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combinatorial nature. In addition, basing on the fact that the total load varies
throughout the day and reaches a peak value different from 1 day to another, each
utility company must decide in advance which generators must start and when they
should be connected to the electrical grid as well as the sequence in which pro-
duction units should be turned off and for how long. The solution method devel-
oped to solve this problem can handle the presence of wind power plants, which
creates additional uncertainty. In this problem it is assumed that the system under
consideration is an isolated one such that it does not have access to an electricity
market. In such a system, the utility needs to specify the probability level that the
system should operate under. This is taken into consideration by solving a chance
constrained program (Wu et al.
2000
; Mantawy et al.
1998
). Instead of using a set
level of energy reserve, the chance constrained model determines the level prob-
abilistically which is superior to using an arbitrary approximation. Under such
probability of generator operating, various number of optimization method are
usually used to reduce the production cost during a speci
ed horizon time. This
time horizon vary from 24 h to 1 week allowing the determination of a production
set units that should be connected to the electrical grid to respond to the request
among a minimum production cost (Mantawy et al.
1998
; Saber et al.
2007
).
We pose the problem of
t-maximizing commitment policy of a
generating plant that has elected to self-commit in response to exogenous but
uncertain energy and reserve price forecasts. Typically, one generator
'
s output does
not physically constrain the output of a different generator 1, so this policy can be
applied to each generator in the merchant
finding the pro
s portfolio separately and independently.
Therefore, for ease of exposition, we assume the case of a single generator. Gen-
erators characteristics such as start-up and shutdown costs, minimum and maximum
up and down times, ramping rates, etc., of this generator are assumed known. The
variation of prices for energy and reserves in future time frames is known only
statistically. In particular, the prices follow a stochastic rather than deterministic
process. We model the process using a Markov chain. The method is applicable to
multiple markets (e.g., day-ahead, hour-ahead) and multiple products (energy,
reserves), (Victoire and Jeyakumar
2005
; Attaviriyanupap et al.
2002
; Juste et al.
1999
). Recently, some methods based on arti
'
cial intelligence, such as meta-heu-
ristics have been applied to overcome this problem. The introduction of arti
cial
intelligence techniques in control software and decision-making is an essential
element in research and in the development of tomorrow
s networks (Victoire and
Jeyakumar
2005
). Thus, to have a good result in operational planning of production
units and ensure a minimum production cost, we proposed two strategies for
solving the Unit Commitment problem, the
'
first one is based on the combination of
two calculations methods, the genetic algorithm and the gradient method and the
second one based on the fuzzy logic approach.
The chapter is organized as follows; Sect.
2
describes the reviews for existing
works related to the use of the optimization method to solve the Unit Commitment
Problem. Section
3
is reserved to formulate the Unit Commitment Problem. Next, in
Sect.
4
, Methodology of resolution through fuzzy logic and gradient genetic
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