Jitendiys

“OPTIMAL PLACEMENT AND SIZING OF MULTI-DISTRIBUTED
GENERATION (DG) INCLUDING DIFFERENT LOAD MODELS
USING PARTICLE SWARM OPTIMIZATION (PSO)”
DISSERTATION
Submitted in Partial Fulfilment of the
Requirements for the Award of the Degree of
MASTER OF TECHNOLOGY
In
INSTRUMENTATION
By
Jitendra Singh Bhadoriya
(DE/11/10)
Under The Supervision
Of
Dr. (Mrs.) Ganga Agnihotri
(MANIT, Bhopal)
School of Instrumentation, Devi Ahilya University
Indore-452001, INDIA.
JULY-2013

Dedicated to
my mother
Smt. Seema Bhadoriya
and
my father
Shri. Sambhu Singh Bhadoriya

Department of Electrical Engineering
MAULANA AZAD NATIONAL INSTITUTE OF TECHNOLOGY (MANIT)
Bhopal-462051, INDIA
CERTIFICATE
This is to certify that the dissertation entitled “OPTIMAL PLACEMENT AND SIZING
OF MULTI-DISTRIBUTED GENERATION (DG) INCLUDING DIFFERENT
LOAD MODELS USING PARTICLE SWARM OPTIMIZATION (PSO) ” submitted
by Mr. Jitendra Singh Bhadoriya, to School of Instrumentation, DEVI AHILYA
VISHWAVIDYALAYA, Indore during the period 28/08/2012 to 05/07/2013 is a
satisfactory account of the bona-fide work done under our supervision at Department of
Electrical Engineering MAULANA AZAD NATIONAL INSTITUTE OF
TECHNOLOGY, Bhopal and is recommended towards the partial fulfilment for the
award of the degree of Master of Technology in Instrumentation Engineering with
Specialization in Instrumentation by Devi Ahilya Vishwavidyalaya, Indore.
I wish his all professional success in her future.
PROJECT GUIDE
Dr. (Mrs.) Ganga Agnihotri
Professor & Dean Academic
MANIT, Bhopal (M.P.)

JITENDRA SINGH BHADORIYA-SCHOOL OF INSTRUMENTATION,DEVI AHILYA UNIVERSITY
INDORE(M.P.) INDIA
3
SCHOOL OF INSTRUMENTATION
DEVI AHILYA VISHWAVIDYALAYA, INDORE
Dissertation Approval
This is to certify that the dissertation entitled “OPTIMAL PLACEMENT AND
SIZING OF MULTI-DISTRIBUTED GENERATION (DG) INCLUDING
DIFFERENT LOAD MODELS USING PARTICLE SWARM OPTIMIZATION
(PSO)”submitted by JITENDRA SINGH BHADORIYA (DE/11/10) to School of
Instrumentation, Devi Ahilya University, Indore during the year 2012-13 is
approved as partial fulfilment for the award of the degree of Master of
Technology with Specialization in Instrumentation.
External Examiner
Date:

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4
CANDIDATE’S DECLARATION
I declare that the work entitled “OPTIMAL PLACEMENT AND SIZING OF
MULTI-DISTRIBUTED GENERATION (DG) INCLUDING DIFFERENT LOAD
MODELS USING PARTICLE SWARM OPTIMIZATION (PSO)” is my own work
conducted under the supervision Of Dr. (Mrs.) Ganga Agnihotri .The
research work was carried out by me at Department of Electrical
Engineering, MAULANA AZAD NATIONAL INSTITUTE OF
TECHNOLOGY (MANIT) Bhopal.
I further declare that to the best of my knowledge the present work does not
contain any part of the work which has been submitted for the award of any
degree either in this University or in any other University/Deemed University
without proper citation.
(Jitendra Singh Bhadoriya)

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ACKNOWLEDGEMENT
I would like to articulate my profound gratitude and indebtedness to my thesis guide
Dr. (Mrs.) Ganga Agnihotri who has always been a constant motivation and
guiding factor throughout the thesis time in and out as well. It has been a great
pleasure for me to get an opportunity to work under him and complete the project
successfully.
I wish to extend my sincere thanks to Prof. A. L. Sharma, Head of our
Department, and Dr. Ratnesh Gupta for approving my project work with great
interest. I would also like to mention Mr. Aashish Bohre, PhD Scholar, for his
cooperation and constantly rendered assistance and my friend, Mr. Akash Khakre
for his help and moral support.
I feel a deep sense of gratitude for my father Sri. Shambhu Singh Bhadoriya and
mother Smt. Seema Bhadoriya who formed a part of my vision and taught me the
good things that really matter in life.
Apart from my efforts, the success of any project depends highly on the
encouragement and guidance of many others. I take this opportunity to express my
gratitude to the people who have been instrumental in the successful completion of
this project. The guidance and support received from all the members who
contributed and who are contributing to this project, was vital for the success of the
project. I am grateful for their constant support and help.
JITENDRA SINGH BHADORIYA
ROLL NO: (DE/11/10)

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ABSTRACT
This research work proposes a multi-objective index-based approach for
optimally determining the size and location of multi-distributed generation
(multi-DG) units in distribution systems with different load models. It is
shown that the load models can significantly affect the optimal location and
sizing of DG resources in distribution systems. The proposed multi-objective
function to be optimized includes a short circuit level parameter to represent
the protective device requirements. The proposed function also considers a
wide range of technical issues such as active and reactive power losses of the
system, the voltage profile, the line loading, and the Mega Volt Ampere
(MVA) intake by the grid. An optimization technique based on particle swarm
optimization (PSO) is introduced. An analysis of the continuation power flow
to determine the effect of DG units on the most sensitive buses to voltage
collapse is carried out. The proposed algorithm is tested using a 38-bus radial
system. The results show the effectiveness of the proposed algorithm.
Keywords- Particle swarm optimization (PSO), Optimal placement
Distributed Generation (DG), Load models .Short circuit level, Voltage
stability.

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CONTENTS
CERTIFICATE…........................................................................................................1
DISSERTATION APPROVAL……………. ……………………………………….........3
CANDIDATE’S DECLARATION………………………………………………..............4
ACKNOWLEDGEMENTS..........................................................................................5
ABSTRACT..................................................................................................................6
CONTENTS..................................................................................................................7
LIST OF FIGURES.......................................................................................................9
LIST OF TABLES.........................................................................................................11
CHAPTER-1………………………………………………………………………...12
INTRODUCTION…………………………………………………………………..12
1.1 Background……………………………………………………………………....13
1.2 The main drawbacks of the centralized paradigm……………………………….14
1.3 DG Insertion in to grid…………………………………………………………..19
1.4 Problem Definition..……………………………….…………….………………22
1.5 Multi-objective-based problem formulation……………….……………………23
1.6 Thesis Layout……………………………………………………………….…..28
Summary……………………………………………………………………………29
CHAPTER-2……………………………………………………………………….30
LITERATURE REVIEW ………………………………………………………..30
Summary ……………………………………………………………………………38
CHAPTER-3………………………………………………………………………..39
DISTRIBUTED GENERATION (DG)…………………………………………...39

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3.1 Introduction………………………………………………………..................40
3.2 Distributed Generation Technologies…………………………………………44
3.3 Distributed Generation Applications……………………………..................50
3.4 The Benefits of Distributed Power………………………………..................55
3.5 The main characteristics of distributed generation…………………..............58
Summary…………………………………………………………………………..60
CHAPTER-4……………………………………………………………………….....61
Particle Swarm Optimization (PSO)…..............................................................61
4.1 Background of Artificially Intelligence…………………………………………...62
4.2 PSO as A Optimization Tool……………………………………………………...64
4.3 Algorithm of PSO ………………………………………………………………...68
4.4 Superiority of PSO………………………………………………………………..79
Summary……………………………………………………………………………...81
CHAPTER-5………………………………………………………………………....82
SIMULATION & RESULTS ANALYSIS...……………………………………….82
5.1 Load modeling…………………………………………………………………...83
5.2 PSAT……………………………………………………………………………..88
5.3 Modeling of IEEE 38 Radial Distribution System……………………………….90
5.4 Results Analysis………………………………………………………………….96
CHAPTER-6………………………………………………………………………...
CONCLUSION …………………………………………………………………….107
Publications & Workshops………………………………………………………...109
REFERENCES……………………………………………………………………...110

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LIST OF FIGURE
Figure-1.1 Central electricity paradigm ….……………………………….............18
Figure 1.2 Distributed Electricity Paradigm………………………………...........21
Figure 1.3 Thesis Layout ……………………………………………………….......28
Figure 3.1 Distributed generation types and technologies.........................…......47
Figure 4.1 Concept of a searching point by PS………………………….…..........71
Figure 4.2 Searching concepts with agents in a solution space by PSO……......72
Figure 4.3 Flow Chart Of PSO………………………………………………........74
Figure 4.4 Solution Procedure…......................................................................78
Figure5.1 General Configuration of the MATLAB Toolbox for Power
SystemAnalysis…………………………………………………………….………......88
Figure 5.2 IEEE 38 BUS SYSTEMS…………………………………..…….......92
Figure 5.3 Simulation model of 38 bus system……………………………….....93
Figure 5.4 Simulation model of 38 bus system with DG…………………….....94
Figure 5.5 Voltage Profile Under Constant Load …………………………......96

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Figure 5.6 Voltage Profile Under Industrial Load ………………………….....97
Figure 5.7 Voltage Profile Under Residential Load ………………………..........97
Figure 5.8 Voltage Profile Under Commercial Load ………..…………….........98
Figure 5.9 Voltage Profile Under Mixed Load ……………………………...........98
Figure 5.10 Line loading under Constant Load ………………………....…..........99
Figure5.11 Line loading under Industrial Load ………………………….............99
Figure 5.12 Line loading under Residential Load ……………………..…….......100
Figure 5.13 Line loading under Commercial Load ……….……………….........100
Figure 5.14 Line loading under Mixed Load ……………………………….........101
Figure 5.15 Short Circuit Level Difference of the System under different Load
Models………………………….......................................................................................102
Figure 5.16 PV curve at (weakest bus of the system ) bus 18……………….......103
Figure 5.17 PV curve at (weakest bus of the system ) 37 bus ……………….......104
Figure 5.18 Multi Objective Function (MOF) is minimized under Different Load
Models…………………………………............................................................................105

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LIST OF TABLE
Table 1.1 Impact Indices weighting …………………………………………......... 25
Table 3. 1 Technologies for distributed generation…………………………..........45
Table 3.2 Comparison between common energy types for power and time
duration…...............................................................................................................50
Table 4.1 SOME KEY TERMS USED TO DESCRIBE PSO…............................68
Table 4.2 Solution Procedure………………………………………………...........77
Table 5.1 Load Data for 38-bus system………………………………….…..........84
Table 5.2 Common values for the exponent’s np and nq, for different load
components………………………………………………………………………............87
Table5.3 Matlab Toolboxes for Power System Analysis………………..…...........89
Table5.4 Impact indices for penetration of a DG unit in the 38 bus system with load
models using PSO……………………………………………………............................101
Table 5.5 Size and Location of DG unit in the 38 bus radial system……….........103
Table 5.6 System power losses and MVA intake for different load models
in the 38-bus radial system, and the value of MOF………….............106

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Introduction
Background
The main drawbacks of the centralized paradigm
DG Insertion in to grid
Problem Definition
Multi-objective-based problem formulation
Objective and Approaches
Thesis Layout
Summary
Chapter 1
Introduction

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1.1 Background
Since the 1990s, electricity production has been driven towards generation concentration
and a higher degree of integration leading to the current centralized electricity paradigm.
This move was driven by several factors:
The search for high energy efficiency: gains in efficiency were achieved
through larger facilities capable of handling higher pressures and temperatures
in steam used in electricity generation. At a certain point, the gains were
however offset by the increase in operating and maintenance costs as materials
were unable to sustain operation at high specification over the long run;
Innovation in electricity transmission: the use of alternative current instead of direct
current permitted to transmit electricity over long distances with a significant loss
reduction;
The search for reliability: so as to increase the reliability at the customer’s end,
large electricity production facilities were connected to the transmission networks.
Pooling resources helped reduce the reliance of each customer on a particular
generator as other generators were often able to compensate for the loss.
Environmental constraints: the use of transmission networks made it possible to
relocate the generation facilities outside the city centers thus removing pollution
due to exhaust from coal fired plants.
Regulation favoring larger generation facilities.
In the sector’s layout resulting from this move towards concentration and Integration
electricity is generated, transported over long distances through the Transmission network
and medium distances through the distribution network to be finally used by the end
customer. This can be summed up as follows:
“Traditional electrical power system architectures reflect historical strategic policy
drivers for building large-scale, centralized, thermal- (hydro-carbon- and nuclear-)
based power stations providing bulk energy supplies to load centers through integrated
electricity transmission (high-voltage: 400, 275 and 132 kV) and distribution (medium,
low-voltage: 33 kV, 11 kV, 3.3 kV and 440V) three-phase systems.” (Mc Donald, 2008).

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Though dominant, centralized generation has always been operating along a smaller
distributed generation capacities that were never phased out of the market. The persistence
of the first historical form of energy generation whereby energy is consumed near its
generation point seems puzzling in the light of the properties of centralized generation
mentioned above. The significant size of distributed generation in countries such as
Denmark clearly implies that it is capable of overcoming shortfalls of the centralized
generation paradigm .
1.2 The main drawbacks of the centralized paradigm
Several studies were conducted to emphasize the main shortfalls of the centralized
generation paradigm and to explicit the motivation of the agents in keeping distributed
generation as a primary source of electricity or as a backup generator the main drivers
listed in the literature are summarized below:
Transmission and distribution costs: transmission and distribution costs amount for up to
30% of the cost of delivered electricity on average. The lowest cost is achieved by
industrial customers taking electricity at high to medium voltage and highest for small
customers taking electricity from the distribution network at low voltage (IEA, 2002).
The high price for transmission and distribution results mainly from losses made up of:
line losses: electricity is lost when flowing into the transmission and distribution
lines;
Unaccounted for electricity and
Conversion losses when the characteristics of the power flow are changed to fit the
specifications of the network (e.g. changing the voltage while flowing from the
transmission network to the distribution network) (EIA, 2009).
The total amount of the losses is significant. In addition to the cash cost, these electricity
losses have an implicit cost in terms of greenhouse gas emissions: fuel is consumed thus
generating greenhouse gases to produce electricity that is actually not used by the final
consumer.
Rural electrification: in an integrated power system, rural electrification is challenging for
two reasons. As large capital expenditures are required to connect remote areas due to the
distance to be covered through overhead lines, connecting remote areas with small

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consumption might prove uneconomical. This effect is amplified when taking into account
transmission and distribution losses because both tend to increase with the distance
covered. Rural electrification is thus costly. It often proves more economical to rely on
distributed generation in such cases .This has often been the case for mountain areas or low
density areas remote from the main cities.
Investment in transmission and distribution networks: over the next 20 years, significant
investment will be required to upgrade the transmission and distribution networks. The
International Energy Agency (2003) estimated the total amount to be invested in
generation, transmission and distribution up to 2030 for the OECD countries to stands
between 3,000 and 3,500 billion dollars (base case predictions). In order to cut these costs,
distributed generation can be used as a way to bypass the transmission and distribution
networks. In its alternative scenario – under this scenario distributed generation and
renewable energy are more heavily supported by policy makers- the IEA forecasts the
overall amount to be invested to be lower than 3,000 billion dollars (electricity generation
investments remaining constant).
Energy efficiency: in the 1960s, the marginal gains in energy efficiency through size
increase and use of higher temperature and pressure started to diminish. Higher
temperatures and pressure resulted in high material wear and tear leading to lower than
expected operating life for steam turbines (Hirsch, 1989). In order to increase energy
efficiency without requiring to higher pressure, cogeneration systems have been developed
to reuse the waste steam in a neighbourhood heating system or cooling system through
district heating and/or cooling district. The total energy efficiency achieved when
combining both electricity and heat goes up to 90% (IPPC, 2007). Comparatively, the sole
electricity generation hardly goes above 40%. The main problem, however, is that steam
and heat are even less easily transported than electricity, thus justifying the use of
distributed generation through production next to the point of consumption. Modern
electrical industry is facing a paradigm shift in the production, delivery and in the end use
of electricity. The introduction and integration of decentralized energy resources has a
positive impact on emerging systems such as micro grids and smart grids .
Security and reliability: The persistence of distributed generation contributed to energy
security through two effects:

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• Fuel diversity: as distributed generation technologies can accommodate a larger
range of fuel that centralized generation, distributed generation has been used to
diversify away from coal, fuel, natural gas and nuclear fuel (IEA, 2002). For
instance, distributed generation has been used at landfills to collect biogas and
generate energy;
• Back up generation: the main use of distributed generation is for back up capacities
to prevent operational failures in case of network problems. Backup generators have
been installed at critical location such as hospitals, precincts etc.
Electricity deregulation and cost control device: in a deregulated electricity market, the
diminution of reserve margins or the failure of generators to supply the network (due for
example to unplanned outages etc) can lead to capacity shortfalls resulting in high
electricity prices to the consumers. In order to hedge against negative price impacts, large
electricity consumers have developed acquired distributed generation capacities. Such a
move was possible thanks to the increase in flexibility in the market regulation following
the deregulation including, among other, reducing barriers to entry.
Environmental Impact: the environmental impact of the centralized energy system is
significant due to the heavy reliance on fuel, coal and to a lesser extent natural gas.
The electricity sector is responsible for ¼ of the NO emissions, 1/3 of the CO2
emissions and 2/3 of the SO2 emissions in the United States (EPA, 2003). Distributed
generation Has been used to mitigate the impact both in terms of emissions associated
with transmission and distribution losses, to increase efficiency through cogeneration
and distributed renewable energy. As distributed generation has been able to overcome
the aforementioned Shortfalls of the centralized generation paradigm, it kept on
average a small share in the overall generation mix. The following subsection will
focus on the main features n of distributed generation and why it has been the source
of an increased attention recently. In figure 1.1 central electricity paradigm is given at
that time generated power is passed to transmission meanwhile some losses are
generated, but a lot of work has been done in smart transmission network .problem
arises when power is further given by transmission lines to distribution system ,where
power demand gets changing in every respect residentially, commercially so load
remains constant only for some time or in ideal conditions . Power demand is
increasing of development of certain area by establishing new industry etc. This

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additional amount of power creates problem for distribution system because it gets
only certain amount of power from generating power plants. Extra amount of load will
get responsible for power fluctuation increasing reactive power , increase real power
loss . We have to reconstruct the distribution system to get overcome on this problem,
it needs heavy amount not economical because if we construct a distribution sub-
station with existing amount of power than we have to again reconstruct the
distribution system & if we reconstruct distribution system keeping future estimate in
mind than it will also creates problem of wasting power in island mode. So we install
distributed generator in that distribution sub-station , the type depends on the locality
of that area which one is better available and reliable(shown in figure 1.2 ) , it will
reduce the losses and also very economical compared to reconstruct a new distributed
network.

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Transmission Network
Distribution Network
Central Power Station
Figure-1.1 Central electricity paradigm

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1.3 DG Insertion in to grid
As seen in the previous part, distributed generation has been historically used in several
ways to complement centralized generation. The reason behind the recent revival of
distributed generation is two-fold: the liberalization of the electricity markets and concerns
over greenhouse gas emissions.
The electricity and gas deregulation process started in Europe following the application of
two directives aimed at providing a free flow of gas and electricity across the continent.
These directives and the subsequent legislation created a new framework making it possible
for distributed generators to increase their share in the total electricity generation mix. The
effect of deregulation is two-fold (IEA, 2002):
• Thanks to the reduction of barriers to entry and clearer prices signals, distributed
generators were able to move in niche markets and exploit failures of centralized
generation. These new applications took the form of standby capacity generators,
peaking generators (i.e. producing electricity only in case of high price and
consumption periods), generators improving reliability and power capacities,
generators providing a cheaper alternative to network use or expansion, provision
of grid support (i.e. provision of ancillary services permitting better and safer
operation of the network and/or shortening the recovery time).
• As distributed generators tend to be of smaller size and quicker to build, they have
been able to benefit from price premiums. Geographical and operational flexibility
made it possible to set up distributed generators in Congested areas or use it only
during consumption peaks. Besides, for small excess demand, it is often
uneconomical to build an additional centralized generation plant whereas with
lower CAPEX and capacities, distributed generation might come in handy (IEA,
2002).
The second driver behind the rebirth of distributed generation is to be related to
environmental constraints. Environmental and economic constraints led to look for cleaner
and more efficient use of energy. Distributed generation has been able to achieve this
target.

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The current model for electricity generation and distribution in the India is dominated by
centralized power plants. The power at these plants is typically combustion (coal, oil, and
natural) or nuclear generated. Centralized power models, like this, require distribution from
the center to outlying consumers. Current substations can be anywhere from 10s to 100s of
miles away from the actual users of the power generated. This requires transmission across
the distance.
This system of centralized power plants has many disadvantages. In addition to the
transmission distance issues, these systems contribute to greenhouse gas emission, the
production of nuclear waste, inefficiencies and power loss over the lengthy transmission
lines, environmental distribution where the power lines are constructed, and security related
issues.
Many of these issues can be mediated through distributed energies. By locating, the source
near or at the end-user location the transmission line issues are rendered obsolete.
Distributed generation (DG) is often produced by small modular energy conversion units
like solar panels. As has been demonstrated by solar panel use in the United States, these
units can be stand-alone or integrated into the existing energy grid. Frequently, consumers
who have installed solar panels will contribute more to the grid than they take out resulting
in a win-win situation for both the power grid and the end-user.

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Central plant
Distributed Generation
Distributed Load
Solar Power
Source
Wind-Power Source
Micro Turbine
Fuel cell
Figure 1.2 Distributed Electricity Paradigms

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1.4 Problem Definition.
The newly introduced distributed or decentralized generation units connected to local
distribution systems are not dispatch able by a central operator, but they can have a
significant impact on the power flow, voltage profile, stability, continuity, short circuit
level, and quality of power supply for customers and electricity suppliers. Optimization
techniques should be employed for deregulation of the power industry, allowing for the
best allocation of the distributed generation (DG) units.
There are many approaches for deciding the optimum sizing and siting of DG units in
distribution systems. The optimum locations of DG in the distribution network were
determined. These works aimed to study several factors related to the network and the DG
unit itself such as the overall system efficiency, system reliability, voltage profile, load
variation, network losses, and the DG loss adjustment factors. The optimal sizing of a small
isolated power system that contains renewable and/or conventional energy technologies
was determined to minimize the system’s energy cost. The authors succeeded in merging
both the DG location and size in one optimization problem. The main factors included in
the optimization problem were investment cost, operation cost, network configuration,
active and reactive power costs, heat and power requirements, voltage profile, and system
losses.
Several methods have been adopted to solve such an optimization problem. Some of them
rely on conventional optimization methods and others use artificial intelligence-based
optimization methods.
In some research, the optimum location and size of a single DG unit is determined while in
others the optimum locations and sizes of multiple DG units are determined a mixed integer
linear program was formulated to solve the optimization problem. The objective was to
optimally determine the DG plant mix on a network section. However, that required
dealing with the power system approximately as a linear system, which is not the real case.
A particle swarm optimization (PSO) algorithm was introduced to determine the optimum
size and location of a single DG unit to minimize the real power losses of the system. The
problem was formulated as one of constrained mixed integer nonlinear programming, with
the location being discrete and the size being continuous. However, the real power loss of
the system was the only aspect considered in this research work, while trying to optimally
find the size of only one DG unit to be placed. Different scenarios were suggested for

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optimum distribution planning. One of these scenarios was to place multiple DG units at
certain locations pre-determined by the Electric Utility Distribution Companies (DISCOs)
aiming to improve their profiles and minimize the investment risk.
An adaptive-weight PSO (APSO) algorithm was used to place multiple DG units, but the
objective was to minimize only the real power loss of the system. PSO used to find the
optimal location of a fixed number of DG units with specific total capacity such that the
real power loss of the system is minimized and the operational constraints of the system are
satisfied. In [24], three types of multi- DG unit were optimally placed, also to minimize the
real power loss of the system using PSO.
The proposed algorithm was applied to test systems, a radial 38-bus system. The algorithm
is built using MATLAB script functions. A continuation power flow is carried out to
determine the effect of DG units on the voltage stability limits using the Power System
Analysis Toolbox (PSAT).
1.5 Multi-objective-based problem formulation
The multi-objective index for the performance calculation of distribution systems for DG
size and location planning with load models have considered by mentioning following
indices by giving a weight to each index.
In this thesis, several indices will be computed in order to describe the effect of load
models due to the presence of DG. These indices are defined as follows.
(1) Real and reactive power loss indices (ILP and ILQ): The real and reactive power loss
indices are defined as.
ILP = (1)
ILQ = (2)
Where PLDG and QLDG are the real and reactive power losses of the distribution system
after the inclusion of DG. PL and QL are the real and reactive system losses without DG in
the distribution system.
(2) Voltage profile index (IVD): One of the advantages of proper location and size of the
DG is the improvement in voltage profile. This index penalizes a size–location pair which

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gives higher voltage deviations from the nominal value (Vnom). In this way, the closer the
index is to zero better is the network performance. The IVD can be defined as
IVD= (3)
where n is the number of buses. Normally, the voltage limit (Vmin ≤ Vi ≤ Vmax) at a
particular bus is taken as a technical constraint, and thus the value of the IVD is normally
small and within the permissible limits.
(3) MVA capacity index (IC): As a consequence of supplying power near to loads, the
MVA flows may diminish in some sections of the network, thus releasing more capacity,
but in other sections they may also increase to levels beyond the distribution line limits (if
the line limits are not taken as constraints). The index (IC) gives important information
about the level of MVA flow/currents through the network regarding the maximum
capacity of conductors. This gives information about the need for system line upgrades.
Values higher than unity (calculated MVA flow values higher than the MVA capacity) of
the index given the amount of capacity violation in term of line flow, whereas lower values
indicate the capacity available
IC= (4)
where NOL is the number of lines, Si is the MVA flow in line i, and CSi is the MVA
capacity of line i.
The benefit of placing DG in a system in the context of line capacity released is measured
by finding the difference in IC between the system with and without DG. The avoidance of
flow near to the flow limits is an important criterion, as it indicates that how earlier the
system needs to be upgraded and thus adding to the cost. Normally, the constraint
(Si ≤ Si, max) at a particular line is taken as a strict constraint.
(4) Short circuit level index (ISC): This index is related to protection and sensitivity issues,
since it evaluates the short circuit current at each bus with and without DG
ISC= (5)

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Where I without DG SC is the short circuit current before installing the DG and I with DG
SC is the short circuit current after installing the DG.
The PSO-based multi-objective function (MOF) is given by
MOF=(σ1.ILP+ σ2.ILQ+ σ3.IC+ σ4.IVD+ σ5.ISC)+MVAsys(pu) (6)
Where MVA sys(pu) is the total intake from the grid expressed per unit, and
=1.0 σp Є [0,1]. (7)
Table 1.1 Impact Indices weighting
Index weights.
ILP
Indices σp
0.3
ILQ 0.2
IC 0.25
IVD 0.1
ISC 0.15
These weights are indicated to give the corresponding importance to each impact index for
the penetration of DG with load models, and they depend on the required analysis (e.g.,
planning, operation, etc.). The weighted normalized indices used as the components of the
objective function are due to the fact that the indices get their weights by translating their
impacts in terms of cost. It is desirable if the total cost is decreased. Table 2 shows the
values for the weights used in present work, considering normal operation analysis, and
they are selected guided by the weights. However, these values may vary according to
engineer concerns.
For this analysis, active losses have the higher weight (0.3) since they are important in
many applications of DG. The current capacity index (IC) has the second highest weight

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(0.25) since it gives important information about the level of currents through the network
regarding the maximum capacity of conductors in distribution systems. Protection and
selectivity impact (ISC) received a weighting of 0.15 since it evaluates important reliability
problems that DG presents in distribution networks. The behavior of the voltage profile
(IVD) received a weight of 0.1 due to its power quality impact.
The multi-objective function (6) is minimized subject to various
operational constraints to satisfy the electrical requirements for a distribution network.
These constraints are the following.
(1) Power-conservation limits: The algebraic sum of all incoming and outgoing power
including line losses over the whole distribution network and power generated from the DG
unit should be equal to zero
PSS(i, V) = + -PDGi (8)
where NOL=number of lines and PD = power demand (MW).
(2) Distribution line capacity limits: The power flow through any distribution line must not
exceed the thermal capacity of the line:
Si ≤ Simax. (9)
(3) Voltage limits: The voltage limits depend on the voltage regulation limits provided by
the DISCO:
Vmin ≤ Vi ≤ Vmax. (10)
The implementation of PSO starts by random generation of an initial population of possible
solutions. For each solution, size–location pairs of the DG units type and capacity
according to homer introduced to the system are chosen within technical limits of
locations and sizes of the DG units. Each solution must satisfy the operational constraints
represented .If one of these constraints is violated, such a solution is rejected. After
generating a population of solutions satisfying the pre-specified constraints, the objective
function of each solution (individual) is evaluated. Once the population cycle is initialized,
the position of each individual in the solution space is modified using the PSO parameters,
e.g., pbest, Gbest, and the agent velocity, to generate the new population. If the DG size
and/or location exceed the limit, they are adjusted back within the specified limits (the

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boundaries). The operational constraints are then checked. If any of them is violated the
new solution is rejected and another one is generated and checked until a solution that
satisfies the specified limits is found. The algorithm stops when the maximum number of
generations is reached. According to PSO theory, the optimal solution is the best solution
ever found throughout the generations (Gbest
).
To validate the proposed method, it is applied to the 38-bus system of under the same load
conditions and using the same objective function (IMO) and same values of index weights
used in to optimally place multi DG unit in the system.

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1.6 Thesis Layossut
Figure 1.3 Thesis Layout
Chapter 3
Distributed
Generation (DG)
Chapter 4
Particle Swarm Optimization a new
Methodology for installation &
planning of Distributed Generations
(DG)
Chapter 5
PSAT -TOOL. By using this mat-lab tool we
have simulated IEEE-38 bus system and we
have continuation power flow, optimum
power flow of the grid. We have found the
DG place & capacity on the required bus and
also SIMULATION & GRAPHICAL
RESULTS with Observation.
Chapter 6
Conclusion
Chapter 1
Introduction
Chapter 2
LITERATURE REVIEW

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Summary
In this thesis work chapter 1 describes about the centralized electricity paradigm and
problem include in this type of system .the solution is carried out by introducing
DISTRIBUTED GENERATION (DG) in to the existing grid , by doing so we are able to
construct a decentralized electricity paradigm and the problems can be minimize at a
greater extent. in this chapter the benefits of distributed generation also presented when
DG has inserted in to grid. Problem has been identifying by taking IEEE-38 bus system
and before insertion of DG we have to check several load models and impact indices.
While inserting DG we should know where and how much capacity of DG should be
introduced for this we have taken PSO to determine exact location of DG. At last Thesis
layout is given.

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Chapter 2
LITERATURE REVIEW

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[1]` This paper proposes a multi-objective index-based approach for optimally
determining the size and location of multi-distributed generation (multi-DG) units in
distribution systems with different load models. It is shown that the load models can
significantly affect the optimal location and sizing of DG resources in distribution systems.
The proposed multi-objective function to be optimized includes a short circuit level
parameter to represent the protective device requirements. The proposed function also
considers a wide range of technical issues such as active and reactive power losses of the
system, the voltage profile, the line loading, and the Mega Volt Ampere (MVA) intake by
the grid. An optimization
Technique based on particle swarm optimization (PSO) is introduced. An analysis of the
continuation power flow to determine the effect of DG units on the most sensitive buses to
voltage collapse is carried out. The proposed algorithm is tested using a 38-bus radial
system and an IEEE 30-bus meshed system.
Multi-objective optimization analysis, including load models, for size–location
planning of distributed generation in distribution systems has been presented. The proposed
optimization algorithm was applied to a 38-bus radial test system and an IEEE 30-bus mesh
test system. The results showed that the proposed algorithm is capable of optimal and fast
placement of DG units. The results clarified the efficiency of this algorithm for
improvement of the voltage profile, reduction of power losses, and reduction of MVA
flows and MVA intake from the grid, and also for increasing the voltage stability margin
and maximum loading.
[2] Distributed generators (DGs) sometimes provide the lowest cost solution to
handling low-voltage or overload problems. In conjunction with handling such problems, a
DG can be placed for optimum efficiency or optimum reliability. Such optimum
placements of DGs are investigated. The concept of segments, which has been applied in
previous reliability studies, is used in the DG placement. The optimum locations are sought
for time-varying
load patterns. It is shown that the circuit reliability is a function of the loading level. The
difference of DG placement between optimum efficiency and optimum reliability varies
under different load conditions. Observations and recommendations concerning DG
placement for optimum reliability and efficiency are provided in this paper. Economic
considerations are also addressed.

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This paper discusses two criteria for the optimal placement of a DG for time-
varying loads. One is to maximize the reliability improvement, and the other is to minimize
the power loss in the system. A three-circuit example is used for quantitative analysis. It is
pointed out that both reliability and losses vary as a function of loading or time. There are
additional practical constraints that must be considered, such as what locations are
available to the utility for installing the DG. Also, modifying the protection system either
due to the additional fault currents supplied by the DG or due to switching operations
anticipated are other practical aspects that need to be considered.
[4] The distributed generation (DG) plant mix connected to any network section has a
considerable impact on the total amount of DG energy exported and on the amount of
losses incurred on the network. A new method for the calculation of loss adjustment factors
(LAFs) for DG is presented, which determines the LAFs on a site specific and energy
resource specific basis. A mixed integer linear program is formulated to optimally utilize
the available energy resource on a distribution network section. The objective function
incorporates the novel LAFs along with individual generation load factors, facilitating the
determination of the optimal DG plant mix on a network section. Results are presented for
a sample section of network illustrating the implementation of the optimal DG plant mix
methodology for two representative energy resource portfolios.
A novel method for the calculation of loss adjustment factors for distributed
generation has been presented. These LAFs take account of the average impact of different
generation technologies at each bus on losses. The LAFs provide a pricing signal for the
optimal DG plant mix, whereby generators’ revenue will increase if they connect at the
appropriate bus. These novel LAFs have been incorporated into an optimal plant mix
methodology using MILP. This methodology determines the optimal DG plant mix for a
section of distribution network subject to a number of constraints. The methodology is
tested on two representative energy portfolios, in both cases performing well. Both cases
demonstrate that there is significant scope for optimization of the DG plant mix, to
maximize both the revenue for the generators and the benefit to society.
[6] This paper presents a novel particle swarm optimization based approach to optimally
incorporate a distribution generator into a distribution system. The proposed algorithm
combines particle swarm optimization with load flow algorithm to solve the problem in a

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single step, i.e. finding the best combination of location and size simultaneously. In the
developed algorithm, the objective function to be minimized is the total network power
losses while satisfying the voltage constraints imposed on the system. It is formulated as
constrained mixed integer nonlinear programming problem with the location being discrete.
The 69−bus radial distribution system has been used to validate the proposed method. Test
results demonstrate the effectiveness and robustness of the developed algorithm.
This paper presents solving the optimal DG allocation and sizing problem through
applying novel hybrid particle swarm optimization based approach algorithm. By
combining the particle swarm optimization with the load flow algorithm the problem was
solved in a single step that is finding the best combination of location and sizing
simultaneously. The effectiveness of the PSO was demonstrated and tested. The proposed
algorithm was tested on 69−bus distribution system to solve the DG mixed integer
nonlinear problem with both equality and inequality constraints imposed on the system.
The hybrid PSO significantly minimized the distribution network real power losses and
converged to the same bus for the DG to be installed in every single run.
[11] Recent changes in the energy industry initiated by deregulation have accelerated the
introduction of distributed generation at the sub-transmission and distribution levels. In
light of the well-known benefits as well as the various issues involved in DG incorporation,
this paper proposes two new quadratic voltage profile improvement indices VPI1 and
VPI2The primal dual interior-point (PDIP) method has been employed to identify the
optimal location and real and reactive power generation on the basis of the newly proposed
indices. A simplified model of a 33-bus radial distribution system has been simulated in
MATLAB to illustrate the use of the new indices.
Employing DG in a distribution system results in several benefits such as
increased overall system efficiency, reduced line losses, improved system voltage profile
and transmission and distribution capacity relief to both utilities and the customers. This
paper has proposed two indices: VPI1 VPI2, to quantify voltage profile improvement in a
distribution system. Primal-dual interior-point method has been employed to determine the
optimal location for the DG units in a distribution system.
[17] Evaluating the technical impacts associated with connecting distributed generation
to distribution networks is a complex activity requiring a wide range of network operational

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and security effects to be quailed and quantized. One means of dealing with such
complexity is through the use of indices that indicate the benefit or otherwise of
connections at a given location and which could be used to shape the nature of the contract
between the utility and distributed generator. This paper presents a multi objective
performance index for distribution networks with distributed generation which considers a
wide range of technical issues. Distributed generation is extensively located and sized
within the IEEE-34 test feeder, wherein the multi objective performance index is computed
for each configuration.
Various impact indices were addressed in this work, aimed at characterizing the
benefits and negative impacts of DG in distribution networks. Furthermore, a multi
objective performance index that relates impact indices by strategically assigning a
relevance factor to each index was proposed. Though the selection of values of relevance
factors will depend on engineering experience, the presented values solved, in a satisfactory
and coherent fashion, the DG location problem, considering different power generation
outputs for the IEEE-34 test feeder. Nevertheless, the proposed relevance factors are
flexible since electric utilities have different concerns about losses, voltages, protection
schemes, etc. This flexibility makes the proposed methodology even more suitable as a tool
for finding the most beneficial places where DGs may be located, as viewed from an
electric utility technical perspective.
[22] This paper proposes an adaptive weight particle swarm optimization (APSO) for
solving optimal distributed generation (DG) placement. APSO has ability to control
velocity of particles. The objective is to minimize the real power loss within acceptable
voltage limits. Four types of DG are considered including DG supplying real power only,
DG supplying reactive power only, DG supplying real power and consume reactive power,
DG supplying real power and reactive power, representing photovoltaic, synchronous
condenser, wind turbines, and hydro power, respectively. The test systems include 33-bus
and 69-bus radial distribution systems. With a given number of DGs in each type, APSO
could find the optimal sizes and locations of multi-DG which result in less total power
system loss than basic particle swarm optimization (BPSO) and repetitive load flow.
Moreover, if the number of DG increases from one to three, the total power loss will
decrease for all types.

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In this paper, APSO is proposed for optimal multi-distributed generation placement. Test
results indicate that the PSO-based algorithm is efficiently finding the optimal multi-DG
placement, compared to BPSO and repetitive load flows,
[27] A concept for the optimization of nonlinear functions using particle swarm
methodology is introduced. The evolution of several paradigms is outlined, and an
implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is
described, and applications, including nonlinear function optimization and neural network
training, are proposed. The relationships between particle swarm optimization and both
artificial life and genetic algorithms are described,
Particle swarm optimization is an extremely wimple algorithm that seems to be effective
for optimizing a wide range of functions. We view it as a ]mid-level form of A-life or
biologically derived algorithm, occupying the space in nature between evolutionary
search, which requires eons, and neural processing, which occurs on the order of
milliseconds. Social optimization occurs in the time frame of ordinary experience - in
fact, it is ordinary experience. In addition to its ties with A-life, particle swarm
optimization has obvious ties with evolutionary computation. Conceptually, it seems to
lie somewhere between genetic algorithms and evolutionary programming. It is highly
dependent on stochastic processes, like evolutionary programming. The adjustment
toward pbest and gbest by the particle swarm optimizer is conceptually similar to the
crossover operation utilized by genetic algorithms. It uses the concept of fitness, as do
all evolutionary computation paradigms.
[37] In this paper, a fuzzy system is implemented to dynamically adapt the inertia
weight of the particle swarm optimization algorithm (PSO). Three benchmark functions
with asymmetric initial range settings are selected as the test functions. The same fuzzy
system has been applied to all the three test functions with different dimensions. The
Experimental results illustrate that the fuzzy adaptive PSO is a promising optimization
method, which is especially useful for optimization problems with a dynamic
environment.
In this paper, a fuzzy system is implemented to dynamically adjust the inertia weight to
improve the performance of the PSO. Three benchmark functions have been used for

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testing the performance of the fuzzy adaptive PSO. For comparison, simulations are
conducted for both the fuzzy adaptive PSO and the PSO with a linearly decreasing
inertia weight. The simulation results illustrate the performance of PSO is not sensitive
To the population size, and the scalability of the PSO is acceptable.
[13] Recently, there has been a great interest in the integration of distributed generation
units at the distribution level. This requires new analysis tools for understanding system
performance. This paper presents a simple methodology for placing a distributed
generator with the view of increasing the load ability of the distribution system. The
effectiveness of the proposed placement technique is demonstrated in a test distribution
system that consists of 30 nodes 32 segments.
A methodology is presented in the paper for distributed generator placement in the
distribution system for maximizing the load ability of the system. In practice, there will
be many factors deciding the location of DG such as fuel availability, land availability
and local ordnance, etc. Given a choice, as corroborated through results the weakest bus
of the system is the best location for DG to increase the loading margin of the system.
[40] This paper presents a genetic algorithm based distributed generator placement
technique in a distribution system for minimizing the total real power losses in the
system. Both the optimal size and location are obtained as outputs from the genetic
algorithm toolbox. The results are verified using two popular power flow analytical tools
for distribution system load flow. The paper also evinces the importance of selecting the
correct size and suitable location for minimizing the system losses.
A genetic algorithm based distributed generator placement technique in a distribution
System for reducing the total real power losses in the system is presented in the paper.
The genetic algorithm toolbox gives both optimal size and the locations as outputs.
These results are verified using two popular load flow programs. This study shows that
the proper placement and size of DG units can have a significant impact on system loss
reduction. It also shows how improper choice of size would lead to higher losses than the
case without DG. However, in practice there will be many constraints to be considered in
selecting the site. Given the choices, the correct sizes of DG units should be placed in the
right location to enjoy the maximum technical benefits.

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[46] This paper describes the Power System Analysis Toolbox (PSAT), an open source
Matlab and GNU/Octave-based software package for analysis and design of small to
medium size electric power systems. PSAT includes power flow, continuation power flow,
optimal power flow, small-signal stability analysis, and time-domain simulation, as well as
several static and dynamic models, including nonconventional loads, synchronous and
asynchronous machines, regulators, and FACTS. PSAT is also provided with a complete
set of user-friendly graphical interfaces and a Simulink-based editor of one-line network
diagrams. Basic features, algorithms, and a variety of case studies are presented in this
paper to illustrate the capabilities of the presented tool and its suitability for educational
and research purposes.
This paper has presented a new open-source PSAT which runs on Matlab and
GNU/Octave. PSAT comes with a variety of procedures for static and dynamic analysis,
several models of standard and unconventional devices, a complete GUI, and a Simulink-
based network editor. These features make PSAT suited for both educational and research
purposes.

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Summary
In this chapter brief literature review is presented. Previous work is carried out on
distributed generation (DG) planning is discussed. The optimal placement is carried out
by different methods are shown. Only main research work papers are considered here for
modelling of 38-bus system and optimal placements & penetration power of DG are
mainly considered .Research papers shows that after insertion of DG in to grid voltage
profile becomes flat , reduction in real power losses & several advantages achieved . PSO
is landmark optimization method for complex engineering problems now-days; in last
PSAT research paper gave information about its capability & advantages over other
power system tools.

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Chapter-3
DISTRIBUTED GENERATION (DG)
Introduction
Distributed Generation Technologies-
Distributed Generation Applications
The Benefits of Distributed Power.
The main characteristics of distributed generation
Summary

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3.1 Introduction
Distributed generation (DG) is not a new concept but it is an emerging approach for
providing electric power in the heart of the power system. The concept of distributed
Generation, which is now gaining worldwide acceptance, was started in the USA almost a
decade ago .Distributed generation (DG) technologies can provide energy solutions to some
customers that are more cost-effective, more environmentally friendly, or provide higher
power quality or reliability than conventional solutions.
Distributed generation is also known as:
Back-up generation
Stand-by generation
Cogeneration
Combined Heat and Power (CHP)
Renewable generation
Remote power
There is not a unique definition of Distributed Generation in all respect covering all the
relevant issues of that like range, location, and siltation. So we have some exist definitions
from different research centers.
DPCA (Distributed Power Coalition of America)
Distributed power generation is any small-scale power generation technology that provides
electric power at a site closer to customers than central station generation. A distributed
power unit can be connected directly to the consumer or to a utility's transmission or
distribution system.
CIGRE (International Conference on High Voltage
Electric Systems)

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Distributed generation is
• Not centrally planned
• Today not centrally dispatched
• Usually connected to the distribution network
• Smaller than 50 or 100 MW
IEA (International Energy Agency)
Distributed generation is generating plant serving a customer on-site, or providing support
to a distribution network, and connected to the grid at distribution level voltages. The
technologies generally include engines, small (including micro) turbines, fuel cells and
photovoltaic. It does not generally include wind power, since most wind power is produced
in wind farms built specifically for that purpose rather than for meeting an on-site power
requirement.
Arthur D. Little
Distributed generation is the integrated or standalone use of small, modular electricity
generation resources by utilities, utility customers, and/or third parties in applications that
benefit the electric system, specific end-user customers, or both. Cogeneration
and combined heat and power (CHP) are included. From a practical perspective, it is a
facility for the generation of electricity that may be located at or near end users within an
industrial area, a commercial building, or a community.
Swedish Electric Power Utilities
Distributed generation is a source of electric power connected directly to the distribution
network or on the customer site of meter.
US Department of Energy
Distributed generation - small, modular electricity generators sited close to the customer
load that can enable utilities to defer or eliminate costly investments in transmission and
distribution (T&D) system upgrades, and provide customers with better quality, more
reliable energy supplies and a cleaner environment.
INDIA
Distributed power means modular electric generation or storage located near the point of
use. It includes biomass generators, combustion turbines, micro turbines, engines/generator
sets and storage and control technologies. It can be either grid connected or independent.
Distributed power connected to the grid is the typically interfaced added distribution

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system. Distributed power generation systems range typically from less than a kilowatt
(kW) to ten megawatts (MW) in size.
By definitions, distributed generation involves the technology of using small-scale power
generation technologies located in close proximity to the load being served. The move
toward on-site distributed power generation is accelerating because of the impending
deregulation and restructuring of the utility industry. In the appropriate configuration,
distributed generation technologies can improve power quality, boost system reliability,
reduce energy costs and help delay or defray substantial utility capital investment.
It mainly depends upon the installation and operation of a portfolio of small size, compact,
and clean electric power generating units at or near an electrical load (customer). The
premise of distributed generation is to provide electricity to a customer at a reduced cost
and more efficiently with reduced losses than the traditional utility central generating plant
with transmission and distribution wires. Other benefits that distributed generation could
potentially provide, depending on the technology, include reduced emissions, utilization of
waste heat, improved power quality and reliability and deferral f transmission or
distribution upgrades.
Distributed power generation or simply distributed - generation (DG), is in the focal point
when it comes to providing possible solutions for a number of socio-economic energy
problems that have taken on Considerable importance as we move into the new
millennium. The enhanced efficiency, environmental friendliness, flexibility and
scalability of the emerging technologies involved in distributed generation have put these
systems at the forefront of solutions to provide power generation for the future.
Moving away from the classical "standby" image of small generator sets and battery based
UPS, the use of DG is expected to grow through a wide range of applications . In many
parts of the world, where there is no power grid, DG can be the only source of power. On
the other hand, in regions well provided with power supply networks, there are few who
contemplate totally replacing connection to the grid by complete reliance on DG, and it is
this aspect of integration of DG into the network that has led to a number of issues which
need to be resolved. The issues involve, not only technical aspects of introducing DG as a

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power source in the network, but also safety and financial concerns of the utility
companies, and inherently, the costs of installing DG with connections to the grid.
DGs are close to the end users, their capacity is small and can operate independently or
Grid- connected. The application of DGs can improve the security and reliability of
electricity supply. DGs are based on the development of power electronic, computer,
communication and control technology. The traditional network topology will be changed
by introducing a large number of power electronic devices, thus there will be uncertainty
generating to network stability. DGs can generate power in time, and reduce the operate
failure to improve the stability of the power system. With the appropriate layout and
voltage regulation, DGs can mitigate the voltage dips and improve voltage regulation
ability and reliability of the system . This is also the main reason for the rapid development
of DGs in recent years.
Till now, not all DG technologies and types are economic, clean or reliable. Some literature
studies delineating the future growth of DGs are:
a) The Public Services Electric and Gas Company (PSE&G), New Jersey, started
to participate in fuel cells (FCs) and photovoltaic’s (PVs) from 1970 and micro-
turbines (MTs) from 1995 till now. PSE&G becomes the distributor of
Honeywell’s 75kW MTs in USA and Canada. Fuel cells are now available in
units range 3–250kW size.
b) The Electric Power Research Institutes (EPRI) study shows that by 2010, DGs
will take nearly 25% of the new future electric generation, while a National Gas
Foundation study indicated that it would be around 30%.
Surveying DG concepts may include DG definitions, technologies, applications, sizes,
locations, DG practical and operational limitations, and their impact on system operation
and the existing power grid. This work focuses on surveying different DG types,
technologies, definitions, their operational constraints, placement and sizing with new
methodology particle swarm optimization. Furthermore, we aim to present a critical survey
by proposing new DG in to conventional grid to make it smart grid.

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3.2 Distributed Generation Technologies-
There are different types of DGs from the constructional and technological points of view
as shown in Fig. 1. These types of DGs must be compared to each other to help in taking
the decision with regard to which kind is more suitable to be chosen in different situations.
However, in our work we are concerned with the technologies and types of the new
emerging DGs: micro-turbines and fuel cells. The different kinds of distributed generation
Technologies are discussed below. Often the term distributed generation is used in
combination with a certain generation technology category, e.g. renewable energy
technology. According to our definition, however, the technology that can be used is not
limited.
DG technologies can meet the needs of a wide range of users, with applications in the
residential, commercial, and industrial sectors. Decision makers at all levels need to be
aware of the potential benefits DG can offer. In some instances, DG technologies can be
more cost effective than conventional solutions.

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Table 3. 1 Technologies for distributed generation
Technology Typical available size per module
Wind turbine 200 Watt–3 MW
Micro-Turbines 35 kW–1 MW
Combined cycle gas T. 35–400 MW
Internal combustion engines 5 kW–10 MW
Combustion turbine 1–250 MW
Small hydro 1–100 MW
Micro hydro 25 kW–1 MW
Photovoltaic arrays 20 Watt–100 kW
Solar thermal, central receive 1–10 MW
Solar thermal, Lutz system 10–80 MW
Biomass, e.g. based on gasification 100 kW–20 MW
Fuel cells, phos acid 200 kW–2 MW
Fuel cells, molten carbonate 250 kW–2 MW
Fuel cells, proton exchange 1 kW–250 kW
Fuel cells, solid oxide 250 kW–5 MW
Geothermal 5–100 MW
Ocean energy 100 kW–1 MW
Stirling engine 2–10 kW
Battery storage 500 kW–5 MW

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3.2.1 Reciprocating Engines
Reciprocating engines, developed more than 100 years ago, were the first of the fossil fuel-
driven DG technologies. Both Otto (spark ignition) and Diesel cycle (compression ignition)
engines have gained widespread acceptance in almost every sector of the economy and are
in applications ranging from fractional horsepower units powering small hand-held tools
to60 MW base load electric power plants. Reciprocating engines are ones in which pistons
move back and forth in cylinders. Reciprocating engines are a subset of internal
combustion engines which also include rotary engines. Smaller engines are primarily
designed for transportation and can be converted to power generation with little
modification. Larger engines are, in general, designed for power generation, mechanical
drive, or marine propulsion. Reciprocating engines are currently available from many
manufacturers in all DG size ranges. For DG applications, reciprocating engines offer low
costs and good efficiency, but maintenance requirements are high, and diesel-fueled units
have high emissions.
3.2.2 Micro-turbine (MT)
Micro-turbine technologies are expected to have a bright future. They 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, recuperate small turbine, and generator.
Sometimes, they have only one moving shaft, and use air or oil for lubrication. MTs are
small scale of 0.4–1m3 in volume and 20–500kW in size. Unlike the traditional
combustion turbines, MTs run at less temperature and pressure and faster speed (100,000
rpm), which sometimes require no gearbox. Some existing commercial examples have low
costs, good reliability, fast speed with air foil bearings ratings range of 30–75kW are
installed in North-eastern US and Eastern Canada and Argentina by Honeywell Company
and 30–50kW for Capstone and Allison/GE companies, respectively . Another example is
ABB MT: of size 100kW, which runs at maximum power with a speed of 70,000 rpm and
has one shaft with no gearbox where the turbine, compressor, and a special designed high
speed generator are on the same shaft.

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Consist of
LIKE
Such as
Fig.3.1 . Distributed generation types and technologies.
Distributed
Generation type
and Technologies
Non-traditional generatorsTraditional Generators
Combustion Engines
MICRO
TURBINE
MT
Natural gas
turbine
Simple
cycle
Combined
cycleRecuperated
cycle
Electrochemical
device
Storage
device
Renewable
device
Fuel
cells
Batterie
s
Flywheels
(PV) Wind
Turbine
(WT)

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3.2.3 Electrochemical devices: fuel cell (FC)
The fuel cell is a device used to generate electric power and provide thermal energy from
chemical energy through electrochemical processes. It can be considered as a battery
supplying electric energy as long as its fuels are continued to supply. Unlike batteries, FC
does not need to be charged for the consumed materials during the electrochemical process
since these materials are continuously supplied. FC is a well-known technology from the
early 1960s when they were used in the Modulated States Space Program and many
automobile industry companies. Later in 1997, the US Department of Energy tested
gasoline fuel for FC to study its availability for generating electric power. FC capacities
vary from kW to MW for portable and stationary units, respectively.
3.2.4 The Internal Combustion Engine:
The most important instrument of the DG systems around the world has been the Internal
Combustion Engine. Hotels, tall buildings, hospitals, all over the world use diesels as a
back-up. Though the diesel engine is efficient, starts up relatively quickly,
it is not environment friendly and has high O & M costs. Consequently its use in the
developed world is limited. In India, the diesel engine is used very widely on account of the
immediate need for power, especially in rural areas, without much concern either for long-
term economics or for environment.
3.2.5 Biomass Based on Gasification
Biomass gasifier systems of up to 500 kW capacity based on fuel wood have been
indigenously developed and being manufactured in the country. Technology for producing
biomass briquettes from agricultural residues and forest litter at both household and
industry levels has been developed. A total capacity of 51.3 MW has so far been installed,
mainly for stand-alone applications.
3.2.6 Wind Turbine Systems
Windmills have been used for many years to harness wind energy for mechanical work
such as pumping water. Before the Rural Electrification Act in the 1920’s provided funds
to extend electric power to outlying areas, farms were using windmills to produce
electricity with electric generators. In the US alone, eight million mechanical windmills
have been installed. Wind energy became a significant topic in the 1970s during the energy
crisis in the U.S. and the resulting search for potential renewable energy sources. Wind

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turbines, basically wind mills dedicated to producing electricity, were considered the most
economically viable choice with in the renewable energy portfolio. During this time,
subsidies in the form of tax credits and favorable Federal regulations were available for
wind turbine projects to encourage the penetration of wind turbines and other renewable
energy sources. Today, attention has remained]focused on this technology as an
environmentally sound and convenient alternative. Wind turbines can produce electricity
without requiring additional investments in infrastructure such as new transmission lines,
and are thus commonly employed in remote locations. Most wind turbines currently being
used are small units (less than 5 kW) designed for the residential sector or larger units
installed by electric companies so they can sell green power to their customers.
3.2.7 Storage devices
It consists of batteries, flywheels, and other devices, which are charged during low load
demand and used when required. It is usually combined with other kinds of DG types to
supply the required peak load demand. These batteries are called “deep cycle”. Unlike car
batteries, “shallow cycle” which will be damaged if they have several times of deep
discharging, deep cycle batteries can be charged and discharged a large number of times
without any failure or damage. These batteries have a charging controller for protection
from overcharge and over discharge as it disconnects the charging process when the
batteries have full charge. The sizes of these batteries determine the battery discharge
period. However, flywheels systems can charge and provide 700kW in 5 s.
3.2.8 Renewable devices
Green power is a new clean energy from renewable resources like; sun, wind, and water. Its
electricity price is still higher than that of power generated from conventional oil sources.
3.2.9 Gas Turbines:
gas turbines are widely used for electricity generation thanks to the regulatory incentives
induced to favor fuel diversification towards natural gas and thanks to their low emission
levels. Conversely to reciprocating engines, gas turbines ordered over the period covered
by the survey were widely used as continuous generators (58%), 18% were used as standby
generators and 24% as peaking generators (DGTW, 2008). Gas turbines are
Widely used in cogeneration;

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DG capacities are not restrictedly defined as they depend on the user type (utility or
customer) and/or the used applications. These levels of capacities vary widely from one
unit to a large number of units connected in a modular form.
Table 3.2 Comparison between common energy types for power and time duration
Power supplied period DG type Remarks
Long period supply Gas turbine and FC
stations
Provide P and Q except FC provides P
only.
Used as base load provider.
Unsteady supply Renewable energy
systems; PV arrays,
WT
Depend on weather conditions.
Provide P only and need a source of Q in
the network.
Used in remote places.
Need control on their operation in some
applications.
Short period supply FC storage units,
batteries, PV cells
Used for supply continuity.
Store energy to use it in need times for a
short period.
3.3 Distributed Generation Applications
Distributed generation (DG) is currently being used by some customers to provide some or
all of their electricity needs. There are many different potential applications for DG
technologies. For example, some customers use DG to reduce demand charges imposed by
their electric utility, while others use it to provide premium power or reduce environmental
emissions. DG can also be used by electric utilities to enhance their distribution systems.

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Many other applications for DG solutions exist. The following is a list of those of potential
interest to electric utilities and their customers.
Continuous Power - In this application, the DG technology is operated at least 6,000 hours
a year to allow a facility to generate some or all of its power on a relatively continuous
basis.
Important DG characteristics for continuous power include:
· High electric efficiency,
· Low variable maintenance costs, and
· Low emissions.
Currently, DG is being utilized most often in a continuous power capacity for industrial
Application such as food manufacturing, plastics, rubber, metals and chemical production.
Commercial sector usage, while a fraction of total industrial usage, includes sectors such as
grocery stores and hospitals.
Combined Heat and Power (CHP) - Also referred to as Cooling, Heating, and Power or
Cogeneration, this DG technology is operated at least 6,000 hours per year to allow a
facility to generate some or all of its power. A portion of the DG waste heat is used for
water heating, space heating, steam generation or other thermal needs. In some instances
this thermal energy can also be used to operate special cooling equipment. Important DG
characteristics for combined heat and power include:
· High useable thermal output (leading to high overall efficiency),
· Low variable maintenance costs, and
· Low emissions.
CHP characteristics are similar to those of Continuous Power, and thus the two applications
have almost identical customer profiles, though the high thermal demand necessary here is
not a requisite for Continuous Power applications. As with Continuous Power, CHP is most
commonly used by industry clients, with a small portion of overall installations in the
commercial sector.
Peaking Power - In a peaking power application, DG is operated between 200-3000 hours
per year to reduce overall electricity costs. Units can be operated to reduce the utility’s
demand charges, to defer buying electricity during high-price periods, or to allow for lower
rates from power providers by smoothing site demand. Important DG characteristics for
peaking power include:

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· Low installed cost,
· Quick startup, and
· Low fixed maintenance costs.
Peaking power applications can be offered by energy companies to clients who want to
reduce the cost of buying electricity during high-price periods. Currently DG peaking units
are being used mostly in the commercial sector, as load factors in the industrial sector are
relatively flat.
The most common applications are in educational facilities, lodging, miscellaneous retail
sites and some industrial facilities with peaky load profiles.
Green Power - DG units can be operated by a facility to reduce environmental emissions
from generating its power supply. Important DG characteristics for green power
applications include:
· Low emissions,
· High efficiency, and
· Low variable maintenance costs.
Green power could also be used by energy companies to supply customers who want to
purchase power generated with low emissions.
Premium Power - DG is used to provide electricity service at a higher level of reliability
and/or power quality than typically available from the grid. The growing premium power
market presents utilities with an opportunity to provide a value-added service to their
clients. Customers typically demand uninterrupted power for a variety of applications, and
for this reason, premium power is broken down into three further categories:
Emergency Power System - This is an independent system that automatically provide
electricity within a specified time frame to replace the normal source if it fails. The system
is used to power critical devices whose failure would result in property damage and/or
threatened health and safety. Customers include apartment, office and commercial
buildings, hotels, schools, and a wide range of public gathering places.
Standby Power System - This independent system provides electricity to replace the
normal source if it fails and thus allows the customer’s entire facility to continue to operate
satisfactorily. Such a system is critical for clients like airports, fire and police stations,
military bases, prisons, water supply and sewage treatment plants, natural gas transmission
and distribution systems and dairy farms.

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True Premium Power System - Clients who demand uninterrupted power, free of all
power quality problems such as frequency variations, voltage transients, dips, and surges,
use this system. Power of this quality is not available directly from the grid – it requires
both auxiliary power conditioning equipment and either emergency or standby power.
Alternatively, a DG technology can be used as the primary power source and the grid can
be used as a backup. This technology is used by mission critical systems like airlines,
banks, insurance companies, communications stations, hospitals and nursing homes.
Important DG characteristics for premium power (emergency and standby) include:
• Quick startup,
• Low installed cost, and
• Low fixed maintenance costs.
Transmission and Distribution Deferral - In some cases, placing DG units in strategic
locations can help delay the purchase of new transmission or distribution systems and
equipment such as distribution lines and substations. A thorough analysis of the life-cycle
costs of the various alternatives is critical and contractual issues relating to equipment
deferrals must also be examined closely. Important DG characteristics for transmission and
distribution deferral (when used as a “peak deferral”) include:
· Low installed cost, and
· Low fixed maintenance costs.
Transmission and distribution DG applications in the U.S. are rare and are not discussed in
the main sections of this report.
Ancillary Service Power - DG is used by an electric utility to provide ancillary services
(interconnected operations necessary to effect the transfer of electricity between purchaser
and seller) at the transmission or distribution level. The market for ancillary services is still
unfolding in the U.S., but in markets where the electric industry has been deregulated and
ancillary services unbundled (in the United Kingdom, for example), DG applications offer
advantages over currently employed technologies. Ancillary services include spinning
reserves (unloaded generation, which is synchronized and ready to serve additional
demand) and non-spinning, or supplemental, reserves (operating reserve is not connected to
the system but is capable of serving demand within a specific time or interruptible demand
that can be removed from the system within a specified time). Other potential services
range from transmission market reactive supply and voltage control, which uses generating
facilities to maintain a proper transmission line voltage, to distribution level local area

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security, which provides back up power to end users in the case of a system fault. The
characteristics that may influence the adoption of DG technologies for ancillary service
applications will vary according to the service performed and the ultimate shape of the
ancillary service market. Ancillary service DG applications in the India.
Distributed power technologies are typically installed for one or more of the following
purposes:
(i) Overall load reduction – Use of energy efficiency and other energy saving measures
for reducing total consumption of electricity, sometimes with supplemental power
generation.
(ii) Independence from the grid – Power is generated locally to meet all local energy
needs by ensuring reliable and quality power under two different models.
a. Grid Connected – Grid power is used only as a back up during failure of
maintenance of the onsite generator.
b. Off grid – This is in the nature of stand-alone power generation. In order to attain
self-sufficiency it usually includes energy saving approaches and an energy storage
device for back-up power. This includes most village power applications in
developing countries.
(iii) Supplemental Power- Under this model, power generated by the grid is augmented
with distributed generation for the following reasons: -
a. Standby Power- Under this arrangement power availability is assured during grid
outages.
b. Peak shaving – Under this model the power that is locally generated is used for
reducing the demand for grid electricity during the peak periods to avoid the peak
demand charges imposed on big electricity users.
(iv) Net energy sales – Individual homeowners and entrepreneurs can generate more
electricity than they need and sell their surplus to the grid. Co-generation could fall into this
category.

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(v) Combined heat and power - Under this model waste heat from a power generator is
captured and used in manufacturing process for space heating, water heating etc. in order to
enhance the efficiency of fuel utilization.
(vi) Grid support – Power companies resort to distributed generation for a wide variety of
reasons. The emphasis is on meeting higher peak loads without having to invest in
infrastructure (line and sub-station upgrades).
3.4 The Benefits of Distributed Power.
DG is a competitive power generation in the future electricity market. Application of DG
brings the following advantages to electric power system operation .
1) DG is a useful addition for a large power grid: as the implementation of networking,
the emergence of AC/ DC hybrid transmission system and electricity market reforms, the
loss of accident caused by major power system blackouts has a great relationship with a
reasonable and feasible "Black Start" program. In DG the hydro and gas turbine with easy
start and fast recovery characteristics, can be used as black start power supply.
2) DG can be used for military and humanitarian tasks:
electrical safety is an important component of national security. Large power grids are
vulnerable to the destruction of war or terrorism or catastrophe, it will seriously endanger
national security. Such as the Kosovo War and the Gulf war. after "911 event", many
experts proposed developing DG is an effective means to solve these electrical safety
issues, such as from the support of Isolated small villages to the support of entire large
operational plan can take advantage of DG.
3) DG can make up the deficiency of large power grids stability: When electric power
system is failure, it can provide emergency power support, making use of local DG
technology which can launch to gradual recovery important load of local power grid in a
short time, then it will ensure electricity supply of important users, but also will prevent
system accident to expand. It not only increases power grid flexibility, and improves power
quality, increases reliability.
4) Need not build power transformer and distribution
station: With the development of social, Load fluctuations is increasing, for short-term
peak load, the investment of building power plants is large and economically inefficient,
but a great deal of nearby supply power reduces transmission and distribution investment,

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and line losses is small. Reduction of transmission and distribution lines can reduce outlet
corridor, reduce electromagnetic pollution of high voltage transmission lines.
5) High efficiency and environmental protection: DG's environmental protection
performance is excellent, it has high energy efficiency up to 65% to 95%. DG also makes
study of using clean energy and renewable energy to generate
electricity possible. Fuel cells, solar photovoltaic, Solar thermal collectors power, wind
power will be effectively applied.
6) can break the power monopoly: In recent years, China has continuously carried the
electricity market reform, the intent is to introduce competition, lower costs of power
production and supply, optimize resource allocation. DG can contribute to the realization of
these purposes. Because DG investment is small, construction time of installation is
short,so it is conducive to investment of independent power producers, which can realize
the power industry market
7) can promote the sustainable development of China's economy:
In order to support sustainable development of China's economic growth, China need to
increase power capacity, expand power production. If using the traditional
power generation mode, it will pose a great threat to energy supply in China. Another
constraint can not be ignored is serious environmental pollution caused by the large
number of fossil energy consumption and large amounts of greenhouse gas emissions.
Active using renewable energy and developing DG can ensure sustainable economic
development.
8) can achieve load power demand in remote areas:
Remote area load is too far away from the existing power system, it is too much investment
to built transmission and distribution systems; and because of natural conditions are too
harsh, from the existing power system to user's transmission line is fully impossible to set
up or after the completion it will often fail. Using DG mode such as small hydropower,
wind power, solar photovoltaic and biomass power generation is an
effective method to solve users electricity in remote areas .
Energy consumers, power providers and all other state holders are benefited in their own
ways by the adoption of distributed power. The most important benefit of distributed power
stems from its flexibility, it can provide power where it is needed and when it is needed.
The major benefits of distributed power to the various stakeholders are as follows:

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3.4.1 Major Potential Benefits of Distributed Generation
Consumer-Side Benefits: Better power reliability and quality, lower energy cost, wider
choice in energy supply options, better energy and load management and faster response to
new power demands are among the major potential benefits that can accrue to the
consumers.
Grid –Side Benefits: The grid benefits by way of reduced transmission and distribution
losses, reduction in upstream congestion on transmission lines, optimal use of existing grid
assets, higher energy conversion efficiency than in central generation and improved grid
reliability. Capacity additions and reductions can be made in small increments closely
matching the demands instead of constructing Central Power Plants which are sized to meet
a estimated future rather than current demand under distributed generation.
Energy Shortage –States likes California and New York that experienced energy
shortages decided to encourage businesses and homeowners to install their own generating
capacity and take less power from the grid. The California Public Utilities Commission for
instance approved a programme of 125 US million $ incentives programme to encourage
businesses and homeowners to install their own generating capacity and take less power
from the grid. In the long run the factors enumerated below would play a significant part in
the development of distributed generation.
Digital Economy –Though the power industry in the USA met more than 99% of the
power requirements of the computer based industries, these industries found that even a
momentary fluctuation in power supply can cause computer crashes. The industries, which
used computer, based manufacturing processes shifted to their own back-up systems for
power generation.
Continued Deregulation of Electricity Markets – The progressive deregulation of the
electricity markets in the USA led to violent price fluctuations because the power
generators, who were not allowed to enter into long-term wholesale contracts, had to pass
on whatever loss they suffered only on the spot markets. In a situation like that in
California where prices can fluctuate by the hour, flexibility to switch onto and off the grid

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alone gives the buyer the strength to negotiate with the power supplier on a strong footing.
Distributed generation in fact is regarded as the best means of ensuring competition in the
power sector.
Both in the USA and UK the process of de-regulation did not make smooth progress on
account of the difficulties created by the regulated structure of the power market and a
monopoly enjoyed the dominant utilities. In fact, the current situation in the United States
in the power sector is compared to the situation that arose in the Telecom Sector on account
of the breakup of AT&T Corporation’s monopoly 20 years ago. In other words distributed
generation is a revolution that is caused by profound regulatory change as well as profound
technical change.
3.5 The main characteristics of distributed generation
As seen in the chapter-1, distributed generation is The main drivers behind the revival of
distributed generation has been historically used in several ways to complement centralized
generation. The reason behind the recent revival of distributed generation is two-fold:
The liberalization of the electricity markets and concerns over greenhouse gas emissions.
The electricity and gas deregulation process started in Europe following the application of
two directives aimed at providing a free flow of gas and electricity across the continent.
These directives and the subsequent legislation created a new framework making it possible
for distributed generators to increase their share in the total electricity generation mix.
The effect of deregulation is two-fold (IEA, 2002):
• Thanks to the reduction of barriers to entry and clearer prices signals, distributed
generators were able to move in niche markets and exploit failures of centralized
generation. These new applications took the form of standby capacity generators,
peaking generators (i.e. producing electricity only in case of high price and
consumption periods), generators improving reliability and power capacities,
generators providing a cheaper alternative to network use or expansion, provision
of grid support (i.e. provision of ancillary services permitting better and safer
operation of the network and/or shortening the recovery time)
• As distributed generators tend to be of smaller size and quicker to build, they have
been able to benefit from price premiums. Geographical and operational flexibility
made it possible to set up distributed generators in Congested areas or use it only
during consumption peaks. Besides, for small excess demand, it is often

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