A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation.

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Publication Details

Published
2025-02-10
DOI
https://doi.org/10.1109/tpec63981.2025.10906982
Citations
2
Primary Topic
Power Systems and Technologies
Type
article
Field-Weighted Citation Impact
1.46
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article

A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

Lei Fan, Yongpei Guan, Weihang Ren, Mingze Li
2 citations
Power Systems and Technologies
1.46
article

A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

Lei Fan, Yongpei Guan, Weihang Ren, Mingze Li
article en
2 citations

Abstract

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation.

University of Florida (US), University of Houston (US)
Openalex Percentile: Top 19%
Power Systems and Technologies
1.46
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