LUẬN VĂN CAO HỌC HỆ THỐNG ĐIỆN CẢI THIỆN CHẤT LƯỢNG ĐIỆN ÁP VÀ GIẢM TỔN THẤT TRONG HỆ THỐNG LƯỚI PHÂN PHỐI HÀ NỘI CÓ XEM XÉT ĐẾN NGUỒN PHÂN TÁN VÀ BỘ TỤ

IMPROVING VOLTAGE PROFILE AND REDUCING LOSS IN
THE HANOI POWER DISTRIBUTION SYSTEM
CONSIDERING DISTRIBUTED GENERATIONS AND CAPACITOR
BANKS
CẢI THIỆN CHẤT LƯỢNG ĐIỆN ÁP VÀ GIẢM TỔN THẤT TRONG
HỆ THỐNG LƯỚI PHÂN PHỐI TP. HÀ NỘI
CÓ XEM XÉT ĐẾN NGUỒN PHÂN TÁN VÀ BỘ TỤ
A thesis submitted in partial fulfillment of the requirements for the
Degree of Master of Engineering in
Energy
Asian Institute of Technology
School of Environment, Resources and Development
ii
Acknowledgements
The author would like to express his deepest gratitude to his advisor,
the chairman of the thesis examination committee, Dr. Mithulananthan.
N.
The author would also like to thank Dr. Weerakorn. O and Prof. Sam R.
Shretha for their kindness in serving as members of examination
committee and for their valuable suggestions and advice throughout
this study.
The author wishes to convey his thank to the Electricity of Vietnam for
generously granting the scholarship so that he could pursue this
valuable master degree.
The author also thanks Ha Noi Power Company (HPC) for providing
him the opportunity to pursue this valuable master degree, to the staff
and officers of HPC, for their assistance during the data collection
phase.
Many thanks are also sending to the faculty and staff members of
Energy Program, especially to Mr. Pukar Mahat, for their help during
the study.

The author thanks to all of my Vietnamese classmates, Ninh, Dung,
Minh, Hieu, for their kindly support.
Finally, the author would like to express his deepest appreciation to his
family – his parents, his wife, and his son for their utmost support,
encouragement and understanding during his study in AIT.
iii
Abstract
Power losses and voltage drop are always major concerns to electricity utility.
Study about the methods to reduce power loss and improve voltage profile has
been carried for many years.
Nowadays, the interest in distributed generation around the world is sharply
increasing. DGs are predicted to be a major component of future power
system with all the benefits that come with them. If placed properly, they will
improve the system in various ways, and of course, reduce power loss and
voltage drop. So, it becomes essential to place them in such a way that all
parties associated with them will be benefited.
In this study, the author would like to present the methodology to improve the
utility grid in term of power loss and voltage drop. The method will find out
the optimal DG and capacitor banks in distribution system. There are two
parts in this study. The first one finds the optimal DG size and the location to
minimize real power loss in the system. Different DG types, namely DG
supplying real or reactive power only, DG supplying real power but
consuming proportionate reactive power, are considered to solve the optimal
DG placement problem. In the second part, the capacitor banks are optimally
placed.
The methodology will be carried out with the primary feeders of one
substation in Ha Noi Power Company. These feeders are modeled as 40 bus
system and 62 bus systems.
iv
List of abbreviations

CAPO – Optimal Capacitor placement
DG – Distributed Generation
EVN – Electricity of Viet Nam
E2 – Long Bien distribution substation
HPC – Ha Noi Power Company
kWh – kilowatt hour
kW – kilowatt
kV, V – kilovolt, volt
kVAr – kilovar
kVA – kilovolt ampe
km – kilometer
MW – megawatt
PSS/ADEPT – Power System Simulator – Advance Distribution Engineering
Productivity Tool
pf – power factor
pu – per unit
v
Tables of Contents
Chapter Title
Page
Title i
Acknowledgements ii
Abstract iii
List of abbreviations iv
Tables of Contents v
List of tables ix
1. Introduction 1
1. Mở đầu Error! Bookmark not defined.
1.1 Background 1
1.2 Statement of problem 2

1.3 Objectives of Study 2
1.4 Scope and limitations 3
1.5 Expected results 3
2. Literature review 5
2.1 Distribution network power loss 5
2.2 Distributed Generation 6
2.2.1 Development of Applications DGs 6
2.2.2 Benefits of DG 7
2.2.3 Distribution Generation Technologies 8
2.2.4 Standard Sizes of Distributed Generation on Market 11
2.3 Distribution Power Flow Algorithms 12
2.4 Shunt Capacitor Placement 14
2.5 DG Placement Techniques 15
3. Distribution Load Flow 17
3.1 Distribution System Characteristics 17
3.2 Modeling system elements 18
3.2.1 Line Modeling 18
3.2.2 Load Modeling 19
3.2.3 Shunt Capacitor Modeling 20
3.2.4 Distributed Generation Modeling 20
3.2.5 Distribution Transformer 21
3.2.6 Network Indexing 21
3.3 Load Flow Algorithm 22
3.3.1 Backward Sweep 22
3.3.2 Forward Sweep 22
3.3.3 Stopping Criteria 23
4. Optimal Placement of the Distributed Generation 25
4. Vị trí tối ưu của nguồn phân tán Error! Bookmark not defined.
vi
4.1 Optimal DG Placement to Reduce Loss 25

4.2 Optimal DG placement when DG Supply Real Power Only 25
4.3 Optimal DG placement when DG Supply Reactive Power Only 27
4.4 Optimal DG placement when DG supply P and consumes Q 27
5. Methodology 29
5. Phương pháp luận Error! Bookmark not defined.
5.1 Overview of methodology 29
5.2 Optimal DG placement to reduce system real power loss 30
Software Tools 31
5.3 Optimal Capacitor Placement Using PSS/ADEPT Application 33
5.3.1 About the PSS/ADEPT Software 33
5.3.2 Analyze Network in PSS/ADEPT 33
5.3.3 Load Flow Analysis in PSS/ADEPT 34
5.3.4 Calculating Capacitor Placement 35
5.4 System data 37
6. Results and conclusions 38
6.1 Optimal Distributed Generation 38
6.1.1 Results of Radial Feeder 983-E2 38
1. Type 1: DG supply real power only: 38
2. Type 2: DG supply real power and consume reactive power: 40
6.1.2 Results of Radial Feeder 979-E2 42
1. Type 1: DG supply real power only: 42
2. Type 2: DG supply real power and consume reactive power: 45
6.2 Optimal Capacitor Placement 47
6.2.1 Results of Radial Feeder 983-E2 48
1. Results of Load flow analysis 48
2. Results of CAPO 48
6.2.2 Results of Radial Feeder 979-E2 50
1. Results of Load flow analysis 50
2. Results of CAPO 51
7. Conclusions 54

7.1 Conclusions 54
7.2 Further study 54
References 55
Appendix A Phụ lục A
Appendix B Phụ lục B
Appendix C Phụ lục C
vii
List of figures
Figure Title Page
Figure 3-1: Model of a line section for single phase (π) representation. 18
Figure 3-2: Model of a line section. 19
Figure 3-3: General form of 3-phase transformer model. 21
Figure 3-4: Numbering of buses and branches. 22
Figure 3-5: Basic steps in the iterative algorithm. 24
Figure 5-1: The flow chart of works. 30
Figure 5-2: Flow chart to find the optimal DG size and the location to reduce
loss in the system 31
Figure 6-1: Optimal DG size at each bus type 1 case 983-E2 38
Figure 6-2: Real power loss when DG installed at each bus with optial size
type 1 case 983-E2 39
Figure 6-3: Voltage profile type 1 case 983-E2 40
Figure 6-4: Optimal DG size for type 2 case 983-E2 40
Figure 6-5: Real power loss when DG installed at each bus with optial size
type 2 case 983-E2 41
Figure 6-6: Voltage profile before and after DG installed type 2 case 983-E2
42
Figure 6-7: Optimal DG size at each bus type 1 case 979-E2 43
Figure 6-8: Real power loss when DG installed at each bus with optial size
type 1 case 979-E2 44
Figure 6-9: Voltage profile before and after DG installed type 1 case 979-E2

45
Figure 6-10: Optimal DG size at each bus type 2 case 979-E2 46
Figure 6-11: Real power loss when DG installed at each bus with optial size
type 2 case 979-E2 46
Figure 6-12: Voltage profile before and after DG installed type 2 case 979-E2
47
Figure 6-13: Voltage profile of feeder 983E2 before capacitor placement -
plotted by PSS/ADEPT 48
Figure 6-14: Voltage profile before and after capacitors placement by CAPO
50
Figure 6-15: Voltage profile of feeder 979E2 before capacitor placement 51
viii
Figure 6-16: Voltage profile of feeder 979E2 before and after capacitor
placement by CAPO 52
ix
List of tables
Table Title Page
Table 2-1: Available capacities of DG for various technologies 11
Table 6-1: Ranking of buses for loss reduction type 1 case 983-E2 ( Appendix
B) 39
Table 6-2: Ranking of buses for loss reduction type 1 case 983-E2 (Appendix
B) 41
Table 6-3: Ranking of bus for loss redution type 1 case 979-E2 44
Table 6-4: Ranking of bus for loss redution type 2 case 979-E2 46
Table 6-5: Compare the results of case 983-E2 52
Table 6-6: Compare the results of case 979-E2 52
1
1. Introduction
1.1 Background
Electric power distribution system engineering has been designed to deal with

problems related to the rapidly expanding distribution system, load
management and reduction of distribution loss. Voltage drop and power loss
are major concerns for utilities as they limit the load ability of feeders and
reduce revenue. All utility have found and applied the optimal method to
improve voltage drop and power loss.
Traditionally, there are many options available for reducing loss and voltage
drops such as network reconfiguration, load balancing, introduction of higher
voltage level, reconductoring, and capacitor installation. Among them,
capacitor placement is one of most economical options for loss reduction,
especially in distribution systems of developing countries.
Recently, the application of small generators, called Distributed Generation
(DG) has been considered to address the issue of loss reduction in distribution
system. DGs have some advantages to replace capacitor banks in order to
improve voltage profile and power loss. DGs can supply both real and
reactive power. DGs can also keep the voltage at some buses in stable by
adjusting reactive power smoothly and automatically. However, DGs also
have some disadvantages such as coordination protection, high initial cost…
This would be lead to a question that which option would be the best among
all the alternatives available?
Distributed Generation (DG) includes the application of small generators,
typically ranging in capacity from few kW to as high as 10,000 kW, scattered
throughout a power system, to provide the electric power needed by electrical
customers[1]. Distributed Generation (DG) uses small-scale power generation
technologies to generate electricity in close proximity to its utilization point.
DG technology portfolios typically include small or micro hydro power
plants, wind turbines, photovoltaic, fuel cells, reciprocating engines,
combustion gas turbines and micro turbines.
This study presents the methodology to find the best solution for improving
voltage drops and power loss in distribution system. The first part presents a
method using MATLAB software to develop a program that finds optimal DG

sizes and the locations to take part in the distribution networks in order to
improve voltage profile and minimize loss. The second part uses PSS/ADEPT
software to find the optimal capacitor banks placement
2
1.2 Statement of problem
Hanoi Power Company (HPC) is a utility that serves almost 450,000
customers in seven urban districts and seven rural districts of Ha Noi. In the
recent years, HPC had implemented a lot of management methods and
upgrading projects to reduce voltage drop, technical and non-technical power
loss However, regardless of all the attempts made, losses are running at
unacceptable percentage, about 11%.
At presently, the growth of energy consumption in Ha Noi is very high. In
order to meet customer demand, Ha Noi distribution network needs to be
planned to improve quantity and quality of electricity supply to meet social
and economical development of Ha Noi in coming years.
Two effective options to reduce power loss and voltage drops are using shunt
capacitors to compensate reactive power in primary feeders and using DG as
an alternative. Now there is a few shunt capacitors installed in primary
feeders. On the other hand, distributed generation is a new term with Viet
Nam, so there is no DG installed in HPC network at that time.
For a long time, capacitor bank is contributed in the whole distribution system
operation. However, with number of benefits that installation of DGs in the
distribution system can bring [see 2.3.2], the DG alternative becomes very
attractive to achieve the objectives.
In recent years, many researches and studies show that DGs provide various
benefits to the system when they are properly planed and operated. On the
other hand, improper placement and operation will degrade the power quality,
reliability and control of the power system, also may lead to even higher loss.
Thus, feasibility studying about DGs should be also carried out as good
options in planning period.

1.3 Objectives of Study
The main objectives of this study are to find the optimal solution to reduce
power loss and improve voltage profile of HPC distribution system.
PSS/ADEPT software is used to find optimal capacitor placement, and a
MATLAB program will be used to find optimal DG size and location so as to
minimize power loss and voltage drop. Then the author will evaluate and
compare the results of two options.
Specifically, objectives of the study are as follows:
1. To calculate power loss and voltage drops of the existing main primary
distribution system of HPC.
2. To develop a program that find optimal DG size and location to
minimize power loss and improving voltage profile in the network.
3
3. To find optimal capacitor placements and sizes in primary feeders for
improving voltage profile and reducing power loss.
4. To evaluate and to compare the solutions, i.e. capacitor placements and
DG placements and give some conclusions and recommendation on the
most appropriate option for power loss reduction.
1.4 Scope and limitations
This study focuses on the optimal method in installation DG into distribution
system. This also implements the reduction of power loss and voltage drop by
placing shunt capacitor banks in the primary feeders.
The distribution system in Hanoi has been rehabilitated. Some distribution
substation has been reconfigured in primary feeders. Therefore, this study will
deal with one distribution substation that has high power loss and poor
voltage profile in primary feeders.
Because of time limited, and the secondary system of the distribution
substation is too large, so this study considers the matter only in the primary
system.
The limitations of work for this study can be summarized as follows:

1- Existing transmission and distribution system in Hanoi Power
Company will be used in the study
2- This study is implemented in one of distribution substation of Hanoi
Power distribution network.
3- Only power balance constraint is considered.
4- DG in this study implies small size generation at the distribution
level and these DGs must have ability to generate reactive power.
1.5 Expected results
This thesis studies the methods how to reduce power loss and voltage drop in
distribution system by compensating reactive power using shunt capacitor
banks and DGs. The expected results are the followings:
1. Examination of one existing distribution substation loss and capabilities
of reduction in power loss as well as improvement of voltage profile.
2. Desirable locations and sizes of DGs.
3. Desirable locations and sizes of shunt capacitors.
4. Expected loss reduction, voltage profile improvement.
With the results will be obtained, the author expect that HPC will put these
methods in operation to reduce power loss and to improve voltage profile.
At present, the author viewpoint is that the optimal capacitor placement
method is more feasible than the optimal DG placement method. Further, the
4
author expects that the second method will be applied in HPC network in near
future.
5
2. Literature review
Reducing voltage drop and power loss is one of the biggest challenges in
electric power utility of developing countries. The electricity demand is
growing sharply. As a result of this, a poor voltage profile as well as higher
loss can be reality if no proper measures are put in placed. While the utilities
are not having sufficient funds for expansion their grid and source, it is

necessary to reduce power losses.
This chapter will review the literature of power loss and voltage drop
distribution network and methods to reduce them. Distributed generation
development and application, and load flow in distribution system are also
reviewed in this chapter.
2.1 Distribution network power loss
Electric power distribution system is the part of power system that delivers
energy directly from suppliers to customers operating at several voltage levels
[1]. In the situation of increasing scarcity of resource and escalating cost of
energy supply, the important of energy conservation and reduction in loss in
the system is felt vital.
Power system loss reduction is one of principle ways of achieving
conservation in the electric power supply sector. In the process of delivering
electricity to customers, loss is occurred in generation, transmission, and
distribution system. In the literature, there are many reports that discuss about
loss. In distribution system, the grid is almost in radial so the level voltages,
load density are the main factors concerning about the system loss.
Distribution network power loss includes both technical and non-technical
loss. In this study, only the first one is considered. Technical losses are natural
and consisting mainly of power dissipation in the electrical system
components such as transmission lines, power transformers, measurement
systems, etc. It is possible to compute and control and provided the power
system in question consists of known quantities of loads. Using computation
tools for calculating power flow, loss and equipment status in power systems
has been developed in nearly years. Improvements in information technology
and data acquisition have made the calculation and verification easier.
Losses depend on various factors, such as load density, inadequate designs,
and improper maintenance, etc. There may be significant proportion of
unaccounted loss due to inaccuracy in meters, flat rate tariff structure, error of
customer billing, pilferage of energy and unauthorized use of electricity. Most

of them fall under non-technical losses. The reduction in system loss can
6
result in substantial saving in energy as well as increase in the power capacity
supply.
Generally, the loss in distribution system is higher in comparing with
transmission system. According to EVN report, in 2004, the shares of loss are
technical distribution loss 61%, non-technical loss 5%, and transmission loss
34%.
Traditionally, there are number of solutions to reduce distribution system
losses, such as network reconfiguration, load balancing, introduction of higher
voltage level, reconductoring, and capacitor installation, etc. Among these,
capacitor placement is considered as one of the most economical option.
Nearly years, distributed generation has been considered as the good
alternative with various benefits.
2.2 Distributed Generation
2.2.1 Development of Applications DGs
Trend in supplying electricity, for few decades now, is mainly through the
hierarchical systems include generation, transmission, and distribution
system. In recent year, with the development of new technologies, many types
of Distribution Generation are successfully applied, mainly in developed
countries.
Although having lot of challenges, more and more DGs are coming to the
market basically with electricity liberalization. This is clearly indicated by
increasing share of DG in electricity market. In the United State, for example,
DG resources or on-site generation cover more than 30% of installed capacity.
This trend is likely to accelerate as deregulation of electric power markets is
materialized. According to International Council on Large Electric System
(CIGRE) report, contribution of DGs in Denmark and the Netherlands has
reached 37% and 40% respectively [29]. Electric Power Research Institute’s
(EPRI) study forecasts that 25% of new generation will be distributed by 2010

and similar study by Natural Gas Foundation believes that the share of DG in
news generation will be 30% by the year 2010 [33].
Energy policies worldwide are encouraging installation of DGs in both
transmission and distribution networks along with large scale power
generating plants. But, the fact is that the distribution systems were not
planned to support the installation of these power generating units in it. Many
studies have reported that this type of integration may create technical and
safety problems [34].
In the literature, a large number of terms and definitions are used to designate
distributed generation. For instance, in Anglo-Saxon countries the term
“embedded generation” is often used, in North American countries the term
7
“dispersed generation”, and in Europe and parts of Asia, the term
“decentralized generation” are used to denote the same type of generation.
This thesis will follow the general definition proposed: Distributed generation
is an electric power source connected directly to the distribution network and
usually at the customer side.
Further, distributed generation may be defined as a generating resource, other
than central generating station, that is placed close to load being served,
usually at customer side. It may be connected to the supply side or demand
side of meter. It may also be defined as any modular generator located at or
near the load center. It can be renewable source like micro hydro, solar, wind
and photovoltaic or fuel based like fuel cells and micro turbine. It may be
understood as small-scale electricity generation.
Among various benefits, DGs can offer significant lower cost and higher
reliability than the electricity grid. Grid along with DGs can result in better
performance than either could alone [3]. In wholesale power market, customer
owned DGs can reduce prices volatility by responding to the extreme price
swings [27]. Despite all these benefits, lack of technology maturation,
interconnection requirements, permitting and siting, and building and

electrical codes are hampering the development of the DGs and these barriers
are expected to be overcome by the restructuring of electricity industry [28].
DG’s penetration in power system is increasing, and in the future power
system might look like the one shown in Figure 2.2 (Source: Distributed
Utility Associates).
2.2.2 Benefits of DG
• Development in distributed generation technologies, constraints on
construction of new transmission lines, increasing customer demand for
highly reliable electricity, electricity market liberalization, and concern
about climate change [26].
• It provides a relatively low capital cost compared to its central counterpart
in response to incremental increases in power demand and avoid
transmission and distribution (T&D) capacity upgrades by locating power
where it is most needed [36].
• DG can defer capital cost of new transmission and distribution lines, reduce
transmission and distribution line loss, and improve power quality and
system reliability [32].
• DG can provide standby power during interruption and can significantly
increase the efficiency of energy utilization and thus may reduce global
emissions at lower costs [38], [39].
• It can result in peak shaving helping the energy supplier to reduce the cost
of generation and also provide ancillary service [38], [39].
8
• DG may level the load curve, improve the voltage profile across the feeder
and reduce the loading of the branches [40].
• Central generating companies can reduce load on their transmission
equipment, provide local voltage support and increase economical benefits
with DG [41].
• Government can use them to introduce competition in the electricity supply
market and thus create price reduction [41].

• DG can reduce wholesale power price by supplying power to the grid,
which leads to reduction in demand [42].
• With the distributed generation, we can have a micro-grid operation by
isolating a particular part of the system from the utility supply and thus can
prevent the sensitive equipments from voltage dips [43].
• Distributed Generation along with the back up support from the grid can
enhance the system reliability and security [44].
2.2.3 Distribution Generation Technologies
There are different types of DGs from the constructional and technological
points of view. The different types of distributed generation are shown in
Figure 2-1. Some suitable DGs for HNC network are fuel cells, micro
turbines, photovoltaic, and reciprocating engines.
Figure 2-2 shows the present and future of power generation.
Figure 0-1: Distributed generation types and technologies.
9
Figure 0-2: Present and the future of power generation.
a Micro-Turbines
Micro-turbine technologies are small capacity combustion turbines, which can
operate using natural gas, propane, and fuel oil. In a simple form, they consist
of a compressor, combustor, small turbine, and generator. There are different
types of micro-turbines according to their operation such as gas turbines and
combustion turbines. Gas turbines are combustion turbines that produce high
temperature and high-pressure gas. Gas turbines are always used above one
MW, but nowadays we can generate electricity through small modular with a
micro-gas turbine of 200 kW sizes [3]. They have the capability of operating
independent of the grid and have black start capability when supplied with
batteries [27]. Like fuel cells and reciprocating engines, they can also regulate
the bus voltage at which they are connected.
* Advantages:
− Durability and low maintenance

− Compact, easy to install, easy to repair.
− Lower capital cost and low electricity cost compared to any other DG
technologies
* Disadvantages:
− Not a quietest of DG, they require considerable muffling, which reduce
output and fuel efficiency.
− Fuel efficiency rather low compared to some other DG types
b Reciprocating Engines
Reciprocating engines are the most widely used type of power source for
distributed generators. They use diesel or natural gas as their fuel. Almost all
engines used for power generation are four-stroke and operate in four cycles.
10
* Advantages
− Low-cost manufacturing base and simple maintenance needs.
− Quick start-time and can be used for peak load shaving.
* Disadvantages
− Exhaust emissions, noise and vibration.
− Power quality is not as high as in inverter-based technologies such as
fuel cells and micro-turbines.
c Photovoltaic
The basic unit of PV is a cell that may be square or round in shape, made of
doped silicon crystal. Cells are connected to form a module or panel and
modules are connected to form an array to generate the required power. Cells
absorb solar energy from the sunlight, where the light photons force cell
electrons to flow, and convert it to dc electricity. Normally an array, cells
connected in series, provides 12V to charge batteries.
* Advantages:
− No fuel and maintenance costs.
− No pollution.
− Extremely reliable and durable.

* Disadvantages:
− Low efficiency (24% in lab and 10% in reality).
− High initial price
− Require storage and conversion device
− Low output power.
− Depend on the weather and geographic conditions.
d Wind Turbines
Wind energy is not a new form it has been used for decades. A wind turbine
consists of a rotor, turbine blades, generator, drive or coupling device, shaft,
and the nacelle (the turbine head) that contains the gearbox and the generator
drive. The wind rotates the windmill-like blades, which in turn rotate their
attached shaft. This shaft operates a pump or a generator that produces
electricity. Although, the energy characteristics of larger wind turbine farms
are closer to the centralized energy sources, small wind turbines (working as
modules) can be combined with PV and battery systems to serve the load of
25–100 kW [3]. A generator driven by wind turbines can produce only real
power because the synchronous generator requires that a very constant
rotational speed be maintained while it cannot be met by “constant-speed”
wind turbine.
* Advantages
11
− No fuel charge
− No pollution.
− Suitable for remote application
* Disadvantages
− Depend on the weather and geographic conditions.
e Fuel Cell
Fuel cells use the technology where oxygen and hydrogen combine to
generate electricity with water and heat being produced as byproducts. Fuel
cell is a device used to generate electric power and provide thermal energy

from chemical energy through electrochemical processes. It provides clean
power and heat for several applications by using gaseous and liquid fuels.
Fuel cell capacities vary from kW to MW for portable and stationary units,
respectively. There are different types depending on the electrolyte used such
as proton exchange membrane or polymer electrolyte membrane fuel cell
(PEMFC), alkaline fuel cell (AFC), direct methanol fuel cell (DMFC),
phosphoric acid fuel cell (PAFC), molten carbonate fuel cell (MCFC) and
solid oxide fuel cell (SOFC). Like the reciprocating engines, they can regulate
the voltage of the bus at which they are connected.
* Advantages:
− Higher efficiency compared to conventional generations (about 60%)
− Low noise and pollution level
* Disadvantages
− Need power electronic interface to regulate the output voltage.
2.2.4 Standard Sizes of Distributed Generation on Market
The typical available sizes per module of DG on the market in [32] will help
system planners to select the correct size that they need. Table 3.1 shows the
range of capacity for DG of different technologies.
Table 0-1: Available capacities of DG for various technologies
N
o
Technology
Typical available size per
module
1.
Internal combustion engines
5 kW -10 MW
2.
Combustion turbines
1 -250 MW

3.
Micro-turbines
35 kW -1 MW
4.
Small hydro
1 -100 MW
5.
Micro hydro
25 kW -1 MW
6.
Wind turbines
200 Watt -3 MW
12
7.
Photovoltaic arrays
20 Watt – 100 kW
8.
Solar thermal, central
receiver
1 – 10 MW
9.
Solar thermal, Lutz system
10 – 80 MW
10.
Biomass, e.g. based on
gasification
100 kW – 20 MW
11.
Fuel cells, phosphoric acid
200 kW – 2 MW

12.
Fuel cells, molten carbonate
250 kW – 2 MW
13.
Fuel cells, proton exchange
1 kW – 250 kW
14.
Fuel cells, solid oxide
250 kW – 5 MW
15.
Geothermal
5 – 100 MW
16.
Ocean energy
100 kW – 1 MW
17.
Sterling engine
2 – 10 kW
18.
Battery storage
500 kW -5 MW
2.3 Distribution Power Flow Algorithms
In order to analysis DG in the distribution system, the first step is to run a
distribution load flow with DG. The results can be analyzed to see the
technical viability of a DG in the distribution system.
Load flow is very important in power system operation and planning as it
provides the picture of steady state operating condition. It is desirable to know
the system operating condition at different loading levels for efficient and
reliable operation of the power system. Many real time and planning
applications require an efficient and robust power flow algorithm. Newton

Raphson and fast decoupled power flow solution techniques, being used for
decades now, to solve transmission system and well behaved power system
cannot be used directly in the distribution system, as it may lead to
convergence problem [51], [52]. This is basically due to the difference in
characteristics of the transmission and distribution systems. Furthermore,
introduction of DGs has changed the way the distribution system is operated.
For all these reasons distribution load flow algorithm needs to be more robust
and faster even for the static studies [53].
Distribution systems usually fall into the category of ill-conditioned power
systems with its special features such as radial or weakly meshed topologies,
high R/X ratio of the distribution lines, unbalanced operation and loading
conditions, non-linear load models and dispersed generation, etc. Numerous
efforts have been made to develop power flow algorithms for distribution
systems.
Recently many researchers have paid attention to obtain the load flow
solution of distribution network. In [52], compensation-based power flow
method is presented by D. Shirmohammadi for meshed systems. And the
13
method was extended to a dispersed generation system with PV node
compensation in [54] by G. X. Luo and A. Semlyen. By adding voltage
correction, it was illustrated that the iterative process power flow calculation
is faster and reliable [55].
Luo et al. [54] presented a compensation method for weakly meshed
networks. This method started from a network structure analysis to find the
interconnection points. Then it breaks these interconnection points using the
compensation method so that the meshed system structure could be changed
to simple tree-type radial system. This method is also suitable for the system
with multiple voltage control buses.
A general load flow method for distribution systems presented by M.H.
Haque in [57] proposed a new approach for meshed networks with more than

one feeding node. The method first converts the multiple-source mesh
network into an equivalent single-source radial type network by setting
dummy nodes for the break points at distributed generators and loop
connecting points. Then the traditional ladder network method can be applied
for the equivalent radial system. Following each of the iterations of the
equivalent radial system, the power injected at the break points must be
updated by an additional calculation through a reduced order impedance
matrix.
Salama et al. [58] have presented a very simple but robust method - the ladder
formula. Essentially, the ladder network method treats the radial system as
two basic element types: the network natural elements (impedance) and
voltage control current sources (system loads) at each load node. The forward
sweep is mainly a voltage drop calculation from the sending end to the far end
of a feeder or a lateral; and the backward sweep is primarily a current
summation based on the voltage updates from the far end of the feeder to the
sending end.
Berg et al.[59] presented a backward method which used a backward
procedure to update the equivalent impedance at the sending end. The main
idea of this method is to treat the load as constant impedance. So if the
equivalent impedance is convergent, the whole system convergence will be
reached. This method is very costly and quite sensitive to the system load
level and load distribution, as well as the system structures.
Baran et al.[60] presented a forward method. In this method, the sending end
voltage becomes the main concern of the system convergence. Voltage drop
and the information on system structure have been considered in the forward
sweep. The voltage-sensitive load current can be included in the system
model. However, this method still has disadvantages. Oriented from ladder
network concepts, the 'branch flow equations' are essentially solved by a
14
Newton-Raphson approach which makes this method complex and costly.

Sometimes the influence of the load distribution would cause slow
convergence.
2.4 Shunt Capacitor Placement
Capacitors have been very commonly used to provide reactive power
compensation in distribution systems. They are provided to minimize power
and energy losses and to maintain the voltage profile within the acceptable
limits. The amount of compensation provided is very much linked to the
placement of capacitors in the distribution system which is essentially
determination of the location, size, number and type of capacitors to be placed
in the system.
The capacitor placement problem is a well researched topic and has been
addressed by many authors in the past. Initially, the problem of capacitor
location has been handled with analytical methods. The famous “two-thirds”
rule [1] advocates for maximum loss reduction. As this rule, a capacitor rated
at two-thirds of the peak reactive load should be installed at a position two-
thirds of the distance along the total feeder length. Although it is easy to
understand, this method had some unrealistic assumptions, such as uniform
load, uniform conductor sizes, etc. Thus, to achieve more accuracy results, it
should have more effective methods.
More recently, thanks to robustness of computer, some methodologies have
been proposed, such as mixed integer programming; linear programming
models; methods based on heuristic search techniques such as genetic
algorithms [12]–[20]; fuzzy approach [21]– [22]; simulated annealing; expert
systems, artificial neutral networks (ANN).
For optimal capacitor allocation, the savings function would be the objective
function and the locations, sizes, number of capacitors, bus voltages, and
current would be the decision variables.
In most of these approaches, the objective function is considered as an
unconstrained maximization of savings due to energy loss reduction and peak
power loss reduction against the capacitor cost [6]-[12]. Others have

formulated the problem with some variations of the above objective function.
Some of the early works have not considered capacitor cost in the
formulation. Some others have included system capacity release and load
growth into the problem formulation.
GA is a well tool to use in optimal capacitor placement [12-14], [18-20] and it
is the fact that GA has been successfully applied to the capacitor placement
problem. In this method, the parameter sets (sizes and locations) are coded.
GA operation will select a population of the coded parameters with highest
15
level and perform a combination of mutation, crossover to generate the better
set of parameters.
A technique applying fuzzy set theory [21], [22] is also applied to solve the
problem. In this technique, voltage and power loss indices are modeled by
membership functions and a fuzzy expert system containing a set of heuristic
rules that performs the inference to determine a capacitor placement
suitability index of each node. Capacitor would be placed at the node with
highest suitability.
Based on all of the method above, many programs are developed to solve the
problem of optimal capacitor placement. Most of current commercial software
is using numerical programming method to solve the optimal capacitor
problem.
Among these, the PSS/ADEPT software written by Shaw Power Technologies
proved an efficient tool for optimal capacitor placement in distribution system
that considers both fixed and switched capacitors, as well as economics
concerns [24].
2.5 DG Placement Techniques
With more and more DGs coming into distribution system, it is always
desirable to place them properly. Proper placement will reduce distribution
system loss, also provide free available capacity for power transmission and
reduce equipment stress. By proper DG placement, we can defer investment

in transmission line expansion as well as improve overall efficiency of the
power delivery. In case of heavily loaded feeder, transmission corridors can
be relieved. Furthermore power quality can be improved by the proper
placement of DGs.
A near optimal placement technique to reduce the system loss has been
presented in [53]. By this technique, loss sensitivity of load bus is calculated.
The buses are ranked according to their loss sensitivity and size of DG in that
bus is kept increasing until the loss starts to increase. DG is placed in the bus
which gives the most loss reduction after placement. In case of the
distribution system, four feeders are chosen based upon the transmission
system study and DGs of fixed sizes are placed in one or more of these
feeders. This method is basically about finding the location and does not give
the size of the DG and furthermore, loss sensitivity of buses changes as we
increase the size of the DG.
The optimal location to place a DG, with unity power factor, in a radial or
networked system is presented in [62], with loss minimization objective. It is
based on bus admittance matrix, generation information, and load distribution
of the system. This technique is basically concerned with finding the optimal
16
location not the optimal size. A practically useful point of the technique is that
it considers time varying load.
Celli et al. in [63] have proposed a multi-objective technique to solve DG
placement and sizing problem with the aim of reducing cost of energy loss,
cost of network upgrading and cost of purchased energy. Genetic algorithm
and ε-constrained method is used to find the size and place to place the DG in
the distribution system. Though the paper considers the various economic
parts related to DG placement and sizing, it is computationally demanding
due to the use of GA.
In [64], optimal DG sizing and placement model is presented with the aim of
minimizing the investment cost and operating cost of the DG. Proposed

methodology aims to minimize the capital investment and operation cost of
distribution company along with payment made toward the loss reduction,
power generated and cost of the power purchased from the grid and it also
compares the cost of network expansion with the DG installation. This
technique sometimes gives the solutions which are not practical and hence
planner experience decisions are required.
A genetic algorithm (GA) based distribution generation placement technique
to reduce overall power loss in distribution system is presented in [65]. B-loss
coefficient is used to find the system loss. The technique uses Genetic
Algorithm Toolbox and both the optimal size and location can be found from
it. Three more genetic algorithm based method for determining the DG size
and location is presented in [79]. Another genetic algorithm based DG
placement technique is presented in [48]. This algorithm finds the optimal
size and place where we can install DG, so as to reduce the system loss. The
technique is used to solve the DG placement problem in medium voltage
distribution system. GA is suitable for multi-objective problem and gives near
optimal solution but it is computationally intensive and suffers from excessive
convergence time and premature convergence.