minimizing power loss in a distribution system by optimal

i

The Arab Academy for Science & Technology

and Maritime Transport

M. Sc. Thesis

Minimizing Power Loss in a Distribution

System by Optimal Sizing and Sitting of

Distributed Generators with Network

Reconfiguration using Grey Wolf,

Particle Swarm, and Hybrid Grey Wolf-

Particle Swarm Optimizers

Presented By:

Eng. Mirna Fouad Abd-Elsalam Abu-Haggar

Supervised By:

Prof. Dr. Mahmoud Magdy Bahgat Eteiba

Dr. Eman Hassan Beshr

ii

DECLARATION

I certify that all the material in this thesis that is not my own work has been identified

and that no material is included for which a degree has previously been conferred on

me.

The contents of this thesis reflect my own personal views and are not necessarily

endorsed by the University.

Signed: _____________________________

Date:

iii

APPROVAL OF EXAMINING COMMITTEE

We hereby certify that we have read the present work and that in our opinion it is fully

adequate in scope and quality as thesis towards the partial fulfillment of the Master

Degree requirements in

Electrical Power and Control Department

From

College of Engineering (AASTMT)

Date …………….…………

Supervisor (s): Prof. Mahmoud Magdy Bahgat Eteiba Professor of Electrical Power and Machines Dept. Faculty of Engineering, Fayoum University.

Name: Position: Signature:

Dr. Eman Hassan Beshr Professor of Electrical Power and Computer Dept. Faculty of Engineering, AASTMT.


Examiners:



iv

ACKNOWLEDGMENT

Most importantly I thank God for discernment in what I was undertaking and also for

keeping me healthy during the thesis period.

My sincere gratitude goes to Prof. M. Magdy Eteiba and Dr. Eman Beshr, the

supervisors of this thesis for supporting, continuously guiding, helpful suggestions,

sharing with me their experience, continued monitoring of my progress during the thesis

work and giving me their time. Thank you for believing in me.

Much appreciation goes to my lecturers in Electrical and Control Department for

imparting on me this worthy knowledge. I am also very grateful to Eng. Ibtihal Zahran

for her time help, and support.

I am very grateful to my dearest fiancé Eng. Ahmed Ellakany for his motivation,

encouragement, and support. His continuous care had always inspired me.

I would like to express my love and sincere gratitude to my beloved family; my parents,

grandma, and brother. They are always there for me for every up and down of my life.

They have been blessing me with unconditional love and support through my life.

Finally, I would like to thank all my sweetest friends especially Nourhan Tarek and

Sandra Amir for their unconditional love, encouragement, and help. They are always

there for me.

God bless you all.

v

ABSTRACT

The necessity for implementing new solutions in distribution systems using advanced

technologies is growing rapidly due to the increased use of distributed and variable

power generation sources and system reconfiguration. As distributed generators

penetration levels increase, instability of the distribution network rises. Thus, there is a

need to raise the reliability and stability of the network by using advanced techniques. It

is a requirement to find the optimal solution for distributed generators sizing and

location while taking into consideration the reduction of electrical power losses and

voltage profile improvement. Many types of research have been done to find the optimal

DG size and location and system reconfiguration aiming to improve the voltage profile

and reduce the energy losses. Although most of this work in the literature use different

techniques like analytical approaches, computation, and artificial intelligence, they still

have several disadvantages. This leads to searching for new techniques and algorithms

in order to improve the already existing work and try to overcome their disadvantages

and come up with better results.

This thesis presents three metaheuristic based algorithms Grey Wolf Optimizer

(GWO), Particle Swarm Optimizer (PSO), and the hybridization of the two

metaheuristics based GWO and PSO to solve network reconfiguration problem in the

presence of installing multiple DGs with different types (conventional, renewable).

Results of new Hybrid GWO-PSO were compared with both GWO and PSO techniques

to show the improvement of the results obtained by this hybridization. The proposed

algorithm is applied to IEEE 33-, IEEE 69-bus radial distribution system, and 78-bus

practical real distribution system in 6th October city, Egypt to minimize the real power

loss. The results clearly indicate that there is a considerable improvement in the voltage

profile, active losses, and reactive losses. MATPOWER and MATLAB® software are

used for simulations. The simulated results illustrate well the performance and

effectiveness of the proposed techniques.

vi

TABLE OF CONTENTS

TABLE OF CONTENTS ............................................................................................................................VI

LIST OF FIGURES ................................................................................................................................ VIII

LIST OF TABLES ....................................................................................................................................... X

LIST OF ACRONYMS/ABBREVIATIONS ............................................................................................XI

1 INTRODUCTION .................................................................................................................................. 1

1.1 ELECTRICAL DISTRIBUTION NETWORK .......................................................... 1 1.2 DISTRIBUTION NETWORK CONGESTION ......................................................... 2 1.3 NETWORK RECONFIGURATION .......................................................................... 2 1.4 DISTRIBUTED GENERATORS ................................................................................ 3 1.5 SYSTEM LOSSES MINIMIZATION ........................................................................ 6 1.6 VOLTAGE PROFILE IMPROVEMENT .................................................................. 7 1.7 THESIS OUTLINE ...................................................................................................... 7

2 LITERATURE SURVEY ...................................................................................................................... 9

2.1 INTRODUCTION ........................................................................................................ 9 2.2 PREVIOUS RESEARCH WORKS ON SYSTEM RECONFIGURATION .......... 10 2.3 PREVIOUS RESEARCH WORKS ON DISTRIBUTED GENERATORS

LOCATING AND SIZING .................................................................................................... 10 2.4 PREVIOUS RESEARCH WORKS ON SYSTEM RECONFIGURATION with

DISTRIBUTED GENERATORS LOCATING AND SIZING ............................................ 13

3 PROBLEM FORMULATION ............................................................................................................ 15

3.1 INTRODUCTION ...................................................................................................... 15 3.2 OBJECTIVE FUNCTION ......................................................................................... 15 3.3 CONSTRAINTS ......................................................................................................... 16 3.4 POWER LOSS USING SYSTEM RECONFIGURATION ..................................... 17 3.5 POWER LOSS USING DG INSTALLATION ........................................................ 18

4 OPTIMIZATION TECHNIQUES ..................................................................................................... 19

4.1 PROPOSED ALGORITHMS.................................................................................... 19 4.1.1 Grey Wolf Optimizer ............................................................................................................. 19 4.1.2 Mathematical Model of PSO ................................................................................................. 20 4.1.3 Implementation Steps of GWO ............................................................................................. 22 4.1.4 Particle Swarm Optimizer ..................................................................................................... 25 4.1.5 Mathematical Model of PSO ................................................................................................. 26 4.1.6 Implementation Steps of PSO ............................................................................................... 27 4.1.7 Hybrid GWO-PSO Optimizer ............................................................................................... 30 4.1.8 Implementation Steps of Hybrid GWO-PSO ........................................................................ 30

vii

4.2 IMPLEMENTATION FOR SYSTEM RECONFIGURATION AND DG

ALLOCATION ....................................................................................................................... 33

5 SIMULATIONS & RESULTS ............................................................................................................ 35

5.1 INTRODUCTION ...................................................................................................... 35 5.2 IEEE 33-BUS TEST SYSTEM .................................................................................. 36

5.2.1 Active Power Loss Reduction ............................................................................................... 36 5.2.2 Reactive Power Loss Reduction ............................................................................................ 41 5.2.3 Voltage Profile Improvement ................................................................................................ 42 5.2.4 Methods Performance ............................................................................................................ 44

5.3 IEEE 69-BUS TEST SYSTEM .................................................................................. 46 5.3.1 Active Power Loss Reduction ............................................................................................... 46 5.3.2 Reactive Power Loss Reduction ............................................................................................ 51 5.3.3 Voltage Profile Improvement ................................................................................................ 52 5.3.4 Methods Performance ............................................................................................................ 54

5.4 78-BUS REAL TEST SYSTEM ................................................................................ 56 5.4.1 Active Power Loss Reduction ............................................................................................... 57 5.4.2 Reactive Power Loss Reduction ............................................................................................ 62 5.4.3 Voltage Profile Improvement ................................................................................................ 63 5.4.4 Methods Performance ............................................................................................................ 66

6 CONCLUSIONS & FUTURE WORK ............................................................................................... 67

6.1 CONCLUSIONS ........................................................................................................ 67 6.2 FUTURE WORK ....................................................................................................... 69

REFERENCES ............................................................................................................................................ 70

APPENDICES ............................................................................................................................................. 79

viii

LIST OF FIGURES

Figure 1-1: Illustrative Radial Distribution System .......................................................................3

Figure 1-2: Illustrative penetration of distributed generators ........................................................4

Figure 1-3: Installed buildings sector renewable DG capacity in AEO2017 Reference case

(gigawatts)......................................................................................................................................5

Figure 1-4: Installed buildings sector non-renewable DG capacity in AEO2017 Reference case

(gigawatts)......................................................................................................................................6

Figure 4-1: Hierarchy of the grey wolf. .......................................................................................20

Figure 4-2: Hunting behavior of grey wolves ..............................................................................21

Figure 4 -3: Flow chart of GWO. .................................................................................................24

Figure 4-4: Group of fish movement in the search space. ...........................................................25

Figure 4-5: Group of bird movement in the search space ............................................................25

Figure 4-6: Search point modification by PSO. ...........................................................................26

Figure 4-7: Flowchart of PSO. .....................................................................................................29

Figure 4-8: Flowchart of hybrid GWO-PSO. ...............................................................................32

Figure 5-1: Single line diagram of the 33-bus system. ................................................................36

Figure 5-2: Single line diagram of a 33-bus system for scenario 8..............................................40

Figure 5-3: Power loss of 33-bus system using three different techniques. .................................40

Figure 5-4: Reactive loss of 33-bus system using three different techniques. .............................41

Figure 5-5: Voltage profile of a 33-bus system using GWO technique. ......................................42

Figure 5-6: Voltage profile of a 33-bus system using PSO technique. ........................................43

Figure 5-7: Voltage profile of a 33-bus system using the hybrid technique. ...............................43

Figure 5-8: Conversion curve of the 33-bus system using three different techniques for scenario

8. ..................................................................................................................................................44

Figure 5-9: Single line diagram of the 69-bus system. ................................................................46

Figure 5-10: Single line diagram of a 69-bus system for scenario 8. ..........................................50

Figure 5-11: Power loss of 69-bus system using three different techniques. ...............................51

Figure 5-12: Reactive loss of 69-bus system using three different techniques. ...........................52

Figure 5-13: Voltage profile of a 69-bus system using GWO technique. ....................................53

Figure 5-14: Voltage profile of a 69-bus system using PSO technique. ......................................53

Figure 5-15: Voltage profile of a 69-bus system using the hybrid technique. .............................54

Figure 5-16: Conversion curve of the 69-bus system using three different techniques for

scenario 8. ....................................................................................................................................56

Figure 5-18: Single line diagram of a 78-bus system for scenario 8. ..........................................61

ix

Figure 5-19: Power loss of 78-bus system using three different techniques. ...............................62

Figure 5-20: Reactive loss of 78-bus system using three different techniques. ...........................63

Figure 5-21: Voltage profile of a 78-bus system using GWO technique. ....................................64

Figure 5-22: Voltage profile of a 78-bus system using PSO technique. ......................................65

Figure 5-23: Voltage profile of a 78-bus system using the hybrid technique. .............................65


scenario 8. ....................................................................................................................................66

x

LIST OF TABLES

Table 5-1: Different penetration of DG units for the 33-bus system. ......................................... 37

Table 5-2: Comparison of simulation results for P-type DG units of the 33-bus system. ........... 38

Table 5-3: Comparison of simulation results for PQ+-type DG units of the 33-bus system. ...... 39

Table 5-4: Comparison of methods performance for the 33-bus system. ................................. 45


Table 5-6: Comparison of simulation results for P-type DG units of the 69-bus system. ........... 48

Table 5-7: Comparison of simulation results for PQ+-type DG units of the 69-bus system. ...... 49

Table 5-8: Comparison of methods performance for the 69-bus system. ................................... 55


Table 5-10: Comparison of simulation results for P-type DG units of the 78-bus system. ........ 59

Table 5-11: Comparison of simulation results for PQ+-type DG units of the 78-bus system. .... 60

xi

LIST OF ACRONYMS/ABBREVIATIONS

ACRONYM Definition of Acronym

ABC Artificial Bee Colony algorithm

A⃗⃗ Coefficient Vector

AC-LF AC-Load Flow

BA Bat Algorithm

BB-BC Big Bang Big Crunch method

BFOA Bacterial Foraging Optimization Algorithm

BSOA Back Tracking Search Optimization Algorithm

BPSO Binary Particle Swarm Optimization

BFOA Bacterial Foraging Optimization Algorithm

CSA Cuckoo Search Algorithm

CSOS Chaotic Symbiotic Organisms Search algorithm

C⃗ Coefficient Vector

DE Differential Evolutionary

DNR Distribution Network Reconfiguration

DGs Distributed Generators

DABC Discrete Artificial Bee Colony

DCGA Decimal Codification Genetic Algorithm

xii

𝐷 Distance from source to the DG bus location in km

EP Evolutionary Programming

EA Efficient Analytical

EA Evolutionary Algorithm

EVs Electric Vehicles

FA Firefly Algorithm

FMGA Fuzzy Mutated Genetic Algorithm

FWA Fire Work Algorithm

FW/BW Forward-Backward Sweep algorithm

FCM Fuzzy C-means Clustering algorithm

GA Genetic Algorithms

GWO Grey Wolf Optimizer

GSA Gravitational Search Algorithm

G⃗⃗ (k) Global Best Particle

HGWO Hybrid Grey Wolf Optimizer

HSA Harmony Search Algorithm

INSGA-II Improved Non dominated Sorting Genetic Algorithm–II

𝐼𝑖,𝑖+1 Current in the line section between buses i and i+1

𝐼𝑖,𝑖+1,𝑚𝑎𝑥 Current’s maximum limit of the line between buses i and i+1

k Iteration Number

LSF Loss Sensitivity Factor

xiii

LP linear programming

𝐿 The total length of the feeder from source to bus

𝐿 Location of DG

LMP Location Marginal Pricing

MFO Moth Flame Optimization

MHA Meta-Heuristic Algorithms

MPGSA Modified Plant Growth Simulation Algorithm

MOF Multi-Objective Function

NSGA-II Non-Dominated Sorting Genetic Algorithm II

OPF Optimal Power Flow

ODGP optimal DG placement

OCOA Oppositional Cuckoo Optimization Algorithm

𝑂𝑆 Opened Switch

PSO Particle Swarm Optimizer

PSO-CFA Particle Swarm Optimization with Constriction Factor Approach

technique

𝑃𝐿𝑜𝑠𝑠(𝑖,𝑖+1) Real power loss from buses 𝑖 to 𝑖 + 1

𝑃𝑖 Real power flowing out of bus 𝑖

𝑃𝐷𝑖 Real power supplied by DG at bus 𝑖

𝑃𝐷𝑖,𝑚𝑎𝑥 Maximum power supplied by DG

𝑃𝐷𝑖,𝑚𝑖𝑛 Minimum power supplied by DG

xiv

𝑃𝐿𝑖+1 Real load power at bus 𝑖 + 1

𝑃′𝑇,𝐿𝑜𝑠𝑠 Summation of all real power losses after reconfiguration

𝑃𝐷𝐺,𝐿𝑜𝑠𝑠 Real power loss with DG installation

𝑃𝐷 Real power supplied by DG

P𝑖⃗⃗ (k) Personal Best Particle 𝑖

PV Photovoltaic

𝑄𝐿𝑜𝑠𝑠(𝑖,𝑖+1) Reactive power loss from buses 𝑖 to 𝑖 + 1

𝑄𝑖 Reactive power flowing out of bus 𝑖

𝑄𝐿𝑖+1 Reactive load power at bus 𝑖 + 1

𝑄𝑖 Reactive power flowing out of bus 𝑖

𝑄𝐷 Reactive power supplied by DG

RDS Radial Distribution System

𝑅𝑖 Resistance of the line section between buses 𝑖 and 𝑖 + 1

𝑟1 Random numbers between 0 and 1

𝑟2 Random numbers between 0 and 1

SFLA Shuffled Frog Leaping Algorithm

RGA Refined Genetic Algorithm

SOCP Second-Order Cone Programming

SPSO Selective Particle Swarm Optimization

𝑠1 Weighting Factor

𝑠2 Weighting Factor

xv

𝑆 Size of DG unit

SA Simulated Annealing algorithm

TLBO Teaching Learning Based Optimization technique

t Iteration Number

VSI Voltage Stability Index

VLI Voltage Limitation Index

𝑉𝑖 The voltage at bus 𝑖

𝑉𝑚𝑎𝑥 Maximum bus voltage

𝑉𝑚𝑖𝑛 Minimum bus voltage

V𝑖⃗⃗⃗ (k) The velocity of particle 𝑖 at iteration k

V𝑖⃗⃗⃗ (k + 1) Updated Velocity of particle 𝑖

𝑊 Weighting Function

𝑋𝑖 The reactance of the line section between buses 𝑖 and 𝑖 + 1

X⃗⃗ p Position vector of the prey

X⃗⃗ Position vector of the grey wolf

𝑌𝑖 Shunt Admittance at bus 𝑖

Y𝑖⃗⃗⃗ (k) Particle 𝑖 Position at iteration k

Y𝑖⃗⃗⃗ (k + 1) Updated Position of Particle 𝑖

α Alphas Wolves

β Betas Wolves

xvi

δ Delta Wolves

ω Omega Wolves

1

C h a p t e r O n e

1 INTRODUCTION

1.1 ELECTRICAL DISTRIBUTION NETWORK

A power system is a network consisting of generation, transmission, and distribution

systems. It aims to fulfill all the networks loads with the most reliability and efficiency.

The generation system can be classified into traditional energy resources such as

thermal, nuclear and fossil fuels, and renewable energy resources such as wind,

hydroelectric, photovoltaic cells, and biomass. The operation of a power system relies

on a centralized control unit. For the time being, the use of a central power plant is less

needed considering the draining of conventional generation, the high costs of

transmission and distribution systems, the technological developments, and the massive

environmental concerns. The generation is connected to the transmission system

through step-up transformers to increase the voltage of the generated electrical power

hereby reduce its current. At the end of the transmission, the voltage is decreased using

a step-down transformer to be distributed. The distribution system is considered the

final stage of the power system as it delivers the amount of electric power required by

the consumers.

A distribution system is classified, according to the nature of the operation, as radial

and ring (mesh) systems. In a radial system, the main feeders supply the electric power

from the distribution substation to the consumers by the means of sub-feeders and

lateral distributors. It is the most used system since it is simple and has a low initial

cost. Radial feeders are characterized by having unidirectional electricity transportation

from the substation to each load. Therefore, without adding DG units, a radial

distribution system is considered to be passive. It can be considered as active by

inserting DG units to the network, as the power flow becomes bidirectional.

In comparison with the passive network, an active network has fewer power losses

while transmitting electricity as it is generated closer to the loads. Active networks also

have many benefits such as improvement of voltage profile, minimized pollutants

emission, improved power quality, high overall efficiency, and relieved transmission

and distribution congestion. The voltage stability improvement and the reduction of line

2

losses are the most crucial advantages because they determine the location and size of

the DG unit to be inserted in the distribution system. Studies show that poor choice of

size and locations of the DG unit would result in more energy losses than before

inserting the DG.

1.2 DISTRIBUTION NETWORK CONGESTION

Congestion of the distribution system is an issue that can take different forms such as

a sudden increase in the load demand and an outage of transmission lines and

generators.

In order to solve this issue, several methods are used such as Distribution Network

Reconfiguration (DNR) and Optimal Placement and sizing of Distributed Generators

(DGs). Network Reconfiguration is a method that deals with the uncertainty of loads by

opening a few sectionalizing switches and closing a few tie switches. Optimal

Penetration of DGs has many advantages including improvement in the voltage profile,

security, reliability, and minimization of transmission losses by installing DGs in

proximity to the user.

Several algorithms have been proposed for distributed generation placement and

sizing in distribution networks to minimize real power loss and improve the voltage

stability of the power system. However, very few of these algorithms have used network

reconfiguration in parallel with the DG location and size for the maximum system loss

reduction.

1.3 NETWORK RECONFIGURATION

Distribution networks consist of normally closed switches (sectionalized switches)

and normally open switches (tie switches) as shown in Figure 1-1. Tie switches

normally operate as ‘’radial networks ’’and connect between loops type laterals or two

substations. Sectionalize switches connect the line sections between busses. These

switches are used to permit configuration management and alternative protection

functions.

Network reconfiguration is a very important operational issue in the networks;

improve active and reactive power in the network, improve the voltage profile and

3

minimize the losses. It’s obvious that network reconfiguration can considerably enhance

the reliability of the system, security, balance system load and reduce the system losses

of the distribution network within short-time. Within the long-term, reconfiguration will

shave the peak load demand and produce concerning vital economic advantages.

Distribution network switches are reconfigured periodically, therefore, reduce current

losses in line sections, transfer system loads from overloaded feeders to lightly loaded

feeders or to assist service to be renovated when the fault happens.

Figure 1-1: Illustrative Radial Distribution System

1.4 DISTRIBUTED GENERATORS

Distributed generation (DG) penetration has become attractive because of its

technical, economic and environmental benefits, although placement and sizing of DGs

in the distribution network is an issue, as any inappropriate location and size of DGs

may increase the overall system loss. DG is a small-scale power generation that is

Su

bst

ati

on

=Tie Switches =sectionalized switches

4

connected to the distribution system. The Electric Power Research Institute (EPRI)

introduces Distributed generation as a generation from a few kilowatts up to 50MW [1].

As shown in Figure 1-2, Penetration of distributed generators (DGs) and particularly

small roof-top photovoltaic installations are widely accepted due to their different

advantages:

DGs are installed close to the customer on the distribution side, leading to

lower transmission losses.

Optimal placement and size of DGs improve voltage profile, the reliability,

and security of the system.

Most of DGs can be easily moved to other locations as they have short

installation periods.

DGs are classified into two types: Renewable Energy Resources (RES) DGs and

non-RES DGs. On the one hand, some of the RES DGs are only capable of injecting

active power such as photovoltaic cells and fuel cells (P-type) or injecting active and

Figure 1-2: Illustrative penetration of distributed generators

Su

bsta

tio

n

DG

DG

DG

5

reactive power by adding smart inverters to them. Others are capable of injecting active

power and consuming reactive power like induction generators of wind turbines (PQ--

type).

The main advantage of RES DGs is the minimization of the total cost, given that they

are cheaper than conventional DGs, minimizing global warming and reducing system

losses.

On the other hand, some of the Non-RES DGs are capable of injecting both active

and reactive power such as combined combustion technology (PQ+-type), the internal

combustion engine and combined cycle-based DGs. Non-RES DGs are characterized by

minimizing active and reactive losses whereas their main disadvantage is that they have

a small effect on the total generation cost reduction and lead to an increase in global

warming. Reference [2] shows in Figure 1-3 and Figure 1-4 the upcoming penetration of

renewable and non-renewable DG units.

Figure 1-3: Installed buildings sector renewable DG capacity in AEO2017 Reference case

(gigawatts)

6

Figure 1-4: Installed buildings sector non-renewable DG capacity in AEO2017 Reference case

(gigawatts)

1.5 SYSTEM LOSSES MINIMIZATION

As previously declared, among the numerous advantages of distributed generation,

reduction of line losses in the system is one of them. Normally, the minimization of real

power loss draws more attraction for the utilities, because real power loss reduces the

efficiency of transmitting energy to the users. However, reactive power loss is certainly

not less important than real power loss. This is because of the fact that the system

reactive power flow has to be preserved at a specific amount for adequate voltage level.

Moreover, Due to the presence of reactive power, real power is transferred to

customers through distribution and transmission lines. Reduction of System loss by

system reconfiguration and placing distributed generators along the network can be very

valuable if the objective function is to reduce system losses and to improve the

performance of the network.

7

1.6 VOLTAGE PROFILE IMPROVEMENT

In the power system, the voltage level of each bus has to be maintained by the

system operator within specific limits. In order to guarantee that the voltage profiles are

satisfying in distribution systems, many standards have been determined to provide

recommendations or stipulations.

For instance, the American National Standards Institute (ANSI) standard C84.1 has

pledged that voltage variations of the distribution system have to be limited among the

range of -13% to 7% [3]. In fact, most of the electricity companies control the voltage

variations in the range of ±6%. System reconfiguration and placement of distributed

generators are upcoming widely accepted methods for improving distribution systems

voltage profiles. Distributed generators location and size have a considerable impact on

voltage profile improvement.

1.7 THESIS OUTLINE

In order to accomplish the above-mentioned objectives, this thesis is organized into

six chapters:

Chapter 1:

This chapter introduces a brief introduction about the electrical distribution

network, distribution network congestion, network reconfiguration, distributed

generators, system losses minimization, and voltage profile improvement and

thesis outline.

Chapter 2:

A summarized literature review is presented on three main aspects of previous

research works; system reconfiguration, distributed generators placement and sizing,

and system reconfiguration in parallel with distributed generators placement and

sizing.

Chapter 3:

This chapter presents the problem formulation of the objective function and its

constraints. Furthermore, the objective function formulation will be illustrated

after system reconfiguration and distributed generators penetration.

8

Chapter 4:

This chapter illustrates three optimization techniques; Grey Wolf Optimizer

(GWO), Particle Swarm Optimizer (PSO), hybrid GWO-PSO, and mathematical

model and implementation steps of each optimization.

Chapter 5:

This chapter presents all the simulation results for IEEE 33-, IEEE 69-bus radial

distribution system, and 78-bus practical real distribution system. Minimization

of system losses and voltage profile improvement will be highlighted.

Comparison between the performances of the above mentioned three optimizers.

Moreover, Comparison between the results of the above mentioned three

optimizers with those of Fire Work Algorithm, Harmony Search Algorithm,

Genetic Algorithm and Refined Genetic Algorithm in terms of power loss

minimization.

Chapter 6:

Finally, the conclusion of this thesis and future work fields will be presented.

9

C h a p t e r T w o

2 LITERATURE SURVEY

2.1 INTRODUCTION

This chapter presents previous work that had been mentioned in the three areas;

System Reconfiguration, Distributed Generators (DGs) Locating and Sizing, System

Reconfiguration with Distributed Generators Locating and Sizing. Latter research

studies develop optimization techniques, which are classified into meta-heuristic

methods, heuristic methods, hybrid methods and analytical methods to solve single or

multiple objective functions. Meta-heuristic methods such as Particle Swarm Optimizer

(PSO), Genetic Algorithms (GA), Bat Algorithm (BA), Grey Wolf Optimizer (GWO),

and Artificial Bee Colony algorithm (ABC), and deterministic methods such as the

analytical method.

This work proposes a new hybrid GWO-PSO technique to solve system

reconfiguration, DGs sizing and DGs sitting. This hybridization eliminates the

disadvantages and emphasizes the advantages of both techniques simultaneously. Many

researchers use metaheuristic or heuristic methods to determine the optimal allocation

and sizing of DGs using the single optimization technique to solve both location and size

of DG however it may not reach the optimal solution every time especially in large

systems.

Other researchers use sensitivity analysis to find constant placement for DG units to

minimize the number of iterations but do not reach the optimal solution as well. In the

present investigation, minimizing the number of iterations is not considered as the most

important issue compared with the vital concern that the system would be able to

withstand the increase of load demand requirements. The presented work uses this

hybridization to find not only the optimal sizing and sitting of DGs but also the optimal

reconfiguration of the system.

Moreover, this work injects active and reactive power into the system unlike most of

the studies that inject active power only. Some of the results will be compared to a

reference that uses analytical analysis to identify DG sitting.

10

2.2 PREVIOUS RESEARCH WORKS ON SYSTEM

RECONFIGURATION

Several studies use system reconfiguration to both minimize real power loss and

improve the voltage stability of the power system. The metaheuristic and heuristic

methods are used for system reconfiguration only and they include Discrete Artificial

Bee Colony (DABC) algorithm, which is used to maximize system load ability [4],

Cuckoo Search Algorithm (CSA) that is used to minimize active power loss and

maximize voltage magnitude [5], Bacterial Foraging Optimization Algorithm (BFOA)

that is used to minimize real power loss [6], and Fuzzy multi-objective optimization [7].

Authors in [8] have used two algorithms namely Fuzzy Mutated Genetic Algorithm

(FMGA) and Evolutionary Programming (EP) to reconfigure the Radial Distribution

System (RDS) by minimizing the real and reactive power losses and improving the

power quality at the same time. PSO and GA using graph theory are applied to find the

radial configuration for two different distribution networks in order to minimize losses

and improve voltage profile [9]. Improved Binary Particle Swarm Optimization is used

to reconfigure system with capacitor placement for power loss reduction of distribution

system [10]. Heuristic algorithm and optimal power flow (OPF) have been considerably

enhanced to find out optimal system reconfiguration for minimizing total reconfiguration

cost [11].

A computational implementation of an Evolutionary Algorithm (EA) is proposed in

order to solve the reconfiguring problem of radial distribution systems [12]. A mixed-

integer conic programming formulation is presented for the minimum loss distribution

network reconfiguration problem [13]. A simple and fast heuristic approach for solving

the distribution feeder reconfiguration problem with an objective of system losses

reduction and improvement of voltage profile [14].

2.3 PREVIOUS RESEARCH WORKS ON DISTRIBUTED

GENERATORS LOCATING AND SIZING

Another metaheuristic, heuristic and hybrid methods are used to determine the

optimal allocation and sizing of DGs to improve the network performance. Some of

these methods are used to tackle Multi-Objective Function (MOF) such as Moth Flame

11

Optimization (MFO) [15], GWO algorithm in [16] and [17], combination of Genetic

Algorithm (GA), PSO in [18], [19], [20], [21] ,[22],and [23], Improved Non dominated

Sorting Genetic Algorithm–II (INSGA-II) [24] , GA and PSO [25], a novel Chaotic

Symbiotic Organisms Search (CSOS) algorithm [26], Dynamic Adaptation of Particle

Swarm Optimization (DAPSO) [27], and Non-Dominated Sorting Genetic Algorithm II

(NSGA-II) [28].

Others such as Artificial Bee Colony algorithm (ABC) [29] and [30],Selective

Particle Swarm Optimization (SPSO) [31],Hybrid Grey Wolf Optimizer (HGWO)

algorithm [32], PSO algorithm in [33], [34], and [35] is used to minimize power loss. In

[36] the authors came up with the DG location and size using Bat Algorithm (BA).

Optimal DG Placement (ODGP) and sizing are presented using selected four heuristic

algorithms; Cuckoo Search Algorithm (CSA); Gravitational Search Algorithm (GSA);

Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) so as to minimize real

power loss [37]. Simulated Annealing (SA) algorithm and Forward-Backward Sweep

(FW/BW) algorithm are used for determining the optimal placement of multiple

distributed generations in the radial distribution system in order to solve multi-objective

function [38].

GA is presented for a distribution generation (DG) allocation strategy for radial

distribution networks under uncertainties of load and generation so as to minimize

network power loss and node voltage deviation [39]. PSO and Differential Evolutionary

(DE) algorithms are used for optimum placement of DGs in radial distribution systems

with the objective of minimizing real power losses of distribution system by the least

injected power from DGs [40]. New Modified Differential Evolution (MDE) technique is

proposed to find the optimal placement and size of multiple distributed generator units

for minimizing overall system losses[41]. Cuckoo Optimization Algorithm (COA) and

Oppositional Cuckoo Optimization Algorithm (OCOA) are proposed for the optimal of

sizing and sitting of DG in 33-bus and 69-bus radial distribution systems where, the

results are compared with those by Genetic Algorithm (GA), Particle Swarm

Optimization (PSO), GA-PSO algorithm, Bacteria Foraging Optimization Algorithm

(BFOA) [42].

Authors in [43] use Firefly Algorithm (FA) to find out optimal DG sitting in order to

minimize power loss and results are compared with those obtained by Genetic Algorithm

(GA) for IEEE 69-bus radial distribution system and Shuffled Frog Leaping Algorithm

12

(SFLA) for IEEE 33-bus radial distribution system. Authors in [44] present Optimal

location and sizing of the voltage controlled DG units using Big Bang Big Crunch (BB-

BC) method.

The analytical methods are also used for DGs installment so as to reduce losses. They

include two different sensitivity analyses is employed for single DG placement [45].

Efficient Analytical (EA) method for multiple DGs placement [46]. In [47] the

formulated sensitivity factor is used for the determination of the optimal size and sitting

of DG to minimize total power losses by an analytical method without use of admittance

matrix, the inverse of admittance matrix or Jacobian matrix. In [48] Loss Sensitivity

Analysis and Voltage Sensitivity Analysis are used to find single DG allocation and DG

sizes are taken in step size of 0.5 MVA starting from 0.5 MVA till 4 MVA at different

power factors. For maximizing Voltage stability, An analytical approach is used for

multiple DG allocation [49]. Trust-Region Sequential Quadratic Programming (TRSQP)

method is proposed to investigation optimal power flow (OPF) problem for distribution

networks with the integration of DGs with a nonconvex multi-objective problem which

is transformed to the single-objective problem [50]. Monte-Carlo Simulation is presented

to find out optimal DG allocation [51]. A hybrid Decimal Codification Genetic

Algorithm (DCGA) and linear programming (LP) technique are proposed in [52] for the

extension planning of the sub-transmission system in the presence of distributed

generators units.

In order to combine the advantages and avoid the disadvantages of the latter methods,

a hybridization between the metaheuristic method and the analytical approach has been

implemented in [53] which uses Loss Sensitivity Factor (LSF) and Back Tracking Search

Optimization Algorithm (BSOA). In [1] the authors used PSO to determine the optimal

size of distributed generators and Loss sensitivity to determine optimal locations. In [54]

authors present the optimal placement of Electric Vehicles (EVs) on IEEE-33 radial

distribution standard test system using The Particle Swarm Optimization with

Constriction Factor Approach (PSO-CFA) technique with the main objective is to

minimize the total power losses and improve the system voltage profile.

Other researches carried on the effect of optimal DG placement in the deregulated

electricity market to maximize Social welfare [55], minimize location marginal pricing

(LMP) in [56] and [57], congestion management [58], and minimization of total

generation cost [59].

13

2.4 PREVIOUS RESEARCH WORKS ON SYSTEM

RECONFIGURATION WITH DISTRIBUTED GENERATORS

LOCATING AND SIZING

Few studies use different methods to tackle the network reconfiguration problem in

parallel with the DG locating and sizing. Reference [60] proposes Binary Particle Swarm

Optimization (BPSO) for system reconfiguration, Loss Sensitivity Factor (LSF) for

finding DG optimal location, and Harmony Search Algorithm (HSA) for DG sizing.

Reference [61] presents Mixed-Integer Second-Order Cone Programming (SOCP) to

determine network reconfiguration, DG locating, and DG sizing problems. Reference

[62] maximizes system load ability by solving the above mentioned three problems

based on Discrete Artificial Bee Colony (DABC) algorithm. Reference [63] solved the

three problems based on Genetic Algorithm (GA).

Reference [64] solves network reconfiguration and DG sizing the based on Harmony

Search Algorithm (HSA) and relies on sensitivity analysis to determine DG units

allocation. Reference [65] suggests the solution of reconfiguration and DG sizing based

on Fire Work Algorithm (FWA) and DGs allocation based on Voltage Stability Index

(VSI). The authors in [66] proposed a system reconfiguration problem of an unbalanced

distribution network using Fuzzy Firefly algorithm, where the loss sensitivity factor is

used to get the appropriate location of distribution generator and Bacterial Foraging

optimization Algorithm (BFOA) is used to find the rating of DGs.

In [67] the authors developed a modified Teaching Learning Based Optimization

technique (TLBO) to reconfigure the distribution network and find the optimal sizing

and location of DGs so as to minimize the total system loss. In [68] the authors proposed

a technique to solve the DG location and size problem, which they named Meta-

Heuristic Algorithms (MHA) and proposed a Binary Particle Swarm Optimization

algorithm (BPSO) for solving network reconfiguration which cannot be used for solving

the DG sizing problem.

The authors in [69] used Selective Particle Swarm Optimization (SPSO) to solve the

network reconfiguration problem and sensitivity analysis method to determine optimal

size and location. In [70] the authors developed an analytical method which is Voltage

Limitation Index (VLI) to solve network reconfiguration, DG sizing, and sitting. In [71]

the authors proposed the Modified Plant Growth Simulation Algorithm (MPGSA) to

14

solve reconfiguration and DG sizing and used Loss Sensitivity Factor (LSF) to find the

optimal location of DG. In [72] and [73] the authors used Particle Swarm Optimizer

(PSO) to solve reconfiguration and DG sizing and locations of DGs are fixed at buses

with the lowest voltage profile.

Further, utilizing power demand and DG profile data are found using fuzzy C-means

(FCM) clustering algorithm and optimum system configurations are found using a

genetic algorithm (GA) to minimize annual energy losses [74].

15

C h a p t e r T h r e e

3 PROBLEM FORMULATION

3.1 INTRODUCTION

This chapter presents the problem formulation of the presented work. The problem

involves minimizing power loss based on system reconfiguration, DGs sizing and

sitting. Eight case studies will be illustrated to reach the maximum reduction of losses.

Using real power loss as an objective function will not only reduce real power losses but

also will reduce reactive power losses and improve the voltage profile of the system.

This problem will be solved using the proposed Grey Wolf Optimizer (GWO), Particle

Swarm Optimizer (PSO) and hybrid GWO-PSO technique.

3.2 OBJECTIVE FUNCTION

The total losses in the line section connecting buses 𝑖 and 𝑖 + 1 are derived in [75] as

follows:

𝐿𝑜𝑠𝑠𝑒𝑠 =

|𝑉𝑖 − 𝑉𝑖+1|2

𝑅𝑖 − 𝑗 𝑋𝑖

(3-1)

𝑃𝐿𝑜𝑠𝑠(𝑖,𝑖+1) = 𝑅𝑒𝑎𝑙|𝐿𝑜𝑠𝑠𝑒𝑠| (3-2)

𝑄𝐿𝑜𝑠𝑠(𝑖,𝑖+1) = 𝐼𝑚𝑎𝑔|𝐿𝑜𝑠𝑠𝑒𝑠| (3-3)

Where,

𝑉𝑖 is voltage at bus 𝑖.

𝑅𝑖 is the resistance of the line section between buses 𝑖 and 𝑖 + 1.

𝑋𝑖 is the reactance of the line section between buses 𝑖 and 𝑖 + 1.

𝑃𝐿𝑜𝑠𝑠(𝑖,𝑖+1) is real power loss from buses 𝑖 to 𝑖 + 1 .

𝑄𝐿𝑜𝑠𝑠(𝑖,𝑖+1) is reactive power loss from buses 𝑖 to 𝑖 + 1 .

16

3.3 CONSTRAINTS

The problem inequality constraints are given as follows:

1) The voltage at each bus should be within specific limits:

𝑉𝑚𝑖𝑛 ≤ |𝑉𝑖| ≤ 𝑉𝑚𝑎𝑥 (3-4)

Where,

𝑉𝑚𝑎𝑥 is the maximum bus voltage.

𝑉𝑚𝑖𝑛 is the minimum bus voltage.

2) Current at each line should be within specific limits:

|𝐼𝑖,𝑖+1| ≤ |𝐼𝑖,𝑖+1,𝑚𝑎𝑥| (3-5)

Where,

𝐼𝑖,𝑖+1 is the current in the line section between buses i and i+1.

𝐼𝑖,𝑖+1,𝑚𝑎𝑥 is the current’s maximum limit of the line between buses i and i+1.

3) Total generated power at each bus should be less than the summation of total load

and total losses:

Where,

𝑃𝑖 is real power flowing out of bus 𝑖.

𝑃𝐷𝑖 is real power supplied by DG at bus 𝑖.

4) Size of DG units should be within specific limits:

𝑃𝐷𝑖,𝑚𝑖𝑛 ≤ 𝑃𝐷𝑖 ≤ 𝑃𝐷𝑖,𝑚𝑎𝑥 (3-7)

∑𝑃𝐷𝑖

𝑛

𝑖=1

≤∑(𝑃𝑖 +

𝑛

𝑖=1

𝑃𝐿𝑜𝑠𝑠(𝑖,𝑖+1)) (3-6)

17

Where,

𝑃𝐷𝑖,𝑚𝑎𝑥 is maximum power supplied by DG

𝑃𝐷𝑖,𝑚𝑖𝑛 is minimum power supplied by DG.

5) The following balance equations [76] must be applied at each bus :

𝑃𝑖+1 = 𝑃𝑖 − 𝑃𝐿𝑜𝑠𝑠,𝑖 − 𝑃𝐿𝑖+1

= 𝑃𝑖 −𝑅𝑖|𝑉𝑖|2

{𝑃𝑖2 + (𝑄𝑖 + 𝑌𝑖|𝑉𝑖|

2)2} − 𝑃𝐿𝑖+1 (3-8)

𝑄𝑖+1 = 𝑄𝑖 − 𝑄𝐿𝑜𝑠𝑠,𝑖 − 𝑄𝐿𝑖+1

=𝑄𝑖 −𝑋𝑖

|𝑉𝑖|2{𝑃𝑖2 + (𝑄𝑖 + 𝑌𝑖1|𝑉𝑖|

2)2}

−𝑌𝑖1|𝑉𝑖|2 − 𝑌𝑖2|𝑉𝑖+1|

2 − 𝑄𝐿𝑖+1

(3-9)

|𝑉𝑖+1|2 = |𝑉𝑖|

2 +𝑅𝑖2 + 𝑋𝑖

2

|𝑉𝑖|2(𝑃𝑖

2 + 𝑄𝑖′ 2) − 2(𝑅𝑖𝑃𝑖 + 𝑋𝑖𝑄𝑖)

= |𝑉𝑖|2 +

𝑅𝑖2 + 𝑋𝑖

2

|𝑉𝑖|2(𝑃𝑖

2 + (𝑄𝑖 + 𝑌𝑖|𝑉𝑖|2)2)

−2(𝑅𝑖𝑃𝑖 + 𝑋𝑖(𝑄𝑖 + 𝑌𝑖|𝑉𝑖|2)) (3-10)

Where,

𝑄𝑖 is the reactive power flowing out of bus 𝑖 .

𝑌𝑖 is shunt admittance at bus 𝑖.

𝑃𝐿𝑖+1 is real load power at bus 𝑖 + 1.

𝑄𝐿𝑖+1 is reactive load power at bus 𝑖 + 1.

3.4 POWER LOSS USING SYSTEM RECONFIGURATION

The network reconfiguration is used to reduce system losses and to handle the system

during any emergencies such as supplying loads during faults. The solution to the

reconfiguration problem is to divide the system into five loops formed by each tie switch.

𝑃′𝑇,𝐿𝑜𝑠𝑠 is the summation of all real power losses after reconfiguration.

18

𝑃′𝑇,𝐿𝑜𝑠𝑠 =∑𝑃′𝐿𝑜𝑠𝑠(𝑖,𝑖+1)

𝑛

𝑖=1

(3-11)

3.5 POWER LOSS USING DG INSTALLATION

Distributed Generators optimal allocation and sizing will postpone the system

upgrade, and shave peak demand. The real power loss when a DG is installed at any

location in the system is given by:

𝑃𝐷𝐺,𝐿𝑜𝑠𝑠 =𝑅𝑖

𝑉𝑖2(𝑃𝑖

2 + 𝑄𝑖2) +

𝑅𝑖

𝑉𝑖2 (𝑃𝐷

2 + 𝑄𝐷2

− 2𝑃𝑖𝑃𝐷 − 2𝑄𝑖𝑄𝐷)(𝐷

𝐿) (3-12)

Where,

𝑄𝑖 is the reactive power flowing out of bus 𝑖.

𝑃𝐷 is real power supplied by DG.

𝑄𝐷 is reactive power supplied by DG.

𝐷 is the distance from the source (DG) to the DG bus location in km.

𝐿 is the total length of the feeder from source to bus.

19

C h a p t e r F o u r

4 OPTIMIZATION TECHNIQUES

4.1 PROPOSED ALGORITHMS

This chapter proposes three optimization techniques; Grey Wolf Optimizer (GWO),

Particle Swarm Optimizer (PSO), and a new hybrid GWO-PSO technique to solve

system reconfiguration, DGs sizing and DGs sitting problems in parallel. This work

eliminates the disadvantages of using sensitivity analysis to find DG units location and

emphasizes the advantages of solving the three problems simultaneously.

4.1.1 Grey Wolf Optimizer

The Grey Wolf Optimizer (GWO) is a meta-heuristic based optimization algorithm

presented by Mirjalili, Mirjalili, and Lewis in 2014 [77]. Grey Wolf (Canis lupus)

belongs to the Canidae family. The grey wolves prefer to live in a pack, following a

social strict dominant hierarchy. The pack size is 5-12 members. The hierarchy level

decreases from α to ω as shown in Figure 4-1. Alphas (α) are at the highest level of the

hierarchy and are the leaders (male or a female). They take all decisions pertaining to

walking time, hunting, sleeping location, and so on. Betas (β) are at the second highest

level and they help group leaders in making decisions. Delta (δ) wolves have to follow

and submit to alphas and betas. The lowest level gray wolf is omega (ω). Omega wolves

have to submit to all other controlling wolves. The mathematical formulation steps are

simulated by:

I. The social hierarchy of GWO.

II. Encircling prey.

III. Hunting prey.

20

Figure 4-1: Hierarchy of the grey wolf.

4.1.2 Mathematical Model of PSO

I. Social Hierarchy of GWO

α is considered to be the best solution. β and δ are considered to be the second and

third best solution respectively. The rest of the solutions are considered to be ω wolves.

II. Encircling Prey

First, the grey wolf encircles the prey. In [77] the encircling procedure is determined

as follows:

D⃗⃗ = |C⃗ . X⃗⃗ p(t) − X⃗⃗ (t)| (4-1)

X⃗⃗ (t + 1) = X⃗⃗ p(t) − A⃗⃗ . D⃗⃗ (4-2)

Where,

t is the iteration number.

A⃗⃗ and C⃗ are coefficient vectors.

X⃗⃗ p indicates the position vector of the prey.

X⃗⃗ is the position vector of the grey wolf.

FIGURE 2. Hierarchy of grey wolf

21

A⃗⃗ and C⃗ vectors are calculated as follows:

A⃗⃗ = 2a⃗ . r 1 − a⃗ (2-3)

C⃗ = 2. r 2 (4-4)

III. Hunting

After encircling the prey as shown in Figure 4-2, the hunting process of grey wolf is

simulated mathematically by supposing that α, β, and δ have better information about the

position of prey. The prey is considered to be the objective function. α, β, and δ are the

first three best solutions so far to reach the objective function. The rest of the solutions

are considered to be the ω wolves and they will update their location according to the

location of α, β, and δ.

Figure 4-2: Hunting behavior of grey wolves

22

The hunting procedure is given in [77] as follows:

D⃗⃗ α = |C⃗ 1. X⃗⃗ α − X⃗⃗ | (4-5)

D⃗⃗ β = |C⃗ 2. X⃗⃗ β − X⃗⃗ | (4-6)

D⃗⃗ δ = |C⃗ 3. X⃗⃗ δ − X⃗⃗ | (4-7)

X⃗⃗ 1 = X⃗⃗ α − A⃗⃗ 1. (D⃗⃗ α) (4-8)

X⃗⃗ 2 = X⃗⃗ β − A⃗⃗ 2. (D⃗⃗ β) (4-9)

X⃗⃗ 3 = X⃗⃗ δ − A⃗⃗ 3. (D⃗⃗ δ) (4-10)

X⃗⃗ (t + 1) =X⃗⃗ 1 + X⃗⃗ 2 + X⃗⃗ 3

3 (4-11)

4.1.3 Implementation Steps of GWO

In GWO algorithm, there is a number of population wolves that represent a candidate

of solutions. Every wolf has dimension real value vector. Where the dimension is the

number of parameters that have to be optimized. Further, each optimized parameter has

upper and lower limits of the solution space. Figure 4 -3 shows the flowchart of GWO.

The GWO technique can be illustrated in the following steps:

Step 1: Set number of iterations t.

Step 2: Set initial random generation of hunting wolves.

Step 3: Run AC-load flow using MATPOWER software package of Matlab®.

Step 4: Determine the objective function and fitness value of each wolf.

Step 5: Identify the best alpha wolf, the second best beta wolf and the third best Delta

wolf using equation (4-5) to equation (4-10).

Step 6: Update the location of the wolves using equation (4-11).

23


Step 8: calculate the fitness value of each wolf.

Step 9: update alpha, beta, and delta wolves.

Step 10: update the iteration t counter.

Step 11: If the stopping criteria are satisfied go to step 12, else go to step 6.

Step 12: Stop. The alpha wolf is the optimal solution of GWO optimizer.

24

Start

Set iteration(it) = 0

it<Maximum iteration

For i=1:number of search agents

Run AC-load flow with constraints

Success Objective function =

infinity

Calculate objective function

Update alpha, beta and delta


Update alpha, beta , delta and omega positions

Yes

No

Convergence_curve(it)=alpha_score

End

Initialize GWO: Dimension of search agents, number of search agents, maximum iteration, and search

boundaries (alpha, beta and delta) positions

Yes

No

it=it+1

Figure 4 -3: Flowchart of GWO.

25

4.1.4 Particle Swarm Optimizer

The Particle Swarm Optimizer (PSO) is a meta-heuristic-based optimization

technique presented by James Kennedy and Russell Eberhart in 1995 [33]. It is inspired

by the social behavior of birds and fishes as shown in Figure 4-4 and Figure 4-5. The

fundamental idea of PSO is that a group of particles is moving in the search space

looking for the food or best solution mathematically. Each particle has a position and

velocity vector.

Figure 4-4: Group of fish movement in the search space.

Figure 4-5: Group of bird movement in the search space

26

Figure 4-6 shows how the particles update their movements depending on their

experiences, and personal and global best particles.

Figure 4-6: Search point modification by PSO.

4.1.5 Mathematical Model of PSO

The updating procedure of the particle position is given in [33] as follows:

V𝑖⃗⃗⃗ (k + 1) = 𝑊. V𝑖⃗⃗⃗ (k) + 𝑠1𝑟1. (P𝑖⃗⃗ (k) − Y𝑖⃗⃗⃗ (k))

+ 𝑠2𝑟2. (G⃗⃗ (k) − Y𝑖⃗⃗⃗ (k)) (4-12)

Y𝑖⃗⃗⃗ (k + 1) = Y𝑖⃗⃗⃗ (k) + V𝑖⃗⃗⃗ (k + 1) (4-13)

Where,

k is the iteration number.

𝑠1 and 𝑠2are the weighting factors.

𝑟1 and 𝑟2are random numbers between 0 and 1.

𝑊 indicates the weighting function.

V𝑖⃗⃗⃗ (k) is particle 𝑖 velocity at iteration k

a V𝑖⃗⃗⃗ (k + 1) indicates the updated veloca ity of particle 𝑖,

27

Y𝑖⃗⃗⃗ (k) is particle 𝑖 position at iteration k.

Y𝑖⃗⃗⃗ (k + 1) indicates the updated position of particle 𝑖.

P𝑖⃗⃗ (k) is the personal best particle 𝑖.

G⃗⃗ (k) is the global best particle.

4.1.6 Implementation Steps of PSO

In the PSO algorithm, the number of population particles represents a candidate for

solutions. Every particle has a dimensional vector. Where this dimension is the number

of parameters that have to be optimized. Consequently, each optimized parameter has

upper and lower boundaries of the solution space. Further, Figure 4-7 shows the

flowchart of the PSO optimizer.

The PSO technique can be illustrated in the following steps:

Step 1: Set number of iterations k.

Step 2: the Set an initial random population of particles

Step 3: Set random initial velocity of each particle for evaluating of the fitness function.


Step 5: Determine the fitness function and fitness value of each particle. During the first

iteration, the fitness value of each particle becomes its personal best. The best fitness

value among all the personal best particles is denoted as a global best particle.

Step 6: Evaluate the velocity of each particle using equation (4-12).

Step 7: Calculate the new particles positions based on equation (4-13).


Step 9: Updating of personal best particle: Compare the fitness value of each new

particle with its personal best particle. If the fitness value of the new particle is better

than the previous personal best particle, then the personal best particle is updated with

the current value of the new particles.

28

Step 10: Updating of global best particle: If the best personal best particle is better than

the global best particle, then the global best particle is substituted with the best personal

best particle.

Step 11: Update the iteration k counter.


Step 13: Stop. The global best particle is the optimal solution of PSO optimizer.

29

Start

Initialize PSO: Number of decision variables(Swarm dimension), swarm size(population size), maximum

iteration, and swarm positions

For i=1: swarm size



infinity

Calculate objective function (fitness)

Update personal best and set best of personal best as global

best

Yes

No

For it=1: maximum iteration

For i=1: swarm size

Update velocities and positions

Run optimal load flow with constraints


infinity



best

Yes

No

Best fitness(Iteration)=GlobalBest.fitness

End

Figure 4-7: Flowchart of PSO.

30

4.1.7 Hybrid GWO-PSO Optimizer

The system reconfiguration problem consists of discrete line numbers while the DG

allocation problem consists of discrete bus numbers while the DG unit capacities

problem is limited by system constraints.

Instead of relying on sensitivity analysis to find the optimal allocation of DG units, a

code will be formulated to search for an optimal reconfiguration, DG allocation, and

capacity at the same time.

Due to the nature of the nonlinear behavior of our problem, running GWO or PSO

optimizers particularly in large systems, will not lead to the same results at each run and

may not reach the optimal solution. Using the proposed hybridization technique

eventually will solve this problem and the same optimal solution will be obtained at each

simulation.

4.1.8 Implementation Steps of Hybrid GWO-PSO

In Hybrid GWO-PSO algorithm, the number and dimension of searching agents that

represent solutions candidate are the same for both optimization techniques GWO and

PSO. Consequently, each optimized parameter has the same upper and lower boundaries

of the solution space for both optimization techniques.

Further, Figure 4-8 shows a flowchart with the main steps of the hybrid GWO-PSO

optimizer.

The Hybrid GWO-PSO technique can be illustrated in the following steps:

Step 1: Set number of iterations.

Step 2: the Set an initial random population of search agents.

Step 3: Run GWO optimizer.

Step 4: pass the minimized searching space points to PSO optimizer as starting points.

Step 5: Run PSO optimizer.

Step 6: Pass these updated new searching space points back to GWO optimizer.

Step 7: Update the iteration counter.

31


Step 9: Stop. The global best particle is the optimal solution of the Hybrid GWO-PSO

optimizer.

32

Start

Set iteration(it) = 0

it<Maximum iteration




infinity


Update alpha, beta and delta


Update alpha, beta , delta and omega positions

Yes

No

End

Initialize GWO: Dimension of search agents, number of search agents, maximum iteration, and search boundaries

(alpha, beta and delta) positions

Yes

No

Call PSO

Initialize PSO: Swarm dimension=Dimension of search agents of GWO, swarm size=number of search agents, and

swarm positions=Updated GWO positions

For i=1: swarm size



infinity

Calculate objective function (fitness)


best

Yes

No

For i=1: swarm size

Update velocities and positions



infinity



best

Yes

No

it=it+1

Convergence_curve(it)=alpha_score

Convergence_curve_hybrid(it)=GlobalBest.fitness

GWO positions=Updated PSO positions

Figure 4-8: Flowchart of hybrid GWO-PSO.

33

4.2 IMPLEMENTATION FOR SYSTEM RECONFIGURATION

AND DG ALLOCATION

System reconfiguration and DG units’ allocation in appropriate places reduce system

losses, improve the system voltage profile, and reduce distribution lines overloading.

The problem control variables are the system reconfiguration, DGs allocation, and

DGs capacities, which control the fitness function. The complexity of solving those three

variables in parallel lies in the fact that they have been solved them separately using

several optimization techniques or using sensitivity analysis with optimization

techniques.

In the present study, these three problems are dealt with simultaneously by using

GWO, PSO, and the hybrid GWO-PSO technique.

The solution vector V for the three optimization techniques to solve scenario 2 to 8 is

given below:

V={𝑂𝑆1 𝑂𝑆2 𝑂𝑆3 𝑂𝑆4 𝑂𝑆5⏟ 𝑟𝑒𝑐𝑜𝑛𝑓𝑖𝑔𝑢𝑟𝑎𝑡𝑖𝑜𝑛

} (4-14)

V={ 𝐿1 𝐿2 𝐿3⏟ 𝐷𝐺𝑠 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑠

𝑆1 𝑆2 𝑆3⏟ 𝐷𝐺𝑠 𝑠𝑖𝑧𝑖𝑒𝑠 𝑜𝑓 𝑝+

} (4-15)


𝐿1 𝐿2 𝐿3⏟ 𝐷𝐺𝑠 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑠


} (4-16)

V={ 𝐿1 𝐿2 𝐿3⏟ 𝐷𝐺𝑠 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑠


𝑆4 𝑆5 𝑆6⏟ 𝐷𝐺𝑠 𝑠𝑖𝑧𝑖𝑒𝑠 𝑜𝑓 𝑄+

} (4-17)

34


𝐿1 𝐿2 𝐿3⏟ 𝐷𝐺𝑠 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑠


𝑆4 𝑆5 𝑆6⏟ 𝐷𝐺𝑠 𝑠𝑖𝑧𝑖𝑒𝑠 𝑜𝑓 𝑄+

} (4-18)

Where,

𝑂𝑆1, 𝑂𝑆2, 𝑂𝑆3, 𝑂𝑆4, and 𝑂𝑆5are five opened switches corresponding to 69, 70, 71,

72, and 73 tie switches.

𝐿1, 𝐿2, and 𝐿3are locations of DGs units.

𝑆1, 𝑆2, and 𝑆3are sizes of DGs units in MW.

𝑆4, 𝑆5, and 𝑆6are sizes of DGs units in MVar.

35

C h a p t e r F i v e

5 SIMULATIONS & RESULTS

5.1 INTRODUCTION

This chapter shows the validity of the proposed three methods for solving DG units’

installation and network reconfiguration using GWO, PSO, and hybrid GWO-PSO. It is

tested on two IEEE standard radial distribution systems (33-bus, 69-bus) and 78-bus

real distribution system in 6th October city, Egypt. The results are compared to

reference [65]. It is proved that there is a small improvement in loss reduction

percentage when DG locations are more than three units. The number of DGs in each

bus is limited to one. Most of the previous studies focused on the injection of active

power only. In this work, the effect of active and reactive power injection of DG units is

studied. The whole simulations had been implemented on Matlab® software package.

Eight scenarios are considered to demonstrate the performance of the proposed

techniques with two different DG types:

1) Scenario 1: Base Case which is only AC-LF (AC-load flow).

2) Scenario 2: System Reconfiguration.

3) Scenario 3: P type (photovoltaic (PV)) DGs installations before Reconfiguration.

4) Scenario 4: P-type (PV) DGs installations after Reconfiguration.

5) Scenario 5: P-type (PV) DGs installations while Reconfiguration.

6) Scenario 6: PQ+ type (Conventional Combustion Turbine) DGs installations

before Reconfiguration.


after Reconfiguration.


while Reconfiguration.

For all test systems, the minimum and maximum voltage constraints are set at 0.9

p.u. and 1.1 p.u. respectively and the substation voltage is 1 p.u. Voltage decreases from

the source to the end nodes. The voltage profile is improved by adding DG units to the

bus to cover part of the load, sequentially, reducing flowing current and line losses.

36

5.2 IEEE 33-BUS TEST SYSTEM

IEEE 33-bus test system data are given in [Appendix A]. This system base

configuration has 1-32 sectionalized switches normally closed and 33-37 tie switches

are normally opened as shown in Figure 5-1.

Five loops are formed by each tie switch. Tie switches are closed during an

emergency case such as faults to cover unsupplied loads or to reduce system losses.

The total real and reactive power loads are 3.715 MW and 2.3 MVAR respectively.

The system base voltage is 12.66 KV. The limits of real and reactive power injected by

DGs are 0 to 2 MW and 0 to 2 MVAR respectively.

1

2

32

3 4

54

5 6

76

7 8

98

9 10

1110

11 12

12

13

13

14

14

15

1

Su

bsta

tio

n1

32

/1

2.6

6 k

V

1615

16 17

17

18

23

23

24

26

26 27

27

28

28

29

29

30

3130

31 32

32

33

25

1819

19

20

20

21

21

22


LP1LP3

LP2

LP4LP522

24

25

36

34

37

33

35

Figure 5-1: Single line diagram of the 33-bus system.

5.2.1 Active Power Loss Reduction

Table 5-1 shows that there is a small improvement in loss reduction percentage when

DG locations are more than three units. Table 5-2 and Table 5-3 show switches opened

for all the scenarios.

37

The comparison between the results using the hybrid GWO-PSO and the individual

use of GWO and PSO all scenarios are simulated with GWO and PSO results are

provided in Table 5-2 and Table 5-3. The population size is 50 in all techniques and

scenarios. The below tables show that the proposed hybrid technique yields the lowest

iteration numbers in most of the scenarios. The optimal candidate location using two

DG types for scenario 3 to 8 are highlighted in Table 5-2 and Table 5-3.

It can also be observed from Table 5-2 and Table 5-3 that the base case power loss is

202.67 kW which is reduced to 139.55, 71.4571, 58.8769, 50.8905, 11.6570, 25.1486,

and 8.9540 by GWO using scenario 2 to 8 respectively. Also, Power loss is reduced to

139.55, 71.4571, 58.8768, 51.3088, 11.6299, 18.3104, and 10.8466 by PSO using

scenario 2 to 8 respectively. Consequently, Power loss is reduced to 139.55, 71.4571,

58.8768, 50.7175, 11.6299, 16.3000, and 8.9162 by Hybrid GWO-PSO using scenario 2

to 8 respectively.

From Table 5-2 and Table 5-3, Power loss percentage reduction is 31.14%, 64.74%,

70.95%, 74.89%, 94.24%, 87.59%, and 94.42% by GWO using scenario 2 to 8

respectively. Also, Power loss percentage reduction is 31.14%, 64.74%, 70.95%,

Table 5-1: Different penetration of DG units for the 33-bus system.

Scenarios Proposed Hybrid GWO-PSO

Base case P loss(kW) 202.67

One DG DG size in MW (bus) 2.5753(6)

P loss(KW ) 103.9659

reduction% 48.703%

Two DGs DG size in MW (bus) 0.8465(13),1.1585(30)

P loss( kW ) 85.9101

reduction% 57.61%

Three DGs

DG size in Mw (bus) 1.0717(30),1.1003(24),0.7540(14)

P loss( kW ) 71.4571

reduction% 64.74%

Four DGs DG size in MW (bus) 0.96600(6),0.6856(31),0.9688(24), 0.6035(14)

P loss( kW ) 65.9861

reduction% 67.4428 %

38

74.68%, 94.26%, 90.96%, and 94.64% by PSO using scenario 2 to 8 respectively.

Further, Power loss percentage reduction is 31.14%, 64.74%, 70.95%, 74.97%, 94.26%,

91.95%, and 95.60% by Hybrid GWO-PSO using scenario 2 to 8 respectively.

Table 5-2: Comparison of simulation results for P-type DG units of the 33-bus

system.

Scenarios GWO PSO Proposed Hybrid GWO-PSO

Scenario 1

Switches opened 33,34,35,36,37

33,34,35,36,37

33,34,35,36,37

P loss(kW) 202.67

202.67 202.67

Scenario 2


7,9,14,32,37 7,9,14,32,37

P loss( kW ) 139.55

139.55 139.55

reduction% 31.14%

31.14% 31.14%

Iterations 50

50 10

Scenario 3


33,34,35,36,37 33,34,35,36,37

DG size in MW (bus)

1.0709(30), 1.0997(24), 0.7541(14)

1.0714(30), 1.0994(24), 0.7539(14)

1.0717(30), 1.1003(24), 0.7540(14)

P loss( kW ) 71.4571

71.4571 71.4571

reduction% 64.74%

64.74% 64.74%

Iterations 200

100 60

Scenario 4


7,9,14,32,37 7,9,14,32,37

DG size in Mw (bus)

0.9317(8), 1.0670(24), 0.9520(30)

0.9316(8), 1.0681(24), 0.9503(30)

0.9316(8), 1.0678(24), 0.9507(30)

P loss( kW ) 58.8769

58.8768 58.8768

reduction% 70.95%

70.95% 70.95%

Iterations 100

100 100

Scenario 5


11,28,31,33,34 11,28,30,33,34

DG size in MW (bus)

0.9581(7), 1.1257(25), 0.8546(33)

0.8141(8), 0.7540(17), 1.3085(25)

0.9569(7), 0.7529(17), 1.2795(25)

P loss( kW ) 50.8905

51.3088 50.7175

reduction% 74.89%

74.68% 74.97%

Iterations 6000 6000 2000

39

Table 5-3: Comparison of simulation results for PQ+-type DG units of the 33-

bus system.


Scenario 6

Switches opened

33,34,35,36,37 33,34,35,36,37 33,34,35,36,37

DG size in MW (bus)

0.7401+j 0.3533(14), 1.0703+j 0.4869(24), 1.0389+j 1.0118(30)

0.74748+j 0.3501(14), 1.0782+j 0.5212(24), 1.0485+j 1.0209(30)

0.7474+j 0.3501(14), 1.0782+j 0.5212(24), 1.0485+j 1.0209(30)

P loss(KW) 11.6570

11.6299 11.6299

reduction% 94.24%

94.26% 94.26%

Iterations 200

100 100

Scenario 7

Switches opened

7,9,14,32,37 7,9,14,32,37 7,9,14,32,37

DG size in Mw (bus)

0.5314+j 0.3147(12), 0.5030+j 0.1485(16), 1.0403+j 0.9996(30)

1.2444+j 0.6028(21), 1.0413+j 0.5036(24), 0.9281+j 0.9510(30)

0.9316+j 0.4345(8), 0.9321+j 0.9530(30), 1.0547+j 0.5108(24)

P loss(KW) 25.1486

18.3104 16.3000

reduction% 87.59%

90.96% 91.95%

Iterations 600

600 200

Scenario 8

Switches opened

5,11,13,15,26 7,16,21,25,34 5,11,13,15,23

DG size in MW (bus)

1.0818 +j 0.5138(8), 1.1327 +j 0.8311(25), 0.7528 +j 0.5720(32)

0.7826 +j 0.3752(12), 0.9533+j 0.4627 24), 1.1959 +j 1.0738(30)

1.09745+j 0.5593(8), 1.1523+j 0.8047(25), 0.7491+j 0.5620(32)

P loss(KW) 8.9540

10.8466 8.9162

reduction% 94.42%

94.64% 95.60%


40

Figure 5-2 shows the single line diagram of scenario 8 for hybrid GWO-PSO

technique.

1

2

32

3 4

54

5 6

76

7 8

98

9 10

1110

11 12

12

13

13

14

14

15

1

Sub

stat

ion

13

2/1

2.6

6 k

V

1615

16 17

17

18

23

23

24

26

26 27

27

28

28

29

29

30

3130

31 32

32

33

25

1819

19

20

20

21

21

22

=Tie Switches =Reconfigured Lines

LP1 LP3

LP2

LP4LP522

24

25

36

34

37

33

35

DG

2

DG

3

DG

1

Figure 5-2: Single line diagram of a 33-bus system for scenario 8.

In Figure 5-3, Scenario 7 shows that power loss for the PQ+ type DG installation

after reconfiguration is not less than DG installation before reconfiguration. Power loss

reduction for scenario 8 is higher than any other scenario using the three proposed

techniques.

Figure 5-3: Power loss of 33-bus system using three different techniques.

0

50

100

150

200

250

P Lo

ss (k

W)

GWO PSO Hybrid

41

5.2.2 Reactive Power Loss Reduction

From Figure 5-4, Base case reactive power loss is 135.141 kVar, which is reduced to

102.305, 49.3907, 44.2879, 40.1381, 9.6676, 17.8765, and 7.5318 for scenarios 2, 3, 4,

5, 6, 7, and 8 respectively using GWO. Also, reactive power loss is reduced to 102.305,

49.3908, 44.2867, 38.6601, 9.6918, 17.0472, and 8.7988 for scenarios 2, 3, 4, 5, 6, 7,

and 8 respectively using PSO. Consequently, reactive power loss is reduced to 102.305,

49.3921, 44.2868, 38.7201, 9.6926, 14.8282, and 7.4668 for scenarios 2, 3, 4, 5, 6, 7,

and 8 respectively using the proposed hybrid technique. It is observed that some

reconfigured lines increase losses in Q injection.

Reactive power loss percentage reduction is 24.2976%, 63.4525%, 67.2283%,

70.299%, 92.8462%, 86.7719%, and 94.4267% by GWO using scenario 2 to 8

respectively. Also, Reactive power loss percentage reduction is 24.2976%, 63.4525%,

67.2292%, 71.3928%, 92.8284%, 87.3855%, and 93.4891% by PSO using scenario 2 to

8 respectively. Further, Reactive power loss percentage reduction is 24.2976%,

63.4525%, 67.2291%, 71.3483%, 92.8291%, 89.0283%, and 94.4749% by Hybrid

GWO-PSO using scenario 2 to 8 respectively.

Figure 5-4: Reactive loss of 33-bus system using three different techniques.

0

20

40

60

80

100

120

140

160

Q L

oss

(k

Va

r)

GWO PSO Hybrid

42

5.2.3 Voltage Profile Improvement

Voltage profile curves for all scenarios are shown in Figure 5-5, Figure 5-6, and

Figure 5-7 using GWO, PSO, and hybrid GWO-PSO techniques respectively. It is

clearly indicated that the system voltage profile for scenario 8 is the best.

The minimum voltage magnitude of the network is 0.91309 (p.u.), which is improved

to 0.93782, 0.96864, 0.97406, 0.96997, 0.99167, 0.98035, and 0.99154 using scenarios

2, 3, 4, 5, 6, 7, and 8 respectively for GWO technique. Also, the minimum voltage

magnitude of the network is improved to 0.93782, 0.96866, 0.97406, 0.97343, 0.99207,

0.97473, and 0.99208 using scenarios 2, 3, 4, 5, 6, 7, and 8 respectively for PSO

technique. Further, the minimum voltage magnitude of the network is improved to

0.93782, 0.96867, 0.97406, 0.97344, 0.99206, 0.98051, and 0.99165 using scenarios 2,

3, 4, 5, 6, 7, and 8 respectively for hybrid GWO-PSO technique.

Figure 5-5: Voltage profile of a 33-bus system using GWO technique.

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

1.02

1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 3 0 3 1 3 2 3 3

Vo

lta

ge

P

ofi

le (

pu

)

Bus No

scenario 1 scenario 2 scenario 3 scenario 4

scenario5 scenario 6 scenario7 scenario 8

43

Figure 5-6: Voltage profile of a 33-bus system using PSO technique.

Figure 5-7: Voltage profile of a 33-bus system using the hybrid technique.

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

1.02

1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 3 0 3 1 3 2 3 3

Vo

lta

ge

P

ofi

le (

pu

)

Bus No



0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

1.02

1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 3 0 3 1 3 2 3 3

Vo

lta

ge

P

ofi

le (

pu

)

Bus No



44

5.2.4 Methods Performance

In order to show the performance of the proposed hybrid GWO-PSO, some of the

results are compared to different techniques in Table 5-4. It is observed that the

performance of the proposed technique is better than FWA, HSA, GA and Refined

Genetic Algorithm (RGA) in terms of power loss minimization.

Figure 5-8 shows the conversion characteristics of GWO, PSO, and hybrid GWO-

PSO for scenario 8. PSO reaches a reasonable solution but not the optimal. GWO and

hybrid technique reach the optimal solution. It can be observed that the proposed hybrid

technique provides the best improvement for both the optimal solution and convergence

speed.


scenario 8.

0

10

20

30

40

50

60

70

17

01

39

20

82

77

34

64

15

48

45

53

62

26

91

76

08

29

89

89

67

10

36

11

05

11

74

12

43

13

12

13

81

14

50

15

19

15

88

16

57

17

26

17

95

18

64

19

33

20

02

20

71

21

40

22

09

22

78

23

47

24

16

24

85

25

54

26

23

26

92

27

61

28

30

28

99

29

68

P L

oss (

kW

)

Iteration

Hybrid GWO PSO

45

Table 5-4: Comparison of methods performance for the 33-bus system.

Scenarios Proposed

hybrid

GWO-PSO

FWA [65] HSA [65] GA [65] RGA [65]

Scenario

2

Switches

Opened

7,9,14, 32,37

7,9,14,

32,28

7,9,14,

32,37

33,34,9,

36,28

7,9,14,

32,37

P loss( kW )

139.55

139.98 138.06 141.60 139.46

Reduction%

31.14%

30.93% 31.88% 30.15% 31.20%

Vworst(p.u.)

0.93782

0.9413 0.9342 0.9310 0.9315

Scenario

3 Switches

opened

33,34,35,

36,37

33,34,35,

36,37

33,34,35,

36,37

33,34,35,

36,37

33,34,35,

36,37

P loss( kW ) 71.4571 88.68 96.76 100.1 97.60

Reduction% 64.74% 56.24% 52.26% 50.60% 51.84%

Vworst(p.u.) 0.96867

0.9680 0.9670 0.9605 0.9687

Scenario

4

Switches

opened

7,9,14,

32,37

7,9,14,

32,28

7,9,14,

32,37

33,34,9,

36,28

7,9,14,

32,37

P loss( kW ) 58.8769 83.91 97.13 98.36 98.23

Reduction% 70.95% 58.59% 52.07% 51.46% 51.53%


0.9612 0.9479 0.9506 0.9479

Scenario

5 Switches

open

11,28,30,

33,34

7,14,11,

32,28

7,14,10,

32,28

7,34,10,

32,28

7,12,9,

32,27

P loss( kW ) 50.8905 67.11 73.05 75.13 74.32

Reduction% 74.89% 66.89% 63.95% 62.92% 63.33%


0.9713 0.9700 0.9766 0.9691

46

5.3 IEEE 69-BUS TEST SYSTEM

IEEE 69-BUS test system data are given in [Appendix B]. The system base

configuration is having 1-68 sectionalize switches normally closed and 69-73 tie

switches are normally open as shown in Figure 5-9. Five loops formed by each tie

switch.

The total real and reactive power loads are 3.8 MW and 2.69 MVAR respectively.

The system base voltage is 12.66 KV. The limits of real and reactive power injected by

DGs are same as IEEE 33-bus test system.

1

2

32

3 4

54

5 6

76

7 8

98

9 10

1110

11 12

12

13

13

14

14

15

1

Su

bsta

tio

n1

32

/12

.66

kV

1615

16 17

1817

18 19

2019

20 21

21

22

22

23

23

24

24

25

25

26

26

27

34

33

34

35

3332

3231

3130

3029

2928

28

27

47

46

47

48

48

49

49

50

5352

53 54

54

55

55

56

56

57

5857

58 59

6059

60 61

6261

62 63

63

64

64

65

65

66

66

67

67

68

68

6952

51

51

5036

37

38

35

39

40

40

41

41

42

42

43

43

44

44

45

45

46

72

70

73

36

37

38

39

71

69LP1

LP3

LP2

LP4LP5




Table 5-5 shows that there is no improvement in loss reduction percentage when DG

locations are more than three units. Table 5-6 and Table 5-7 show switches opened for

all the scenarios.

47

In order to compare the performance of hybrid GWO-PSO, all scenarios are

simulated with GWO and PSO results are provided in Table 5-6 and Table 5-7. The

population size using all the techniques is 50, 50, 50, 100, 60, 60, and 100 in scenarios 2

to 8 respectively. The proposed hybrid technique shows the least iteration numbers for

most of the scenarios, similar to IEEE 33-bus test system. The optimal candidate

location using two DG types for scenario 3 to 8 are highlighted in Table 5-6 and

Table 5-7.

From Table 5-6 and Table 5-7, base case power loss is 224.9295 kW which is

reduced to 98.5687, 71.4131, 39.01865, 35.5060, 9.59208, 8.47849, and 5.47987 by

GWO using scenario 2 to 8 respectively. Also, Power loss is reduced to 98.5687,

69.6525, 36.7430, 36.74120, 7.17099, 7.73888, and 4.40472 by PSO using scenario 2 to

8 respectively. Consequently, Power loss is reduced to 98.5687, 69.3873, 35.5060,

35.13378, 4.486375, 5.88686, and 3.71327 by Hybrid GWO-PSO using scenario 2 to 8

respectively.

From Table 5-6 and Table 5-7, Power loss percentage reduction is 56.1779%,

68.25%, 82.65%, 84.21%, 95.73%, 96.23%, and 97.56% by GWO using scenario 2 to 8



Base case P loss(kW) 224.9295

One DG DG size in MW (bus)

1.87262(61)

P loss(KW )

83.1679

reduction% 63.02 %

Two DGs DG size in MW (bus)

1.7817(61),0.53114(17)

P loss( kW )

71.6356

reduction% 68.15%

Three DGs

DG size in Mw (bus)

0.5271(11),1.7189(61), 0.3799(18)

P loss( kW )

69.3873

reduction% 69.15%

Four DGs DG size in MW (bus)

0.4055(61),0.3121(12),0.1554(21), 0.0806(2)

P loss( kW )

71.8322

reduction% 68.064%

48



Additionally, Power loss percentage reduction is 56.17%, 69.15%, 84.21%, 84.38%,

98.00%, 97.38%, and 98.34% by Hybrid GWO-PSO using scenario 2 to 8 respectively.

Table 5-6: Comparison of simulation results for P-type DG units of the 69-bus system.


Scenario 1

Switches opened

69,70,71,72,73 69,70,71,72,73 69,70,71,72,73

P loss(kW)

224.9295 224.9295 224.9295

Scenario 2

Switches opened

14,57,61, 69,70 14,57,61, 69,70 14,57,61, 69,70

P loss( kW )

98.5687 98.5687 98.5687

reduction%

56.17% 56.17% 56.17%

Iterations

300 200 30

Scenario 3

Switches opened

69,70,71,72,73 69,70,71,72,73 69,70,71,72,73

DG size in MW (bus)

0.5223(18), 1.7779(61), 0.0257( 68)

0.3992(18), 1.7269(61), 0.4596(66)

0.5271(11), 1.7189(61), 0.3799(18)

P loss( kW )

71.4131 69.6525 69.3873

reduction%

68.25% 69.03% 69.15%

Iterations

300 300 100

Scenario 4

Switches opened

14,57,61, 69,70 14,57,61, 69,70 14,57,61, 69,70

DG size in Mw (bus)

1.4344(61), 0.5670(27), 1.0457(2)

1.4339(61),0.5659(27),0.6146(51)

1.4341(61), 0.5661(27), 0.5374(11)

P loss( kW )

39.0186 36.7430 35.5060

reduction%

82.65% 83.66% 84.21%

Iterations

200 200 50

Scenario 5

Switches opened

14,57,61,69,70 13,56,61,69,70 14,55,61, 69,70

DG size in MW (bus)

1.4339(61), 0.5659(27), 0.5375(11)

1.4339(61), 0.5694(27), 0.6072(51)

1.4340(61), 0.4902(64) , 0.5375(11)

P loss( kW )

35.5060 36.7412 35.1337

reduction%

84.21% 83.66% 84.38%


49

Table 5-7: Comparison of simulation results for PQ+-type DG units of the 69-bus system.


Scenario 6

Switches opened

69,70,71,72,73 69,70,71,72,73 69,70,71,72,73

DG size in MW (bus)

0.0006+j 0.0711(69), 1.6913+j 1.2438(61), 0.7718+j 0.2386(68)

0.4402+j 0.3143(36), 1.7345 +j 1.2383(61), 0.5219 +j 0.3530(17)

0.4530+j 0.3219(68), 1.6917+j 1.2081(61), 0.3180+j 0.2111(21)

P loss(KW) 9.5920

7.1709 4.4863

reduction% 95.73%

96.81% 98.00%

Iterations 300

300 100

Scenario 7

Switches opened

14,57,61, 69,70 14,57,61, 69,70 14,57,61, 69,70

DG size in Mw (bus)

0.0871+j 0.2096(68), 1.4155+j 1.0131(61), 0.5643+j 0.3856(27)

0.6137+j 0.4385(51), 1.4171+j 1.01236(61), 0.5629+j 0.3904(27)

0.5366+j 0.3826(11), 1.4167+j 1.0129(61), 0.5629+j 0.3900(27)

P loss(KW) 8.4784

7.7388 5.8868

reduction% 96.23%

96.55% 97.38%

Iterations 300

300 100

Scenario 8

Switches opened

8,13, 20, 24, 55 12,21,40,53,70 14,16,41, 55,64

DG size in MW (bus)

0.08778+j 0.5722(2), 0.8475 +j 0.5899(11), 1.7651+j 1.2605(61)

1.7298+j 1.2346(61), 0.7649+j 0.5493(50), 0.7791+j 0.5339(43)

0.4319+j 0.2913(21), 0.5897+j 0.4161(11), 1.6770+j 1.1979(61)

P loss(KW)

5.4798 4.40472 3.7132

reduction%

97.56% 98.04% 98.34%

Iterations 10000 10000 3000

50

Figure 5-10 shows the single line diagram of scenario 8 for hybrid GWO-PSO

technique.

1

2

32

3 4

54

5 6

76

7 8

98

9 10

1110

11 12

12

13

13

14

14

15

1

Su

bsta

tio

n1

32

/1

2.6

6 k

V

1615

16 17

1817

18 19

2019

20 21

21

22

22

23

23

24

24

25

25

26

26

27

34

33

34

35

3332

3231

3130

3029

2928

28

27

47

46

47

48

48

49

49

50

5352

53 54

54

55

55

56

56

57

5857

58 59

6059

60 61

6261

62 63

63

64

64

65

65

66

66

67

67

68

68

6952

51

51

5036

37

38

35

39

40

40

41

41

42

42

43

43

44

44

45

45

46

72

70

73

36

37

38

39

71

69D

G1

DG

2

DG

3


LP1

LP3

LP2

LP4LP5


From Figure 5-11, Scenario 7 shows that power loss for PQ+ type DG installation

after reconfiguration is not less than DG installation before reconfiguration and the best

improvement in power loss reduction is for scenario 8 using the three proposed

techniques same as IEEE 33-bus test system.

51



From Figure 5-12, Base case reactive power loss is 102.1456 kVar, which is reduced

to 92.02372, 35.84050, 35.84100, 34.175139, 9.5035477, 8.274102, and 6.54042 for

scenarios 2, 3, 4, 5, 6, 7, and 8 respectively using GWO. Also, reactive power loss is

reduced to 92.02372, 35.05094, 34.61280, 34.672879, 8.020511, 7.55022, and 2.798343

for scenarios 2, 3, 4, 5, 6, 7, and 8 respectively using PSO. Further, the reactive loss is

102.1456 kVar, which is reduced to 92.0237, 34.9527, 34.1729, 34.2659, 7.2140,

6.8968, and 5.6053 using scenarios 2, 3, 4, 5, 6, 7, and 8 respectively using the proposed

hybrid technique. Some reconfigured lines increase losses in Q injection similar to IEEE

33-bus test system.




66.1143%, 66.0554%, 92.148%, 92.6084%, and 97.2604% by PSO using scenario 2 to 8

respectively. Additionally, Reactive power loss percentage reduction is 9.9093%,

65.7815%, 84.2146%, 66.4539%, 92.937%, 93.2348%, and 94.5125% by Hybrid GWO-

PSO using scenario 2 to 8 respectively.

0

50

100

150

200

250

P L

oss

(k

W)

GWO PSO Hybrid

52




Figure 5-15 using GWO, PSO, and hybrid GWO-PSO techniques respectively. It is

indicated that the system voltage profile for scenario 8 is the best same as IEEE 33-bus

test system.


to 0.94947, 0.97891, 0.98134, 0.98133, 0.98669, 0.99288, and 0.99377using scenarios





0.94947, 0.97898, 0.98134, 0.98133, 0.99426, 0.99369, and 0.99486 for scenarios 2, 3,

4, 5, 6, 7, and 8 respectively using the proposed hybrid technique.

53



0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

1.02

1 4 7 1 0 1 3 1 6 1 9 2 2 2 5 2 8 3 1 3 4 3 7 4 0 4 3 4 6 4 9 5 2 5 5 5 8 6 1 6 4 6 7

Vo

lta

ge

Pro

file

(P

U)

Bus No



0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

1.02

1 4 7 1 0 1 3 1 6 1 9 2 2 2 5 2 8 3 1 3 4 3 7 4 0 4 3 4 6 4 9 5 2 5 5 5 8 6 1 6 4 6 7

Vo

lta

ge

Pro

file

(P

U)

Bus No



54



Some of the results are compared to several techniques and this is shown in

Table 5-8. The performance of the proposed technique is better than the other

techniques in terms of power loss minimization similar to IEEE 33-bus test system.


PSO for scenario 8. GWO and PSO reach a reasonable solution but not the optimal.

PSO is faster than GWO with a better solution. It can be observed that the proposed

hybrid technique provides the best improvement for both optimal solution and

convergence speed similar to IEEE 33-bus test system.

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

1.02

1 4 7 1 0 1 3 1 6 1 9 2 2 2 5 2 8 3 1 3 4 3 7 4 0 4 3 4 6 4 9 5 2 5 5 5 8 6 1 6 4 6 7

Vo

lta

ge

Pro

file

(P

U)

Bus No



55

Table 5-8: Comparison of methods performance for the 69-bus system.

Scenarios Proposed

hybrid

GWO-PSO

FWA [65] HSA [65] GA [65] RGA [65]

Scenario

2

Switches

opened

14,57,61, 69,70

14,56,61, 69,70

69,18,13, 56,61

69,70,14, 53,61

69,17,13, 55,61

P loss( kW ) 98.5687 98.59 99.35 103.29 100.28

Reduction% 56.17% 56.17% 55.85% 54.08% 55.42%

Vworst(p.u.) 0.94947 0.9495 0.9428 0.9411 0.9428

Scenario

3

Switches

opened

69,70,71, 72,73

69,70,71, 72,73

69,70,71, 72,73

69,70,71, 72,73

69,70,71, 72,73

P loss( kW ) 69.3873 77.85 86.77 88.5 87.65

Reduction% 69.15% 65.39% 61.43% 60.66% 61.04%

Vworst(p.u.) 0.97898 0.9740 0.9677 0.9687 0.9678

Scenario

4

Switches

opened

14,57,61, 69,70

14,56,61, 69,70

69,18,13, 56,61

69,70,14,53,61

69,17,13, 55,61

P loss( kW ) 35.5060 43.88 51.30 54.53 52.34

Reduction% 84.21% 80.49% 77.20% 75.76% 76.73%

Vworst(p.u.) 0.98134 0.9720 0.9619 0.9401 0.9611

Scenario

5

Switches

opened

14,55,61, 69,70

69,70,13, 55,63

69,17,13, 58,61

10,15,45, 55,62

10,16,14, 55,62

P loss( kW ) 35.1337 39.25 40.30 46.20 44.23

Reduction% 84.38% 82.55% 82.08% 73.38% 80.32%

Vworst(p.u.) 0.98133 0.9796 0.9736 0.9727 0.9742

56

Figure 5-16: Conversion curve of the 69-bus system using three different techniques for scenario 8.

5.4 78-BUS REAL TEST SYSTEM

This system test data are given in [Appendix C]. The system base configuration

consists of having 1-78 sectionalized switches normally closed whereas five switches

are normally opened as shown in Figure 5-17.

The total real and reactive power loads are 48.25 MW and 20.99 MVAR

respectively. The system base capacity is 1.5 MVA and base voltage is 22 KV. The

limits of real and reactive power injected by DGs are 0 to 20 MW and 0 to 10 MVAR

respectively.

0

10

20

30

40

50

60

70

80

1

92

18

3

27

4

36

5

45

6

54

7

63

8

72

9

82

0

91

1

10

02

10

93

11

84

12

75

13

66

14

57

15

48

16

39

17

30

18

21

19

12

20

03

20

94

21

85

22

76

23

67

24

58

25

49

26

40

27

31

28

22

29

13

P L

oss (

kW

)

Iterations

Hybrid GWO PSO

57

1

2

3

2

3

4

5

4

5

6

7

6

7

8

9

8

9

10

11

10

11

1212

1313

1414

15

16

17

16

17

18

15

32

33

32

18

19

20

19

20

21

22

21

22

23

24

23

24

25

26

25

26

27

28

27

28

2929

3030

31

35

33

37

36

34

35

39

38

36

37

41

40

38

39

43

42

40

41

45

44

42

43

47

44

49

48

45

46

51

50

47

48

53

52

49

50

55

54

51

52

75

71

77

76

72

73

79

78

74

75

81

80

76

7782

78

34

1

46

31

57

56

53

54

59

58

55

56

61

60

57

58

63

62

59

60

65

64

61

62

74

67

66

63

64

69

68

65

66

71

70

67

68

73

72

69

70

Substation22 kV




Table 5-5 shows that there is a small improvement in loss reduction percentage when

DG locations are more than three units. Table 5-10 and Table 5-11 show switches

opened for all the scenarios.

58

Table 5-10 and Table 5-11 illustrate the comparison in the same way as test systems

A and B. The population size using all techniques is 50, 60, 60, 100, 60, 60, and 100 in

scenarios 2 to 8 respectively. Tables show that the proposed hybrid technique takes the

least number of iterations for the most of the scenarios, similar to IEEE 33-bus test

system and IEEE 69-bus test system. The optimal candidate location using two DG

types for scenario 3 to 8 are highlighted in Table 5-10 and Table 5-11.

As shown in Table 5-10 and Table 5-11, base case power loss is 421.7192kW, which

is reduced to 209.373, 142.02, 107.68, 88.95, 123.06, 92.96, and 48.93 by GWO using

scenario 2 to 8 respectively. Also, Power loss is reduced to 209.3731, 154.9977, 109.25,

115.61, 101.6025, 90.3978, and 61.5560 by PSO using scenario 2 to 8 respectively.

Further, Power loss is reduced to 209.37, 141.96, 107.6448, 88.95, 89.2335, 88.4952,

and 48.6045 by Hybrid GWO-PSO using scenario 2 to 8 respectively.

From Table 5-10 and Table 5-11, Power loss percentage reduction is 50.3525%,

66.32%, 74.46%, 78.90%, 70.81%, 77.95%, and 88.39% by GWO using scenario 2 to 8





Base case

P loss(kW) 421.7192

One DG DG size in MW (bus)

13.0322(29)

P loss(KW )

223.743

reduction% 46.945%

Two DGs DG size in MW (bus)

13.0318(29), 9.08572(7)

P loss( kW )

174.9617

reduction% 58.51%

Three DGs

DG size in Mw (bus)

9.0871(7),13.0333(29),6.6383(67)

P loss( kW )

141.9624

reduction% 66.33%

Four DGs DG size in MW (bus)

6.6436(67 ),9.2305(6),9.2397(25), 6.14282(49)

P loss( kW )

114.4634

reduction% 72.85%

59

Additionally, Power loss percentage reduction is 50.35%, 66.33%, 74.47%, 78.90%,

78.84%, 79.01%, and 88.4747% by Hybrid GWO-PSO using scenario 2 to 8

respectively.

Table 5-10: Comparison of simulation results for P-type DG units of the 78-bus

system.


Scenario 1

Switches opened

32,34,40,48,63 32,34,40,48,63 32,34,40,48,63

P loss(kW)

421.7192 421.7192 421.7192

Scenario 2

Switches opened

10,28,34,45, 64 10,28,34,45, 64 10,28,34,45, 64

P loss( kW )

209.3731 209.3731 209.3731

reduction%

50.3525% 50.3525% 50.3525%

Iterations

100 100 30

Scenario 3

Switches opened

32,34,40,48,63 32,34,40,48,63 32,34,40,48,63

DG size in MW (bus)

6.6347(67), 9.4411(5), 13.0352(29)

6.6392(67), 8.3307(32), 11.4460(52)

9.0871(7), 13.0333(29), 6.6383(67)

P loss( kW )

142.0250 154.9977 141.9624

reduction%

66.32% 63.24% 66.33%

Iterations

300 300 100

Scenario 4

Switches opened 10,28,34,45, 64 10,28,34,45, 64 10,28,34,45, 64 DG size in Mw (bus)

5.4594(75), 10.5558(3), 6.5508(67)

5.1046(43), 10.3781(16), 6.55061(67)

5.5491(25), 10.3774(16), 6.5501(67)

P loss( kW )

107.6866 109.2588 107.6448

reduction%

74.4648% 74.092% 74.47%

Iterations

300 300 100

Scenario 5

Switches opened

8,23,30,43,64 8,26,34,41,64 8,23,30,43,64

DG size in MW (bus)

15.8913(32), 5.4580(75), 6.5507(67)

9.1835(32), 5.8525(31), 6.9573(25)

6.5505(67), 5.4581(75), 15.8910(32)

P loss( kW )

88.9550 115.6147 88.9550

reduction%

78.90% 72.5849% 78.90%


60

Table 5-11: Comparison of simulation results for PQ+-type DG units of the

78-bus system.


Scenario 6

Switches opened

32,34,40,48,63 32,34,40,48,63 32,34,40,48,63

DG size in MW (bus)

4.2811+j 0.0002 (11), 6.2412+j 4.5606 (3), 12.9952+j 5.6954 (29)

7.0371+j 3.0773(31), 9.0865+j 3.9673 (7), 8.3189+j 3.6267 (25)

13.0438+j 5.7339(29), 6.6376+j 2.8963(67), 9.0840+j 3.9653(7)

P loss(KW)

123.0646 101.6025 89.2335

reduction%

70.81% 75.91% 78.84%

Iterations

300 300 100

Scenario 7

Switches opened

10,28,34,45, 64

10,28,34,45, 64

10,28,34,45, 64

DG size in Mw (bus)

6.5494+j 2.7644(67), 5.4412+j 0.0273(75), 10.5141+j 4.5881(3)

10.3709+j 4.5219(16), 5.1071+j 2.2269(43), 6.5514+j 2.8547(67)

6.5530 +j 2.8517(67), 10.3680 +j 4.5201(16), 5.5462 +j 2.4208(25)

P loss(KW) 92.9695

90.3978 88.4952

reduction% 77.95%

78.56% 79.01%

Iterations 300

300 100

Scenario 8

Switches opened

8,21,42,55,63 8, 20,41,51,63 8,20,42, 55,63

DG size in MW (bus)

5.4565+j 2.3783 (75), 18.2086+j 7.9589(32), 6.6405+j 2.8940(67)

6.6404 +j 2.8937 (67), 16.2463+j 7.0932(24), 5.7228 +j 2.4958(31)

18.4208+j 8.0511(24), 6.6400+j 2.8937(67), 5.4565 +j 2.3779(75)

P loss(KW) 48.93

61.5560 48.6045

reduction% 88.39%

85.40% 88.47%


61

System Losses are very small compared to the load capacity due to the fact that the

loads are commercial and are located directly after the substation directly at the primary

side of the transformer. Therefore the line current is small compared to the secondary

side of the transformer that feeds residential loads. Figure 5-18 shows the single line

diagram of scenario 8 for hybrid GWO-PSO technique.

1

2

3

2

3

4

5

4

5

6

7

6

7

8

9

8

9

10

11

10

11

1212

1313

1414

15

16

17

16

17

18

15

32

33

32

18

19

20

19

20

21

22

21

22

23

24

23

24

25

26

25

26

27

28

27

28

2929

3030

31

35

33

37

36

34

35

39

38

36

37

41

40

38

39

43

42

40

41

45

44

42

43

47

44

49

48

45

46

51

50

47

48

53

52

49

50

55

54

51

52

75

71

77

76

72

73

79

78

74

75

81

80

76

7782

78

34

1

46

31

57

56

53

54

59

58

55

56

61

60

57

58

63

62

59

60

65

64

61

62

74

67

66

63

64

69

68

65

66

71

70

67

68

73

72

69

70

Substation22 kV


DG2

DG1

DG3


62

Figure 5-19 shows that Power loss reduction for scenario 8 is higher than any other

scenario using the three proposed techniques same as IEEE 33-bus test system and

IEEE 69-bus test system.

.



As shown in Figure 5-20, the base case reactive power loss is 572.3431 kVar, which

is reduced to 284.15455, 192.75180, 146.14884, 120.7269, 167.01948, 126.17517, and

66.410482 for scenarios 2, 3, 4, 5, 6, 7, and 8 respectively using GWO. Also, reactive

power loss is reduced to 284.15455, 210.357906, 148.282588, 156.90870, 137.89190,

122.685028, and 83.5420028 for scenarios 2, 3, 4, 5, 6, 7, and 8 respectively using PSO.

Further, reactive power loss is reduced to 284.15455, 192.66676, 146.092127,

120.72697, 121.10492, 120.102971, and 65.964515 using scenarios 2, 3, 4, 5, 6, 7, and

8 respectively using the proposed hybrid technique. Some reconfigured lines increase

losses in Q injection similar to IEEE 33-bus test system.

0

50

100

150

200

250

300

350

400

450

P L

oss

(K

W)

GWO PSO Hybrid

63




74.092%, 72.5849%, 75.9075%, 78.5644%, and 85.4035% by PSO using scenario 2 to

8 respectively. Further, Reactive power loss percentage reduction is 50.3524%,

66.3372%, 74.4747%, 78.9065%, 78.8404%, 79.0155%, and 88.4747% by Hybrid

GWO-PSO using scenario 2 to 8 respectively.




Figure 5-23 using GWO, PSO, and hybrid GWO-PSO techniques respectively. Similar

to IEEE 33-bus test system and IEEE 69-bus test system, the voltage profile for scenario

8 is the best.

0

100

200

300

400

500

600

700

Q L

oss (

kV

ar)

GWO PSO Hybrid

64


to 0.99139, 0.98552, 0.99253, 0.99231, 0.99128, 0.99253, and 0.99598 using scenarios





0.99139, 0.98552, 0.99161, 0.99231, 0.99161, 0.99161, and 0.99598 using scenarios 2,

3, 4, 5, 6, 7, and 8 respectively using the proposed hybrid technique.


0.955

0.96

0.965

0.97

0.975

0.98

0.985

0.99

0.995

1

1.005

1 4 7 1 0 1 3 1 6 1 9 2 2 2 5 2 8 3 1 3 4 3 7 4 0 4 3 4 6 4 9 5 2 5 5 5 8 6 1 6 4 6 7 7 0 7 3 7 6

Vo

lta

ge

P

ofi

le (

pu

)

Bus No



65



0.955

0.96

0.965

0.97

0.975

0.98

0.985

0.99

0.995

1

1.005

1 4 7 1 0 1 3 1 6 1 9 2 2 2 5 2 8 3 1 3 4 3 7 4 0 4 3 4 6 4 9 5 2 5 5 5 8 6 1 6 4 6 7 7 0 7 3 7 6

Vo

lta

ge P

ofi

le (

pu

)

Bus No



0.955

0.96

0.965

0.97

0.975

0.98

0.985

0.99

0.995

1

1.005

1 4 7 1 0 1 3 1 6 1 9 2 2 2 5 2 8 3 1 3 4 3 7 4 0 4 3 4 6 4 9 5 2 5 5 5 8 6 1 6 4 6 7 7 0 7 3 7 6

Vo

lta

ge P

ofi

le (

pu

)

Bus No



66



PSO for scenario 8 where GWO and PSO did not reach the optimal solution. PSO is

faster than GWO. However, GWO is with a better solution than PSO. Furthermore, the

proposed technique is the best, similar to IEEE 33-bus test system and IEEE 69-bus test

system.

Figure 5-24: Conversion curve of the 78-bus system using three different techniques for scenario 8.

0

20

40

60

80

100

120

140

1

98

19

5

29

2

38

9

48

6

58

3

68

0

77

7

87

4

97

1

10

68

11

65

12

62

13

59

14

56

15

53

16

50

17

47

18

44

19

41

20

38

21

35

22

32

23

29

24

26

25

23

26

20

27

17

28

14

29

11

P L

oss

(K

W)

Iterations

Hybrid GWO PSO

67

C h a p t e r s i x

6 CONCLUSIONS & FUTURE WORK

6.1 CONCLUSIONS

This thesis presents system reconfiguration and DGs sitting and sizing for (33-bus,

69-bus) IEEE system and 78- real distribution system by comparing the performance of

the proposed new hybrid GWO-PSO technique to Grey Wolf Optimizer (GWO) and

Particle Swarm Optimizer (PSO) individually. This combination of the two

metaheuristic techniques leads to the elimination of the disadvantages of both

techniques, the minimization of the number of iterations and helps reach the optimal

solution at every simulation.

This thesis also compares the proposed technique to several techniques in terms of

power loss minimization; Fire Work Algorithm, Harmony Search Algorithm, Genetic

Algorithm, and Refined Genetic Algorithm.

The results of this proposed technique have proved to be better than the other

published studies that have used sensitivity factors to solve the DG location problem.

Also, the results show that using the proposed new hybrid technique have proved to be

better than using GWO and PSO individually. It is also concluded that the PSO

technique reaches the optimal solution in a shorter time while the GWO gives a more

accurate solution. Further, it can be observed that using a hybrid GWO-PSO solver will

make use of the advantages of both techniques. The presented hybrid GWO-PSO

technique provides the best improvement for both optimal solution and convergence

speed. Using GWO or PSO optimizers especially in the practical large systems, will not

lead to the same results at each simulation and sometimes may not reach the optimal

solution. However, using the proposed hybridization technique eventually solved this

problem and the same optimal solution obtained at each code simulation.

In the IEEE 33-bus system, active power loss decreases from 202.67 to 8.9540 kW,

reactive power loss decreases from 135.141 to 7.5318 kVar and the minimum voltage

magnitude improved from 0.91309 to 0.99154 (p.u.) using GWO. Also, active power

loss decreases from 202.67 to 10.8466 kW, reactive power loss decreases from 135.141

68

to 8.7988 kVar and the minimum voltage magnitude improved from 0.91309 to 0.99208

(p.u.) using PSO. Further, active power loss decreases from 202.67 to 8.1962 kW,

reactive power loss decreases from 135.141 to 7.4668 kVar and the minimum voltage

magnitude improved from 0.91309 to 0.99165 (p.u.) using hybrid GWO-PSO relative to

the scheme without considering system reconfiguration and DGs placement.

In the IEEE 69-bus system, active power loss decreases from 224.9295 to 5.4798

kW, reactive power loss decreases from 102.1456 to 6.5404 kVar and the minimum

voltage magnitude improved from 0.90919 to 0.99377 (p.u.) using GWO. Also, active

power loss decreases from 224.9295 to 4.4047 kW, reactive power loss decreases from

102.1456 to 2.7983 kVar and the minimum voltage magnitude improved from 0.90919

to 0.99524 (p.u.) using PSO. Further, active power loss decreases from 224.9295 to

3.7132 kW, reactive power loss decreases from 102.1456 to 5.6053 kVar and the

minimum voltage magnitude improved from 0.90919 to 0.99486 (p.u.) using hybrid

GWO-PSO.

In the 78-bus real system, active power loss decreases from 421.7192 to 48.9331

kW, reactive power loss decreases from 572.3431 to 66.41048 kVar and the minimum

voltage magnitude increased from 0.97046 to 0.99598 (p.u.) using GWO. Also, active

power loss decreases from 421.7192 to 61.5560 kW, reactive power loss decreases from

572.3431 to 83.5420 kVar and the minimum voltage magnitude increased from 0.97046

to 0.99161 (p.u.) using PSO. Further, active power loss decreases from 421.7192 to

48.6045 kW, reactive power loss decreases from 572.3431 to 65.9645 kVar and the

minimum voltage magnitude increased from 0.97046 to 0.99598 (p.u.) using hybrid

GWO-PSO.

From the latter results, it is observed that using power loss as an objective function

improves all the other elements of the network. Real power loss as an objective function

will not only reduce real power losses but also will reduce reactive power losses and

improve the voltage profile of the system. Moreover, the combination of network

reconfiguration and optimal placement of DG units has the best improvement compared

to solving each one of them separately.

69

6.2 FUTURE WORK

Up to the presented research in this thesis, more studies could be illustrated in the

future, some of which are:

Compare the results of the proposed objective function with another

objective; minimize reactive power loss, and maximize system load ability.

Study the effect of optimal DG placement in the deregulated electricity

market; Social welfare maximization, minimization of location marginal

pricing (LMP), and minimization of total generation cost.

Investigate the inclusion of congestion management by optimal DG

penetration and system reconfiguration.

Study the impact of capacitor placement only, capacitor placement with

system reconfiguration, capacitor placement in parallel with DG placement,

and capacitor placement with system reconfiguration and DG placement on

the system performance.

Study the effect of optimal capacitor allocation to maximize the cost savings

for different load levels.

70

REFERENCES

[1] S. Kansal, B. B. R. Sai, B. Tyagi, and V. Kumar, “Optimal placement of

distributed generation in distribution networks,” Int. J. Eng. Sci. Technol., vol. 3,

no. 3, pp. 47–55, 2011.

[2] EIA, “Modeling distributed generation in the buildings sectors,” no. November,

p. 7, 2017.

[3] M. Wang and J. Zhong, “A novel method for distributed generation and capacitor

optimal placement considering voltage profiles,” in IEEE Power and Energy

Society General Meeting, 2011.

[4] M. M. Aman, G. B. Jasmon, A. H. A. Bakar, and H. Mokhlis, “Optimum network

reconfiguration based on maximization of system loadability using continuation

power flow theorem,” Int. J. Electr. Power Energy Syst., vol. 54, pp. 123–133,

2014.

[5] T. T. Nguyen and A. V. Truong, “Distribution network reconfiguration for power

loss minimization and voltage profile improvement using cuckoo search

algorithm,” Int. J. Electr. Power Energy Syst., vol. 68, pp. 233–242, 2015.

[6] K. Sathish Kumar and T. Jayabarathi, “Power system reconfiguration and loss

minimization for an distribution systems using bacterial foraging optimization

algorithm,” Int. J. Electr. Power Energy Syst., vol. 36, no. 1, pp. 13–17, 2012.

[7] J. S. Savier and D. Das, “Impact of network reconfiguration on loss allocation of

radial distribution systems,” IEEE Trans. Power Deliv., vol. 22, no. 4, pp. 2473–

2480, 2007.

[8] B. Venkatesh, S. Chandramohan, N. Kayalvizhi, and R. P. K. Devi, “OPTIMAL

RECONFIGURATION OF RADIAL DISTRIBUION SYSTEM USING

ARTIFICIAL INTELLIGENCE METHODS,” 2009.

[9] M. Assadian, M. M. Farsangi, and H. Nezamabadi-pour, “Optimal

Reconfiguration of Distribution System by PSO and GA using graph theory,” pp.

71

83–88, 2007.

[10] M. Sedighizadeh, M. Dakhem, M. Sarvi, and H. H. Kordkheili, “Optimal

reconfiguration and capacitor placement for power loss reduction of distribution

system using improved binary particle swarm optimization,” Int. J. Energy

Environ. Eng., vol. 5, no. 1, pp. 1–11, 2014.

[11] F. V. Gomes et al., “Approach Using Optimum Power Flow and Sensitivity

Analysis for Loss Reduction,” vol. 21, no. 4, pp. 1616–1623, 2006.

[12] J. C. Cebrian and N. Kagan, “Reconfiguration of distribution networks to

minimize loss and disruption costs using genetic algorithms,” Electr. Power Syst.

Res., vol. 80, no. 1, pp. 53–62, 2010.

[13] R. A. Jabr, R. Singh, and B. C. Pal, “Minimum loss network reconfiguration

using mixed-integer convex programming,” IEEE Trans. Power Syst., vol. 27,

no. 2, pp. 1106–1115, 2012.

[14] S. Elsaiah and J. Mitra, “A method for minimum loss reconfiguration of radial

distribution systems,” IEEE Power Energy Soc. Gen. Meet., vol. 2015–

September, 2015.

[15] S. A. Hussien and H. Mahmoud, “Optimal Placement and Sizing of DGs in

Distribution System for Improving Voltage Profile and Reducing the Power Loss

using Moth Flame Optimization Technique,” vol. 6, no. 3, pp. 161–167, 2017.

[16] R. Sanjay, T. Jayabarathi, T. Raghunathan, V. Ramesh, and N. Mithulananthan,

“Optimal allocation of distributed generation using hybrid grey Wolf optimizer,”

IEEE Access, vol. 5, pp. 14807–14818, 2017.

[17] U. Sultana, A. B. Khairuddin, A. S. Mokhtar, N. Zareen, and B. Sultana, “Grey

wolf optimizer based placement and sizing of multiple distributed generation in

the distribution system,” Energy, vol. 111, pp. 525–536, 2016.

[18] A. Sobieh, M. Mandour, E. M. Saied, and M. M. Salama, “Optimal Number Size

and Location of Distributed Generation Units in Radial Distribution Systems

Using Grey Wolf Optimal Number Size and Location of Distributed Generation

72

Units in Radial Distribution Systems Using Grey Wolf Optimizer,” no. February,

2017.

[19] A. Ameli, S. Bahrami, F. Khazaeli, and M. R. Haghifam, “A multiobjective

particle swarm optimization for sizing and placement of DGs from DG owner’s

and distribution company’s viewpoints,” IEEE Trans. Power Deliv., vol. 29, no.

4, pp. 1831–1840, 2014.

[20] A. M. El-Zonkoly, “Optimal placement of multi-distributed generation units

including different load models using particle swarm optimisation,” IET Gener.

Transm. Distrib., vol. 5, no. 7, p. 760, 2011.

[21] M. P. Lalitha, V. C. V. Reddy, and V. Usha, “Optimal DG placement for

minimum real power loss in radial distribution systems using PSO,” J. Theor.

Appl. Inf. Technol., vol. 13, no. 2, pp. 107–116, 2010.

[22] M. R. Haghifam and S. M. M. Larimi, “DG allocation based on modified nodal

price with consideration of loss and reliability using PSO,” 22nd Int. Conf. Exhib.

Electr. Distrib. (CIRED 2013), no. January, pp. 0851–0851, 2013.

[23] W. Krueasuk and W. Ongsakul, “Optimal placement of distributed generation

using Particle Swarm Optimization,” Australas. Univ. Power Eng. Conf. -

AUPEC, p. 6, 2006.

[24] W. Sheng, K. Y. Liu, Y. Liu, X. Meng, and Y. Li, “Optimal Placement and

Sizing of Distributed Generation via an Improved Nondominated Sorting Genetic

Algorithm II,” IEEE Trans. Power Deliv., vol. 30, no. 2, pp. 569–578, 2015.

[25] G. Agnihotri, M. Dubey, and A. K. Bohre, “Optimal sizing and sitting of DG

with load models using soft computing techniques in practical distribution

system,” IET Gener. Transm. Distrib., vol. 10, no. 11, pp. 2606–2621, 2016.

[26] S. Saha and V. Mukherjee, “Optimal placement and sizing of DGs in RDS using

chaos embedded SOS algorithm,” IET Gener. Transm. Distrib., vol. 10, no. 14,

pp. 3671–3680, 2016.

[27] P. M. Branch and P. Moghan, “Customer reliability improvement and power loss

73

reduction in distribution systems using distributed generations,” vol. 5, no. 3, pp.

2313–2317, 2012.

[28] K. Buayai, “Optimal multi-type DGs placement in primary distribution system by

NSGA-II,” Res. J. Appl. Sci. Eng. Technol., vol. 4, no. 19, pp. 3610–3617, 2012.

[29] M. Dixit, P. Kundu, and H. R. Jariwala, “Optimal placement and sizing of DG in

Distribution system using Artificial Bee Colony Algorithm,” 2016 IEEE 6th Int.

Conf. Power Syst., pp. 1–6, 2016.

[30] A. A. Seker and M. H. Hocaoglu, “Artificial Bee Colony algorithm for optimal

placement and sizing of distributed generation,” 2013 8th Int. Conf. Electr.

Electron. Eng., pp. 127–131, 2013.

[31] Sarfaraz, A. Bansal, and S. Singh, “Optimal allocation and sizing of distributed

generation for power loss reduction,” IET Conf. Publ., vol. 2016, no. CP700, pp.

1244–1252, 2016.

[32] K. R. Guerriche and T. Bouktir, “Optimal Allocation and Sizing of Distributed

Generation with Particle Swarm Optimization Algorithm for Loss Reduction,”

Rev. des Sci. la Technol. - RST-, vol. 6, no. July, pp. 59–69, 2015.

[33] K. Bhumkittipich and W. Phuangpornpitak, “Optimal placement and sizing of

distributed generation for power loss reduction using particle swarm

optimization,” Energy Procedia, vol. 34, pp. 307–317, 2013.

[34] M. T. El-mohandes and E. Shaker, “PSO-based Performance Improvement of

Distribution Systems Using DG Sources,” no. 1, pp. 2–6, 2016.

[35] S. C. Srivastava and S. Member, “<A Generalized Approach for DG Planning

and Viability Analysis Under Market Scenario.pdf>,” vol. 60, no. 11, pp. 5075–

5085, 2013.

[36] S. Remha, S. Chettih, and S. Arif, “Optimal placement of different DG units type

in distribution networks based on voltage stability maximization and

minimization of power losses,” Proc. 2016 8th Int. Conf. Model. Identif. Control.

ICMIC 2016, pp. 867–873, 2017.

74

[37] A. Uniyal and A. Kumar, “Comparison of optimal DG placement using CSA,

GSA, PSO and GA for minimum real power loss in radial distribution system,”

2016 IEEE 6th Int. Conf. Power Syst. ICPS 2016, pp. 1–6, 2016.

[38] K. Dharageshwari and C. Nayanatara, “Multiobjective optimal placement of

multiple distributed generations in IEEE 33 bus radial system using simulated

annealing,” IEEE Int. Conf. Circuit, Power Comput. Technol. ICCPCT 2015, no.

2014, 2015.

[39] S. Ganguly and D. Samajpati, “Distributed generation allocation on radial

distribution networks under uncertainties of load and generation using genetic

algorithm,” IEEE Trans. Sustain. Energy, vol. 6, no. 3, pp. 688–697, 2015.

[40] H. Manafi, N. Ghadimi, M. Ojaroudi, and P. Farhadi, “Optimal placement of

distributed generations in radial distribution systems using various PSO and de

algorithms,” Elektron. ir Elektrotechnika, vol. 19, no. 10, pp. 53–57, 2013.

[41] S. Kumar, D. P. K. K. Mandal, and N. Chakraborty, “Optimal allocation of

multiple DG units in radial distribution system using modified differential

evolution technique,” Int. Conf. Control. Instrumentation, Energy Commun.

CIEC 2014, pp. 379–383, 2014.

[42] S. Roy, S. Sultana, and P. K. Roy, “Oppositional cuckoo optimization algorithm

to solve DG allocation problem of radial distribution system,” 2015 Int. Conf.

Recent Dev. Control. Autom. Power Eng. RDCAPE 2015, pp. 44–49, 2015.

[43] K. Nadhir, D. Chabane, and B. Tarek, “Distributed Generation Location and Size

Determination to Reduce Power Losses of a Distribution Feeder by Firefly

Algorithm,” vol. 56, pp. 61–72, 2013.

[44] Y. G. Hegazy, M. M. Othman, W. El-Khattam, and A. Y. Abdelaziz, “Optimal

sizing and siting of distributed generators using Big Bang Big Crunch method,”

Proc. Univ. Power Eng. Conf., 2014.

[45] K. . b Mahmoud, N. . Yorino, and A. . Ahmed, “Optimal Distributed Generation

Allocation in Distribution Systems for Loss Minimization,” IEEE Trans. Power

Syst., vol. 31, no. 2, pp. 960–969, 2016.

75

[46] A. El-Fergany, “Optimal allocation of multi-type distributed generators using

backtracking search optimization algorithm,” Int. J. Electr. Power Energy Syst.,

vol. 64, pp. 1197–1205, 2015.

[47] T. Gözel and M. H. Hocaoglu, “An analytical method for the sizing and siting of

distributed generators in radial systems,” Electr. Power Syst. Res., vol. 79, no. 6,

pp. 912–918, 2009.

[48] K. M. Nitin Singh, Smarajit Ghosh, “Optimal Sizing and Placement of DG in a

Radial Distribution Network using Sensitivity based methods,” Int. Electr. Eng.

J., vol. 6, no. July, pp. 1727–1734, 2015.

[49] S. Kalambe, G. Agnihotri, and A. K. Bohre, “an Analytical Approach for

Multiple Dg Allocation in Distribution System,” vol. 2, no. 3, pp. 39–48, 2013.

[50] K. Liu, W. Sheng, and S. Cheng, “Optimal power flow algorithm and analysis in

distribution system considering distributed generation,” IET Gener. Transm.

Distrib., vol. 8, no. 2, pp. 261–272, 2014.

[51] B. N. Rao and A. R. Abhyankar, “Optimal Placement of Distributed Generator

using Monte Carlo Simulation,” Power Syst. Conf. (NPSC), 2014 Eighteenth

Natl., pp. 1–6, 2014.

[52] H. Shayeghi and A. Bagheri, “Dynamic sub-transmission system expansion

planning incorporating distributed generation using hybrid DCGA and LP

technique,” Int. J. Electr. Power Energy Syst., vol. 48, no. 1, pp. 111–122, 2013.

[53] M. H. Moradi and M. Abedini, “A combination of genetic algorithm and particle

swarm optimization for optimal DG location and sizing in distribution systems,”

Int. J. Electr. Power Energy Syst., vol. 34, no. 1, pp. 66–74, 2012.

[54] M. Dixit and R. Roy, “PSO-CFA based optimal placement of EVs in radial

distribution network for loss minimization,” in Proceedings of 2015 IEEE

International Conference on Electrical, Computer and Communication

Technologies, ICECCT 2015, 2015.

[55] D. Gautam and N. Mithulananthan, “Optimal DG placement in deregulated

76

electricity market,” Electr. Power Syst. Res., vol. 77, no. 12, pp. 1627–1636,

2007.

[56] T. Soares et al., “ANN-Based LMP Forecasting in a Distribution Network with

Large Penetration of DG,” pp. 1–8, 2012.

[57] N. R. Battu, A. R. Abhyankar, and N. Senroy, “Optimal location and quantum of

DG in a multi-feed system with varying LMPs,” 12th IEEE Int. Conf. Electron.

Energy, Environ. Commun. Comput. Control (E3-C3), INDICON 2015, pp. 1–6,

2016.

[58] S. Mohammad, H. Nabavi, and S. Hajforoosh, “Placement and Sizing of

Distributed Generation Units for Congestion Management and Improvement of

Voltage Profile using Particle Swarm Optimization,” pp. 1–6, 2011.

[59] B. Mohan, “Optimal DG placement under Standard Market Design using GA,”

pp. 148–153, 2012.

[60] K. Y. Liu, W. Sheng, Y. Liu, and X. Meng, “A network reconfiguration method

considering data uncertainties in smart distribution networks,” Energies, vol. 10,

no. 5, pp. 1–17, 2017.

[61] H. Xing and X. Sun, “Distributed generation locating and sizing in active

distribution network considering network reconfiguration,” IEEE Access, vol. 5,

pp. 14768–14774, 2017.

[62] H. Mokhlis, G. B. Jasmon, M. M. Aman, and A. H. A. Bakar, “Optimum tie

switches allocation and DG placement based on maximisation of system

loadability using discrete artificial bee colony algorithm,” IET Gener. Transm.

Distrib., vol. 10, no. 10, pp. 2277–2284, 2016.

[63] S. A. Taher and M. H. Karimi, “Optimal reconfiguration and DG allocation in

balanced and unbalanced distribution systems,” Ain Shams Eng. J., vol. 5, no. 3,

pp. 735–749, 2014.

[64] R. S. Rao, K. Ravindra, K. Satish, and S. V. L. Narasimham, “Power Loss

Minimization in Distribution System Using Network Reconfiguration in the

77

Presence of Distributed Generation,” IEEE Trans. Power Syst., vol. 28, no. 1, pp.

1–9, 2012.

[65] A. Mohamed Imran, M. Kowsalya, and D. P. Kothari, “A novel integration

technique for optimal network reconfiguration and distributed generation

placement in power distribution networks,” Int. J. Electr. Power Energy Syst.,

vol. 63, pp. 461–472, 2014.

[66] M. Kaur and S. Ghosh, “Effective Loss Minimization and Allocation of

Unbalanced Distribution Network,” Energies, vol. 10, no. 12, p. 1931, 2017.

[67] A. Ghosh, D. Kumar, and S. R. Samantaray, “Simultaneous reconfiguration with

DG placement using bit-shift operator based TLBO,” 2016 Natl. Power Syst.

Conf. NPSC 2016, 2017.

[68] M. Ali Hormozi, “Optimal Network Reconfiguration and Distributed Generation

Placement in Distribution System Using a Hybrid Algorithm,” Int. J. Energy

Power Eng., vol. 5, no. 5, p. 163, 2016.

[69] S. Nawaz, M. Imran, A. K. Sharma, and A. Jain, “Optimal feeder reconfiguration

and DG placement in distribution network,” Int. J. Appl. Eng. Res., vol. 11, no. 7,

pp. 4878–4885, 2016.

[70] S. Vidyasagar, K. Vijayakumar, R. Ramanujam, D. Sattianadan, and N. Kumar,

“Voltage profile improvement using DG in reconfigured distribution system,”

Int. J. Control Autom., vol. 8, no. 6, pp. 393–410, 2015.

[71] R. Rajaram, K. Sathish Kumar, and N. Rajasekar, “Power system reconfiguration

in a radial distribution network for reducing losses and to improve voltage profile

using modified plant growth simulation algorithm with Distributed Generation

(DG),” Energy Reports, vol. 1, pp. 116–122, 2015.

[72] M. N. M. Nasir, N. M. Shahrin, M. F. Sulaima, M. H. Jali, and M. F. Baharom,

“Optimum network reconfiguration and DGs sizing with allocation

simultaneously by using particle swarm optimization (PSO),” Int. J. Eng.

Technol., vol. 6, no. 2, pp. 773–780, 2014.

78

[73] W. M. Dahalan and H. Mokhlis, “Network reconfiguration for loss reduction

with distributed generations using PSO,” IEEE Int. Conf. Power Energy

(PECon), 2012, no. December, pp. 823–828, 2012.

[74] A. M. Tahboub, V. R. Pandi, H. H. Zeineldin, and S. Member, “Distribution

System Reconfiguration for Annual Energy Loss Reduction Considering

Variable Distributed Generation Profiles,” vol. 30, no. 4, pp. 1677–1685, 2015.

[75] R. D. Zimmerman and C. Murillo-S´anchez, MATPOWER User’s Manual.

[Online]. Available: http://www.pserc.cornell.edu/matpower/

[76] S. Ghosh and K. S. Sherpa, “An Efficient Method for Load Flow Solution of

Radial Distribution Networks,” vol. 2, no. 9, pp. 2094–2101, 2008.

[77] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv. Eng.

Softw., vol. 69, pp. 46–61, 2014.

http://www.pserc.cornell.edu/matpower/

79

APPENDICES

80

Appendix A

Test data for the 33-bus system

Switch

No.

From bus

i

To bus

i+1

Ri, i+1 Xi, i+1 P(KW) Q(kVar)

1 1 2 0.0922 0.0470 100 60

2 2 3 0.4930 0.2511 90 40

3 3 4 0.3660 0.1864 120 80

4 4 5 0.3811 0.1941 60 30

5 5 6 0.8190 0.7070 60 20

6 6 7 0.1872 0.6188 200 100

7 7 8 0.7114 0.2351 200 100

8 8 9 1.0300 0.7400 60 20

9 9 10 1.0440 0.7400 60 20

10 10 11 0.1966 0.0650 45 30

11 11 12 0.3744 0.1238 60 35

12 12 13 1.4680 1.1550 60 35

13 13 14 0.5416 0.7129 120 80

14 14 15 0.5910 0.5260 60 10

15 15 16 0.7463 0.5450 60 20

16 16 17 1.2890 1.7210 60 20

17 17 18 0.7320 0.5740 90 40

18 2 19 0.1640 0.1565 90 40

19 19 20 1.5042 1.3554 90 40

20 20 21 0.4095 0.4784 90 40

21 21 22 0.7089 0.9373 90 40

22 3 23 0.4512 0.3083 90 50

23 23 24 0.8980 0.7091 420 200

24 24 25 0.8960 0.7011 420 200

25 6 26 0.2030 0.1034 60 25

26 26 27 0.2842 0.1447 60 25

27 27 28 1.0590 0.9337 60 20

28 28 29 0.8042 0.7006 120 70

81

Base kV=12.66,

Tie switches=21-8; 9-15; 12-22; 18-33; 25-29

29 29 30 0.5075 0.2585 200 600

30 30 31 0.9744 0.9630 150 70

31 31 32 0.3105 0.3619 210 100

32 32 33 0.3410 0.5302 60 40

33 21 8 2.0000 2.0000 - -

34 9 15 2.0000 2.0000 - -

35 12 22 2.0000 2.0000 - -

36 18 33 0.5000 0.5000 - -

37 25 29 0.5000 0.5000 - -

82

Appendix B


Switch

No.

From bus

i

To bus

i+1


1 1 2 0.0005 0.0012 0 0

2 2 3 0.0005 0.0012 0 0

3 3 4 0.0015 0.0036 0 0

4 4 5 0.0251 0.0294 0 0

5 5 6 0.366 0.1864 2.6 2.2

6 6 7 0.381 0.1941 40.4 30

7 7 8 0.0922 0.047 75 54

8 8 9 0.0493 0.0251 30 22

9 9 10 0.819 0.2707 28 19

10 10 11 0.1872 0.0619 145 104

11 11 12 0.7114 0.2351 145 104

12 12 13 1.03 0.34 8 5

13 13 14 1.044 0.345 8 5.5

14 14 15 1.058 0.3496 0 0

15 15 16 0.1966 0.065 45.5 30

16 16 17 0.3744 0.1238 60 35

17 17 18 0.0047 0.0016 60 35

18 18 19 0.3276 0.1083 0 0

19 19 20 0.2106 0.069 1 0.6

20 20 21 0.3416 0.1129 114 81

21 21 22 0.014 0.0046 5 3.5

22 22 23 0.1591 0.0526 0 0

23 23 24 0.3463 0.1145 28 20

24 24 25 0.7488 0.2475 0 0

25 25 26 0.3089 0.1021 14 10

26 26 27 0.1732 0.0572 14 10

27 3 28 0.0044 0.0108 26 18.6

28 28 29 0.064 0.1565 26 18.6

83

29 29 30 0.3978 0.1315 0 0

30 30 31 0.0702 0.0232 0 0

31 31 32 0.351 0.116 0 0

32 32 33 0.839 0.2816 14 10

33 33 34 1.708 0.5646 19.5 14

34 34 35 0.3978 0.1315 6 4

35 3 36 0.0044 0.0108 26 18.55

36 36 37 0.064 0.1565 26 18.55

37 37 38 0.1053 0.123 0 0

38 38 39 0.0304 0.0335 24 17

39 39 40 0.0018 0.0021 24 17

40 40 41 0.7283 0.8509 1.2 1

41 41 42 0.31 0.3623 0 0

42 42 43 0.041 0.0478 6 4.3

43 43 44 0.0092 0.0116 0 0

44 44 45 0.1089 0.1373 39.22 26.3

45 45 46 0.0009 0.0012 39.22 26.3

46 4 47 0.0034 0.0084 0 0

47 47 48 0.0851 0.2083 79 56.4

48 48 49 0.2898 0.7091 384.7 274.5

49 49 50 0.0822 0.2011 384.7 274.5

50 8 51 0.0928 0.0473 40.5 28.3

51 51 52 0.3319 0.1114 3.6 2.7

52 9 53 0.174 0.0886 4.35 3.5

53 53 54 0.203 0.1034 26.4 19

54 54 55 0.2842 0.1447 24 17.2

55 55 56 0.2813 0.1433 0 0

56 56 57 1.59 0.5337 0 0

57 57 58 0.7837 0.263 0 0

58 58 59 0.3042 0.1006 100 72

59 59 60 0.3861 0.1172 0 0

60 60 61 0.5075 0.2585 1244 888

84

Base kV=12.66,

Tie switches=11-43; 13-21; 15-46; 50-59; 27-65

61 61 62 0.0974 0.0496 32 23

62 62 63 0.145 0.0738 0 0

63 63 64 0.7105 0.3619 227 162

64 64 65 1.041 0.5302 59 42

65 11 66 0.2012 0.0611 18 13

66 66 67 0.0047 0.0014 18 13

67 12 68 0.07394 0.2444 28 20

68 68 69 0.0047 0.0016 28 20

69 11 43 0.5 0.5 - -

70 13 21 0.5 0.5 - -

71 15 46 1 0.5 - -

72 50 59 2 1 - -

73 27 65 1 0.5 - -

85

Appendix C


Switch

No.

From bus

i

To bus

i+1


1 1 2 0.178125 0.241746 685.8921 298.3631

2 2 3 0.016875 0.022902 685.8921 298.3631

3 3 4 0.0525 0.071251 717.069 311.925

4 4 5 0.0125 0.016965 748.2459 325.487

5 5 6 0.0125 0.016965 174.5907 75.94696

6 6 7 0.00875 0.011875 286.8276 124.77

7 7 8 0.02 0.027143 748.2459 325.487

8 8 9 0.03 0.040715 244.427 106.3257

9 9 10 0.06125 0.083127 255.6507 111.2081

10 10 11 0.04375 0.059376 1153.546 501.7924

11 11 12 0.015625 0.021206 236.9446 103.0709

12 12 13 0.055625 0.075492 265.0038 115.2766

13 13 14 0.0175 0.02375 717.069 311.925

14 14 15 0.02 0.027143 685.8921 298.3631

15 3 16 0.008125 0.011027 717.069 311.925

16 16 17 0.0375 0.050894 685.8921 298.3631

17 17 18 0.025 0.033929 436.4768 189.8674

18 1 19 0.13125 0.178128 286.8276 124.77

19 19 20 0.0125 0.016965 729.5398 317.3498

20 20 21 0.03375 0.045804 717.069 311.925

21 21 22 0.00375 0.005089 271.2392 117.989

22 22 23 0.06625 0.089912 654.7152 284.8011

23 23 24 0.06875 0.093305 685.8921 298.3631

24 24 25 0.0525 0.071251 717.069 311.925

25 25 26 0.0125 0.016965 748.2459 325.487

26 26 27 0.0975 0.132324 729.5398 317.3498

27 27 28 0.05375 0.072948 735.7752 320.0622

86

28 28 29 0.00625 0.008482 654.7152 284.8011

29 29 30 0.10625 0.144199 249.4153 108.4957

30 30 31 0.025 0.033929 467.6537 203.4294

31 31 52 0.01875 0.025447 1153.546 501.7924

32 32 15 0.02 0.027143 149.6492 65.0974

33 32 16 0.055 0.074644 202.6499 88.15273

34 24 32 0.015 0.020358 277.4745 120.7014

35 1 33 0.1375 0.186611 685.8921 298.3631

36 33 34 0.03125 0.042412 779.4229 339.0489

37 34 35 0.04 0.054287 286.8276 124.77

38 35 36 0.06375 0.086519 685.8921 298.3631

39 36 37 0.0025 0.003393 748.2459 325.487

40 37 38 0.0375 0.050894 717.069 311.925

41 38 39 0.00875 0.011875 717.069 311.925

42 39 40 0.0875 0.118752 723.3044 314.6374

43 40 41 0.02125 0.02884 654.7152 284.8011

44 41 42 0.005 0.006786 748.2459 325.487

45 42 43 0.05375 0.072948 717.069 311.925

46 43 25 0.0375 0.050894 748.2459 325.487

47 1 44 0.019625 0.026634 748.2459 325.487

48 44 45 0.0225 0.030536 685.8921 298.3631

49 45 46 0.1225 0.166253 748.2459 325.487

50 46 47 0.015 0.020358 685.8921 298.3631

51 47 48 0.09125 0.123842 174.5907 75.94696

52 48 49 0.01375 0.018661 717.069 311.925

53 49 50 0.0625 0.084823 717.069 311.925

54 50 51 0.0375 0.050894 174.5907 75.94696

55 51 52 0.025 0.033929 717.069 311.925

56 1 53 0.03625 0.049197 221.3561 96.2899

57 53 54 0.04375 0.059376 729.5398 317.3498

58 1 55 0.05 0.067858 717.069 311.925

59 55 56 0.025 0.033929 236.9446 103.0709

87

Base kV = 22,

Tie switches=32-15; 24-32; 37-38; 44-45; 59-60

60 56 57 0.01625 0.022054 26.81215 11.66328

61 57 58 0.08125 0.11027 748.2459 325.487

62 58 59 0.00625 0.008482 685.8921 298.3631

63 59 60 0.01125 0.015268 1122.369 488.2305

64 60 61 0.04875 0.066162 1122.369 488.2305

65 61 62 0.005 0.006786 374.123 162.7435

66 62 63 0.015 0.020358 685.8921 298.3631

67 63 64 0.09375 0.127235 1122.369 488.2305

68 64 65 0.06875 0.093305 685.8921 298.3631

69 65 66 0.025 0.033929 1091.192 474.6685

70 66 67 0.015625 0.021206 748.2459 325.487

71 67 68 0.053125 0.0721 143.4138 62.38501

72 68 69 0.0375 0.050894 1122.369 488.2305

73 69 70 0.039375 0.053438 685.8921 298.3631

74 61 54 0.05375 0.072948 1091.192 474.6685

75 1 71 0.15625 0.212058 717.069 311.925

76 71 72 0.12625 0.171342 685.8921 298.3631

77 72 73 0.015 0.020358 1028.838 447.5446

78 73 74 0.06875 0.093305 - -

79 74 75 0.01875 0.025447 - -

80 75 76 0.0625 0.084823 - -

81 76 77 0.1525 0.206968 - -

82 77 78 0.02 0.027143 - -

minimizing power loss in a distribution system by optimal

Documents