Optimal placement and sizing of multi dg using pso

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  • sir this is prathap i am doing this project and i am new to matlab can u pls send matlab to my email i d (prathap.sacs@gmail.com) which is very useful to me sir
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  • 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
  • Optimal placement and sizing of multi dg using pso

    1. 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.20 December 12012 JITENDRA SINGH BHADORIYA
    2. 2. CONTENTSINTRODUCTION OF DISTRIBUTION GENERATOR (DG)PROPOSED WORK OPTIMAL PLACEMENT AND SIZING OFMULTI DGMETHODOLOGY: PSO ALGORITHMRESEARCH TOOL: MATLAB/PSATCONCLUSIONSREFERENCES20 December2012 2 Jitendra Singh Bhadoriya
    3. 3. INTRODUCTION DISTRIBUTIONGENERATOR“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
    4. 4. INTRODUCTION DG TYPES & RANGE20 December 2012 Jitendra Singh Bhadoriya 4 Jitendra Singh Bhadoriya
    5. 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 support20 December 2012 Jitendra Singh Bhadoriya 5 Jitendra Singh Bhadoriya
    6. 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 Areas20 December 2012 Jitendra Singh Bhadoriya 6 Jitendra Singh Bhadoriya
    7. 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.pdf20 December 2012 Jitendra Singh Bhadoriya 7 Jitendra Singh Bhadoriya
    8. 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.20 December 2012 Jitendra Singh Bhadoriya 8 Jitendra Singh Bhadoriya
    9. 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.120 December 2012 Jitendra Singh Bhadoriya 9 Jitendra Singh Bhadoriya
    10. 10. 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 gbest20 December 2012 Jitendra Singh Bhadoriya 10 Jitendra Singh Bhadoriya
    11. 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.20 December 2012 Jitendra Singh Bhadoriya 11 Jitendra Singh Bhadoriya
    12. 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)20 December 2012 Jitendra Singh Bhadoriya 12 Jitendra Singh Bhadoriya
    13. 13. 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 EndWhile maximum iterations or minimum error criteria is not attained 20 December 2012 Jitendra Singh Bhadoriya 13 Jitendra Singh Bhadoriya
    14. 14. RESEARCH TOOL: MATLAB/PSAT20 December 2012 Jitendra Singh Bhadoriya 14 Jitendra Singh Bhadoriya
    15. 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.20 December 2012 Jitendra Singh Bhadoriya 15 Jitendra Singh Bhadoriya
    16. 16. PSAT20 December 2012 Jitendra Singh Bhadoriya 16 Jitendra Singh Bhadoriya
    17. 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 • Machines20 December 2012 Jitendra Singh Bhadoriya 17 Jitendra Singh Bhadoriya
    18. 18. PSAT SIMULATION LIBRARY20 December 2012 Jitendra Singh Bhadoriya 18 Jitendra Singh Bhadoriya
    19. 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 busα and β are real and reactive power exponents20 December 2012 Jitendra Singh Bhadoriya 19 Jitendra Singh Bhadoriya
    20. 20. LOAD TYPESAll Load types depend on the value of α and βLOAD TYPE & EXPONENT VALUE LOAD TYPE α β CONSTANT 0 0 RESIDENTIAL .92 4.04 INDUSTRIAL .18 6 MIXED 1.51 3.420 December 2012 Jitendra Singh Bhadoriya 20 Jitendra Singh Bhadoriya
    21. 21. IEEE 38 BUS SYSTEM20 December 2012 Jitendra Singh Bhadoriya 21 Jitendra Singh Bhadoriya
    22. 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 4GENCO 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 3020 December 2012 Jitendra Singh Bhadoriya 22 Jitendra Singh Bhadoriya
    23. 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 above20 December 2012 Jitendra Singh Bhadoriya 23 Jitendra Singh Bhadoriya
    24. 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 energy20 December 2012 Jitendra Singh Bhadoriya 24
    25. 25. SMART GRID20 December 2012 Jitendra Singh Bhadoriya 25
    26. 26. DG CONNECTED SMART GRID20 December 2012 Jitendra Singh Bhadoriya 26 Jitendra Singh Bhadoriya
    27. 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 system20 December 2012 Jitendra Singh Bhadoriya 27 Jitendra Singh Bhadoriya
    28. 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 Jitendra Singh Bhadoriya 28 Jitendra Singh Bhadoriya
    29. 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. 20 December 2012 Jitendra Singh Bhadoriya 29 Jitendra Singh Bhadoriya
    30. 30. 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/20 December 2012 Jitendra Singh Bhadoriya 30
    31. 31. THANK YOU20 December2012 Jitendra Singh Bhadoriya 31 Jitendra Singh Bhadoriya

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