Large-scale unit commitment under uncertainty: an updated literature survey

W. Ackooij (), I. Danti Lopez (), A. Frangioni (), F. Lacalandra () and M. Tahanan ()
Additional contact information
W. Ackooij: EDF R&D
I. Danti Lopez: EDF R&D
A. Frangioni: Università di Pisa
F. Lacalandra: The Italian Regulatory Authority for Energy, Networks and Environment (ARERA)
M. Tahanan: Supply Chain Consultants

Annals of Operations Research, 2018, vol. 271, issue 1, No 3, 85 pages

Abstract: Abstract The Unit Commitment problem in energy management aims at finding the optimal production 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, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and 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, focussing 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, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper “Large-scale Unit Commitment under uncertainty: a literature survey” that appeared in 4OR 13(2):115–171 (2015); this version has over 170 more citations, most of which appeared in the last 3 years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject.

Keywords: Unit commitment; Uncertainty; Large-scale optimization; Survey (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (23)

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DOI: 10.1007/s10479-018-3003-z

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