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Robust Transmission Network Expansion Planning Problem Considering Storage Units

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Álvaro García-Cerezo, Luis Baringo, Raquel García-Bertrand

Optimization and Control · Submitted 10 Jul 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim This algorithm guarantees convergence to the global optimum in a finite number of iterations.

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arXiv ID1907.04775 (version 1)
Pith IDDYX4CAGA, Pith's permanent record of this paper and its review
Citation\'Alvaro Garc\'ia-Cerezo, Luis Baringo, Raquel Garc\'ia-Bertrand. "Robust Transmission Network Expansion Planning Problem Considering Storage Units." arXiv:1907.04775 (2019). https://pith.science/paper/DYX4CAGA
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,792 input tokens.

What the abstract itself states, as probabilities: claims first 0.09 · states limitation 0.29 · human participants 0.01 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.61 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This paper addresses the transmission network expansion planning problem considering storage units under uncertain demand and generation capacity. A two-stage adaptive robust optimization framework is adopted whereby short- and long-term uncertainties are accounted for. This work differs from previously reported solutions in an important aspect, namely, we include binary recourse variables to avoid the simultaneous charging and discharging of storage units once uncertainty is revealed. Two-stage robust optimization with discrete recourse problems is a challenging task, so we propose using a nested column-and-constraint generation algorithm to solve the resulting problem. This algorithm guarantees convergence to the global optimum in a finite number of iterations. The performance of the proposed algorithm is illustrated using the Garver's test system.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper addresses the transmission network expansion planning problem considering storage units under uncertain demand and generation capacity. background 0.73 0.52 0.05 asserted 0.96
s1 A two-stage adaptive robust optimization framework is adopted whereby short- and long-term uncertainties are accounted for. method 0.98 0.83 0.04 asserted 1.00
s2 This work differs from previously reported solutions in an important aspect, namely, we include binary recourse variables to avoid the simultaneous charging and discharging of storage units once uncertainty is revealed. contribution 0.97 0.89 0.12 asserted 1.00
s3 Two-stage robust optimization with discrete recourse problems is a challenging task, so we propose using a nested column-and-constraint generation algorithm to solve the resulting problem. method 0.85 0.81 0.07 asserted 0.90
s4 This algorithm guarantees convergence to the global optimum in a finite number of iterations. result 0.33 0.77 0.17 asserted 1.00
s5 The performance of the proposed algorithm is illustrated using the Garver's test system. method 0.72 0.88 0.07 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.98.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This paper addresses the transmission network expansion planning problem considering storage units under uncertain demand and generation capacity. A two-stage adaptive robust optimization framework is adopted whereby short- and long-term uncertainties are accounted for. This work differs from previously reported solutions in an important aspect, namely, we include binary recourse variables to avoid the simultaneous charging and discharging of storage units once uncertainty is revealed. Two-stage robust optimization with discrete recourse problems is a challenging task, so we propose using a nested column-and-constraint generation algorithm to solve the resulting problem. This algorithm guarantees convergence to the global optimum in a finite number of iterations. The performance of the proposed algorithm is illustrated using the Garver's test system.

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