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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
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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.
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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
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DG ADVANTAGE
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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
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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
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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.
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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.
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RESEARCH TOOL: PSAT
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PSAT
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PSAT
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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
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IEEE 38 BUS SYSTEM
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IEEE 38 BUS SYSTEM
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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
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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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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
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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.
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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/
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THANK YOU
Jitendra Singh Bhadoriya