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DG PLACEMENT USING PSOI

PSAT,PSO,DG,

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DG PLACEMENT USING PSOI

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  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:- Dr. GangaAgnihotri Prof. Electrical Engg. Deptt.MANIT, Bhopal Presented By:- Jitendra Singh Bhadoriya M-Tech(INSTRUMENTATION) IIIrd Sem. 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 Jitendra Singh Bhadoriya

  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. Jitendra Singh Bhadoriya

  4. INTRODUCTION DG TYPES & RANGE Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  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 Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  6. DG ADVANTAGE • Rural Electrification • Peak Load Shortages • Transmission and Distribution Losses • Digital Economy • Benefits To Other Stake Holders • Consumer-Side Benefits • Grid –Side Benefits • Continued Deregulation of Electricity Markets • Energy Shortage • Remote and Inaccessible Areas Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  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 RusselEberhart [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 Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  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. Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  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 Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  10. PSO PARAMETER 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 Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  11. PSOEquation • 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. Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  12. weighting function w • Thefollowing 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) Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  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 Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  14. RESEARCH TOOL: MATLAB/PSAT Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  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. Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  16. PSAT Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  17. PSAT • PSAT core is the power flow routine, which also takes care ofstate variable initialization. • Once the power flow has been solved, further static and/or dynamic analysis can be performed. • These routines are: • Controls • Regulating Transformers • FACTS • Other Models • Power Flow Data • CPF and OPF Data • Switching Operations • Loads • Machines Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  18. PSAT SIMULATION LIBRARY Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  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 Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  20. LOAD TYPES All Load types depend on the value of α and β LOAD TYPE & EXPONENT VALUE LOAD TYPE αβ CONSTANT 0 0 RESIDENTIAL .92 4.04 INDUSTRIAL .18 6 MIXED 1.51 3.4 Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  21. IEEE 38 BUS SYSTEM Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  22. IEEE 38 BUS SYSTEM Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  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 Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  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 Jitendra Singh Bhadoriya

  25. SMART GRID Jitendra Singh Bhadoriya

  26. DG CONNECTED SMART GRID Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  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 DECENTRAILIZEDsystem Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  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 AnshuBharadwaj and RahulTongia, 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 Jitendra Singh Bhadoriya

  29. REFERENCES • Smart grid initiative for power distribution utility in India Power and Energy Society General Meeting, 2011 IEEE24-29 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. Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

  30. REFERENCES • http://www.powermin.nic.in/ • 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/ • http://www.mathworks.in/ Jitendra Singh Bhadoriya

  31. THANK YOU Jitendra Singh Bhadoriya Jitendra Singh Bhadoriya

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