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Stochastic optimization of unit commitment: a new decomposition framework

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4 Author(s)
Carpentier, P. ; Centre Autom. et Syst., Ecole des Mines de Paris, Fontainebleau, France ; Gohen, G. ; Culioli, J.-C. ; Renaud, A.

This paper presents a new stochastic decomposition method well-suited to deal with large-scale unit commitment problems. In this approach, random disturbances are modeled as scenario trees. Optimization consists in minimizing the average generation cost over this “tree-shaped future”. An augmented Lagrangian technique is applied to this problem. At each iteration, nonseparable terms introduced by the augmentation are linearized so as to obtain a decomposition algorithm. This algorithm may be considered as a generalization of price decomposition methods, which are now classical in this field, to the stochastic framework. At each iteration, for each unit, a stochastic dynamic subproblem has to be solved. Prices attached to nodes of the scenario trees are updated by the coordination level. This method has been applied to a daily generation scheduling problem. The use of an augmented Lagrangian technique, provides satisfactory convergence properties to the decomposition algorithm. Moreover, numerical simulations show that compared to a classical deterministic optimization with reserve constraints, this new approach achieves substantial savings

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Power Systems, IEEE Transactions on  (Volume:11 ,  Issue: 2 )