The modern power system around the world has grown in complexity of interconnection and power demand. The focus has shifted towards enhanced performance, increased customer focus, low cost, reliable and clean power. In this changed perspective, scarcity of energy resources, increasing power generation cost, environmental concern necessitates optimal economic dispatch. In reality power stations neither are at equal distances from load nor have similar fuel cost functions. Hence for providing cheaper power, load has to be distributed among various power stations in a way which results in lowest cost for generation. Practical economic dispatch (ED) problems have highly non-linear objective function with rigid equality and inequality constraints. Particle swarm optimization (PSO) is applied to allot the active power among the generating stations satisfying the system constraints and minimizing the cost of power generated. The viability of the method is analyzed for its accuracy and rate of convergence. The economic load dispatch problem is solved for three and six unit system using PSO and conventional method for both cases of neglecting and including transmission losses. The results of PSO method were compared with conventional method and were found to be superior. The conventional optimization methods are unable to solve such problems due to local optimum solution convergence. Particle Swarm Optimization (PSO) since its initiation in the last 15 years has been a potential solution to the practical constrained economic load dispatch (ELD) problem. The optimization technique is constantly evolving to provide better and faster results. While writing the report on our project seminar, we were wondering that Science and smart technology are as ever expanding field and the engineers working hard day and night and make the life a gift for us

1. MAJOR PROJECT SEMINAR MAY-2013
SUBMITTED TO
Mr. Neeraj Kr. Garg
Astt. Prof. & Head Of Dept. Electrical Engineering
SUBMITTED BY
1.RADHEY SHYAM MEENA
2.DEEPA SHARMA 3.RAKESH KUMAR 4.TEENA GARG 5.KANWAR LAL
B.TECH(2009-2013) ELECTRICAL ENGINEERING
Govt Engineering College Jhalawar 326023
RAJASTHAN TECHNICAL UNIVERSITY KOTA(RAJASTHAN)

2. IInnddiiaann PPoowweerr SSeeccttoorr
EElleeccttrriicciittyy AAcctt

3. Regulation DeregulatioRegulation Deregulationn
“Process” of removing
restrictions
and regulations
to achieve competitive
wholesale prices without
Compromising adequacy, system
reliability and security
CCoommppeettiittiioonn
PPrriivvaattiizzaattiioonn
OOppeenn aacccceessss
UUnnbbuunnddlliinngg ooff SSeerrvviicceess

4. Electrical Industry Regulation aanndd DDeerreegguullaattiioonn
Deregulated System
Model
Generation Company
Transmission Company
Distribution Company
Retailers
Customers
Generation & Retailing -
Deregulated
Transmission & Distribution -
Regulated

5. GOALS OOFF DDEERREEGGUULLAATTIIOONN
•• LLoowweerr uuttiilliittyy rraatteess
•• CChhooiiccee ooff eelleeccttrriicciittyy pprroovviiddeerrss
•• EEffffiicciieenntt,, ccoosstt bbaasseedd pprriicciinngg
•• EEnnccoouurraaggiinngg rreenneewwaabbllee eenneerrggyy ssoouurrcceess
•• CCuussttoommeerr ssppeecciiffiicc sseerrvviicceess
WHAT ARE THE RISKS?

6. POWER SYSTEM OPTIMIZATIOM
• WHAT IS OPTIMIZATION
• IT’S FUNCTION
• TECHNIQUES
• PROBLEM FOR OPTIMIZATION

7. CONVENTIONAL METHODS
•Linear Programming
•Nonlinear Programming
•Quadratic Programming
•Newton’s Method
INTELLIGENT SEARCH METHODS
•Optimization Neural Network
•Evolutionary Algorithms
•Tabu Search
•Ant Colony Optimization
•Genetic Algorithm
•Particle Swarm Optimization
NONQUANTITY APPROACHES

8. Particle Swarm Optimization

9. The Inventors
Russell Eberhart James Kennedy
Electrical Engineer Social-psychologist

10. Swarm Intelligence
• Collective system capable of accomplishing difficult tasks in
dynamic and varied environments without any external guidance
or control and with no central coordination
• Achieving a collective performance which could not normally be
achieved by an individual acting alone

11. Particle Swarm Optimization (PSO)
• PSO is a robust stochastic optimization
technique based on the movement and
intelligence of swarms.
• PSO applies the concept of social interaction
to problem solving.

12. PSO Search Scheme
• It uses a number of agents, i.e., particles, that
constitute a swarm moving around in the
search space looking for the best solution.
• Each particle is treated as a point in a N-dimensional
space which adjusts its “flying”
according to its own flying experience as well
as the flying experience of other particles.

13. Particle Flying Model
• pbest  the best solution achieved so far by that particle.
• gbest  the best value obtained so far by any particle in the
neighborhood of that particle.
 The basic concept of PSO lies in accelerating each particle toward its
pbest and the gbest locations, with a random weighted acceleration at
each time.

14. Particle Flying Model
vk  w d pbestk  w d gbestk 1 1 w  c  rand()
sk
pbestk
gbestk
vk
vk1
k 1 s 
d pbestk
d gbestk
1 2
2 2 w  c  rand()
vk

15. Particle Flying Model
• Each particle tries to modify
its position using the
following information:
– the current positions,
– the current velocities,
– the distance between the
current position and pbest,
– the distance between the
current position and the gbest.

16. Particle Flying Model
k 1 k k 1
i i i s   s  v 
k 1 k k
i i i v   v  v
1 2 k () ( k k ) () ( k k )
i i i i v  c  rand  pbest  s  c  rand  gbest  s

17. PSO Algorithm
For each particle
Initialize particle
END
Do
For each particle
k 1 k k
i i i v   v  v
1 2 k () ( k k ) () ( k k )
i i i i v  c  rand  pbest  s  c  rand  gbest  s
*
**
Calculate fitness value
If the fitness value is better than the best fitness value (pbest) in history
set current value as the new pbest
k 1 k k
i i i s   s  v
End
Choose the particle with the best fitness value of all the particles as the gbest
For each particle
Calculate particle velocity according equation (*)
Update particle position according equation (**)
End
While maximum iterations or minimum error criteria is not attained

18. The Flowchart of PSO
Generate and initialize particles with
random position (X) and velocity (V)
Update
Position
Termination criterion is met? (e.g., Gbest=sufficient
good fitness or maximum generations)
Return the best solution
Particle m
…..
Particle 1
Evaluate position (Fitness)
If fitness(X) >fitness(Pbest)
Pbest=X
If fitness(X) >fitness(Gbest)
Gbest=X
Update
velocity
Yes
No

19. How to choose parameters
The right way
This way
Or this way

20. Parameters selection
Different ways to choose parameters:
• proper balance between exploration and exploitation
• putting all attention on exploitation
(making possible searches in a vast problem spaces)
• automatization by meta-optimization

21. Type 1” form
Global constriction coefficient
v t  v t  p x t
(  1)  ( ( )  ( 
( )))
x ( t 1) v ( t 1) x ( t
)
  rand(0,1)  rand(0, 2 )  '1 '2
 i g p p
p
' '
 
 

1 2
' 
'
1 2
  
   
with
2

 
   
 

for 4
2
2 4


  

else
Usual values:
=1
=4.1
=> =0.73
swarm size=20
Non divergence criterion

22. Simulation  Initialization

23. Simulation  After 5 Generations

24. Simulation  After 10 Generations

25. Simulation  After 15 Generations

26. Simulation  After 20 Generations

27. Simulation  After 25 Generations

28. Simulation  After 100
Generations

29. Simulation  After 500
Generations

30. Iterations gBest
0 416.245599
5 515.748796
10 759.404006
15 793.732019
20 834.813763
100 837.911535
5000 837.965771
Optimun 837.9658

31. Adaptive swarm size
There has been enough
improvement
although I'm the worst
I'm the best
but there has been not enough
improvement
I try to kill myself
I try to generate a
new particle

32. Swarm

33. APPLICATION TO POWER SYSTEM

34. OTHER APPLICATION

35. Real
applications Médical diagnoses Industriel
Electric vehicle
Electric generator
• Telecommunications
• Signal Processing
• Function Optimization
• Artificial Neural Network Training
• Fuzzy System Control

36. FUTURE APPLICATION

37. Conclusion
Looking ahead …
• Game-changing technologies coming
• World energy portfolio will become more diverse, automated and
integrated
• New opportunities and business models will result
… and the future is closer than we think
“This algorithm belongs ideologically to that philosophical school
that allows wisdom to emerge rather than trying to impose it,
that emulates nature rather than trying to control it,
and that seeks to make things simpler rather than more complex.”

38. QUESTIONS ..?
THANK’S TO ALLL