Optimal placement and sizing of multi

April 12, 2023 1JITENDRA SINGH BHADORIYA

DEVI AHILYA VISHWAVIDYALAYA, INDORESchool of Instrumentation

“OPTIMAL PLACEMENT AND SIZING OF MULTI-DISTRIBUTED GENERATION (DG) INCLUDING

DIFFERENT LOAD MODELS USING PSO” Presented By:-

Jitendra Singh BhadoriyaM-Tech(INSTRUMENTATION) IIIrd Sem.

Guided By:-

Dr. Ganga Agnihotri Prof. Electrical Engg. Deptt. MANIT, Bhopal

A PRESENTATION ON

April 12, 2023 2Jitendra Singh Bhadoriya

CONTENTSINTRODUCTION 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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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.

Jitendra Singh Bhadoriya

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INTRODUCTION DG TYPES & RANGE


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INTRODUCTION DG Technologies

Distributed power technologies are typically installed for one or more of the purposes

Overall load reductionIndependence from the gridSupplemental PowerNet energy salesCombined heat and power Grid support

Jitendra Singh BhadoriyaJitendra Singh Bhadoriya

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DG ADVANTAGE


Consumer-Side BenefitsGrid –Side BenefitsContinued Deregulation of Electricity MarketsEnergy Shortage Remote and Inaccessible Areas

Rural ElectrificationPeak Load ShortagesTransmission and Distribution LossesDigital EconomyBenefits To Other Stake Holders


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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/kennedy95-ijcnn.pdf


http://dsp.jpl.nasa.gov/members/payman/swarm/kennedy95-ijcnn.pdf

http://dsp.jpl.nasa.gov/members/payman/swarm/kennedy95-ijcnn.pdf

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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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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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PSO PARAMETER


sk

vk

vpbest

vgbest

sk+1

vk+1

sk

vk

vpbest

vgbest

sk+1

vk+1

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

Fig.1 Concept of modification of a searching point by PSO


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PSO Equation

The modification of the particle’s position can be mathematically modeled according the following equation :

Vik+1 = wVi

k +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 = si

k + Vik+1 (3)


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PSO ALGORITHMFor each particle Initialize particleENDDoFor 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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RESEARCH TOOL: MATLAB/PSAT


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• 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.


RESEARCH TOOL: PSAT


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PSAT


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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 • CPF and OPF Data• Switching Operations• Loads• Machines

• Controls• Regulating Transformers• FACTS• Other Models


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PSAT SIMULATION LIBRARY


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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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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


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IEEE 38 BUS SYSTEM


GENCO 1Bus_3

Bus_2Bus_1

Bus 9

Bus 8Bus 7Bus 6

Bus 5Bus 4

Bus 38

Bus 37

Bus 36Bus 35

Bus 34

Bus 33Bus 32

Bus 31Bus 30

Bus 29

Bus 28

Bus 27

Bus 26

Bus 25

Bus 24

Bus 23

Bus 22

Bus 21

Bus 20

Bus 19

Bus 18

Bus 17

Bus 16

Bus 15

Bus 14

Bus 13Bus 12

Bus 11Bus 10


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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

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DG CONNECTED SMART GRID


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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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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


http://www.goodreads.com/author/show/220574.James_Kennedy

http://www.goodreads.com/author/show/220573.Yuhui_Shi

http://www.sciencedirect.com/

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=785511&contentType=Conference+Publications&sortType=desc_p_Citation_Count&searchField=Search_All&queryText=pso

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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.


http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6027502


REFERENCEShttp://www.powermin.nic.in/http://www.dg.history.vt.edu/ch1/introduction

.htmlhttp://ieeexplore.ieee.orghttp://www.swarmintelligence.org/http://umpir.ump.edu.my/360/http://www.mnre.gov.in/http://www.isgtf.in/http://www.mathworks.in/

http://www.powermin.nic.in/

http://www.dg.history.vt.edu/ch1/introduction.html

http://www.dg.history.vt.edu/ch1/introduction.html

http://ieeexplore.ieee.org/

http://www.swarmintelligence.org/

http://umpir.ump.edu.my/360/

http://www.mnre.gov.in/

http://www.isgtf.in/

April 12, 2023 31Jitendra Singh Bhadoriya

THANK YOU


Optimal placement and sizing of multi

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