ASEMINAR REPORT

ONMAXIMAL OPTIMAL BENEFITS OF

DISTRIBUTED GENERATION USING GENETIC ALGORITHMS

Submitted by:Supervised by:

Department of Electrical EngineeringMalaviya National Institute of Technology, Jaipur

INTRODUCTION

Distributed Generation is the generation of electrical energy in small, nearest the load centre, with the option to interact (to buy or to sell) with the electrical network and, in some cases, considering the maximum power efficiency

ADVANTAGES Taking power to load

1. Composite technical and economic benefits1. Peak Use capacity: Operating cost reduces and act as a

spinning reserve2. Reliability, Security and Power quality: Can supply

electricity where voltage supply is difficult.

2. Technical benefits1. Reduced environmental and health concern aspects.2. Grid support; stabilize a dropping frequency due to a

sudden under capacity or excess demand

3. Economic benefits1. low maintenance cost

Rating- 5kw to 100mw

MAJOR POLICY ISSUES

o High financial (capital) cost

o System frequency deviation: increases the burden

on the system operator

o Voltage deviation: influence on the local voltage

level

o Change in power flows: may induce power flows

from the low voltage into the medium-voltage grid.

o Bi-directional power flow

o Higher harmonics: some DG technologies produce

direct current

SINGLE OBJECTIVE OPTIMIZATION TECHNIQUE

o Voltage profile improvement (VPI)

o Total spinning reserve increasing (SRI)

o Power flow reduction in critical lines (PFR)

o Total line-loss reduction (LLR)

MULTI OBJECTIVE OPTIMIZATION TECHNIQUE

Optimize more than one objective function simultaneously, which can be solved by using the weighting factors for maximizing benefits of DG.

MBDG= w1VPI%+ w2SRI%+ w3PFR% + w4LLR%

Where w1, w2, w3 and w4 are benefit weighting factors for VPI%, SRI%, PFR% and LLR%, respectively

OPTIMAL PROPOSED APPROACH FOR MAXIMAL BENEFITS OF DG (MBDG)

Linear programming (LP):

The linear programming (LP) is defined as an optimization of a linear objective functions and linear constraints. LP in the standard form can be written as :

Maximize or minimize Z = cx

Subjected to : Ax= b

Where; x ≥ 0,b≥ 0

x is the vector of decision variables to be determined, b is the bounding vector

OPTIMAL PROPOSED APPROACH FOR MAXIMAL BENEFITS OF DG (MBDG)

Genetic algorithm (GA)

A procedure used to find approximate solutions to search problems through application of the principles of evolutionary biology.

Use biologically inspired techniques such as genetic inheritance, natural selection, mutation, and sexual reproduction (recombination, or crossover).

Implemented using computer simulations in which an optimization problem is specified.

GA needs scalar fitness information to work, it is natural to propose a combination of all the objectives into a single one, by using a weighted sum of the single objective functions.

OUTLINE OF GA Step 1. [Start] Generate random population of n chromosomes (suitable

solutions for the problem) Step 2. [Fitness] Evaluate the fitness f(x) of each chromosome x in the

population Step 3. [New population] Create a new population by repeating following

steps until the new population is complete [Selection] Select two parent chromosomes from a population according

to their fitness (the better fitness, the bigger chance to be selected) [Crossover] With a crossover probability cross over the parents to form a

new offspring (children). If no crossover was performed, offspring is an exact copy of parents.

[Mutation] With a mutation probability mutate new offspring at each locus (position in chromosome).

[Accepting] Place new offspring in a new population Step 4. [Replace] Use new generated population for a further run of

algorithm Step 5. [Test] If the end condition is satisfied, stop, and return the best

solution in current population Step 6. [Loop] Go to step 2

BASIC PARAMETERS FOR MBDG

Population size (Pop): This is the number of chromosomes in a population, which describes the number of searching points

Number of populations, generations (Npop): This is a sufficient number of iterations or populations that are required to get the optimal solution and it is used as a stopping criterion.

Probability of crossover (Pc): This parameter is used to determine the number of chromosomes required to be included in the crossover process

Probability of mutation (Pm): The number of bits that undergo the mutation operation is determined by the mutation probability.

Solution precession (Pr): High precession increases the chromosome length and, hence, the computational time.

VALUE OF PARAMETERS

However, these parameters selected to obtain the global optimum solution in minimum time as:

Pop = 80,Npop = 100 , Pc= 50% , Pm = 5% and Pr= 0.001

FITNESS OF THE INDIVIDUALS:

F (S ) = Objective function+ C (i ) × Z (i )i= 1,..., 6

whereThe objective function; might be single or multi-objective and i is the number of constraintsC(i): penalty values if all the constrains (total DG number allowed, traditional generation capacity, DG generation capacity constraints, power balance constraints and voltage limits constraints) go outside their allowed limits

RESULTS

CONCLUSION

o DG sizing and placement plays a very important role in improving the performance of the grid.

o Application of GA for finding optimal sizing and sitting of DG helps in voltage profile improvement (VPI), spinning reserve increasing (SRI), power flow reduction (PFR) and line-loss reduction (LLR).

REFERENCES A.A. Abou El-Ela, S.M. Allam, M.M. Shatla,” Maximal optimal benefits

of distributed generation using genetic algorithms”, Electric Power System Research, vol.80 ,pp. 869–877 Nov. 2009.(Base Paper)

Caisheng Wang, Student Member, IEEE, and M. Hashem Nehrir, Senior Member, IEEE, “Analytical Approaches for Optimal Placement of Distributed Generation Sources in Power Systems”, IEEE transactions on power systems, vol. 19, no. 4, November 2004.

Y. M. Atwa , Student Member, IEEE , E. F. El-Saadany , Senior Member, IEEE , M. M. A. Salama , Fellow, IEEE , and R. Seethapathy, Member, IEEE, “Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization”, IEEE transactions on power systems, VOL. 25, NO. 1, February 2010

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