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:aakashdavda@ieee.org).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: brian.azzopardi@ieee.org).B. R. Parekh is with the Department of Electrical Engineering, Birla

Vishvakarma Mahavidyalaya, 388120 Vallabh Vidyanagar, India. (e-mail:brparekh@ieee.org).M. D. Desai was with the Department of Electrical Engineering, Kalol

Institute of Technology and Research, 382721 Kalol, NG, India (e-mail:d.manhar@yahoo.in).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.

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

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

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

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

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

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.

REFERENCES

[1] C. Wang and M. H. Nehrir, Analytical approaches for optimal place-ment of distributed generation sources in power systems, IEEE Trans.Power Syst., vol. 19, no. 4, pp. 20682076, Nov. 2004.

[2] K. Dang, J. Yu, T. Dang, and B. Han, Benefit of distributed generationon line loss reduction, in Proc. ICECE, 2011, pp. 20422045.

[3] P. Chiradeja, Benefit of distributed generation: A line loss reductionanalysis, presented at the IEEE PES Transmission and DistributionConference and Exhibition, China, Aug. 1517, 2005.

[4] V. H. M. Quezada, J. R. Abbad, and T. G. S. Roman, Assessmentof energy distribution losses for increasing penetration of distributedgeneration, IEEE Trans. Power Syst., vol. 21, no. 2, pp. 533540, May2006.

[5] A. Bhowmik, A. Maitra, S. M. Halpin, and J. E. Schatz, Determina-tion of allowable penetration levels of distributed generation resourcesbased on harmonic limit considerations, IEEE Trans. Power Del., vol.18, no. 2, pp. 619624, Apr. 2003.

[6] J. P. Barton and D. G. Infield, Energy storage and its use with inter-mittent renewable energy, IEEE Trans. Energy Conv., vol. 19, no. 2,pp. 441448, Jun. 2004.

[7] P. Chiradeja and R. Ramakumar, An approach to quantify the tech-nical benefits of distributed generation, IEEE Trans. Energy Conv.,vol. 19, no. 4, pp. 764773, Dec. 2004.

[8] M. S. Naderi, Md. S. Naderi, K. Rahmani, G. B. Gharehpetian, and L.Zahedi, Quantitative assessment of distributed generation benefits toimprove power system indices, presented at the Int. Conf. on Renew-able Energies and Power Quality, Las Palmas de Gran Canaria, Spain,Apr. 1315, 2011.

[9] P. A. V. Raj, S. Senthilkumar, and T. G. Palanivelu, Swarm in-telligence based optimization of distributed generation capacity forpower quality improvement, Acta Electrotechnica, vol. 49, no. 3, pp.334342, 2008.

[10] E. Afzalan and M. A. Taghikhani, DG allocation and sizing usingMSFLA to reduce losses and improve voltage profile in distributionnetwork, J. Sustain. Energy, vol. 3, no. 3, Sept. 2012.

[11] W. El-Khattam, Y. G. Hegazy, and M. M. A. Salama, Investigatingdistributed generation systems performance usingMonte Carlo simula-tion, IEEE Trans. Power Syst., vol. 21, no. 2, pp. 524532, May 2006.

[12] O. Amanifar, Optimal distributed generation placement and sizing forloss and THD reduction and voltage profile improvement in distribu-tion systems using particle swarm optimization and sensitivity anal-ysis, presented at the 16th Conference on Electrical Power and Distri-bution Networks, Bandar Abbas, Iran, Apr. 1920, 2011.

[13] A. T. Davda, B. R. Parekh, and M. D. Desai, Reduction of lossesand improvement in voltage profile for a 2.8 MVA distribution net-work with dispersed generation, presented at the IEEE PES Int. Conf.on Power Systems Technol., Auckland, New Zealand, Oct. 30Nov. 22012.

[14] J. Liu, W. Zhang, R. Zhou, and J. Zhong, Impacts of distributed re-newable energy generations on smart grid operation and dispatch, pre-sented at the IEEE Power and Energy Society General Meeting, Jul.2226, 2012.

[15] CYMDIST. CYME Int. Inc..[16] The Indian Electricity Rules, 1956. [Online]. Available:

http://www.coalindia.in/Documents/MineSafety/Indian_Electri-icity_Rules_1956_28022013.pdf

[17] IEEE task force on load representation for dynamic performance, Bib-liography on load models for power flow and dynamic performancesimulation, IEEE Trans. Power Syst., vol. 10, no. 1, pp. 523538, Feb.1995.

[18] S. Sivanagaraju and S. Satyanarayana, Electric Power Transmissionand Distribution, 1st ed. New Delhi, India: Pearson Education, 2009.

[19] HOMER: Hybrid Optimization Model for Electric Renewables. Nat.Renewable Energy Lab., U.S. Dept. Energy [Online]. Available: www.homerenergy.com

[20] R. C. Dugan, M. F. McGranaghan, S. Santoso, and H. W. Beaty, Elec-trical Power Systems Quality, 2nd ed. New Delhi, India: Tata Mc-Graw-Hill, 2008.

[21] J. Liu, M. M. A. Salama, and R. R. Mansour, An efficient power flowalgorithm for distribution systems with polynomial load, Int. J. Electr.Eng. Education, vol. 39, no. 4, pp. 371386, Oct. 2002.

[22] M. M. Medina, L. Qi, and K. L. Butler-Purry, A three phase load flowalgorithm for shipboard power systems, presented at the 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.

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.