![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](https://image.slidesharecdn.com/dynamiceconomicloaddispatch-areviewofsolutionmethodologies48-130524092704-phpapp01/85/Dynamic-economic-load-dispatch-a-review-of-solution-methodologies48-10-320.jpg)
Dynamic economic load dispatch a review of solution methodologies48
- 1. 1
- 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 Refrences 2
- 3. Power system economic load dispatch is the process of allocating generation among available generating units subject to load and other operational constraints, such that, the cost of operation is minimum. 3
- 4. The rate of increase of power demand is more then the rate of increase of generation. If the plant is located far from the load center transmission losses will be considerably higher and the plant may be uneconomical. 4
- 5. Discretization of entire dispatch period Dispatching of the individual static interval economically, through static economic dispatch 3 HOUR 1 HOUR 2 HOUR 3 HOUR 5
- 6. Total time period Dispatch-able units Total operating cost Fuel cost Quadratic fuel cost function Fit(Pit)=ai + bi Pit + ci P (it) 2 +│ei sin{fi (pit-min –Pit )}│ 6
- 7. Invasive weed optimization technique(IWO) Particle swarm optimization technique(PSO) Genetic algorithm technique(GA) Simulated annealing technique(SA) 7
- 8. The IWO technique is a stochastic optimization method that is based on the simulation of production, mutation, and spatial propagation of weeds. Adapting with their environment, invasive weed cover spaces of opportunity left behind by improper tillage; followed by enduring occupation of the field. They reproduce rapidly by making seeds and raise their population. Their behaviour changes with time as the colony become dense leaving lesser opportunity of life for the ones with lesser fitness. 8
- 9. NO Define the solution space Initialize a population of weeds within the solution space c Evaluate the fitness of each of each weed and rank the population Reproduce new seeds based on the rank of the population Disperse the new seeds over the solution space Solution is the best weed Evaluate the fitness of new weeds, rank the population and retain the most pmax Finished? YES 9
- 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. An individual gains knowledge from other population member. Fish 1 Food : 50 Fish 3 Food : 150 Fish 2 Food : 100 Fish 4 Food : 300 Where should I move to? 11
- 12. The combination vector created by pbest, gbest, pulls each particle to a better direction than previous published versions pbest gbest Standard PSO 12
- 13. Initialize particles with random position and zero velocity Evaluate fitness value Compare & update fitness value with pbest and gbest Meet stopping criterion? Update velocity and position Start End YES NO pbest = the best solution (fitness) a particle has achieved so far. gbest = the global best solution of all particles. 13
- 14. Genetic algorithms are a class of heuristic search methods and computational models of adaptation and evolution based on natural selection. GA is a search system used in finding out the exact or estimated solutions to optimization. Genetic algorithm categorized as global search heuristics. Inspired by Darwin’s Theory about evolution. 14
- 16. 16
- 17. SA algorithm is a powerful optimization technique having the ability to find global or near optimum solutions for large combinatorial optimization problems. SA is a random search technique for optimization that exploits an analogy between the way in which a metal cools and freezes into a minimum energy crystalline structure and the search for a minimum in a more general system. SA was developed in 1983 to deal with highly non linear problems. 17
- 18. Linear cost curve Local optimum solution Highly sensitive to starting point 18
- 20. This paper presents important features of ded problem. This problem is traditional problem and solved using several methods based on the requirements of the problem formulation. These methods are classified into mathematicalprogramming based methods. It is expected that this review of the ded problem based on the solution method to solve them will be helpful for all those who do research related to ded problem. 20
- 21. R.Sharma,Niranjan nayak,Kirshananda and P.K Rout- Modified invasive weed optimization with dual mutation for dynamic economic dispatch published in 2011 IEEE C.kumar and T.Alwarsamy- Dynamic economic dispatch-A Review of solution methodologies published in 2011 EuroJournals. R Chakrabarti , P K Chattopadhay, (Ms) M Basu ,C K Panigrahi- Particle Swarm Optimization Technique for Dynamic Economic Dispatch published in 2006 IE(I) Journal-EL Google image 21