particle swarm optimization application in power system
DESCRIPTION
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 usTRANSCRIPT
MAJOR PROJECT SEMINAR MAY-2013
SUBMITTED TO
Mr. Neeraj Kr. GargAstt. Prof. & Head Of Dept. Electrical Engineering
SUBMITTED BY1.RADHEY SHYAM MEENA
2.DEEPA SHARMA 3.RAKESH KUMAR 4.TEENA GARG 5.KANWAR LALB.TECH(2009-2013) ELECTRICAL ENGINEERING
Govt Engineering College Jhalawar 326023RAJASTHAN TECHNICAL UNIVERSITY KOTA(RAJASTHAN)
Indian Power SectorIndian Power SectorElectricity ActElectricity Act
Regulation DeregulationRegulation Deregulation
“Process” of removing restrictions
and regulations to achieve competitive
wholesale prices without Compromising adequacy, system
reliability and security
CompetitionCompetition
Unbundling of ServicesUnbundling of Services
PrivatizationPrivatization
Open accessOpen access
Electrical Industry Regulation and DeregulationElectrical Industry Regulation and Deregulation
Deregulated System Model
Generation Company
Transmission Company
Distribution Company
Retailers
Customers
Generation & Retailing - Deregulated
Transmission & Distribution - Regulated
GOALS OF DEREGULATIONGOALS OF DEREGULATION•• Lower utility ratesLower utility rates•• Choice of electricity providersChoice of electricity providers•• Efficient, cost based pricingEfficient, cost based pricing•• Encouraging renewable energy sourcesEncouraging renewable energy sources•• Customer specific servicesCustomer specific services
WHAT ARE THE RISKS?
POWER SYSTEM OPTIMIZATIOM
• WHAT IS OPTIMIZATION • IT’S FUNCTION• TECHNIQUES• PROBLEM FOR OPTIMIZATION
CONVENTIONAL METHODS•Linear Programming•Nonlinear Programming•Quadratic Programming•Newton’s MethodINTELLIGENT SEARCH METHODS•Optimization Neural Network•Evolutionary Algorithms•Tabu Search•Ant Colony Optimization •Genetic Algorithm •Particle Swarm Optimization
NONQUANTITY APPROACHES
Particle Swarm Optimization
The Inventors
Russell Eberhart James KennedySocial-psychologistElectrical Engineer
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
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.
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.
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.
Particle Flying Model
kskpbest
kgbest
kv1kv
1ks
kpbestd
kgbestd
1 2
k kpbestk gbestd dv w w 11 ()c rw and
22 ()c rw and
kv
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.
Particle Flying Model
1 1k k ki i is s v
1k k ki i iv v v
1 2() ( ) () ( )k k k k ki i i iv c rand pbest s c rand gbest s
PSO Algorithm
For each particle Initialize particleEND
Do For each particle Calculate fitness value If the fitness value is better than the best fitness value (pbest) in history
set current value as the new pbest 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
For each particle Initialize particleEND
Do For each particle Calculate fitness value If the fitness value is better than the best fitness value (pbest) in history
set current value as the new pbest 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
1k k ki i iv v v
1 2() ( ) () ( )k k k k ki i i iv c rand pbest s c rand gbest s
1k k ki i is s v
*
**
The Flowchart of PSOGenerate and initialize particles with
random position (X) and velocity (V)
Termination criterion is met? (e.g., Gbest=sufficient good fitness or maximum generations)
Return the best solution
Particle m…..
Particle 1Evaluate position (Fitness)
If fitness(X) >fitness(Pbest)Pbest=X
If fitness(X) >fitness(Gbest)Gbest=X
Update velocity
Update Position
Yes
No
How to choose parametersThe right way
This way
Or this way
Parameters selectionDifferent 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
Type 1” form
2121 ''),0(),0( randrand
21
21
''''
gi ppp
)()1()1()))(()(()1(
txtvtxtxptvtv
with
4for 42
22
else
Usual values:
=1
=4.1
=> =0.73swarm size=20
Non divergence criterion
Global constriction coefficient
Simulation Initialization
Simulation After 5 Generations
Simulation After 10 Generations
Simulation After 15 Generations
Simulation After 20 Generations
Simulation After 25 Generations
Simulation After 100 Generations
Simulation After 500 Generations
Iterations gBest0 416.2455995 515.748796
10 759.40400615 793.73201920 834.813763
100 837.9115355000 837.965771
Optimun 837.9658
Adaptive swarm size
There has been enough improvement
but there has been not enough improvement
although I'm the worst
I'm the best
I try to kill myself
I try to generate a new particle
Swarm
APPLICATION TO POWER SYSTEM
OTHER APPLICATION
Real applications
Médical diagnoses Industriel
Electric generator
Electric vehicle
• Telecommunications• Signal Processing• Function Optimization• Artificial Neural Network Training• Fuzzy System Control
FUTURE APPLICATION
Conclusion
“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.”
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
THANK’S TO ALLL
QUESTIONS ..?