By,

Jitendra Kumar Regd. No.-0911019048

ASeminar on

Dynamic Economic load Dispatch-A review of solution methodologies

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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

CONTENTS:-

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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.

DYNAMIC ECONOMIC LOAD DISPATCH

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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.

WHY DYNAMIC ECONOMIC LOAD DISPATCH IS NECESSARY

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Discretization of entire dispatch period Dispatching of the individual static interval

economically, through static economic dispatch

Procedure in dynamic economic load dispatch

3 HOUR

1 HOUR

2 HOUR

3 HOUR

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D.e.d problem formulation

Min F= it (Pit)

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

Invasive weed optimization technique(IWO)Particle swarm optimization technique(PSO)Genetic algorithm technique(GA)Simulated annealing technique(SA)

Types of technique to solve DED problem

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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.

Invasive weed optimization technique(IWO):--

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Invasive weed optimization flow chart:-

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

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1. Xi G=[X1,i,G,X2,i,G,X3,i,G…………….XD,i,G]

Some Iwo formulation:-

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

)

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An individual gains knowledge from other population member.

Particle Swarm Optimization~ Basic Idea: Social Behavior ~

Fish 1Food : 50

Fish 3Food : 150

Fish 2Food : 100

Fish 4Food : 300

Where should I

move to?

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The combination vector created by pbest, gbest, pulls each particle to a better direction than previous published versions

General Pso

pbest

gbest

Standard PSO

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PSO algorithm

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.

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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.

Genetic algorithms(GA) 

GENE

GENE

GENE

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Diagram related to GA:-

Roulette Wheel Selection

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Genetic algorithm flow chart

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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.

Simulated Annealing Technique(SA)

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Linear cost curveLocal optimum solutionHighly sensitive to starting point

Failure of Classical method

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Load d

eman

d

predict

ionDifficult

dynamic opt.

Global opt. solution may not occur

Limitation of D.E.D

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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.

Conclusion:-

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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

References:-

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