Daily generation scheduling optimization with transmission constraints: a new class of algorithms

The authors describe a novel class of algorithm dealing with the daily generation scheduling (DGS) problem. These algorithms have been designed by adding artificial constraints to the original optimization problem; handling these artificial constraints by using a dual approach; using an augmented Lagrangian technique rather than a standard Lagrangian relaxation technique; and applying the auxiliary problem principle which can cope with the nonseparable terms introduced by the augmented Lagrangian. To deal with the DGS optimization problem these algorithms are shown to be more effective than classical ones. They are well suited to solve this DGS problem taking into account transmission constraints.<
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