Stochastic optimization of unit commitment: A new decomposition framework
- Ecole des Mines de Paris, Fontainebleau (France). Centre Automatique et Systemes
- Electricite de France, Clamart (France). Direction des Etudes et Recherches
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.
- OSTI ID:
- 264287
- Report Number(s):
- CONF-950727--
- Journal Information:
- IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 2 Vol. 11; ISSN 0885-8950; ISSN ITPSEG
- Country of Publication:
- United States
- Language:
- English
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