A data-driven mixed integer programming approach for joint chance-constrained optimal power flow under uncertainty

Qin, JC; Jiang, R; Mo, H; Dong, D

A data-driven mixed integer programming approach for joint chance-constrained optimal power flow under uncertainty

Qin, JC Jiang, R Mo, H Dong, D

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Publisher:: SPRINGER HEIDELBERG
Publication Type:: Journal Article
Citation:: International Journal of Machine Learning and Cybernetics, 2025, 16, (2), pp. 1111-1127
Issue Date:: 2025-02-01

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Field	Value	Language
dc.contributor.author	Qin, JC
dc.contributor.author	Jiang, R
dc.contributor.author	Mo, H
dc.contributor.author	Dong, D https://orcid.org/0000-0002-7425-3559
dc.date.accessioned	2025-07-15T05:39:43Z
dc.date.available	2025-07-15T05:39:43Z
dc.date.issued	2025-02-01
dc.identifier.citation	International Journal of Machine Learning and Cybernetics, 2025, 16, (2), pp. 1111-1127
dc.identifier.issn	1868-8071
dc.identifier.issn	1868-808X
dc.identifier.uri	http://hdl.handle.net/10453/188259
dc.description.abstract	This paper introduces a novel mixed integer programming (MIP) reformulation for the joint chance-constrained optimal power flow problem under uncertain load and renewable energy generation. Unlike traditional models, our approach incorporates a comprehensive evaluation of system-wide risk without decomposing joint chance constraints into individual constraints, thus preventing overly conservative solutions and ensuring robust system security. A significant innovation in our method is the use of historical data to form a sample average approximation that directly informs the MIP model, bypassing the need for distributional assumptions to enhance solution robustness. Additionally, we implement a model improvement strategy to reduce the computational burden, making our method more scalable for large-scale power systems. Our approach is validated against benchmark systems, i.e., IEEE 14-, 57- and 118-bus systems, demonstrating superior performance in terms of cost-efficiency and robustness, with lower computational demand compared to existing methods.
dc.language	English
dc.publisher	SPRINGER HEIDELBERG
dc.relation.ispartof	International Journal of Machine Learning and Cybernetics
dc.relation.isbasedon	10.1007/s13042-024-02325-x
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	A data-driven mixed integer programming approach for joint chance-constrained optimal power flow under uncertainty
dc.type	Journal Article
utslib.citation.volume	16
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-07-15T05:39:41Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	2

Abstract:

This paper introduces a novel mixed integer programming (MIP) reformulation for the joint chance-constrained optimal power flow problem under uncertain load and renewable energy generation. Unlike traditional models, our approach incorporates a comprehensive evaluation of system-wide risk without decomposing joint chance constraints into individual constraints, thus preventing overly conservative solutions and ensuring robust system security. A significant innovation in our method is the use of historical data to form a sample average approximation that directly informs the MIP model, bypassing the need for distributional assumptions to enhance solution robustness. Additionally, we implement a model improvement strategy to reduce the computational burden, making our method more scalable for large-scale power systems. Our approach is validated against benchmark systems, i.e., IEEE 14-, 57- and 118-bus systems, demonstrating superior performance in terms of cost-efficiency and robustness, with lower computational demand compared to existing methods.

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http://hdl.handle.net/10453/188259