Standard

A tight MIP formulation of the unit commitment problem with start-up and shut-down constraints. / Gentile, C.; Morales-España, G.; Ramos, A.

In: EURO Journal on Computational Optimization, Vol. 5, No. 1-2, 2017, p. 177-201.

Research output: Scientific - peer-reviewArticle

Harvard

Gentile, C, Morales-España, G & Ramos, A 2017, 'A tight MIP formulation of the unit commitment problem with start-up and shut-down constraints' EURO Journal on Computational Optimization, vol 5, no. 1-2, pp. 177-201. DOI: 10.1007/s13675-016-0066-y

APA

Gentile, C., Morales-España, G., & Ramos, A. (2017). A tight MIP formulation of the unit commitment problem with start-up and shut-down constraints. EURO Journal on Computational Optimization, 5(1-2), 177-201. DOI: 10.1007/s13675-016-0066-y

Vancouver

Gentile C, Morales-España G, Ramos A. A tight MIP formulation of the unit commitment problem with start-up and shut-down constraints. EURO Journal on Computational Optimization. 2017;5(1-2):177-201. Available from, DOI: 10.1007/s13675-016-0066-y

Author

Gentile, C. ; Morales-España, G. ; Ramos, A. / A tight MIP formulation of the unit commitment problem with start-up and shut-down constraints. In: EURO Journal on Computational Optimization. 2017 ; Vol. 5, No. 1-2. pp. 177-201

BibTeX

@article{c68445a09acf46b1b77f99bc05025070,
title = "A tight MIP formulation of the unit commitment problem with start-up and shut-down constraints",
abstract = "This paper provides the convex hull description of the single thermal UnitCommitment (UC) problem with the following basic operating constraints: (1) generation limits, (2) start-up and shut-down capabilities, and (3) minimum up and down times. The proposed constraints can be used as the core of any unit commitment formulation to strengthen the lower bound in enumerative approaches.We provide evidence that dramatic improvements in computational time are obtained by solving the self-UC problem and the network-constrained UC problem with the new inequalities for different case studies.",
keywords = "Unit commitment (UC), Mixed-integer programming (MIP), Facet/convex hull description",
author = "C. Gentile and G. Morales-España and A Ramos",
year = "2017",
doi = "10.1007/s13675-016-0066-y",
volume = "5",
pages = "177--201",
journal = "EURO Journal on Computational Optimization",
issn = "2192-4406",
number = "1-2",

}

RIS

TY - JOUR

T1 - A tight MIP formulation of the unit commitment problem with start-up and shut-down constraints

AU - Gentile,C.

AU - Morales-España,G.

AU - Ramos,A

PY - 2017

Y1 - 2017

N2 - This paper provides the convex hull description of the single thermal UnitCommitment (UC) problem with the following basic operating constraints: (1) generation limits, (2) start-up and shut-down capabilities, and (3) minimum up and down times. The proposed constraints can be used as the core of any unit commitment formulation to strengthen the lower bound in enumerative approaches.We provide evidence that dramatic improvements in computational time are obtained by solving the self-UC problem and the network-constrained UC problem with the new inequalities for different case studies.

AB - This paper provides the convex hull description of the single thermal UnitCommitment (UC) problem with the following basic operating constraints: (1) generation limits, (2) start-up and shut-down capabilities, and (3) minimum up and down times. The proposed constraints can be used as the core of any unit commitment formulation to strengthen the lower bound in enumerative approaches.We provide evidence that dramatic improvements in computational time are obtained by solving the self-UC problem and the network-constrained UC problem with the new inequalities for different case studies.

KW - Unit commitment (UC)

KW - Mixed-integer programming (MIP)

KW - Facet/convex hull description

U2 - 10.1007/s13675-016-0066-y

DO - 10.1007/s13675-016-0066-y

M3 - Article

VL - 5

SP - 177

EP - 201

JO - EURO Journal on Computational Optimization

T2 - EURO Journal on Computational Optimization

JF - EURO Journal on Computational Optimization

SN - 2192-4406

IS - 1-2

ER -

ID: 24868877