OPTIMAL PLACEMENT OF DISTRIBUTED GENERATION

“OPTIMAL PLACEMENT AND SIZING OF MULTI-
DISTRIBUTED GENERATION (DG) INCLUDING DIFFERENT
LOAD MODELS USING PSO”
A
Dissertation
Submitted in partial fulfillment for the award of the Degree of
Master of Technology
in

Department of School of Instrumentation

(Instrumentation Engineering)

Supervisor: Submitted By:
Dr. Ganga Agnihotri Jitendra Singh Bhadoriya

Professor & Dean Acad. Er. No.: 11/2011

MANIT, Bhopal

Department of School of Instrumentation

DEVI AHILYA VISHWAVIDYALAYA, INDORE

JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYA
UNIVERSITY,INDORE INDIA

CANDIDATE’S DECLARATION

I hereby declare that the work, which is being presented in the Dissertation, entitled

“Optimal Placement and Sizing Of Multi-Distributed Generation (DG) Including

Different Load Models Using PSO” in partial fulfillment for the award of Degree of

“Master of Technology” in Department of School of Instrumentation with

Specialization Instrumentation, and submitted to the Department of School of

Instrumentation Engineering, Devi Ahilya Vishwavidyalaya, Indore, is a record of

my own investigations carried under the Guidance of Dr. Ganga Agnihotri,

Department of Electrical Engineering, MANIT, Bhopal.

I have not submitted the matter presented in this Dissertation anywhere for the award

of any other Degree.

(Jitendra Singh Bhadoriya)

Instrumentation Engineering
Enrolment No.: 11/2011
D.A.V.V, INDORE


CERTIFICATE

This is to certify that the work which is being presented in this dissertation entitled

“Optimal Placement and Sizing Of Multi-Distributed Generation (DG) Including

Different Load Models Using PSO” submitted by Mr. Jitendra Singh Bhadoriya,

to the Devi Ahilya Vishwavidyalaya, Indore, towards partial fulfillment of the

requirements for the award of the Degree of Master of Technology in

Instrumentation Engineering (Power System) is a bonafide record of the work

carried out by him under my supervision and that this work has not been submitted

elsewhere for a degree.

Supervisor:

Dr. Ganga Agnihotri

Professor & Dean Acad.

MANIT, Bhopal


ACKNOWLEDGEMENT

For the accomplishment of this thesis work, expression and words run short to convey
my gratitude to many individuals. This thesis work is an outcome of moral support
and persuasive interest dedicated from many individuals directly or indirectly
involved.

Though the idea of the thesis started from characterizing a curricular obligation, never
the less it has taken the interest of learning to ever-new heights for us. I am indebted
to MANIT, Bhopal, for providing such a forum where we can utilize and in a way
experiment with the knowledge acquired over the complete Master of Technology
curriculum.

I would like to take this opportunity in expressing immense gratitude to my guide Dr.
Ganga Agnihotri, Professor, Department of Electrical Engineering, MANIT, Bhopal,
for her constant inspiration, useful criticism and immense support throughout the
work. I am indebted for the hard work she has put in to produce this report in the best
possible form.

I would like to extend my honour to Dr. Appu Kuttan K.K. Director, MANIT,
Bhopal, Dr. R.K. Nema Head of Electrical Department, MANIT, Bhopal, for their
blessings and encouragement.

I am thankful to the Staff Members of Department of Electrical Engineering, MANIT,
Bhopal, for their co-operation in my work. Last but not least I would like to express
my sincere thanks to all of my friends for their valuable support.

Finally, my special thanks to my parents for their moral support and encouragement.

(Jitendra Singh Bhadoriya)


ABSTRACT

This work presents a multi objective performance index-based size and location
determination of distributed generation in distribution systems with different load
models. Normally, a constant power (real and reactive) load model is assumed in most
of the studies made in the literature. It is shown that load models can significantly
affect the optimal location and sizing of distributed generation (DG) resources in
distribution systems. The simulation technique based on particle swarm technology is
studied. The studies have been carried out on 38-bus distribution systems.

This 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 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.
After enactment of Electricity Act ‘2003 in India, a comprehensive change is
happening in Indian power sector, and power distribution utilities are going through a
reformation process to cope up with the regulatory change for reduction in
Aggregated Technical and Commercial Loss, improvement in Power Quality,
Reliability of Power Supply, and Improvement in Customer Satisfaction. Smart Grid
is sophisticated, digitally enhanced power systems where the use of modern
communications and control technologies allows much greater robustness, efficiency
and flexibility than today’s power systems. In a smart grid, all the various nodes need
to interconnect to share data as and where needed. Government of India has recently
formed “Smart Grid Forum” and “Smart Grid Task Force” for enablement of smart
grid technology into Indian Power Distribution Utilities as a part of their Smart Grid
initiative to meet their growing energy demand in similar with the developed country
like USA, Europe etc.

CONTENTS

CANDIDATE’S DECLARATION …………………………………………………i
CERTIFICATE….......................................................................................................ii
ACKNOWLEDGEMENTS.......................................................................................iii
ABSTRACT...................................................................................................................iv
CONTENTS.................................................................................................................v
LIST OF FIGURES....................................................................................................vi
LIST OF TABLES......................................................................................................vi

CHAPTER-1………………………………………………………………………...1
INTRODUCTION…………………………………………………………………..1
1.1 Introduction……………………………………………………………………....1
1.2 DG types and range……………………………………………………………….2
1.3 Distributed Power Applications………………………………….………………..5
1.4 Classic Electricity
Paradigm………………………………………………………6
1.5 The Benefits of Distributed Power.………………………………….....................7

CHAPTER-2…………………………………………………………………….......10
PARTICLE SWARM OPTIMIZATION………………………………...………..10
2.1 Introduction………………………………………...........................................….10
2.2 The PSO algorithm………………………………………………………….........10

CHAPTER-3………………………………………………………………………...12
PSAT/MATLAB RESEARCH TOOL..…………………………………………...12
3.1 Overview……………………………………………………………………….....12

CHAPTER-4…………………………………………………………………….…..16
MODELING OF SMART RADIAL SYSTEM...…………………………………16

4.1 Description of a Power System…………………………………………………..16
4.2 Important of Load Modeling………………………………………………..….16


4.3 Load models and impact indices ………………………………………………...17
4.4 Smart Grid Pilots in India…………………………………………………….......20

CHAPTER-5………………………………………………………………………...23
CONCLUSION……………………………………………………………………...23

REFERENCES……………………………………………………………………...24

LIST OF FIGURE

Figure 1. Distributed generation types and technologies….……………………….....3
Figure 2. Electricity
Paradigm………………………………………………………….7
Figure 3. Main graphical user interface of PSAT…………………………………….15
Figure 4. PSAT Simulink library……………………………………………..……….15
Figure 5. 38-bus test system…………………………………………………………..18
Figure 6. Structure of Smart Grid…………………………………………………….22

LIST OF TABLE

Table -1 Comparison between common energy types for power and time duration…4
Table -2 Functions available on MATLAB and GNU/OCTAVE platforms……..….14
Table -3 Load types and exponent values…………………………………………....17
Table -4 System and load data for 38-bus system………..………………

Dec-2012


Chapter-1
INTRODUCTION

1.1 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. 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). 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 photovoltaics (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.


1.2 DG TYPES AND RANGE

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 paper we are concerned with the technologies
and types of the new emerging DGs: micro-turbines and fuel cells. The different kinds
of distributed generation are discussed below.

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, recuperator, 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.


Fig. 1. Distributed generation types and technologies.

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.

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.

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.

DG capacities: 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 1 Comparison between common energy types for power and time duration
Power supplied period DG type Remarks
Long period supply Gas turbine and FC Provide P and Q except FC provides P
stations only.
Used as base load provider.
Unsteady supply Renewable energy Depend on weather conditions.
systems; PV arrays, Provide P only and need a source of Q
WT in the network.
Used in remote places.
Need control on their operation in some
applications.
Short period supply FC storage units, Used for supply continuity.
batteries, PV cells Store energy to use it in need times for a
short period.


1.3 Distributed Power Applications
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.

(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).

1.4 Classic Electricity Paradigm (Central Power Station Model)

The current model for electricity generation and distribution in the United States 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.


Fig. 2: a) Classic Electricity Paradigm, b) Distributed Generation (DG) Electricity Paradigm

1.5 The Benefits of Distributed Power
A) 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:


1.5.1 Major Potential Benefits of Distributed Generation
1.5.2 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.

1.5.3 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.

1.5.4 Benefits To Other Stake Holders: Energy Service Companies get new
opportunities for selling, financing and managing distributed generation and load
reduction technologies and approaches. Technology developers, manufacturers and
vendors of distributed power equipment see opportunities for new business in an
expanded market for their products. Regulators and policy maker’s support distributed
power as it benefits consumers and promotes competition.

B) The following are among the more important factors that contributed to the
emergence of distributed generation as a new alternative to the energy crisis that
surfaced in the USA.

i. 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.

ii. 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.

iii. 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 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.

C) 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.

D) 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.


Chapter-2
Particle Swarm Optimization
2.1 Introduction

Particle swarm optimization (PSO) is a population based stochastic optimization
technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social
behavior of bird flocking or fish schooling.

PSO shares many similarities with evolutionary computation techniques such as
Genetic Algorithms (GA). The system is initialized with a population of random
solutions and searches for optima by updating generations. However, unlike GA, PSO
has no evolution operators such as crossover and mutation. In PSO, the potential
solutions, called particles, fly through the problem space by following the current
optimum particles. The detailed information will be given in following sections.

Compared to GA, the advantages of PSO are that PSO is easy to implement and there
are few parameters to adjust. PSO has been successfully applied in many areas:
function optimization, artificial neural network training, fuzzy system control, and
other areas where GA can be applied.

2.2 The PSO algorithm

As stated before, PSO simulates the behaviors of bird flocking. Suppose the following
scenario: a group of birds are randomly searching food in an area. There is only one
piece of food in the area being searched. All the birds do not know where the food is.
But they know how far the food is in each iteration. So what's the best strategy to find
the food? The effective one is to follow the bird which is nearest to the food.

PSO learned from the scenario and used it to solve the optimization problems. In
PSO, each single solution is a "bird" in the search space. We call it "particle". All of
particles have fitness values which are evaluated by the fitness function to be
optimized, and have velocities which direct the flying of the particles. The particles
fly through the problem space by following the current optimum particles.


PSO is initialized with a group of random particles (solutions) and then searches for
optima by updating generations. In every iteration, each particle is updated by
following two "best" values. The first one is the best solution (fitness) it has achieved
so far. (The fitness value is also stored.) This value is called pbest. Another "best"
value that is tracked by the particle swarm optimizer is the best value, obtained so far
by any particle in the population. This best value is a global best and called gbest.
When a particle takes part of the population as its topological neighbors, the best
value is a local best and is called lbest.

After finding the two best values, the particle updates its velocity and positions with
following equations.

v[] = v[] + c1 * rand() * (pbest[] - present[]) + c2 * rand() * (gbest[] - present[])
present[] = persent[] + v[]

Where v[] is the particle velocity, persent[] is the current particle (solution). pbest[]
and gbest[] are defined as stated before, rand () is a random number between (0,1). c1,
c2 are learning factors usually c1 = c2 = 2.


Chapter-3
PSAT/MATLAB RESEARCH TOOL
3.1 Overview
Power System Analysis is an analysis that is so important nowadays. It is not only
important in economic scheduling, but also necessary for planning and operation for a
system. Based on that, in recently years, there are many researches, new
developments and analysis was introduced to people in order to mitigate the problems
that involving Power System Analysis such as Load Flow Analysis, Fault Analysis,
Stability Analysis and Optimal Dispatch on Power Generation.

i) Load Flow Analysis is important to analyze any planning for power system
improvement under steady state conditions such as to build new power generation
capacity, new transmission lines in the case of additional or increasing of loads, to
plan and design the future expansion of power systems as well as in determining the
best operation of existing systems.

ii) Fault Analysis is important to determine the magnitude of voltages and line
currents during the occurrence of various types of fault.

iii) Stability Analysis is necessary for reliable operation of power systems to keep
synchronism after minor and major disturbances.

iv) Optimal Dispatch is to find real and reactive power to power plants to meet load
demand as well as minimize the operation cost.

All the analysis discussed above is an importance tool involving numerical analysis
that applied to a power system. In this analysis, there is no known analytical method
to solve the problem because it depends on iterative technique. Iterative technique is
one of the analysis that using a lot of mathematical calculations which takes a lot of
times to perform by hand. So, to solve the problems, the development of this toolbox
based on MATLAB 7.8 with Graphical User Interface (GUI) will help the analysis
become quick and easy.


The PSAT kernel is the power flow algorithm, which also takes care of the state
variable initialization. Once the power flow has been solved, the user can perform
further static and/or dynamic analyses. These are:
1) Continuation Power Flow (CPF);
2) Optimal Power Flow (OPF);
3) Small signal stability analysis;
4) Time domain simulations.

PSAT deeply exploits Matlab vectorized computations and sparse matrix functions in
order to optimize performances. Furthermore PSAT is provided with the most
complete set of algorithms for static and dynamic analyses among currently available
Matlab-based power system softwares (see Table II). PSAT also contains interfaces to
UWPFLOW and GAMS which highly extend PSAT ability to solve CPF and OPF
problems, respectively.

In order to perform accurate and complete power system analyses, PSAT supports a
variety of static and dynamic models, as follows:

- Power Flow Data: Bus bars, transmission lines and transformers, slack buses, PV
generators, constant power loads, and shunt admittances.

- Market Data: Power supply bids and limits, generator power reserves, and power
demand bids and limits.

- Switches: Transmission line faults and breakers.

- Measurements: Bus frequency measurements.
- Loads: Voltage dependent loads, frequency dependent loads, ZIP (polynomial)
loads, thermostatically controlled loads, and exponential recovery loads [14].

- Machines: Synchronous machines (dynamic order from 2 to 8) and induction motors
(dynamic order from 1 to 5).

- Controls: Turbine Governors, AVRs, PSSs, Over-excitation limiters, and secondary
voltage regulation.
- Regulating Transformers: Under load tap changers and phase shifting transformers.


- FACTS: SVCs, TCSCs, SSSCs, UPFCs.

- Wind Turbines: Wind models, constant speed wind turbine with squirrel cage
induction motor, variable speed wind turbine with doubly fed induction generator, and
variable speed wind turbine with direct drive synchronous generator.

- Other Models: Synchronous machine dynamic shaft, subsynchronous resonance
model, solid oxide fuel cell, and subtransmission area equivalents.

Besides mathematical algorithms and models, PSAT includes a variety of additional
tools, as follows:
1) User-friendly graphical user interfaces;
2) Simulink library for one-line network diagrams;
3) Data file conversion to and from other formats;
4) User defined model editor and installer;
5) Command line usage.

TABLE 2 Functions available on MATLAB and GNU/OCTAVE platforms


Fig. 3. Main graphical user interface of PSAT.

Fig. 4. PSAT Simulink library.


Chapter-4
MODELING OF SMART RADIAL
SYSTEM

4.1 Description of a Power System
A power system must be safe, reliable, economical, benign to the environment and
socially acceptable. The power system is subdivided into Generation, Transformer,
Transmission and Sub-Transmission, Distribution and Loads. The following section
will examine each of the sub-system in detailed.

4.1.1 Distribution
The distribution system is the part that the sub-transmission lines typically deliver
their power to locations called substations where the voltage is transformed
downward to a voltage that is required by the customers. The voltage of the
distribution system is between 4.6KV and 25KV.

4.2 Important of Load Modeling

The power system engineer bases decisions concerning system reinforcements and
system performance in large part on the results of power flow and stability simulation
studies. Representation inadequacies that cause under or over building of the system
or degradation of reliability could prove to be costly. In performing power system
analysis, models must be developed for all pertinent system components, including
generating stations, transmission and distribution equipment, and load devices. Much
attention has been given to models for generation and transmission/distribution
equipment. The representation of the loads has received less attention and continues
to be an area of greater uncertainty. Many studies have shown that load representation
can have significant impact on analysis results. Therefore, efforts directed at
improving load modelling are of major importance.

4.3 Load models and impact indices


The optimal allocation and sizing of DG units under different voltage-dependent load
dependent
model scenarios are to be investigated. Practical voltage-dependent load models, i.e.,
dependent
residential, industrial, and commercial, have been adopted for investigations. The load
models can be mathematically expressed as:
as

Where Pi and Qi are real and reactive power at bus i, Poi and Qoi are the active and
reactive operating points at bus i, Vi is the voltage at bus i, and α and β are real and
reactive power exponents. In the constant power model conventionally used in power
flow studies, α = β = 0 is assumed. The values of the real and reactive exponents used
in the present work for industrial, residential, and commercial loads are given in Table
3.

Table 3 Load types and exponent values.


Fig. 5. 38-bus test system.

TABLE 4 System and load data for 38-bus system


4.4 Smart Grid Pilots in India

The following functionalities have been proposed in the 8 pilot projects


4.4.1 Smart grid as distribution
Smart Grid is the modernization of the electricity delivery system so that it monitors,
protects and automatically optimizes the operation of its interconnected elements –
from the central and distributed generator through the high-voltage network and
high
distribution system, to industrial users and building automation systems, to energy
system
storage installations and to end use consumers and their thermostats, electric vehicles,
end-use
appliances and other household devices. Smart grid is the integration of information
and communications system into electric transmission and distribution networks. The
Smart Grid in large, sits at the intersection of Energy, IT and Telecommunication
Technologies. The smart grid (Refer Fig 6) delivers electricity to consumers using
)
two-way digital technology to enable the more efficient management of consum
way consumers’

end uses of electricity as well as the more efficient use of the grid to identify and
correct supply demand-imbalances instantaneously and detect faults in a “self-
healing” process that improves service quality, enhances reliability, and reduces costs.
The emerging vision of the smart grid encompasses a broad set of applications,
including software, hardware, and technologies that enable utilities to integrate,
interface with, and intelligently control innovations.

Some of the enabling technologies & business practice that make smart grid
deployments possible include:

• Smart Meters
• Meter Data Management
• Field area networks
• Integrated communications systems
• IT and back office computing
• Data Security
• Electricity Storage devices
• Demand Response
• Distributed generation
• Renewable energy

Fig 6: Structure of Smart Gri


Chapter-5

CONCLUSIONS

1) Here the problem of DG placement & capacity has presented.

2) PSO methodology used for multi dg placement.

3) IT will make power grid in to smart grid.

4) DG have advantage of islanding, it make consumer less dependent on grid.

5) DG can be work either individually or grid connected so it forms
decentralized system.

REFERENCES
[1] Book of Swarm Intelligence by JamesKennedy, YuhuSh.
[2] THE ELECTRICITY ACT, 2003.
[3] http://www.sciencedirect.com/
[4] Smart Grid Vision & Roadmap for India (benchmarking with other countries)
– Final Recommendations from ISGF.
[5] Islanding Protection of Distribution Systems with Distributed Generators – A
Comprehensive Survey Report S.P.Chowdhury, Member IEEE.
[6] Distributed Power Generation: Rural India – A Case Study, Anshu Bharadwaj
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[15] Ministry of Power, 2003b. Discussion Paper on Rural Electrification Policies,
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[18] http://ieeexplore.ieee.org
[19] http://www.swarmintelligence.org
[20] http://umpir.ump.edu.my/360/
[21] http://www.mnre.gov.in
[22] http://www.isgtf.in
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