MILP Formulations for Generator Maintenance Scheduling in Hydropower Systems

Maintenance activities help prevent costly generator breakdowns but because generators under maintenance are typically unavailable, the impact of maintenance schedules is significant and their cost must be accounted for when planning maintenance. In this paper, we address the generator maintenance s...

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Bibliographic Details
Published in:IEEE transactions on power systems Vol. 33; no. 6; pp. 6171 - 6180
Main Authors: Rodriguez, Jesus A., Anjos, Miguel F., Cote, Pascal, Desaulniers, Guy
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Computing time
Formulations
Hydroelectric power
Hydroelectric power generation
Integer linear programming
Maintenance engineering
Maintenance management
Mathematical programming
Mixed integer linear programming
optimal maintenance scheduling
Reduction
Schedules
Scheduling
Windows (intervals)
ISSN:0885-8950, 1558-0679
Online Access:Get full text
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Summary:Maintenance activities help prevent costly generator breakdowns but because generators under maintenance are typically unavailable, the impact of maintenance schedules is significant and their cost must be accounted for when planning maintenance. In this paper, we address the generator maintenance scheduling problem in hydropower systems. We propose a mixed-integer programing model that considers the time windows of the maintenance activities, as well as the nonlinearities and disjunctions of the hydroelectric production functions. Because the resulting model is hard to solve, we also propose an extended formulation, a set reduction approach that uses logical conditions for excluding unnecessary set elements from the model, and valid inequalities. We performed computational experiments using a variety of instances adapted from a real hydropower system in Canada, and the extended formulation with set reduction achieved the best results in terms of computational time and optimality gap.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2018.2833061