A Novel

Approach for Optimal Exploitation of Distributed Generations Resources in

Distributed Networks : (Novel Fitness Function and Case Study in Iran)

Hamidreza

Mahdian

Department of Electrical Engineering

University of Semnan

Semnan, Iran

Asghar Akbari

Foroud

Department

of Electrical Engineering

University

of Semnan

Semnan,

Iran

Abstract—This paper proposes a novel

method for optimal exploitation

of distributed generation resources (DGs) in

distributed networks which simultaneously minimize the power losses in the

network, minimize the sharing of distributed generation resources (DGs) in the

short circuit level of the network and the voltages profile

improvement in the network. The presented method in this paper with inequality

partnership

of the considered objectives, obtain the possible global minimum of the

considered fitness function without affecting a particular purpose, perfectly

optimized. The genetic algorithm is used for minimization of considered fitness

function and satisfying the considered constraint. The proposed approach is applying

on the radial Iranian 11 bus distributed network and the obtaining results

confirm the robustness of the proposed method for optimal exploiting of the distributed generation resources

(DGs) in distributed networks.

Keywords—

Distributed generation

resources (DGs); Short circuit level and Voltages profile.

I. Introduction

According to the increasing

demand of electrical energy in recent years and adopting the environmental and

restructuring policies in power systems, the tendency to using of the

distributed generation resources (DGs) is increasing. Also, with the advent of

technology and the desire to replacing the energy resources with renewable

energy resources the using of distributed generation units with various

technologies, including solar, wind and geothermal is increasing. According

To the DGs vicinity to energy

consumption centers, the possibility of the power losses reduction, decreasing

in the peak of the power consumption and increasing in the reliability of the

power electrical systems has been prepared. But the using of the dispersed generation

units (DGs) along with the many benefits it brings, they will create the

problems. The most important of these problems is due to DGs impact on the

performance of the protective system of the distribution networks and

coordination between its components. The negative impact of DG on the

protective system of the distribution networks are dependent to the Various

factors, such as the type of technology, location, number, the type of its

connection to the network and the capacity of the distributed generation unit (DGs)

as well as the network structure and the location of the fault in the network. To minimize the negative effects of DG, various methods are presented.

The basis of these techniques is generally

based on changing the protection system and rearranging its equipment, as well

as reducing the capacity of the DG production or cutting it off when faults occur.

Of course, the adoption of these methods has

problems such as lack of comprehensiveness, negative effects on the performance

of other parts of the system, and also being associated with high cost and

complexity. 1 proposes an optimal approach based on the using of the genetic

algorithm for optimal placement and sizing of distributed generation (DG) resources

in an distribution network. An important feature of the proposed method in 1

is equipped with short-circuit fault currents due to DGs and connection /

disconnect mode, thus preventing undesirable effects on the coordination of

protection devices. 2 is proposes a

multi-objective index-based method for optimally determining the size and places

of distribution units (DGs) in a distributed system with asymmetric power

factor based on different load models. It has been shown in 2 which the load models can significantly affect the

position and desirable size of DG resources in distribution systems. The fitness function optimized includes a

short-circuit surface parameter to represent the requirements of the protective

device. 3 describes the desirable

location of a distributed generator of 2.3 megawatts (DG) in a test system

(IEEE 14 bus system) based on the power factor. Optimal Distributed Generator

(DG) is determined optimally with respect to power losses per one. The bus

whose DG is connected. Power losses per bus using the Neplan software are

determined using the Extended Newton Raphson method. The 4 states which The

distributed generation (DG) in the field of power systems is rapidly

increasing, due to its potential solution to issues such as requiring a power

system license to meet demand and lack of transfer capacity. The inadequate allocation

of DG resources in the power system not only raises energy or energy costs, it

can also damage the operating system. Putting optimal DGs to maximize

reliability and stability in the power system is essential. Several studies

exist to solve the DG transfer problem for various purposes and restrictions

imposed on them. However, the clear principle for this issue is still one The

ambiguous problem in this paper is the introduction and general fields of

research and development in the field of various solutions for putting the

optimal DG in literature. In this paper, some of the most popular methods,

including analytical method, optimal power flow (OPF) and computational

evolutionary methods. This article provides a useful guide to future studies

for those who are interested in this problem or intends to do more research in

this area. The 5 mainly focuses on

the impact of distributed generation resources (DGs) on the distribution networks.

The 5 is states which the integration of the DGs is a traditional radical

distribution system to a multi-source system. The 5 is states which distributed

generation refers to a term that refers to power generation near the place of

consumption. Distributed generation effects, increased short circuit, damage

variations, change in reliability and voltage profiles across the network. The

above benefits can be achieved with the ideal position and the measurement of

DG units. An ideal position is obtained from the index vector method. Optimal

Milk Optimization (ALO), a new methadone algorithm is used to determine the

optimal DG size. This paper proposes a novel approach for optimal exploitation

of the dispersed generation resources (DGs) based on the new fitness function

on the Iranian real case study to minimize the short circuit level in the

network, minimization of the power losses in the network and improvement the

voltages profile. The linear coefficient method is used to associate the

mentioned objectives together and the genetic algorithm is used to minimize the

considered objective function and satisfying the considered constraints.

II. PROBLEM STATEMENT

In this

state the multi-objective optimization method for optimal exploitation of distributed generation resources (DGs) in

distributed network for minimization the short circuit level in the network,

minimization of the power losses in the network and improvement the voltages

profile in the network, based on the linear coefficient method are proposed.

A.

Objective

Function

In this paper the distributed generation resources

(DGs) placement and sizing are stated as multi-criteria optimization problem which

the objective of it is taking tradeoff between the minimization the short-circuit

level in the network, minimization of the power losses in the network and

improvement the voltages profile in the network with appropriate weight

coefficient. The objective function of distributed generation resources (DGs) placement

and sizing optimization problem expressed by weighting coefficients method. In

the proposed optimization problem, the objective function of distributed

generation resources (DGs) sizing and placement problem is described in (1).

The constraints of distributed generation resources (DGs) sizing and placement

problem can be expressed as (2), (7) and (8). The objective function of distributed

generation resources (DGs) optimal exploitation problem expressed by weighting

coefficients method.

(1)

Where F is the fitness function for the optimal

exploitation of the dispersed generation resources (DGs), is the

active power losses in the network, is the

symmetrical three phase fault current in the network, is the

i’th bus voltage and is the

number of the buses of the network. In (1),

are

the weighting coefficients which are proportionate to the considered objectives

in the optimal exploitation of the dispersed

generation resources (DGs) problem. The basically point which must be

considered is that the weighting coefficients in the optimal exploitation of the dispersed generation

resources (DGs) problem are the decision variables like the other decision

variables.

B.

Constraints in the Optimal Exploitation of DGs

Problem

The constraints in the optimal exploitation of

the dispersed generation resources (DGs) problem are expressed in the (2), (3)

and (4) equations.

(2)

(3)

(4)

Where

is the per

unit value of the the i’th bus voltage, is the transfusion capacitance

of the dispersed generation resources in the

i’th buses of the network and is the

consumption capacitance in the i’th

buses of the network. The equation (4) expressed that the summation of the

three weighting coefficients must be equal 1. The equation (3) expressed which

the transfusion capacitance of the dispersed

generation resources (DG) in the i’th buses of the network must not be

greater than consumption capacitance in the i’th

buses of the network.

III. Solving Method

For solving

the dispersed generation resources (DGs) optimal

placement and sizing multi-criteria optimization problem the genetic algorithm

is used. This means that to solve the DG’s sizing and placement multi-criteria

optimization problem, initially the number, location and size of DGs are

guessed randomly. Fig (1) illustrates the flowchart of solving method for the dispersed generation resources (DGs) optimal

placement and sizing multi-criteria optimization problem based on the using of

genetic algorithm.

Fig (1): The schematic of genetic algorithm that

proposed for DGs placement and sizing optimization problem.

IV. Investigation and Simulation

To investigate the proposed

method to minimize the negative effects of the dispersed generation resources (DGs) on the protective system of the

network, studies have been conducted on a real distribution feeder. The studied

feeder is a 20 kV distribution feeder, outlet from the Neyshabur Dizbad Post

with an approximate length of 37 km. The mentioned feeder (outlet from the

Neyshabur Dizbad Post with an approximate length of 37 km) feeds on a number of

loads that are mostly agricultural and industrial. The single-line diagram of

this feeder (outlet from the Neyshabur Dizbad Post with an approximate length

of 37 km), with the position of each load of this feeder, is specified in

figure (2). As could be seen, this feeder (outlet from the Neyshabur Dizbad

Post with an approximate length of 37 km) has 11 main nodes and 5 sub-branches.

Information about loads including active and reactive power consumption as well

as impedance data for each of the lines are presented in table (1). According

to the load data, the capacity of the feeder transformer is equal to 5750 kV

and the nominal current according to the voltage of 20 kV is 165.98 amps.

Fig (2) : The

single-line diagram of the studied network.

Table (1) : The information of the

lines and loads of the studied network.

Load Data

Line Data

Nodes

P(kW)

Q(kVAr)

Line

R(ohm)

X(ohm)

1

–

–

2-1

2016/2

679/1

2

5/807

5/500

3-2

8724/0

4262/0

3

510

1/316

4-2

9085/1

9324/0

4

340

75/210

5-2

6243/0

4761/0

5

170

5/105

6-5

3632/1

666/0

6

340

75/210

7-5

2628/0

2004/0

7

170

5/105

8-7

8171/3

8648/1

8

1190

5/737

9-7

69/0

5262/0

9

340

75/210

10-9

9991/2

4652/1

10

510

1/316

11-9

0843/1

8269/0

11

510

1/316

With considering the mutation rate and the

crossover rate respectively be equal to the 0.015 and 0.78 the results of the

implementation of the genetic algorithm for solving the dispersed generation resources

(DGs) optimal placement and sizing

multi-criteria optimization problem in active distribution networks are

presented in figure (3).

Fig (3) : The single-line diagram of the studied network.

The bar diagram of

the short circuit current in the buses of the active distribution network is illustarate in

figure (4).

Fig (4) : The bar diagram of

the short circuit current in the buses of the active distribution network.

The figure (4) clearly shows which the

short circuit current amplitude in the bus number (2) is greater than from

other buses in the active power netwrk. The weighting respectively coefficient

bar diagram shown in the figure (5).

Fig (5) :The

weighting respectively coefficient bar diagram

The figure (4) clearly shows which the weighting respectively

coefficient

corresponding to the active power loss in the mentioned fitness

function is much larger than other coefficients due to the large value

of the maximum short circuit current compared to other parameters considered in

the considered fitness function for optmial placement and sizing of the distributed

generation resources (DGs) in the active power network. The comparing between

three considered parameter in the considered fitness function for optmial

placement and sizing of the distributed generation resources (DGs) in the

active power network be illustrates in the figure (6).

Fig .(6) :The comparing between three

considered parameter in the considered fitness function.

The results of the

implemente of the genetic algorithm for solving the optimal placement and

sizing of the distributed generation resources (DGs) in the active power

network obtain the putting the distributed genetration with capacitance of the

0.67 MVA in bus number 2. The results of implemente of the genetic algorithm

for solving the optimal placement and sizing of the distributed generation

resources (DGs) in the active power network clearly shows that the active power

in the network could be 1.0369 pu and the macimum short circuit current in the

nework could be reach to the 7.5677 pu. The results of the running the genetic

algorithm for optimal exploitation of the distributed generation in the network

cleary shpws that the optiml sizes and optimal location of the sitributed

generation could be clearly determined.

V. Conclousions

This paper proposes a novel approach for optimal

exploitation of the dispersed generation resources (DGs) based on the new

fitness function on the Iranian real case study to minimization the short

circuit level in the network, minimization of the power losses in the network

and improvement the voltages profile. The linear coefficient method is used to

associate the mentioned objectives together and the genetic algorithm is used

to minimize the considered objective function and satisfying the considered

constraints. The results of the implemented the optmal placement and

sizing of distributed generations (DGs) based on the minimization the short circuit

level in the network, minimization of the power losses in the network and

improvement the voltages profile in the active power

network clearly shows that the optimal size and optimal location of the

dispressed generation couled be determined.

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