Large-scale Unit Commitment under uncertainty

The Unit Commitment problem in energy management aims at finding the optimal productions schedule of a set of generation units while meeting various system-wide constraints. It has always been a large-scale, non-convex difficult problem, especially in view of the fact that operational requirements i...

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Bibliographic Details
Published in:4OR Vol. 13; no. 2; pp. 115 - 171
Main Authors: Tahanan, Milad, van Ackooij, Wim, Frangioni, Antonio, Lacalandra, Fabrizio
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2015
Springer Nature B.V
Springer Verlag
Subjects:
Artificial Intelligence
Business and Management
Computer Aided Engineering
Computer Science
Constraints
Dynamic programming
Energy management
Industrial and Production Engineering
Integer programming
Invited Survey
Linear programming
Literature reviews
Mathematical programming
Operations Research
Operations Research/Decision Theory
Optimization
Robustness (mathematics)
Schedules
Uncertainty
Unit commitment
90-02
Uncertainty
Survey
90B30
90C06
Unit Commitment
90C90
Large-scale optimization
ISSN:1619-4500, 1614-2411
Online Access:Get full text
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Summary:The Unit Commitment problem in energy management aims at finding the optimal productions schedule of a set of generation units while meeting various system-wide constraints. It has always been a large-scale, non-convex difficult problem, especially in view of the fact that operational requirements imply that it has to be solved in an unreasonably small time for its size. Recently, the ever increasing capacity for renewable generation has strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex, uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focusing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, also providing entry points to the relevant literature on optimization under uncertainty.
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ISSN:1619-4500
1614-2411
DOI:10.1007/s10288-014-0279-y