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Fig. 4 Flowchart of solving the unit commitment problem via gradient-genetic algorithm method
as quickly as possible. The process of solving the unit commitment problem by
gradient-genetic algorithm method is performed according to the following
fl
ow-
chart (Fig.
4
):
5 Simulations, Results and Comparative Study
In order to test the performance of the optimization proposed method; the strategy
has been applied to an IEEE electrical network 14 buses, having 5 generators, over
a period of 24 h (Fig.
5
). The strategies are occurring at t = 40 s and the scheduling
of the on/off states and the amount of generated power by each production unit is
performed for each 3 h.
The characteristics of the different production units are given in Table
2
.
We have took as population size = 40, crossover probability = 0.6, mutation
probability = 0.02 and the maximum number of generations = 300.
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