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1242 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 29, NO. 3, MAY
2014
Dispersed Generation Enable Loss Reductionand Voltage Profile
Improvement in Distribution
NetworkCase Study, Gujarat, IndiaAkash T. Davda, Member, IEEE,
Brian Azzopardi, Member, IEEE, Bhupendra R. Parekh, Member, IEEE,
and
Manhar D. Desai
AbstractDistribution system operators are often challenged
byvoltage regulation problems, energy losses, and network
capacityproblems. This paper analyses a real-life 3.9-MVA
distributionnetwork in Gujarat State, India. Distributed generation
fromrenewable energy sources like wind and solar, at optimal
locationson distribution feeders, may enable energy loss reduction
andvoltage profile improvement. A methodology is developed
andpresented for deciding the appropriate location of these
embeddedrenewable generators. Simulations are performed to
calculatedifferent scenarios, and the final analysis reveals that
the lowvoltage problem has totally been eliminated on all of the
nodes ofthe distribution network. Complimentary, significant energy
lossreductions are also achieved in the distribution, and the
networkreserve capacity has also increased.
Index TermsDistributed generation (DG), distribution net-work,
embedded renewable generation (ERG), renewable energysources
(RESs).
I. INTRODUCTION
T HE emergence of intermittent local energy production,most
likely by renewable energy sources (RESs) has pre-sented new
challenges to all, such as the electricity supply
chain,transmission system operators (TSOs), distribution system
op-erators (DSOs), and energy supply companies (ESCos). Oneof these
challenges is possibly leading to problems in the net-works that
have not been planned in advance, as, originally,the electric power
system is designed to have centralized gen-erating plants
facilitating unidirectional power flow through anextensive
transmission and distribution network. The traditionalsystem
operation by ESCos was to plan for peak loads rather
Manuscript received April 26, 2013; revised September 05, 2013;
acceptedOctober 28, 2013. Date of publication December 11, 2013;
date of current ver-sion April 16, 2014. Paper no.
TPWRS-00513-2013.A. T. Davda is with the Department of Electrical
Engineering, B. H.
Gardi College of Engineering and Technology, 361162 Rajkot,
India (e-mail:[email protected]).B. Azzopardi is with the
Department of Electric Power Systems and Re-
newable Energy Centre, Kaunas University of Technology, LT-51367
Kaunas,Lithuania (e-mail: [email protected]).B. R. Parekh is
with the Department of Electrical Engineering, Birla
Vishvakarma Mahavidyalaya, 388120 Vallabh Vidyanagar, India.
(e-mail:[email protected]).M. D. Desai was with the Department of
Electrical Engineering, Kalol
Institute of Technology and Research, 382721 Kalol, NG, India
(e-mail:[email protected]).Digital Object Identifier
10.1109/TPWRS.2013.2292117
than net load. The peak load was very predictable, and,
hence,control of the generation station could optimally be
performedeven manually. In contrary, consumers expect an absolute
rightto turn their loads on and off at will, as this have been the
sit-uation through most of the 20th century. With potential
storageunits such as batteries, electric vehicles (EVs), or heat
pumpswith heat storage, increasing shares of consumers tend to
covertheir electricity demand by their own local generation, such
asphotovoltaic (PV) for typical households and combined heatand
power (CHP) units or micro wind turbines and in combina-tion with
renewable energy sources (RESs) generation for largerarea networks,
planning challenges are already existing in gen-eration,
transmission, and distribution systems. This situationwill even
more dramatically evolve when local generation willbe significantly
cheaper than supply provided by electric utili-ties. New strategies
are required to guarantee a secure, reliable,and environmentally
friendly electricity supply with affordabletariffs.Conventional
power generation is accompanied with some
serious environmental problems including the associated
greenhouse gas (GHG) emissions. Nevertheless, the existing
powersystem has several problems like over loaded lines, low
voltageproblems, high losses, and capacity/expansion
problems.Distributed generation (DG) can be defined as small
capacity
power generation integrated on the consumer side (that is,within
the distribution system). If DG uses RESs for gener-ation, it may
be termed as embedded renewable generation(ERG).Various factors can
be considered for deciding the optimal ca-
pacity and location of the ERGs. Since a decade ago, due to
theongoing rapid changes in the electric utility infrastructure,
therehas been a keen interest for researchers and engineers on
theERG (DG integration) issues, its impact on the power systemas a
whole and distribution system in particular, and the bene-fits and
issues associated with it. The performance of the dis-tribution
systems with ERG depend upon various factors likepenetration levels
of ERG, its location uncertainty, and varyingoutput from
ERGs.Energy loss reduction is expected with introduction of ERG
in the distribution system. Looking at the deregulation and
theshortage of transmission capacities, researchers in [1][3]
havepresented analytical methods to determine the optimal
locationof ERG in a networked as well as radial system
consideringpower loss reduction of the system, which can be helpful
to
0885-8950 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.See
http://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
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DAVDA et al.: DISPERSED GENERATION ENABLE LOSS REDUCTION AND
VOLTAGE PROFILE IMPROVEMENT IN DISTRIBUTION NETWORK 1243
system designers in proper selection of the ERGs. The energyloss
reduction will be different for different locations and var-ious
capacities of the ERGs. However, Quezada et al. [4] com-puted the
annual energy losses variations with different pene-tration and
concentration levels of the ERG analyses that this isnot always
true, as the network power flows are modified to asignificant
extent by ERG. The authors also state that higher re-duction in
energy losses can be expected when distributed gen-erators are more
dispersed along the network feeders.Analytical methods have also
been developed for assessment
of the prediction of the allowable DG penetration levels
basedupon the harmonic limit considerations [5]. If energy storage
isused along with ERG, then the type and the capacity of
storagewill also have an impact on the penetration capacity of the
ERG[6]. A set of indices to quantify the technical benefits of
DGsuch as voltage profile improvement, energy loss
reduction,environmental impact reduction, and DG benefit were
proposed[7][10]. Evaluation and investigation of the performance
ofthe distribution system with ERG is done in [11] using MonteCarlo
simulations. In [12], particle swarm optimization is usedas a tool
to minimize the cost of the overall system by changingthe location
and capacity of the DGs. Various real-system casestudies have also
been conducted across the globe. Reference[13] evaluates the impact
of ERG on a real-life 2.8-MVAdistribution network of a particular
area of Gujarat State, India.The improvement in voltage profile and
reduction in line lossesare analyzed for various locations and
capacities of ERGs, andthe results are quiet encouraging. An 89.22%
reduction in linelosses is achieved, and the minimum network
voltage improvesto 0.96 p.u. as compared with the original 0.91
p.u., underpeak loading conditions. In [14], the impacts of DG on
dispatchmodes of power systems based on the Guangdong power grid
inChina are assessed. The paper provides suggestions for
smoothintegration of a large amount of distributed RES generation
inthe future.The distribution networks in India and, particularly,
in many
areas of Gujarat State are operating at maximum capacity andmay
get overloaded under peak loading conditions. Gujarat isone of the
leading states of India, where, currently, industrialdevelopment is
peaking. Owing to the accelerated rate of ur-banization and
industrialization, the way-leave permission forlaying of new lines
for bifurcation of currently overloaded linesis also a problem
faced by the utility. Furthermore, expansionof the distribution
network is inevitable in this scenario, therebyputting in
additional investment and burden for distributioninfrastructure.The
government of Gujarat has announced a photovoltaic
rooftop program across six cities of Gujarat state, wherein
atotal of 25-MW through about 2000 PV rooftop systems of var-ious
capacities on residential and commercial buildings wouldbe added.1
In a true sense, this can be considered as ERG at thedistribution
level, and hence a need has emerged to study theimpact the ERG and
the distribution network will have on eachother.The aim of this
paper is to analyze a real-life 3.9-MVA distri-
bution network in Gujarat State, India. DG from RESs wind
and1[Online]. Available: http://rooftopsolargujarat.com
TABLE IDETAILS OF CONNECTED LOAD
solar are considered to enable energy loss reduction and
voltageprofile improvement at optimal locations. The paper will
pro-vide guidelines for optimal allocation of ERGs and exhibit
theimpact of the addition of these ERGs to support grid
infrastruc-ture. The selected area, close to the sea-shore, has an
averagewind velocity of 5.6 to 6.0 m/s, which is feasible for wind
powergeneration2 and has a significant solar irradiance of four
peaksun hours per day, which is suitable for PV generation.3
Thereis no conventional centralized generating station in the
vicinityof the area. The nearest generating station is
approximately 200km away from the selected area. One of the reasons
to select thisparticular area is a high level of existing technical
losses and thescope of loss reduction with ERGs.This paper is
structured as follows. In Section II, the distribu-
tion network is formulated with the existing grid
infrastructure.Section III provides the load flow analysis modeling
withoutERG and highlights the below-standard voltage profiles
exhib-ited on the grid system, while Section IV describes the
devel-oped methodology for optimal ERGs location. In Section V,
thegrid case scenario analysis with ERG integration is
performed,and, in Section VI, the results of the study, in
particular, the en-ergy loss reduction and the voltage profile
improvements, arehighlighted. Finally, in Section VII, the main
conclusions arepresented.
II. PROBLEM FORMULATIONThe case study is based on a radial
3.9-MVA distribution net-
work in Gujarat State, India, which has a total line length of46
km with 115 buses, supplying power to single-phase andthree-phase
loads. The details of the connected load to the radial3.9-MVA
distribution network under study are given in Table I.In
engineering terms, power is the rate of energy delivered
and is proportional to the product of the voltage and the
cur-rent. The power supply system can only control the quality
ofvoltage; it has no control over the currents that particular
loadsmight draw. Therefore, the standards in power quality area
aredevoted to maintain the supply voltage within certain
limits.Electric distribution networks expand with time on the
demand.ERGs contribute to the improvement of power quality in
theareas where voltage support by grid is difficult.The study and
analysis of existing power distribution network
revels that the major problem faced by the consumers is supplyat
poor voltage and that faced by utility is a high level of
distri-bution losses and limited or no reserve capacity. This
paper, as a2[Online]. Available:
http://www.cwet.tn.nic.in/html/departments_wpdmap.
html3[Online]. Available: www.mnre.gov.in
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1244 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 29, NO. 3, MAY
2014
Fig. 1. Voltage profile of existing network.
case study, evaluates the health of a real-life distribution
feederwith regards to minimum network voltage and network
reservecapacity, along with line loss calculation, before and after
theintegration of DGs with RESs.
III. MODELINGModeling of the distribution system under study is
done using
CYMDIST software [15]. In this paper, the nominal voltage of230
V is considered as 1.0 per unit (p.u.). The voltage profile ofthe
3.9-MVA distribution network is shown in Fig. 1. The min-imum
voltage in the existing network is 0.89 p.u. which is justover 10%
less. A permissible voltage range has been consideredas %. However,
permissible voltage variation is different fordifferent countries.
For India, it is %, for Europe, it is %,and, in a few countries, it
changes from state to state [16].Usually, the load of electrical
appliances and devices vary
with supply voltage. Their demand varies as a function
ofvoltage. Loads can be categorized into Constant Power
Load,Constant Current Load, and Constant Impedance Load. Theload at
a particular point may be a combination of some pro-portion of all
these. In general, these models can be written as
(1)(2)
where , and are nominal real power, reactive power,and voltages
on a per-unit basis, respectively.For a constant power model, we
have , for a constant
current model, we have , and, for a constant impedancemodel, we
have .An exhaustive review of load models to be used for power
flow has been presented in [17]. The constant power model isthe
most severe representation from the system stability pointof view
[18]. To consider the worst conditions of voltage varia-tions,
loads have been modeled as constant power loads, that is,
.Modeling of the ERGs is done using the Hybrid Optimiza-
tion Model for Electric Renewables (HOMER) software [19].For
wind turbines, an average wind speed of 5.6 m/s at 30-mheight is
available in the area where the real-life network is lo-cated,
which is equivalent to wind power density of 200250W per square
meter . The hub height of both of the wind tur-bines considered
here is about 79 m, and the output derived isby extrapolating the
available 30-m wind data. For the solar PV
TABLE IIDETAILS OF EMBEDDED RENEWABLE GENERATORS
system, the peak sun hours available throughout Gujarat stateare
four to five peak sun hours per day , and a value equal tofour peak
sun hours has been considered for deriving the outputfrom the solar
PV system.W1 and W2 are 2.1-MVA and 1.5-MVA wind turbine gener-
ators, respectively, with an average output of 655 and 476
kW,respectively. S1 is a 1.8-MWp PV system with a 300-kW in-verter
average output. ERG sizes up to one third of the feedercapacity do
not require any special communications and controlto work properly
with the utility voltage regulating equipments[20]. Keeping in view
the site conditions, feeder loading con-ditions, and other
parameters like anticipated future expansion,the capacity of ERGs
is predefined for deciding optimal loca-tions for their integration
in to the existing network. The totaloutput of the ERGs here is
nearly one third that of the feederloading. Details of ERGs are
given in Table II.The output of the ERGs was derived from HOMER
software,
and these values were then given as input in CYMDIST soft-ware
for further analysis and simulations.The model of the existing
radial network under consideration
is shown in Fig. 2. All of the nodes of the network are
numbered.As the network under consideration covers a significant
area
with a line length of 46 km with 115 buses and is spread
acrossall directions from the substation supplying power, three
refer-ence nodes at the farthest points on the network have been
con-sidered to check the overall health of the distribution
networksystem.The results of load flow analysis (LFA), as shown in
Fig. 3, re-
veal that voltage falls below the permissible limit in the
majorityof the 3.9-MVA distribution network. The sections indicated
bythick dark lines experience under-voltage beyond
permissiblelimits. Moreover, the sections of the network with thick
lighterlines indicate overloading of that section. The loading of
thefeeder is with respect to the current rating of the conductor.
Asection of conductor is considered to be overloaded when cur-rent
passing through that section exceeds its nominal currentrating,
that is, 183 A for the main line and 123 A for the spurline.The
minimum voltage in the existing network is 0.89 p.u.,
at node number 583. The losses in the existing network are258
kVA under peak loading conditions.
IV. METHODOLOGYTraditional load flow methods, which incorporate
the
GaussSeidel method, the NewtonRaphson method, and fastdecoupled
techniques, were primarily developed for transmis-sion system
analysis. Additionally, a ladder network methodfor radial
distribution systems using basic circuit theories andlaws is
another well-known method. All of these methods have
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DAVDA et al.: DISPERSED GENERATION ENABLE LOSS REDUCTION AND
VOLTAGE PROFILE IMPROVEMENT IN DISTRIBUTION NETWORK 1245
Fig. 2. The 3.9-MVA distribution network in Gujarat State,
India.
Fig. 3. LFA network map results.
been successfully applied in industry for many years [21]. LFAof
the distribution system must incorporate the unique
charac-teristics of distribution systems such as radial topologies,
a highresistance by reactance (R/X) ratio of the distribution
lines,nonlinear load models, and dispersed generation.
Distributionsystems usually fall into the category of
ill-conditioned powersystems having high R/X ratios, due to which
the methods likeNewtonRaphson and fast decoupled may provide
inaccurate
Fig. 4. Typical radial distribution system.
results and may not converge. Therefore, traditional load
flowmethods cannot be directly applied to distribution systemssince
the assumptions made for transmission systems are notvalid for the
unique characteristics of distribution systems. Onthe other hand,
ladder network methods are quite suitable forradial networks with
high R/X ratio [22]. The ladder networkmethod uses backwardforward
sweep for LFA.A methodology is developed and algorithm for the same
is
prepared to integrate ERGs into the existing network at
optimallocations keeping in view the improvement in voltage
profileand reduction in losses. The method used for performing LFA
isbackwardforward sweep method for calculating voltage dropsand
losses of network at different buses/nodes [23].We consider a
typical radial distribution system shown in
Fig. 4, where is the number of nodes in the network understudy
and is the load current at node n, Amp.The load current at each
node is computed by
(3)
where is the connected load in kVA and is the node voltagein
kV.In backward sweep, node voltage will be computed by
(4)
where the line current will be computed by Kirchhoffs Cur-rent
Law (KCL), which for the end branch is as in
(5)
where
line impedance;
line or branch between the nodes and .
In forward sweep, node voltage will be computed by (6),
(6)
where line current is taken from the value stored during
back-ward sweep.Convergence criteria,
(7)
where is the specified source voltage, is the calculatedsource
voltage in backward sweep, is the magnitude of
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1246 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 29, NO. 3, MAY
2014
Fig. 5. Algorithm for LFA.
voltagemismatch of and in the load flow, and is
specifiedtolerance.The backwardforward sweep is repeated until this
conver-
gence is achieved.The real and reactive power losses are
calculated using
(8)
(9)
The line losses in this paper are considered in kVA, as the
re-active power flow directly affects the network reserve
capacityand overloading of network sections, which are two of the
fac-tors considered in this paper for analysis of the distribution
net-work under consideration.The tolerance for convergence
considered for simulation
purpose is 0.1% (V), which is 0.23 V. From the
simulationscarried out, it is observed that a tolerance value
higher than thisgives inaccurate results. Results with a value of
tolerance lowerthan 0.1% (V) remain unaltered.
Fig. 6. Methodology for determining the appropriate ERGs
locations.
Based on the backwardforward sweep method and (3)(9),Fig. 5
describes the algorithm giving the step by step procedurefor LFA.In
the first phase, the existing network is modeled and an-
alyzed for problems like low voltage, overloading of
networksections, and existing losses. In the second phase, naming
theERGs in descending order of their capacities, W1 as G1, W2as G2,
and S1 as G3, these ERGs are integrated in the networkat suitable
locations derived by the developed methodology andagain the network
is analyzed with reference to the above pa-rameters. The detailed
methodology developed for determiningthe appropriate locations of
ERGs for voltage profile improve-ment and loss reduction in the
network is shown in Fig. 6, whichis based on an iterative
approach.
V. CASE SCENARIO ANALYSIS OF NETWORK
ERGs are integrated in 3.9-MVA radial distribution networkin
Gujarat State, India, at identified locations, are probable
tolow-voltage energy supplies. The case scenarios in Table
IIIconsider individual ERGs integration as well as their
combi-nations. In each of the cases, voltage improvement and loss
re-duction was achieved. For the scenario when all of the ERGswere
connected to the network at optimal places as derived by
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DAVDA et al.: DISPERSED GENERATION ENABLE LOSS REDUCTION AND
VOLTAGE PROFILE IMPROVEMENT IN DISTRIBUTION NETWORK 1247
TABLE IIIERGS CAPACITIES FOR VARIOUS CASES
the methodology developed, maximum voltage improvement,and
losses reduction was observed. Details of various cases
aregiven.Without ERGs, the 3.9-MVA radial distribution network
in Gujarat State, India, exhibit problems of low voltage
andoverloading at a number of nodes and branches. Three mainareas
with low voltage problems in the network are identified.Case 1
considers the integration of the 2.1-MVA wind gener-ator (W1) with
average output of 655 kW (as derived by theRESs simulation on HOMER
software) in the area having leastvoltage. W1 was integrated at
several locations within that areaand the most optimal location
with respect to voltage profileimprovement and loss reduction in
the entire network wasidentified, as given in the methodology.
Similarly, cases 2 and 3consider the integration of W2 and S1,
respectively, in the otheridentified areas having low voltage
problems. The simulationresults of all of the above three cases
were analyzed, and stillthe network was found to have low voltage
problems andsignificant losses. In cases 46, a combination of these
ERGswas integrated as described in Table III, keeping the
locationssame for the ERGs, as derived in the first three cases.
Finally,in case 7, all three ERGs were integrated in the network at
thepreviously derived locations, and the simulation results
wereanalyzed for low voltage problems, line losses,
overloadingproblems, and network reserve capacity.The results
revealed that, in case 7, all of the above problems
are resolved, that is, the minimum network voltage is 0.95
p.u.at node 583 and is within permissible limits, significant
energyloss reduction of the order of 47.43% (of the existing
losses) isachieved, no overloading is found in any of the sections
of thenetwork and there is scope of future expansion of the
networkwith about 11% reserve capacity of the network, under
peakloading conditions.Model of the network for case 7, with
integration of all ERGs
as per the methodology developed in this paper is shown inFig.
7.
VI. RESULTS
LFA was performed for each case, and the minimum voltagein the
network was noted. The power loss was also calculated.The results
of simulation for voltage profile of various cases aregiven in
Table IV.With the different values of ERGs for various cases,
the
power losses also change. Table V shows the power losses
Fig. 7. Network model with integration of all ERGs (case 7).
TABLE IVSIMULATIONS RESULTS FOR VOLTAGE PROFILE
TABLE VPOWER LOSS FOR VARIOUS CASE SCENARIOS
for various ERG capacities and savings in power loss as
apercentage of maximum load.It is observed that with the addition
of ERGs in the existing
distribution network at appropriate locations as derived by
themethodology, the minimum voltage of the network increases
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1248 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 29, NO. 3, MAY
2014
from 0.89 pu as existing in case 0, where no ERGs are presentin
the network, to 0.95 p.u. in case 7, where all three ERGs
areintegrated in the network. Savings in losses differ from case
tocase. For the same case 7, in addition to improvement in
thevoltage profile, savings in losses of the order of 83 kVA
arealso achieved.With the use of ERGs, the overloading has
totallybeen eliminated from all of the sections of the network.
VII. CONCLUSION
This paper deals with a real-life 3.9-MVA distribution
systemcase study, which is the first of its kind in the State of
Gujarat.The network in its existing state under peak loading
conditionsexperiences multiple problems of low voltage, high line
losses,overloaded sections, and future expansion constraints. As is
thecurrent scenario across the globe, India too is experiencing
aconsiderable rise in grid-connected renewables, especially at
thedistribution level. A methodology is developed in this paper
fordeciding the proper locations of ERGs with predefined
capaci-ties. Various case scenarios are analyzed for various
combina-tions of the ERGs. It is found that all of the above
problems aresolved by the integration of ERGs in the distribution
network.Considerable voltage profile improvement is achieved by
usingthe methodology described in this paper. Voltages of all of
thesections of the network remain within the permissible limits,and
none of the sections are overloaded. The losses reduce to47.43% of
the existing losses under peak loading conditions ofthe network.
Also, during future expansion, additional load canbe catered by the
same network due to an increase in reservecapacity of the network.
The developed methodology and sce-nario case study results are a
handful tool for DSOs under sim-ilar challenges of RESs
integration.
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IEEE PESTransmission and Distribution Conference and Exposition,
Dallas, TX,USA, Sep. 712, 2003.
[23] W. H. Kersting, Distribution System Modeling and Analysis,
1st ed.Washington, DC, USA: CRC, 2002, LLC.
Akash T. Davda (M12) received the B.E. degree(with honors) from
Gujarat University, Gujarat,India, in 2001, and the M.E. degree
(with honors)from Sardar Patel University, Gujarat, India, in
2003,both in electrical engineering.He is presently an Associate
Professor and Head
of the Department of Electrical Engineering, B. H.Gardi College
of Engineering and Technology, Ra-jkot, India. He has authored and
coauthored sevenpapers in national journals and conferences and
ninepapers in international journals and conferences. He
has authored two research papers presented at IEEE International
Conferences.His current research interests include renewable
energy, distributed generation,energy management and audit, and
power systems.Prof. Davda is a Life Member of the Indian Society
for Technical Educa-
tion, the Solar Energy Society of India, and the Society of
Energy Engineersand Managers, India, and a member of the
International Solar Energy Society.He has also received travel
grants under the Young Scientist Category fromthe Department of
Science and Technology, Government of India, for attendingand
presenting a research paper at IEEE International Conference
organized inCanada in 2012.
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DAVDA et al.: DISPERSED GENERATION ENABLE LOSS REDUCTION AND
VOLTAGE PROFILE IMPROVEMENT IN DISTRIBUTION NETWORK 1249
Brian Azzopardi (M09) received the B.Eng. degree(with honors)
from the University of Malta, Malta,and the Ph.D. degree from The
University of Man-chester, Manchester, U.K., both in electrical and
elec-tronic engineering.He is presently anAssociate Professor and a
Senior
Researcher with the Faculty of Electrical and
ControlEngineering, The Kaunas University of Technology,Kaunas,
Lithuania. His research interests include en-ergy economics as well
as sustainable power systemsand renewable and clean energy
technologies.
Bhupendra R. Parekh (M12) was born in Indiain 1957. He received
the B.E. degree in electricalengineering from Sardar Patel
University, Gujarat,India, in 1979, and the M.E. degree and Ph.D.
degreein electrical engineering from the Indian Instituteof
Technology, Bombay, India, in 1985 and 1995,respectively.He has
authored and coauthored several research
papers and took part in many short-term trainingprograms. He has
also organized a short-termtraining program for teachers sponsored
by ISTE
and approved by AICTE. He has also been a member of the Project
EvaluationCommittee (PEC) for various engineering colleges. He has
guided more than
30 students of PG program and examined dissertations of
approximately 30 PGstudents of Gujarat University and Maharaja
Sayajirao University. He is alsoguiding Ph.D. students at Sardar
Patel University. He is presently a Professorand Head of the
Department of Electrical Engineering, Birla
VishvakamaMahavidyalaya, Vallabh Vidyanagar, India.Dr. Parekh is a
Life Member of the Indian Society for Technical Education
and an associate member of the Institute of Engineers.
Manhar D. Desai born in India in 1941. He receivedthe B.E.
degree in electrical engineering from Gu-jarat University, Gujarat,
India, in 1965, the M.E. de-gree in electrical engineering
(measurement and in-strumentation) fromUniversity of Roorkee,
Roorkee,India, in 1968, and the Ph.D. degree in
biomedicalengineering from the Indian Institute of
Technology,Roorkee, India, in 1983.He has authored and coauthored
nearly 30
papers in national journals and conferences andapproximately ten
papers in international journals
and conferences. His current research interests include
renewable energy,distributed generation, medical image processing
etc.Dr. Desai is a Life Member of the Indian Society for Technical
Education and
National Bio-medical Engineering Society. He was the recipient
of the GoldMedal while at the University of Roorkee.