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Cost-Driven Screening of Network Constraints for the Unit Commitment Problem
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DOI：10.1109/TPWRS.2022.3160016.png)
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Abstract
En 中文
In an attempt to speed up the solution of the unit commitment (UC) problem, both machine-learning and optimization-based methods have been proposed to lighten the full UC formulation by removing as many superfluous line-flow constraints as possible. While the elimination strategies based on machine learning are fast and typically delete more constraints, they may be over-optimistic and result in infeasible UC solutions. For their part, optimization-based methods seek to identify redundant constraints in the full UC formulation by exploring the feasibility region of an LP-relaxation. In doing so, these methods only get rid of line-flow constraints whose removal leaves the feasibility region of the original UC problem unchanged. In this paper, we propose a procedure to substantially increase the line-flow constraints that are filtered out by optimization-based methods without jeopardizing their appealing ability of preserving feasibility. Our approach is based on tightening the LP-relaxation that the optimization-based method uses with a valid inequality related to the objective function of the UC problem and hence, of an economic nature. The result is that the so strengthened optimization-based method identifies not only redundant line-flow constraints but also inactive ones, thus leading to more reduced UC formulations.
Keywords:
Costs
Power transmission lines
Machine learning
Technological innovation
Schedules
Production
Optimization
Bounding
constraint screening
cost-driven approach
optimization-based method
unit commitment
Journal
IF:
7.2
Papers:
1.1W
Citations:
5.0W