Optimal maintenance scheduling of a gas engine power plant using generalized disjunctive programming
Identifieur interne : 004800 ( Main/Exploration ); précédent : 004799; suivant : 004801Optimal maintenance scheduling of a gas engine power plant using generalized disjunctive programming
Auteurs : Pedro M. Castro [Portugal] ; Ignacio E. Grossmann [États-Unis] ; Patrick Veldhuizen [Afrique du Sud] ; Douglas Esplin [Afrique du Sud]Source :
- AIChE Journal [ 0001-1541 ] ; 2014-06.
Abstract
A new continuous‐time model for long‐term scheduling of a gas engine power plant with parallel units is presented. Gas engines are shut down according to a regular maintenance plan that limits the number of hours spent online. To minimize salary expenditure with skilled labor, a single maintenance team is considered which is unavailable during certain periods of time. Other challenging constraints involve constant minimum and variable maximum power demands. The objective is to maximize the revenue from electricity sales assuming seasonal variations in electricity pricing by reducing idle times and shutdowns in high‐tariff periods. By first developing a generalized disjunctive programming model and then applying both big‐M and hull reformulation techniques, we reduce the burden of finding the appropriate set of mixed‐integer linear constraints. Through the solution of a real‐life problem, we show that the proposed formulations are very efficient computationally, while gaining valuable insights about the system. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2083–2097, 2014
Url:
DOI: 10.1002/aic.14412
Affiliations:
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<front><div type="abstract">A new continuous‐time model for long‐term scheduling of a gas engine power plant with parallel units is presented. Gas engines are shut down according to a regular maintenance plan that limits the number of hours spent online. To minimize salary expenditure with skilled labor, a single maintenance team is considered which is unavailable during certain periods of time. Other challenging constraints involve constant minimum and variable maximum power demands. The objective is to maximize the revenue from electricity sales assuming seasonal variations in electricity pricing by reducing idle times and shutdowns in high‐tariff periods. By first developing a generalized disjunctive programming model and then applying both big‐M and hull reformulation techniques, we reduce the burden of finding the appropriate set of mixed‐integer linear constraints. Through the solution of a real‐life problem, we show that the proposed formulations are very efficient computationally, while gaining valuable insights about the system. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2083–2097, 2014</div>
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