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Dynamic economic load dispatch a review of solution methodologies48

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Dynamic economic load dispatch a review of solution methodologies48

  1. 1. 1
  2. 2.  Meaning of ded Why ded is necessary ? Process of ded Ded formulation Types of technique to solve ded problem Invasive technique Invasive flow chart Pso technique Pso flow chart Genetic algorithm technique Simulated annealing technique Failure of classical method Limitation of ded Conclusion Refrences2
  3. 3.  Power system economic load dispatch isthe process of allocating generation amongavailable generating units subject to load andother operational constraints, such that, thecost of operation is minimum.3
  4. 4.  The rate of increase of power demand is more then the rate ofincrease of generation. If the plant is located far from the load center transmissionlosses will be considerably higher and the plant may beuneconomical.4
  5. 5.  Discretization of entire dispatch period Dispatching of the individual static intervaleconomically, through static economic dispatch3 HOUR1 HOUR2 HOUR3 HOUR5
  6. 6.  Total time period Dispatch-able unitsTotal operating cost Fuel costQuadratic fuel cost functionFit(Pit)=ai + bi Pit + ci P (it)2 +│ei sin{fi (pit-min –Pit )}│6
  7. 7.  Invasive weed optimization technique(IWO) Particle swarm optimization technique(PSO) Genetic algorithm technique(GA) Simulated annealing technique(SA)7
  8. 8.  The IWO technique is a stochastic optimization method that is based onthe simulation of production, mutation, and spatial propagation ofweeds. Adapting with their environment, invasive weed cover spaces ofopportunity left behind by improper tillage; followed by enduringoccupation of the field. They reproduce rapidly by making seeds and raise their population. Their behaviour changes with time as the colony become dense leavinglesser opportunity of life for the ones with lesser fitness.8
  9. 9. NODefine the solution spaceInitialize a population ofweeds within the solutionspace cEvaluate the fitness of each ofeach weed and rank thepopulationReproduce new seeds basedon the rank of thepopulationDisperse the newseeds over the solutionspaceSolution is the bestweedEvaluate the fitness of newweeds, rank the populationand retain the most pmaxFinished?YES9
  10. 10. 1. Xi G=[X1,i,G,X2,i,G,X3,i,G…………….XD,i,G]2. SiG= [{Fmax, G-F(XiG )}{Smax - Smin }]/[{Fmax , G – Fmin ,G}]3. =[{(Gmax -G)÷Gmin}n * ( max - min )+ min ]4. = G(S, Xi )/ G(Si , x )10
  11. 11.  An individual gains knowledge from otherpopulation member.Fish 1Food : 50Fish 3Food : 150Fish 2Food : 100Fish 4Food : 300Whereshould Imove to?11
  12. 12.  The combination vector created by pbest, gbest,pulls each particle to a better direction thanprevious published versionspbestgbestStandard PSO12
  13. 13. Initialize particles with random positionand zero velocityEvaluate fitness valueCompare & update fitness valuewith pbest and gbestMeet stoppingcriterion?Update velocity andpositionStartEndYESNOpbest = the bestsolution (fitness)a particle hasachieved so far.gbest = theglobal bestsolution of allparticles.13
  14. 14.  Genetic algorithms are a class of heuristic searchmethods and computational models of adaptationand evolution based on natural selection. GA is a search system used in finding out the exact orestimated solutions to optimization. Genetic algorithm categorized as global searchheuristics. Inspired by Darwin’s Theory about evolution.14
  15. 15. Roulette Wheel Selection15
  16. 16. 16
  17. 17.  SA algorithm is a powerful optimization technique havingthe ability to find global or near optimum solutions forlarge combinatorial optimization problems. SA is a random search technique for optimization thatexploits an analogy between the way in which a metal coolsand freezes into a minimum energy crystalline structureand the search for a minimum in a more general system. SA was developed in 1983 to deal with highly non linearproblems.17
  18. 18.  Linear cost curve Local optimum solution Highly sensitive to starting point18
  19. 19. LoaddemandpredictionLimitation of D.E.D19
  20. 20.  This paper presents important features of ded problem. Thisproblem is traditional problem and solved using severalmethods based on the requirements of the problemformulation. These methods are classified into mathematicalprogrammingbased methods. It is expected that this review of the ded problem based onthe solution method to solve them will be helpful for all thosewho do research related to ded problem.20
  21. 21.  R.Sharma,Niranjan nayak,Kirshananda and P.K Rout-Modified invasive weed optimization with dualmutation for dynamic economic dispatch published in2011 IEEE C.kumar and T.Alwarsamy- Dynamic economicdispatch-A Review of solution methodologiespublished in 2011 EuroJournals. R Chakrabarti , P K Chattopadhay, (Ms) M Basu ,C KPanigrahi- Particle Swarm Optimization Technique forDynamic Economic Dispatch published in 2006 IE(I)Journal-EL Google image21

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