Unit Commitment Optimization Using Gradient-Genetic Algorithm and Fuzzy Logic Approaches - Complex System Modelling and Control Through Intelligent Soft Computations - page 691

Table 5 Optimal binary combination of units operation
Unit Units operation scheduling

using fuzzy logic

Units operation scheduling using
gradient-genetic algorithm

Unit 1 11111111 11111111

Unit 2 00111111 00111110

Unit 3 00011110 00111110

Unit 4 00001110 00001110

Unit 5 00001100 00001000

The superiority of the gradient-genetic algorithm method is obvious. This method
operates better than the individual algorithms in terms of On/Off unit commitment
states scheduling and in term of optimizing the total production cost.
In fact, based on the probability equation of such a combination planning:

2 n

m

P Combination ¼ ð 1 Þ ð 21 Þ

where, n is the number of units and m is the discretized duration. For our case study,
the combining probability P Combination is about 6.2 35 combinations. This number
suggests the ability of the hybrid method to choose a perfect planning, allowing to
guarantee the supply/demand balance and a minimal production cost.
We con

rm that the two approaches have allowed selecting concisely the pro-
duction units that should be available to respond to the demand of the electrical
network over a future period (Fig. 7 ).
Moreover, thanks to the best selection of the fuzzy variables, 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 in presence of the various constraints. Consequently, basing on
the forecasted load curve, this method put generators in guard state for intervening
in cases where there is an additional power demand.

Fig. 7 Unit commitment
scheduling over a period of
24 h

500

Unit 615 MVA
Unit 60 MVA
Unit 60 MVA
Unit 25 MVA
Unit 25 MVA

400

300

200

100

0

10 15 20 25

0 5

Time (h)