1. Use Of Hybrid Method Combining Genetic Algorithm And
Gradient To Solve The UC Problem: Theoretical Investigation
And Comparative Study
LaTICE Laboratory , National Superior School of Engineers
of Tunis (ENSIT), University of Tunis
Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI
sahbimarrouchi@yahoo.fr ; benhessinemoez@yahoo.fr ; chebbi.souad@gmail.com
1
Ministry of Higher Education, Scientific Research and Technology
University of Tunis
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI
LaTICE
Laboratory

2. Conclusion
Simulations and results
2
Introduction
Proposed strategy for unit
commitment problem resolution
Ministry of Higher Education, Scientific Research and Technology
University of Tunis
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI
1
2
3
4

3. Ministry of Higher Education, Scientific Research and Technology
University of Tunis
The industry development
Population growth
Increase of the electric energy
consumption
 Solve technical and economic problems
 Establish a good exploitation of the electrical grid
 Improve the management of electrical energy
 Optimize the electricity production
 Guarantee a balance between production and consumption
3
1- Introduction
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI
Imbalance

4. - Construction of a Unit Commitment Problem (UCP)
- Development of an optimizing methodology for UCP
4
1- Introduction
Ministry of Higher Education, Scientific Research and Technology
University of Tunis
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI
• Minimize cost, cheap units run first
• Expensive ones run only when load demand is high
• Establish a UC scheduling plan while respecting constraints :
start-up cost
shut-down cost
spinning reserve
Combined Use of Genetic Algorithm and
Gradient methods (GA-Gradient)
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5
2- Proposed strategy
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Ministry of Higher Education, Scientific Research and Technology
University of Tunis
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI
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6. Ministry of Higher Education, Scientific Research and Technology
University of Tunis
6
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI
Genetic Algorithm
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2- Proposed strategy

7. 7
Ministry of Higher Education, Scientific Research and Technology
University of Tunis
Gradient Method
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI
2- Proposed strategy
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The resolution process by the gradient method is guaranteed through the research of the descent
direction of the greatest slope corresponding to the minimal amount of power generated by each unit.

8. Ministry of Higher Education, Scientific Research and Technology
University of Tunis
8
2- Proposed strategy
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI
Fig.1. Production Cost
through GA-gradient method
Start
Random Population
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9. Ministry of Higher Education, Scientific Research and Technology
University of Tunis
9
3- Simulations and results
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI
3- Simulations and results Table 1.
Characteristics of production units
U
Pmax
(MW)
Pmin
(MW
)
c b a M
U
T
M
D
T
Hot
start
-up
cost
($)
Cold
start
-up
cost
($)
1 582 110 379.2 30.36 0.0756 8 8 4500 9000
2 55 15 606.6 27.3 0.2274 3 3 170 340
3 53 10 454.8 22.74 0.2274 3 3 170 340
4 23 8 151.8 22.5 0.1518 1 1 30 60
5 23 8 303.6 22.74 0.1518 1 1 30 60
Fig 2: Studied model: IEEE 14
Bus test .
Hour 3 6 9 12 15 18 21 24
Demand
(MW)
259 200 300 450 527 610 480 320
Table 2. Amount of load required

10. 10
H Power
demand
(MW)
Generated power by each unit (MW) production
cost ($)
Optimal
planning
615
MVA
60
MVA
60_Bis
MVA
25 MVA 25_Bis
MVA
1 259 274.6 0 0 0 0 6885 10000
2 259 325.8 0 0 0 0 7942 10000
3 259 361.2 0 0 0 0 8695 10000
4 200 376.3 0 0 0 0 9022 10000
5 200 390.3 0 0 0 0 9329 10000
6 200 417.3 0 0 0 0 9928 10000
7 300 416.3 6.2 3.4 0 0 10156 11100
8 300 417.2 10.3 7.4 0 0 10391 11100
9 300 444.6 19.13 10.2 0 0 11333 11100
10 450 490.8 25 15 0 0 12757 11100
11 450 497.4 29.9 20.25 0 0 13166 11100
12 450 507.7 34.8 21.88 0 0 13605 11100
13 527 519.9 36.59 23.66 3.2 4.6 14189 11111
14 527 522.3 34.63 22.5 3.8 4.688 14416 11111
15 527 522.3 33.01 22.05 3.7 4.7 14102 11111
16 610 535.9 37.58 28.11 4.9 0 14432 11110
17 610 535 36.58 28.89 4.6 0 14658 11110
18 610 539 38 31.34 4.6 0 14886 11110
19 480 482 29.78 23.03 4.1 0 12986 11110
20 480 453 25.1 17.29 3.2 0 11975 11110
21 480 430.1 20.6 12.71 3.6 0 11399 11110
22 320 375.7 12.03 0 0 0 9342 11000
23 320 347.5 14.35 0 0 0 8799 11000
24 320 335.4 16.08 0 0 0 8590 11000
Total Cost($) 2.7750e+005
Total generated power (MW) 9438
Time (sec) 12.57
Ministry of Higher Education, Scientific Research and Technology University of Tunis
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI

11. Ministry of Higher Education, Scientific Research and Technology
University of Tunis
11
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI
3- Simulations and results
Table 3. Production Cost and Time required to converge for each optimized method
Genetic algorithm [20] Gradient-genetic algorithm
Production cost ($) 2.9452e+005 2.7750e+005
Execution time (sec) 10.21 12.57
Fig.3. Scheduling and
produced powers by
production units
using the genetic
algorithm (a) and
gradient genetic
algorithm (b)

12. 12
3- Simulations and results
Ministry of Higher Education, Scientific Research and Technology
University of Tunis
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI
Fig 4. Scheduling of the production units using the
GA (a) and gradient-GA (b)
0 5 10 15 20 25
0
1
2
Temps (H)
UNIT
1
0 5 10 15 20 25
-1
0
1
2
Temps (H)
UNIT
2
0 5 10 15 20 25
-1
0
1
2
Temps (H)
UNIT
3
0 5 10 15 20 25
-1
0
1
2
Temps (H)
UNIT
4
0 5 10 15 20 25
-1
0
1
2
Temps (H)
UNIT
5
0 5 10 15 20 25
0
1
2
Temps (h)
UNIT
1
0 5 10 15 20 25
-1
0
1
2
Temps (h)
UNIT
2
0 5 10 15 20 25
-1
0
1
2
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UNIT
3
0 5 10 15 20 25
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0
1
2
Temps (h)
UNIT
4
0 5 10 15 20 25
-1
0
1
2
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UNIT
5
(a) (b)
.

13. Ministry of Higher Education, Scientific Research and Technology
University of Tunis
13
4- Conclusion
• The proposed strategy has presented a great performance and
capability of convergence to a global optimum as quick as possible
compared to meta-heuristic (GA) methods. Through a well definite
operational planning scheduling.
• The strategy provides a fast enough time to converge to the optimal
solution; which demonstrates its effectiveness
• Compared to approaches based on Genetic Algorithm, the proposed
strategy was promising and has proved that it could be applied for grid
with several production units.
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI

14. Ministry of Higher Education, Scientific Research and Technology
University of Tunis
14
Thank you
for
your attention
ENSIT Sahbi MARROUCHI, Moez BEN HESSINE, Souad CHEBBI