Unit Commitment Optimization Using Gradient-Genetic Algorithm and Fuzzy Logic Approaches - Complex System Modelling and Control Through Intelligent Soft Computations - page 692

Fig. 8 Unit commitment scheduling and generated power through fuzzy logic (a) and gradient-
genetic algorithm (b) methods

The obtained results through Fig. 7 and Table 5 prove the effectiveness of the
Gradient-Genetic algorithm and the fuzzy logic methods in solving the Unit
Commitment problem and in choosing the best plan of On/Off production units.
Figure 8 a, b show the production scheduling of 5 units for a variable power
demand during a discrete time margin (horizon time about 24 h). Indeed, taking into
account the technical constraints related to each generator (limited power, minimum
down-time before restart, minimum operating time before off state), strategies
enabled to get the best On/Off scheduling states of the various units while opti-
mizing the power produced by each unit within the allowable margins. Further-
more, solving the UCP by these optimization methods are considered as reliable
and have presented high performances especially for a problem involving identical
production units, which is not the case for the application of dynamic programming
method to the UCP, established in the work (Dekrajangpetch et al. 1999 ), which can
not in any way applied for the case of identical production units. However, we nd

that the unit commitment scheduling based on the fuzzy logic theory (Fig. 8 a) is
effective and this can be observed through the temporal evolution of the power
produced by the most powerful generator (615 MVA); which suggests the effec-
tiveness of resolution through the fuzzy logic approach especially in presence of
systems that are dif

cult to model. Nevertheless, the strategy based on the use of
gradient-genetic-algorithm method (Fig. 8 b) remains the most promising and could
be applied to solve the UCP for systems having complicated architecture and for
any number of production units. Knowing that the minimization of the production
cost equation is closely related to the optimization of the generated power P ih , the
ef ciency of resolution through Gradient-genetic algorithm approach is guaranteed

with great consideration in the limitation of the produced active power by each
generator per hour in one side and in the allowable voltage levels margins for each
electrical grid in the other side.