Optimal placement and sizing of multi dg using pso
Optimal placement and sizing of multi
1. DEVI AHILYA VISHWAVIDYALAYA, INDORE
School of Instrumentation
A
PRESENTATION ON
“OPTIMAL PLACEMENT AND SIZING OF MULTI-
DISTRIBUTED GENERATION (DG) INCLUDING
DIFFERENT LOAD MODELS USING PSO”
Guided By:- Presented By:-
Dr. Ganga Agnihotri Jitendra Singh Bhadoriya
Prof. Electrical Engg. Deptt. MANIT, Bhopal M-Tech(INSTRUMENTATION)
IIIrd Sem.
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2012 JITENDRA SINGH BHADORIYA
2. CONTENTS
INTRODUCTION OF DISTRIBUTION GENERATOR (DG)
PROPOSED WORK OPTIMAL PLACEMENT AND SIZING OF
MULTI DG
METHODOLOGY: PSO ALGORITHM
RESEARCH TOOL: MATLAB/PSAT
CONCLUSIONS
REFERENCES
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3. INTRODUCTION DISTRIBUTION
GENERATOR
“Distributed power means modular electric
generation or storage located near the point
of use” according to Ministry of Power.
It includes biomass generators, combustion
turbines, micro turbines, engines generator
sets and storage and control technologies.
Distributed power generation systems range
typically from less than a kilowatt (kW) to ten
megawatts (MW) in size.
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4. INTRODUCTION DG TYPES & RANGE
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5. INTRODUCTION DG Technologies
Distributed power technologies are typically
installed for one or more of the purposes
Overall load reduction
Independence from the grid
Supplemental Power
Net energy sales
Combined heat and power
Grid support
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6. DG ADVANTAGE
Consumer-Side Benefits Rural Electrification
Grid –Side Benefits Peak Load Shortages
Continued Deregulation of Electricity Transmission and Distribution Losses
Markets Digital Economy
Energy Shortage Benefits To Other Stake Holders
Remote and Inaccessible Areas
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7. METHODOLOGY PSO
Particle Swarm Optimization is an Optimization Technique
to evaluate the optimal solution .
Evolutionary computational technique based on the
movement and intelligence of swarms looking for the most
fertile feeding location
It was developed in 1995 by James Kennedy and Russel
Eberhart [Kennedy, J. and Eberhart, R. (1995). “Particle Swarm
Optimization”, Proceedings of the 1995 IEEE International
Conference on Neural Networks, pp. 1942-1948, IEEE Press.]
(http://dsp.jpl.nasa.gov/members/payman/swarm/kennedy9
5-ijcnn.pdf
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8. PARTICLE SWARM OPTIMIZATION
• 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.
• It was developed in 1995 by James Kennedy (social-
psychologist) and Russell Eberhart (electrical engineer).
• It uses a number of agents (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.
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9. PSO
• Each particle keeps track of its coordinates in the
solution space which are associated with the best
solution (fitness) that has achieved so far by that
particle. This value is called personal best , pbest.
• Another best value that is tracked by the PSO is
the best value obtained so far by any particle in
the neighborhood of that particle. This value is
called gbest.
• The basic concept of PSO lies in accelerating
each particle toward its pbest and the gbest
locations, with a random weighted accelaration at
each time step as shown in Fig.1
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10. PSO PARAMETER
sk+1
vk
vk+1 vgbest
vpbest
sk
Fig.1 Concept of modification of a searching point by PSO
sk : current searching point.
sk+1: modified searching point.
vk: current velocity.
vk+1: modified velocity.
vpbest : velocity based on pbest.
vgbest : velocity based on gbest
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11. PSO Equation
The modification of the particle’s position can
be mathematically modeled according the
following equation :
Vik+1 = wVik +c1 rand1(…) x (pbesti-sik) + c2
rand2(…) x (gbest-sik) ….. (1)
where, vik : velocity of agent i at iteration k,
w: weighting function,
cj : weighting factor,
rand : uniformly distributed random number between 0 and 1,
sik : current position of agent i at iteration k,
pbesti : pbest of agent i,
gbest: gbest of the group.
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12. weighting function w
• The following weighting function is usually utilized in (1)
• w = wMax-[(wMax-wMin) x iter]/maxIter (2)
• where wMax= initial weight,
• wMin = final weight,
• maxIter = maximum iteration number,
• iter = current iteration number.
• sik+1 = sik + Vik+1 (3)
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13. PSO ALGORITHM
For each particle
Initialize particle
END
Do
For each particle
Calculate fitness value
If the fitness value is better than the best personal fitness value in
history, set current value as a new best personal fitness value
End
Choose the particle with the best fitness value of all the particles, and
if that fitness value is better then current global best, set as a global
best fitness value
For each particle
Calculate particle velocity according velocity change equation
Update particle position according position change equation
End
While maximum iterations or minimum error criteria is not attained
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15. RESEARCH TOOL: PSAT
• PSAT is a Matlab toolbox for electric power
system analysis and control.
• PSAT includes Power Flow , continuation power
flow, optimal power flow, small signal stability
analysis and time domain simulation.
• All PSAT operations can be assessed by means of
graphical user interfaces (GUIs) and a Simulink-
based library provides an user friendly tool for
network design.
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17. PSAT
PSAT core is the power flow routine, which also takes care of
• state variable initialization.
Once the power flow has been solved, further static and/or
dynamic analysis can be performed.
These routines are:
Power Flow Data • Controls
• CPF and OPF Data • Regulating Transformers
• Switching Operations • FACTS
• Loads • Other Models
• Machines
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19. LOAD MODELS
The optimal allocation and sizing of DG units
under different voltage-dependent load model
scenarios are to be investigated.
Practical voltage-dependent load models
Vi=voltage at i bus
α and β are real and reactive power exponents
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20. LOAD TYPES
All Load types depend on the value of α and β
LOAD TYPE & EXPONENT VALUE
LOAD TYPE α β
CONSTANT 0 0
RESIDENTIAL .92 4.04
INDUSTRIAL .18 6
MIXED 1.51 3.4
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21. IEEE 38 BUS SYSTEM
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22. IEEE 38 BUS SYSTEM
Bus 38
Bus 25 Bus 13
Bus 12
Bus 11
Bus 10
Bus 14
Bus 24 Bus 35
Bus 36
Bus 9
Bus 15
Bus 23
Bus 16
Bus 8 Bus 34
Bus 6 Bus 7
Bus 5
Bus_3 Bus 4
GENCO 1
Bus_1 Bus_2 Bus 17
Bus 26
Bus 19
Bus 18
Bus 27
Bus 20
Bus 37
Bus 28
Bus 21
Bus 29
Bus 22 Bus 33
Bus 32
Bus 31
Bus 30
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23. Smart Grid Pilots in India
• Functionality Objective
Residential AMI Demand Response, Reduced AT&C
Industrial AMI Demand Side Management,
Outage Management Improving availability and reliability,
Peak Load Management Optimal resource utilization, Distribution
Power Quality Management Voltage Control, Reduced losses
Micro Grid Improved Power Access in rural areas,
Distributed Generation Improved Power Access in rural areas,
Sustainable Growth, New technology
implementation
Combined Functionality as at 1,2,4,5 above
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24. Smart grid
Some of the enabling technologies & business practice that make
smart grid deployments possible include
Smart Meters
Meter Data Management
Field area networks
Integrated communications systems
Distributed generation
IT and back office computing
Data Security
Electricity Storage devices
Demand Response
Renewable energy
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27. CONCLUSIONS
• Here the problem of DG placement & capacity
has presented
• PSO METHODOLOGY used for multi dg
placement
• IT will make power grid in to smart grid
• DG have advantage of ISLANDING, it make
consumer less dependent on grid
• DG can be work either individually or grid
connected so it forms DECENTRAILIZED system
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28. REFERENCES
Book of Swarm Intelligence by JamesKennedy, YuhuSh
THE ELECTRICITY ACT, 2003
http://www.sciencedirect.com/
Smart Grid Vision & Roadmap for India (benchmarking with
other countries) – Final Recommendations from ISGF
Islanding Protection of Distribution Systems with Distributed
Generators – A Comprehensive Survey Report
S.P.Chowdhury, Member IEEE
Distributed Power Generation: Rural India – A Case Study
Anshu Bharadwaj and Rahul Tongia, Member, IEEE
Interconnection Guide for Distributed Generation
Empirical study of particle swarm optimization
POWER SYSTEM ANALYSIS EDUCATIONAL TOOLBOX USING
MATLAB 7.1
Power System Load Modeling The School of Information
Technology and Electrical Engineering The University of
Queensland byWen Zing Adeline Chan
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29. REFERENCES
Smart grid initiative for power distribution utility in India
Power and Energy Society General Meeting, 2011 IEEE 24-29
July 2011 Energy & Utilities Group of Capgemini India Private
Ltd., Kolkata, India
Distributed generation technologies, definitions and benefits
Electric Power Systems Research 71 (2004) 119–128
Multiobjective Optimization for DG Planning With Load
Models IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 24,
NO. 1, FEBRUARY 2009
Ministry of Power, 2003a. Annual Report 2002–2003,
Government of India, New Delhi.
Ministry of Power, 2003b. Discussion Paper on Rural
Electrification Policies, November 2003, Government of India,
New Delhi.
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31. THANK
YOU
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Hinweis der Redaktion
Distributed energy resources (distributed power) refers to a variety ofsmall modular power generating technologies that can be combined withenergy management and storage systems and used to improve theoperations of the electricity delivery systems, whether or not thesetechnologies are connected to an electric grid. Distributed energyresources support and strengthen the central-station model of electricitygeneration, transmission and distribution. Distributed power can assumea variety of forms. It can be as simple as installing a small electricitygenerator to provide back-up power at an electricity consumer site. Onthe other hand it can be a more complex system highly integrated with theelectricity grid and comprisingelectricity generation, energy storage and power management systems