On Mixed-Integer Programming Formulations for the Unit Commitment Problem

Knueven, Bernard; Ostrowski, James; Watson, Jean-Paul

doi:10.1287/ijoc.2019.0944

On Mixed-Integer Programming Formulations for the Unit Commitment Problem

Journal Article · Thu Jun 25 00:00:00 EDT 2020 · INFORMS Journal on Computing

DOI:https://doi.org/10.1287/ijoc.2019.0944· OSTI ID:1670724

^[1]; Ostrowski, James ^[2]; Watson, Jean-Paul ^[3]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Discrete Math & Optimization
Univ. of Tennessee, Knoxville, TN (United States). Industrial and Systems Engineering
Sandia National Lab. (SNL-CA), Livermore, CA (United States). Data Science & Cyber Analytics

We provide a comprehensive overview of mixed-integer programming formulations for the unit commitment (UC) problem. UC formulations have been an especially active area of research over the past 12 years due to their practical importance in power grid operations, and this paper serves as a capstone for this line of work. We additionally provide publicly available reference implementations of all formulations examined. We computationally test existing and novel UC formulations on a suite of instances drawn from both academic and real-world data sources. Driven by our computational experience from this and previous work, we contribute some additional formulations for both generator production upper bounds and piecewise linear production costs. By composing new UC formulations using existing components found in the literature and new components introduced in this paper, we demonstrate that performance can be significantly improved—and in the process, we identify a new state-of-the-art UC formulation.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories, Livermore, CA; University of Tennessee, Knoxville, TN (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Electricity (OE); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC04-94AL85000; NA0003525; SC0018175

OSTI ID:: 1670724

Alternate ID(s):: OSTI ID: 2339864
OSTI ID: 1761396

Report Number(s):: SAND--2019-13440J; 681098

Journal Information:: INFORMS Journal on Computing, Journal Name: INFORMS Journal on Computing Journal Issue: 4 Vol. 32; ISSN 1091-9856

Publisher:: INFORMSCopyright Statement

Country of Publication:: United States

Language:: English

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The impacts of convex piecewise linear cost formulations on AC optimal power flow Coffrin, Carleton; Knueven, Bernard; Holzer, Jesse Electric Power Systems Research, Vol. 199 https://doi.org/10.1016/j.epsr.2021.107191	journal	October 2021
A New Affinely Adjustable Robust Model for Security Constrained Unit Commitment under Uncertainty Sierra-Aguilar, Juan Esteban; Marín-Cano, Cristian Camilo; López-Lezama, Jesús M. Applied Sciences, Vol. 11, Issue 9 https://doi.org/10.3390/app11093987	journal	April 2021

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