Near-Optimal Solutions for Day-Ahead Unit Commitment: Article No. 110678

Dominic Yang; Bernard Knueven; Jean-Paul Watson; James Ostrowski

doi:10.1016/j.epsr.2024.110678

Near-Optimal Solutions for Day-Ahead Unit Commitment: Article No. 110678

Dominic Yang, Bernard Knueven, Jean-Paul Watson, James Ostrowski

Computational Science

Research output: Contribution to journal › Article › peer-review

1 Scopus Citations

Abstract

Given the difficulty and the time pressure of solving unit commitment problems, near-optimal solutions (those with 0.1 or 0.001% optimality gaps) are often used in practice. The choice in which of the near-optimal solutions is used, however, is random. We investigate the impact of solution choice on the revenues obtained by generator owners across a variety of pricing schemes and problem instances.

Original language	American English
Number of pages	7
Journal	Electric Power Systems Research
Volume	234
DOIs	https://doi.org/10.1016/j.epsr.2024.110678
State	Published - 2024

NREL Publication Number

NREL/JA-2C00-90475

Keywords

market-clearing
mixed-integer linear programming (MILP)
symmetry
unit commitment (UC)

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10.1016/j.epsr.2024.110678

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title = "Near-Optimal Solutions for Day-Ahead Unit Commitment: Article No. 110678",

abstract = "Given the difficulty and the time pressure of solving unit commitment problems, near-optimal solutions (those with 0.1 or 0.001% optimality gaps) are often used in practice. The choice in which of the near-optimal solutions is used, however, is random. We investigate the impact of solution choice on the revenues obtained by generator owners across a variety of pricing schemes and problem instances.",

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journal = "Electric Power Systems Research",

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AU - Yang, Dominic

AU - Knueven, Bernard

AU - Watson, Jean-Paul

AU - Ostrowski, James

PY - 2024

Y1 - 2024

N2 - Given the difficulty and the time pressure of solving unit commitment problems, near-optimal solutions (those with 0.1 or 0.001% optimality gaps) are often used in practice. The choice in which of the near-optimal solutions is used, however, is random. We investigate the impact of solution choice on the revenues obtained by generator owners across a variety of pricing schemes and problem instances.

AB - Given the difficulty and the time pressure of solving unit commitment problems, near-optimal solutions (those with 0.1 or 0.001% optimality gaps) are often used in practice. The choice in which of the near-optimal solutions is used, however, is random. We investigate the impact of solution choice on the revenues obtained by generator owners across a variety of pricing schemes and problem instances.

KW - market-clearing

KW - mixed-integer linear programming (MILP)

KW - symmetry

KW - unit commitment (UC)

U2 - 10.1016/j.epsr.2024.110678

DO - 10.1016/j.epsr.2024.110678

M3 - Article

SN - 0378-7796

VL - 234

JO - Electric Power Systems Research

JF - Electric Power Systems Research

ER -

Near-Optimal Solutions for Day-Ahead Unit Commitment: Article No. 110678

Abstract

NREL Publication Number

Keywords

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