Information Technology Reference
In-Depth Information
Unit Commitment Optimization Using
Gradient-Genetic Algorithm and Fuzzy
Logic Approaches
Sahbi Marrouchi and Souad Chebbi
Abstract The development of the industry and the gradual increase of the popu-
lation are the main factors for which the consumption of electricity increases. In
order to establish a good exploitation of the electrical grid, it is necessary to solve
technical and economic problems. This can only be done through the resolution of
unit commitment problem (UCP). The decisions are which units to commit at each
time period and at what level to generate power meeting the electricity demand.
Therefore, in a robust unit commitment problem,
first stage commitment decisions
are made to anticipate the worst case realization of demand uncertainty and mini-
mize operation cost under such scenarios. Unit Commitment Problem allows
optimizing the combination of the production units
states and determining their
production planning in order to satisfy the expected consumption with minimal cost
during a speci
'
ed period which varies usually from 24 h to 1 week. However, each
production unit has some constraints that make this problem complex, combina-
torial and nonlinear. In this chapter, we have proposed two strategies applied to an
IEEE electrical network 14 buses to solve the UCP in general and in particular to
find the optimized combination scheduling of the produced power for each unit
production. The First strategy is based on a hybrid optimization method, Gradient-
Genetic algorithm, and the second one relies on a Fuzzy logic approach.
Throughout these two strategies, we arrived to develop an optimized scheduling
plan of the generated power allowing a better exploitation of the production cost in
order to bring the total operating cost to possible minimum when it
s subjected to a
series of constraints. A comparison was made to test the performances of the
proposed strategies and to prove their effectiveness in solving Unit Commitment
problems.
'
Search WWH ::
Custom Search