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Unit Commitment problem is applied to identical units. Furthermore, a recent work
(Grey and Sekar
2008
) presented a uni
ed solution of the security constrained unit
commitment (SCUC) using linear programming (LP) as the optimization tool and an
extended DC network model in order to account for the security and the contingency
concerns and to calculate the economic dispatch (ED).
By contrast, the occurrence of meta-heuristics methods, genetic algorithm (Cheng
et al.
2002
; Yingvivatanapong
2006
; Damousis et al.
2004
), Tabu search
(Sudhakaran et al.
2010
), simulated annealing (Lin et al.
1993
; Rajan et al.
2002
) has
improved the quality of the optimal solutions. However, these methods require a
considerable computation time especially for complex problems. In this context,
Maifeld and Sheble (
1996
) have presented a new strategy for solving the UC
problem. The proposed strategy relies on using genetic algorithm (GA) based on a
new mutation technology. The results showed that the proposed algorithm have
found a good list of planning for production units during a fairly reasonable com-
putation time. However, Genetic algorithms are time-consuming since it requires
binary encoding and decoding to represent each unit operation state and to compute
the
fitness function, respectively, throughout genetic algorithm procedures. This
causes huge computation burdens, making it dif
cult to apply to large-scale systems.
GA (Padhy
2001
; Wu et al.
2000
; Hong and Li
2002
) is a general-purpose stochastic
and parallel search method based on the mechanics of natural selection and natural
genetics. It is a search method, which has the potential of obtaining near-global
minimum, and the capability to obtain the accurate results within short time and the
constraints are included easily. The ANN (Sasaki et al.
1992
; Kohonen
1998
; Wood
and Woolenberg
1996
) has the advantages of giving good solution quality and rapid
convergence, and this method can accommodate more complicated unit-wise con-
straints and is claimed for numerical convergence and solution quality problems.
The solution processing in each method is very unique.
Regarding to Zhao et al. (
2006
), they have applied a hybrid optimization method
for solving UC problem: This method is based on the combination of Particle
Swarm Optimization (PSO) method, the technique of sequential quadratic pro-
gramming (SQP), and tabu search (TS) method. The combinatorial part of the UC
problem was solved using the TS method. Nevertheless, the nonlinear part of the
economic dispatch problem (EDP) was solved using a hybrid technique of PSO and
SQP methods. The effectiveness of the hybrid optimization technique has been
tested on a network with 7 production units. In the same context, Kazarlis et al.
(
1996
) have developed a genetic algorithm strategy based on different evaluation
functions to solve the problem of unit commitment. In order to evaluate the algo-
rithm performances, 100 production units have been tested and the results were
compared to those found by the dynamic programming and the Lagrangian method.
Using the approach based on fuzzy logic has undergone major progress in effec-
tiveness due to its resolution of the nonlinear dif
cult problems. Indeed, fuzzy logic
follows an approximation of reasoning while enabling effective decision making.
In studies (Kurban and Filik
2009
; Dieu and Ongsakul
2007
), authors have
adopted a fuzzy dynamic programming algorithm to determine the optimal time
schedule of a power system interconnected with WECS which considers the wind
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