A stabilised scenario decomposition algorithm applied to stochastic unit commitment problems

•We apply scenario decomposition to two-stage and multi-stage unit commitment models.•Dual stabilisation and initialisation are included for rapid convergence.•A novel schedule combination heuristic is used to construct good primal solutions.•Numerical results on a model of Britain demonstrate the e...

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Veröffentlicht in:European journal of operational research Jg. 261; H. 1; S. 247 - 259
Hauptverfasser: Schulze, Tim, Grothey, Andreas, McKinnon, Ken
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 16.08.2017
Schlagworte:
Dantzig–Wolfe decomposition
Heuristics
Lagrangian relaxation
Mixed-integer column generation
Stochastic programming
Dantzig–Wolfe decomposition
Lagrangian relaxation
Heuristics
90C11
90C15
Mixed-integer column generation
Stochastic programming
ISSN:0377-2217, 1872-6860
Online-Zugang:Volltext
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Zusammenfassung:•We apply scenario decomposition to two-stage and multi-stage unit commitment models.•Dual stabilisation and initialisation are included for rapid convergence.•A novel schedule combination heuristic is used to construct good primal solutions.•Numerical results on a model of Britain demonstrate the effectiveness of the method. In recent years the expansion of energy supplies from volatile renewable sources has triggered an increased interest in stochastic optimisation models for hydro-thermal unit commitment. Several studies have modelled this as a two-stage or multi-stage stochastic mixed-integer optimisation problem. Solving such problems directly is computationally intractable for large instances, and alternative approaches are required. In this paper we use a Dantzig–Wolfe reformulation to decompose the stochastic problem by scenarios. We derive and implement a column generation method with dual stabilisation and novel primal and dual initialisation techniques. A fast, novel schedule combination heuristic is used to construct very good primal solutions, and numerical results show that knowing these from the start improves the convergence of the column generation method significantly. We test our method on a central scheduling model based on the British National Grid and illustrate that convergence to within 0.1% of optimality can be achieved quickly.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2017.02.005