A I. Introduction According to the increasing demand

A Novel
Approach for Optimal Exploitation of Distributed Generations Resources in
Distributed Networks : (Novel Fitness Function and Case Study in Iran)

 

Hamidreza
Mahdian

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Department of Electrical Engineering

University of Semnan

Semnan, Iran

[email protected]

Asghar Akbari
Foroud

Department
of Electrical Engineering

University
of Semnan

Semnan,
Iran

[email protected]

 

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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