAREER Award 1453921. Paper no. TPWRS-01455-2017. (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 1Z2, Canada (e-mail: aldigs@ece.ubc.ca; chen@ece.ubc.ca).",

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