Transmission Expansion Planning via Partitioning: Efficient Sensitivity Calculations

Transmission expansion planning is often approached via iterative partitioning, which involves a master problem and a subproblem. The master problem provides investment proposals, while the subproblem computes the resulting operation cost and the sensitivities of the investment proposals with respect to operation cost. These sensitivities are critical for the master problem to progressively refine its investment proposals. We propose an analytical procedure based on the Lagrangian function of the subproblem to compute these sensitivities, efficiently and accurately. The outcomes of the proposed analytical procedure are contrasted with those obtained from the common but cumbersome practice of using auxiliary constraints to fix investment variables to master problem investment proposals, and with those obtained by numerical perturbation.

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Publication Details

Journal
IEEE Transactions on Power Systems
Published
2025-02-28
DOI
https://doi.org/10.1109/tpwrs.2025.3546807
Primary Topic
VLSI and FPGA Design Techniques
Type
article
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article

Transmission Expansion Planning via Partitioning: Efficient Sensitivity Calculations

Antonio J. Conejo, Prudhvi Anand Bhattiprolu
IEEE Transactions on Power Systems
VLSI and FPGA Design Techniques
article

Transmission Expansion Planning via Partitioning: Efficient Sensitivity Calculations

Antonio J. Conejo, Prudhvi Anand Bhattiprolu
article en

Abstract

Transmission expansion planning is often approached via iterative partitioning, which involves a master problem and a subproblem. The master problem provides investment proposals, while the subproblem computes the resulting operation cost and the sensitivities of the investment proposals with respect to operation cost. These sensitivities are critical for the master problem to progressively refine its investment proposals. We propose an analytical procedure based on the Lagrangian function of the subproblem to compute these sensitivities, efficiently and accurately. The outcomes of the proposed analytical procedure are contrasted with those obtained from the common but cumbersome practice of using auxiliary constraints to fix investment variables to master problem investment proposals, and with those obtained by numerical perturbation.

IEEE Transactions on Power SystemsVol. 40(4)
Austral Engineering and Software (United States) (US), The Ohio State University (US)
Openalex Percentile: Top 97%
VLSI and FPGA Design Techniques
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