Optimizing the effect of charging electric vehicles on distribution transformer using demand side management

0.92 MB·English

by  Swapna Ganapaneni, Srinivasa Varma Pinni

IndonesianJournalofElectricalEngineeringandComputerScience
Vol. 25,No. 1,January2022,pp. 25∼34
ISSN:2502-4752,DOI:10.11591/ijeecs.v25.i1.pp25-34 ❒ 25
Optimizing the effect of charging electric vehicles on
distribution transformer using demand side management
SwapnaGanapaneni,SrinivasaVarmaPinni
DepartmentofElectricalandElectronicsEngineering,KoneruLakshmaiahEducationFoundation,Guntur,India
ArticleInfo ABSTRACT
Articlehistory: Thispapermainlyaimstopresentthedemandsidemanagement(DSM)ofelectricve-
hicles(EVs)byconsideringdistributiontransformercapacityataresidentialarea.The
ReceivedMar14,2021
objective functions are formulated to obtain charging schedule for individual owner
RevisedOct27,2021
byi)minimizingtheloadvarianceandii)priceindicatedchargingmechanism. Both
AcceptedNov26,2021
theobjectivefunctionsprofittheownerinthefollowingways:i)fulfillingtheirneeds,
ii) minimizing overall charging cost, iii) lessening the peak load, and iv) avoiding
Keywords: the overloading of distribution transformer. The proposed objective functions were
worked on a residential area with 8 houses each house with an EV connected to a
Charging
20kVAdistributiontransformer. TheformulationsweretestedinLINGOplatform-
Demandsidemanagement
optimizationmodelingsoftwareforlinear,nonlinear,andintegerprogramming. The
Distributiontransformer
resultsobtainedwerecomparedwhichgivesgoodinsightofEVloadschedulingwith-
Electricvehicle outactualpriceprediction.
Overloading
Scheduling
ThisisanopenaccessarticleundertheCCBY-SAlicense.
CorrespondingAuthor:
SwapnaGanapaneni
DepartmentofElectricalandElectronicsEngineering,KoneruLakshmaiahEducationFoundation
GreenFields,Vaddeswaram,GunturDistrict,AndhraPradesh,522502,India
Email: [email protected]
1. INTRODUCTION
Electric vehicle (EV) is the one can satisfy the need of future transportation due to lack of enough
fossilfuelswhichtopsthedemandforelectricalenergy. InsuchasituationifcharginganddischargingofEVs
arenothandledproperlywilloverloadsthegrid. InthisregardchargingtheEVatresidentialplacesisakey
issueresultinginseveraltechnicalproblemsatthelevelofdistributiontransformer,demandsidemanagement
(DSM)ispossiblyagoodsolution.
AnoverviewontheliteratureofDSMtechniquesispresentedhere. Rapidincreaseindaytodayelec-
tricitydemand,DSMhelpstoavoidutilitiesbuildingextracapacityofthegenerationbymeansofdecreasing
thepeakdemandthroughshiftingandadjustingcustomerselectricityconsumption.DSMmainlyinvolvesthree
programslikeefficientenergymanagement(EM),demandresponse(DR),effectiveloadmanagement(ELM)
bythecustomers[1]. Figure1representsthedetailedclassificationofDSM.
Energymanagement(EM)mainlyaimstoreduceenergyconsumptionwhichautomaticallyminimizes
theenergycost. Agoodscopeofenergymanagementcanbeeasilyachievedinvarioussectorslikeindustrial,
commercial, agricultural and even in households if energy saving tips are followed. Proper maintenance of
boilers,steamsystems,compressedairsystems,motoranddrivesystems,andlighteningaspects,willlowers
the energy usage. Time to time audit of energy; awareness and training programs; and good metering and
billingsystems,aretheimportantaspectsthroughwhichefficientenergymanagementisobtained[1].
Demand response (DR) is one important policy of DSM which concentrates mainly on the pricing
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systemtomanagethepeakload.DRdenotes“changesinelectricusagebyend-usecustomersfromtheirnormal
consumption patterns in response to changes in the price of electricity over time, or to incentive payments
designedtoinducelowerelectricityuseattimesofhighwholesalemarketpricesorwhensystemreliabilityis
jeopardized”[2].
Figure1. Classificationofdemandsidemanagementtechniques
Two major classification are done in the DR programs based on the pricing system are time-based
pricing system (TBPS) and incentive-based pricing system (IBPS) [3]. Direct load control (DLC), interrupt-
ible/curtailable service (I/CS), emergency demand response program (EDRP), capacity management (CM),
demandbidding(DB),ancillaryservicemarket(ASM)areclassifiedunderIBPSwhereastimeofuse(ToU),
realtimepricing(RTP),criticalpeakpricing(CPP)arecategorizedunderTBPS.
– Directloadcontrol(DLC):DLCisoneapproachofDRwherecustomer’sloadsareshutsdownbythe
utilitiesremotely onshort noticefor reliabilityproblems. Thisis mainlyexecuted onsmall consumers
likeresidentialandsmallcommercialcustomer[4].
– Interruptible/curtailableservice(I/CS):Itistheprogramwherecustomersoncurtailoftheirequipment
getsadiscountorcreditedontheirbillwhenthesystemisundercontingencyandiftheydonotagreeto
curtail,theyarepenalized.
– Emergencydemandresponseprogram(EDRP):Whenaneventoccurs,emergencyDRisausualprogram
to implement. In the case of reliability events EDRP offers incentives to customers for reducing their
loadsandcannotbepenalizedfornotcurtailingtheirloadbecausethepricesarepre-specified[5].
– Capacity management (CM): It is a demand side resource, during contingencies it commits to reduce
prespecifiedamountofloadandpenalizestheparticipantsiftheydonotcurtailtheloadoninstructions.
Customersobligingtheinstructionsareguaranteedtoreceivepaymentsinexchange.
– ToU: Prices are set in advance but differs depending on the times of the day and will not reflect any
adjustmentstotheactualconditionsofthesystem. Consumerswillnothaveanyincentivesforreduced
consumptioninelectricityduringpeakperiodsandhourlymeteringisnotrequired.
– RTP:Itisalsotermedasdynamicpricingaspricesvarieswithrealtimeconditionsandreflectstheactual
phenomenonofthesystembyprovidingbestinformationaboutthepoweravailableatalocation.Energy
consumptionshouldbemeasuredonhourlybasisasitischargedappropriately,andcustomersareoffered
withincentivesfortheirreducedconsumptionofenergyduringpeakperiods.
– CPP: This is a dynamic pricing scheme where few peak hours are charged with high prices to reduce
peakdemandandothertimeperiodsarechargedwithnormalprices,therebypermitsthecustomersto
minimizetheiroverallenergybill.
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IndonesianJElecEng&CompSci ISSN:2502-4752 ❒ 27
Effectiveloadmanagement(ELM)techniquesaretheonewhereutilitytriestoreducethepeakcon-
sumptionbythesubsequentapproacheslikepeakclipping,valleyfilling,loadshifting,strategicconservation,
strategicloadgrowth,flexibleloadshape[6].
– Peakclipping:ItisthewaywhereloadisreducedduringpeaktimebymeansofDLClikeshuttingdown
theequipmentoftheconsumer. Thismethodwillnotgreatlyinfluencetheentireloadcurvebutreflects
inreductionofloadduringpeakperiod.
– Valleyfilling:Itencouragesenergyconsumptionduringoffpeakhoursbythecustomersoverofferingin-
centiveslikeallowingthemtopaylowtariffforchangingtheirscheduletooffpeakhours,anddiscounts.
– Load shifting: This method aims to shift the load during peak hours to period where load is lessened
howeveroveralldemandremainsconstantinthisphenomenon.
– Strategic conservation: It mainly drives to bring down seasonal energy consumption by encouraging
consumers towards the use of efficient devices and appliances, decreasing wastage of energy. It also
offersincentivestoconsumerswhoadoptstechnologicalchangesintheirusage.
– Strategicloadgrowth:Itmainlytriestocontrolseasonalincreaseinenergyconsumption. Thedealership
employsintelligentsystems,effectivedevicesandmoreviablesourcesofenergytoreachtheirgoals.
– Flexibleloadshape: Itincludessetofactivitiesandintegratedplanningbetweenconcessionaryandthe
customer rendering to the requirement of the moment. Consumer loads are modeled with the help of
loadlimitingequipmentsuchthattherewillnotbemuchdeviationintheactualloadandwillnotdisturb
securityissues.
UpgradingthedistributiontransformerwithpenetrationofEVinthedistributionsystemisacostex-
pensive and unplanned charging of these EVs may cause the grid overloading. Therefore, a strategy DR is
applied to avoid overloading of transformers by considering the priority of each individual home and conve-
niencepreferencesetting. However,impactofvaryingpricesignalsisnotconsideredinapplyingDR[7].
Real time optimal scheduling of a battery energy storage system is proposed to reduce peak load of
abuildingasanDSMtechniquetoreducecostofelectricalenergyin[8]andintegrationofrenewableenergy
sourcesarenotconsidered.MicrogridgridresourceswereintegratedtotheIndiandistributionsystemandhave
beenscheduledtoreducedependencyonmaingridandontheotherhandpeakloadsweremanagedbymeans
offlexibleloadshapingwhichisatoolofDSMminimizesthecustomer’sdissatisfactionevendiminishedthe
operationcostofmicrogrid[9].
PEVs charging control is done based on a new distributed random-access approach which does not
needcentralizedcontrolandcanbeexecutedinrealtime. Theworkdiffersfromtheexistingmethodologiesas
itconsidersthehistoricaldata tocoordinatesmartagentsratherthanRTP[10]. Multiobjectiveformulations
were done in [11] to minimize total energy generation and cost associated for implementing DSM such that
energy planning was done in a decentralized manner, PEVs charging is shifted resulted in reduction of total
emissionsandsavingsincost.
SchedulingofPEVsatabuildinggaragetoreducethepeakloadandenergycostisachievedin[12]
by formulating an optimization model which minimizes the square of the Euclidean distance. Similarly, in
a decentralized system, non-cooperative game approach is followed for obtaining charging and discharging
schedulesofthebatteriesandadistributedalgorithmwasdevelopedwherethetotalenergychargingcostofa
PEVisminimized.However,apricingmechanismforvehicletobuildingmodelisnotproposed,impactofdis-
chargingprocessofbatteryonitslifeisnotevaluated,renewableenergysourcesintegrationisnotconsidered.
DynamicpricingmechanismisoneofthepossiblesolutionstoachieveDSMwasworkedoutin[13]evaluated
theeffectsofherdingunusualparticipationofcustomers, lazinessofcustomers, anddifferentusagegroupof
customers. MinimumsizeoftheenergystoragesystemisproposedinPluginelectricvehiclechargingstation
supportedbyrenewableenergysources[14].
Quadratic programming (QP) and multi agent system (MAS) approaches were discussed and com-
paredbyMetsetal.in[15]reducedthepeakloadandvariabilityintheloadofadistributiongrid.MASproved
tobethebesthoweverQPresultsgivemoreoptimalsolutions. However,EVschargedatoffpeaktimecanbe
helpedtodischargetheirenergyduringpeakperiodsbacktothegridandvehiclesarrivingrandomlytocharge
at the workplace are not considered. A micro grid consisting of wind, photovoltaic generation, utilized the
stationarypluginhybridelectricvehiclesbydevelopinganoptimalscheduleforchargingthemtosupportthe
dynamicnatureofrenewableresourcesisproposedin[16]. AnovelalgorithmisproposedtochargelargeEV
fleetbypredictingtheirloaddayaheadsatisfyinggridconstraints,theindividualrequirementsofthecustomer
Optimizingtheeffectofchargingelectricvehiclesondistributiontransformerusing... (SwapnaGanapaneni)
28 ❒ ISSN:2502-4752
like arrival and departure times, by minimizing their overall charging cost, developing their individual plans
in [17]. Particle swarm optimization is implemented to optimally schedule the EVs in a coordinated manner
andminimizedtheactivepowerlossescomparedtouncoordinatedchargingofEVsforanIEEE-33busradial
system[18]. Ametaheuristicalgorithmusedasoptimizationalgorithmin[19]tooptimizedemandsideofen-
hancetimeofuse(ETOU)pricingforacommercialloaddemandandsignificantlyanalyzedthatthetechnique
shiftedthemaximumdemandfrompeaktimetooffpeaktimewhichminimizedthecostofelectricity. Impacts
ofEVtechnologyandhowtheyhelptheworld’sgrowingdemandforenergyisdemonstratedin[20].
IncreaseinnumberofEVsgrowsdemandforelectricityandtoavoidinterruptionsinthegrid,PVin-
tegrationwithEVchargingstationispresentedthoroughlyin[21].Energycontrollerformicrogridisdesigned
in[22]todevelopcharginganddischargingschedulesofEVsbyabsorbingoverproducedelectricitywiththe
integrationofrenewableenergyandshavesthepeakloadofthemicrogrid.
Thispaperintroducedanobjectivefunctiontominimizetheloadvarianceandpriceindicatedcharging
mechanismforcontrolledschedulingofEVsduringvalleyhours. Finallyresultsobtainedinboththemethods
were compared and highlighted the best solution. The paper is outlined as follows: i) Section 2 consists of
problemformulation,modellingofEVload;ii)Section3consistsoftwoobjectivefunctionsformulations;and
iii)Section4summarizestheresultsbycomparingboththeschedulingschemes.
2. PROBLEMFORMULATION
Theresidentialareaunderconsiderationisservedbya20kVAdistributiontransformerfromthegrid.
It is having 8 houses and each house with an EV as shown in Figure 2. Each household load is the power
consumedforlighting,airconditioner,washingmachine,andwaterheater. andnotincludingtheEVload. The
householdloadprofileisadoptedfrom[23]whichisinfactconsideredfromthewebsiteofelectricreliability
council of Texas (ERCOT), a South-Central Texas residential area. Basic household load profile of a day is
showninFigure3.
2.1. StochasticEVloadmodelling
As EVs charging adds extra load to the distribution transformer there is a need to know about their
daily travelling distances, estimating initial state of charge (SoC), starting time of charging to balance the
energyandtoavoidupgradingtheexistingtransformer. ToaccountuncertaintiesinthebehaviorofEVload,
probabilistic distribution functions are used to estimate arrival time of the vehicle, distance travelled, initial
SoC and time required to charge its battery. As per national household travel survey (NHTS) 2009 report
whichprovidescompletedetailsoftransportationinUS,dailydistancetravelledbyanEVfollowsLognormal
distributionandarrivaltimeofthevehiclefollowsGaussiandistributionfunctions.
Distancetravelledbymostofthepeopleisaround20-25milesadayandmorethanhalfofthepeople
travellessthan30miles/day[23]. ThetravelleddistanceinmilesperdaycanbeapproximatedbyLognormal
distributiongivenby(1)withmean(µ)of3.37andstandarddeviation(σ)of0.5anditisshowninFigure4.
1
F (d)= √ e−(lnd−µ)/2σ2 ford>0 (1)
dist
dσ 2π
%ofsoc =[1−(E ∗d )/C ]∗100 (2)
j C j bat
Basedonthedistancetravelledinitialstateofcharge(SoC)ofallEVscanbeestimatedasfollowing
from (2). Where soc is the initial soc of jth vehicle. d is the distance travelled in miles by jth vehicle.
j j
E is the energy consumed in kWh/miles. C is the battery capacity in kWh. The EV model considered
c bat
inthisstudyisNissanLeaf2016model, acarwhichsolelyrunsonelectricitywith24kWhbatterycapacity
andhaving0.28kWh/milesconsumption. Energystillrequiredandtimeneededtochargethebatterycanbe
obtainedasfrom(3)and(4).
socf −soc
Ereq = j ∗C (3)
j η bat
Where Ereq energy required to fill jth vehicle’s battery in kWh, η is the efficiency of the charger which is
j
consideredas0.95,SoC isthefinalSoCtobeattainedbytheendofthechargingand(4),
f
C =Ereq /P (4)
time j
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IndonesianJElecEng&CompSci ISSN:2502-4752 ❒ 29
whereC isthechargingtimerequiredtochargebatteryinHours,Pistheoutputpowerofthechargerin
time
kW.SinceitisassumedchargingEVsisdoneatresidentialareafrom[24],[25],level1chargingoutputpower
is 1.44 kW (120 V,12 A) and for level 2 it is 3.3 kW (240 V,14 A) are considered. Most of the household
ownerschargetheirEVsaftertheyreturnhomefromworkat16:00to21:00accordingtoNHTS2009report,
therandomnessinconnectingEVstostartchargingfollowsGaussiandistributionasgivenin(6)withamean
(η) of 17:00 and standard deviation (σ) of 2.28. The distribution function is described as follows and the
distributioncurveforarrivaltimeoftheEVisshowninFigure5.
1
F (t)= √ e−(t−µ)/2σ2, for0<t<24 (5)
A
σ 2π
Figure2. Residentialareaservedbydistributiontransformerunderstudy
Figure3. Householdloadprofileofaday
To come up with randomness in the arrival time and distance travelled by the EVs random function
isappliedtotheirprobabilitydistributionfunctionsuchthatarrivaltimeanddistancetravelledbyeachEVare
obtained. TheuncontrolledloadcurvewhichincludeEVsalongwithhouseholdload,householdloadandEV
loadareshowninFigure6andFigure7respectivelyforlevel1andlevel2charging. Theuncontrolledload
resultsfrom householdload andEVs loadwhen everyhousehold ownerconnects theirEV immediatelythey
arrivehome. ItisobservedthatfromFigure5andFigure6duetouncontrolledchargingofEVsdistribution
transformer is overloaded for 4 hours about 25% in level 1 whereas for almost 2 hours to 50% in level 2
charging.
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Figure4. Distributionofdistancetravelledin Figure5. Distributionofarrivaltime
miles ofEVs
Figure6. UncontrolledchargingofEVs Figure7. UncontrolledchargingofEVs
overloadingdistributiontransformerinlevel1 overloadingdistributiontransformerinlevel2
charging charging
3. OPTIMIZATIONPROBLEMSFORCONTROLLEDCHARGINGOFEVS
InthissectiontwoobjectivefunctionaredesignedbasedonDSMmethodologiestocontrolthecharg-
ing of EVs such that optimal schedule of EVs are obtained which allows distribution transformer to operate
withinitscapacitylimits.
3.1. Method1: Minimizingloadvariance
The idea behind this objective function is to reduce load during peak hours, flatten the load curve
whichminimizesthevarianceofenergyrequiredinaday. Minimizingthedifferenceofloadbetweenoffpeak
hoursandpeakhourshelpsthedistributiontransformertofunctionathighefficiency. AppropriateScheduling
ofEVsachievesthisobjectivemoreeasily. TheobjectiveformulationofDSMcanbeexpressedasfollowsand
EV istheoptimizationvariable.
ij
  2
 (cid:88)24 (cid:88)8 
Minimize τ − (Hij +EVij) (6)
 
i=1j=1
Whereτ isthetotalloadprofiletobemetinadayinkW.H isthejthhomeloadprofileatithtimeperiodin
ij
kW.EV isthechargingpowerofthejthvehicleatithtimeperiodinkW.
ij
8 (cid:32) 24 (cid:33)
(cid:88) (cid:88)
andτ = H +Ereq
ij j
j=1 i=1
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IndonesianJElecEng&CompSci ISSN:2502-4752 ❒ 31
Subjectedtofollowingconstraints,
td
(cid:88)
∀j soc + EV ∗ η ≤Ereq
j ij j
i=ta
∀iand∀j 0≤EV ≤Emax
ij j
8
(cid:88)
∀i (H +EV )≤ P
ij ij trans
j=1
whereSoc istheexistingsocofthejthvehiclebeforeitconnectsforcharginginkW.ηistheefficiencyofthe
j
chargerwhichisconsideredas0.95. Ereq chargingpowerneededtofilljth vehicle’sbatteryinkW.Emax
j j
isthepowerratingofthechargerinkW.P transformerloadinkW.ta,tdarearrivalanddeparturetimes
trans
respectively.
3.2. Method2: Priceindicatedchargingmechanism
The charging price of the EVs mainly depends on the fluctuations in the load. Prior information
aboutelectricitypricehelpsconsumerstoshifttheirloadfrompeakperiodtooffpeakperiod. Themainaim
of this objective is to shift load from peak hours to off peak hours with the help of a price indicator. Thus,
this objective function schedules the charging of EVs more optimally without knowledge of real time prices
suchthatitminimizesthechargingcostoftheEVsandpreventsdistributiontransformergettingoverloaded.
Therefore,theobjectivefunctionis(7),
 
24 24 8
(cid:88) (cid:88)(cid:88)
MIN  Ei+ Pij ∗Ci (7)
i=1 i=1j=1
where
C =δ /δ
i i T
andE isthetotalhouseholdloadprofileatith hourinkW.P isthechargingpowerofthejth vehicleatith
i ij
time period in kW. C is the price indicator which is the ratio of household load at i th period to the average
i
householdloadoftheday. δ isthehouseholdloadatithperiodinkW.δ istheaveragehouseholdloadofthe
i T
dayinkW.
4. SIMULATIONRESULTS
This section presents the results of the proposed optimization problems in two different charging
levels. The proposed objectives are evaluated in LINGO platform. The aim is to achieve charging plan of
eachvehiclewhilesatisfyingthecapacityofthedistributiontransformercapacity,minimizingthetotalcostof
chargingEVsandfillingthebatteriesto90%oftheSoC.Theoptimizationisperformedonaresidentialarea
withhouseholdload,EVloadobtainedfromstochasticmodellingandthetotalloadondistributiontransformer
based on uncontrolledcharging of EVs. Here total load representsthe sum of household loadand EVs load.
Figure6andFigure7presentsthe20kVAdistributiontransformeroverloadingconditionforlevel1andlevel2
respectivelyforuncontrolledchargingofEVs. Figure8andFigure9forlevel1andlevel2respectivelyshows
thesameafterimplementingtheoptimizationformethod1alleviatingtheproblemofdistributiontransformer
overloadconditionwhilesatisfyingtheEVownerrequirementslikearrivaltime,departuretimeandensuring
thatthebatteryreaches90%ofSoC.
Theoptimizationprobleminmethod2i.e. Priceindicatedchargingmechanismisanalysedonlevel
2 charging as more power is required to charge EVs in less duration when compared to level 1 and resulted
insignificantpeaksasshowninFigure7. TheoptimizationresultsshowthatEVsloadisscheduledsuchthat
the total load curve is well below within the rated capacity of distribution transformer which minimises the
overallcostoftheelectricity. Howeveroff-peakhoursfrom1:00to8:00areturnedouttobepeakhourswhich
istreatedaspriceindicateduncontrolledchargingasshowninFigure10.
From the household load profile of a day shown in Figure 3 off peak hours are from 1:00 to 10:00.
OverallchargingcostofEVsarefurtherminimisedbyslightlyadjustingthedeparturetimeofvehiclesupto
10:00am. Figure11showsthepriceindicatedcontrolledchargingofEVs.
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Figure8. ControlledchargingofEVsinlevel-1 Figure9. ControlledchargingofEVsinlevel-2
alleviatingoverloadconditionofdistribution alleviatingoverloadconditionofdistribution
transformerafterminimisingloadvariance transformerafterminimisingloadvariance
Figure10. Priceindicateduncontrolledcharging Figure11. Priceindicatedcontrolledcharging
mechanismforlevel2chargingofEVs mechanismforlevel2chargingofEVs
4.1. Comparisonofproposedoptimizationmethodsforlevel2charging
Thehouseholdload,EVloadandtotalloadofthedistributiontransformerwhenEVsareconnectedin
level2chargingmodebasedonuncontrolledcharging,minimisingloadvarianceandpriceindicatedcharging
mechanisms are presented in the Figure 12 and Figure 13 respectively. After performing the proposed opti-
mizationmethodsforDSM,withoutdisturbingnonEVloadi.ehouseholdloadbypropermanagementofEVs,
distributiontransformeroverloadingproblemissolved.
FromtheFigure12itcanbeobservedthataloneconsideringhouseholdloadiswellbelowthelimits
ofthetransformerbutthetotalloadoftransformeralongwithhouseholdloadwhenEVsareconnectedinun-
controlledmanneroverloadedthedistributiontransformerfrom100to150%ofitscapacityforalmost3hours
during peak hours in the night. So, to minimize the load fluctuations and peak load the above implemented
optimizations resulted in total load of controlled charging of EVs, total load of price indicated uncontrolled
chargingofEVs, totalloadofpriceindicatedcontrolledchargingofEVs. Outofwhichpriceindicatedcon-
trolled charging of EVs balanced the system very well in terms of the total load on the transformer and in
minimizing the charging price of the EVs. In price indicated uncontrolled charging though the total load on
distribution transformer is well up to the capacity of transformer, off peak hours are loaded to full extent of
thetransformercapacitywhichmayresultinincreaseofchargingpricesofEVscomparedtopriceindicated
controlledcharging.So,eventhoughthereisaslightviolationinthedeparturetimesofvehiclespriceindicated
controlledchargingseemstobebestwhencomparedtocontrolledandpriceindicateduncontrolledcharging.
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However,withouthavingtheknowledgeofpricefluctuations,notdisturbingthehouseholdloadwhilesatisfy-
ingEVowner’srequirementsEVsloadisscheduledinanoptimalmannerbyadoptingthetransformerlimits
in minimizing the load variance optimization i.e method 1. Therefore, out of the three cases if price is not a
constraint controlled charging of EVs, Price indicated uncontrolled charging yields the good solution to the
residentialareaaswellastothedistributiongridfollowingalltheirrequirementsevenoptimizingthecostof
charging EVs to most possible extent. Similarly, if there is minor flexibility considered in departure time of
EVsaboveallpriceindicatedcontrolledcharginggivesthebestsolutionobeyingthelimitsonthetransformer
aswellasminimizingoverallchargingcostofEVsfurtherproceededtosatisfytheEVownerrequirements.
Figure12. Householdload,comparisonofEV Figure13. Householdload,comparisonoftotal
loadofuncontrolled,controlled,price loadofuncontrolled,controlled,price
indicateduncontrolled,priceindicated indicateduncontrolled,priceindicated
controlledforlevel2chargingofEVs controlledforlevel2chargingofEVs
5. CONCLUSION
ThispaperfirstlypresentedthemethodologytomodelthestochasticEVloadandtotalloadatares-
idential area including EV load is calculated. As uncontrolled charging of EVs resulted in overloading of
distribution transformer, demand side management techniques are implemented. The proposed optimization
methodsalleviatedtheproblemofoverloadingtransformer. Totalloadonthedistributiontransformeriscom-
paredinalltheapproaches. ItisobservedthatmethodoneminimizedtheloadfluctuationsbyshiftingEVload
from peak hours to off peak hours and method two is implemented where EVs load is scheduled during low
pricehoursandnoknowledgeonfluctuationsinrealtimepriceisneeded.Bothmethodssatisfiedtheconstraint
onthetransformercapacity,avoidedpeakloadonthesystem,minimizedthechargingcostofEVsandsched-
uled them within the given time limit. We also presented the price indicated controlled charging mechanism
whichfurtheroptimizedthechargingpriceofEVswithslightdeviationindeparturetimes.
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Optimizingtheeffectofchargingelectricvehiclesondistributiontransformerusing... (SwapnaGanapaneni)
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BIOGRAPHIESOFAUTHORS
Mrs.GanapaneniSwapna workingasAssistantprofessorinthedepartmentofElectrical
andElectronicsEngineeringatKLDeemedtobeuniversity(KLEF)intheareaofPowersystems
control and Automation. She obtained her B.Tech and M.Tech degree from JNTU Kakinada and
currently pursuing PhD in KLEF. She is having nine years of teaching experience. Her research
interestsincludepowersystemsderegulationandoptimalchargingofElectricvehiclesinsmartgrid.
Shepublished7articlesinSCIandScopusindexedjournals. SheisaffiliatedwithIEEEasstudent
memberandACMasprofessionalmember.Shecanbecontactedatemail:[email protected].
Dr. Pinni Srinivasa Varma completed his M. Tech. from JNTU Hyderabad. He has
completedhisPh.D.fromJNTUAnantapur. HisareasofresearcharePowerSystemDeregulation
andPowerSystemReliability.Hehaspublished50researchpapersinvariousinternationaljournals.
HehaswrittenatextbookonPowerSystemDeregulationandispublishedbyLambertpublishers.
Hehaspublished2patentsintheareaofPowerSystems.Now,heisworkingasAssociateProfessor
inEEEDept.,KLUniversity,Guntur,AndhraPradesh,India. AtpresentDr. PSVarmaisserving
asAssociateDeanRD,KLEF.Hecanbecontactedatemail:[email protected].
IndonesianJElecEng&CompSci,Vol. 25,No. 1,January2022: 25–34