Optimal placement and sizing of multi dg using pso

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.

20 December 1
2012 JITENDRA SINGH BHADORIYA

CONTENTS
INTRODUCTION OF DISTRIBUTION GENERATOR (DG)
PROPOSED WORK OPTIMAL PLACEMENT AND SIZING OF
MULTI DG
METHODOLOGY: PSO ALGORITHM
RESEARCH TOOL: MATLAB/PSAT
CONCLUSIONS
REFERENCES

20 December
2012 2
Jitendra Singh Bhadoriya

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.

20 December 2012 Jitendra Singh Bhadoriya 3

INTRODUCTION DG TYPES & RANGE


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


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


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


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.


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

PSO PARAMETER
k
s k+ 1
v
v k+ 1 v g b e st

v p b e st
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

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.


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)


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


RESEARCH TOOL: MATLAB/PSAT


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.

PSAT


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


PSAT SIMULATION LIBRARY


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


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


IEEE 38 BUS SYSTEM


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


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


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


SMART GRID


DG CONNECTED SMART GRID


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

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
Jitendra Singh Bhadoriya 28

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.


REFERENCES
http://www.powermin.nic.in/
http://www.dg.history.vt.edu/ch1/introductio
n.html
http://ieeexplore.ieee.org
http://www.swarmintelligence.org/
http://umpir.ump.edu.my/360/
http://www.mnre.gov.in/
http://www.isgtf.in/
http://www.mathworks.in/

THANK
YOU
20 December
2012 Jitendra Singh Bhadoriya 31

Optimal placement and sizing of multi dg using pso

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