A Data-aided Security Constraint Prescreening Technique and Application to Real-world System

Shubo Zhang; Hongxing Ye; Fengyu Wang; Yonghong Chen; Stephen Rose; Yaming Ma

doi:10.1109/NAPS46351.2019.9000286

A Data-aided Security Constraint Prescreening Technique and Application to Real-world System

Shubo Zhang
, Hongxing Ye
, Fengyu Wang
, Yonghong Chen
, Stephen Rose
, Yaming Ma

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

Security constraints are necessary in unit commitment problems to guarantee reliable commitments. However, only a small subset of security constraints can bind in the unit commitment problem. This paper presents a data-aided prescreening approach that could identify and remove non-dominating security constraints. This approach takes advantage of both deterministic and statistical methods, which leverages the historical data and linear programming (LP) solution. Lazy constraints are used in optimization solver to maintain solution integrity. Case studies presented here use Midcontinent Independent System Operator (MISO) data, and simulation results show that the proposed prescreening approach significantly improves computational performance.

Original language	English
Title of host publication	51st North American Power Symposium, NAPS 2019
Place of Publication	usa
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728104072
DOIs	https://doi.org/10.1109/NAPS46351.2019.9000286
State	Published - Oct 1 2019
Event	51st North American Power Symposium, NAPS 2019 - Wichita, United States Duration: Oct 13 2019 → Oct 15 2019

Conference

Conference	51st North American Power Symposium, NAPS 2019
Country/Territory	United States
City	Wichita
Period	10/13/19 → 10/15/19

Keywords

Lazy Constraint
Optimal Power Flow
Security Constraint
Transmission Congestion
Unit Commitment

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10.1109/NAPS46351.2019.9000286

Cite this

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title = "A Data-aided Security Constraint Prescreening Technique and Application to Real-world System",

abstract = "Security constraints are necessary in unit commitment problems to guarantee reliable commitments. However, only a small subset of security constraints can bind in the unit commitment problem. This paper presents a data-aided prescreening approach that could identify and remove non-dominating security constraints. This approach takes advantage of both deterministic and statistical methods, which leverages the historical data and linear programming (LP) solution. Lazy constraints are used in optimization solver to maintain solution integrity. Case studies presented here use Midcontinent Independent System Operator (MISO) data, and simulation results show that the proposed prescreening approach significantly improves computational performance.",

keywords = "Lazy Constraint, Optimal Power Flow, Security Constraint, Transmission Congestion, Unit Commitment",

author = "Shubo Zhang and Hongxing Ye and Fengyu Wang and Yonghong Chen and Stephen Rose and Yaming Ma",

year = "2019",

month = oct,

day = "1",

doi = "10.1109/NAPS46351.2019.9000286",

language = "English",

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Zhang, S, Ye, H, Wang, F, Chen, Y, Rose, S & Ma, Y 2019, A Data-aided Security Constraint Prescreening Technique and Application to Real-world System. in 51st North American Power Symposium, NAPS 2019., 9000286, Institute of Electrical and Electronics Engineers Inc., usa, 51st North American Power Symposium, NAPS 2019, Wichita, United States, 10/13/19. https://doi.org/10.1109/NAPS46351.2019.9000286

A Data-aided Security Constraint Prescreening Technique and Application to Real-world System. / Zhang, Shubo; Ye, Hongxing; Wang, Fengyu et al.
51st North American Power Symposium, NAPS 2019. usa: Institute of Electrical and Electronics Engineers Inc., 2019. 9000286.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Data-aided Security Constraint Prescreening Technique and Application to Real-world System

AU - Zhang, Shubo

AU - Ye, Hongxing

AU - Wang, Fengyu

AU - Chen, Yonghong

AU - Rose, Stephen

AU - Ma, Yaming

PY - 2019/10/1

Y1 - 2019/10/1

N2 - Security constraints are necessary in unit commitment problems to guarantee reliable commitments. However, only a small subset of security constraints can bind in the unit commitment problem. This paper presents a data-aided prescreening approach that could identify and remove non-dominating security constraints. This approach takes advantage of both deterministic and statistical methods, which leverages the historical data and linear programming (LP) solution. Lazy constraints are used in optimization solver to maintain solution integrity. Case studies presented here use Midcontinent Independent System Operator (MISO) data, and simulation results show that the proposed prescreening approach significantly improves computational performance.

AB - Security constraints are necessary in unit commitment problems to guarantee reliable commitments. However, only a small subset of security constraints can bind in the unit commitment problem. This paper presents a data-aided prescreening approach that could identify and remove non-dominating security constraints. This approach takes advantage of both deterministic and statistical methods, which leverages the historical data and linear programming (LP) solution. Lazy constraints are used in optimization solver to maintain solution integrity. Case studies presented here use Midcontinent Independent System Operator (MISO) data, and simulation results show that the proposed prescreening approach significantly improves computational performance.

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KW - Optimal Power Flow

KW - Security Constraint

KW - Transmission Congestion

KW - Unit Commitment

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BT - 51st North American Power Symposium, NAPS 2019

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T2 - 51st North American Power Symposium, NAPS 2019

Y2 - 13 October 2019 through 15 October 2019

ER -

A Data-aided Security Constraint Prescreening Technique and Application to Real-world System

Abstract

Conference

Keywords

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