Anintegratedtrafficandpowergridsimulator

enablingtheassessmentofe-mobilityimpactonthegrid

LucianoBononiAlbertoBorghe2CarloAlbertoNucciTullioSalmonCino2

UniversityofBologna–DEI–DISI

carloalberto.nucci@unibo.it

1.Introduc=on2.Aimoftheproposedco-simulator3.Descrip=onoftheco-simulator4.Testcaseresults5.Conclusiveremarks

OutlineofpresnetaBon

1.IntroducBon2.Aimoftheproposedco-simulator3.Descrip=onoftheco-simulator4.Testcaseresults5.Conclusiveremarks

OutlineofpresnetaBon

LeCorbusier,whofamouslycalledahouse“amachineforliving”,wasfascinated—evenobsessed—byanotherkindofmachine,theautomobile.In1925hewrote“Cars...havecompletelyrevolu6onizedourpastconcep6onsofurbanplanning.

IntroducBon

In2017:recentMITstudyàacorrectregula=onandanincreaseincarsharingcouldreducethenumberofvehiclesonourroadstojust20to30%ofwhatitistoday.Thiswouldbebadlyneededifthees=matethatthedemandforurbantransportshouldmorethandoubleby2050iscorrect.

IntroducBon

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Hypothetical scenarios of the spread of BEVs and PHEVs in the Italian car fleet as of 2025 and 2030 (absolute number and as percentage of stock).

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1.Introduc=on2.Aimoftheproposedco-simulator3.Descrip=onoftheco-simulator4.Testcaseresults5.Conclusiveremarks

OutlineofpresnetaBon

TheAim

Electro-mobilityintegra=onintocitytrafficàplansfortheinstallaBonofadequatepubliccharginginfrastructuresareofutmostimportance.ToassesstheeffectsoftransientscausedbytheconcurrentchargingofalargenumberofEVstotheopera=ngcondi=onsofMVnetworksàEngineeringtoolsTheseEngineeringtools,moreover,canbeusedtodesignandtestspecificcountermeasuresagainstoverloadofpowercomponents,voltagevaria=onsandunbalances.

TheAim

Thepresenta=onsummarizessomeoftheworkcarriedoutintheauthors’laboratoriesbyamulB-disciplinaryresearchteamtoaccomplishsuchanassessment.Itdescribesaco-simulaBonplaQormintegra=nga)  amobilitysimulatorsuitablyadaptedtoreproducee-

mobilityinatypicalmiddle-sizeItaliancitycenterwith

b)  apowerdistribuBonnetworksimulatorandwith

c)  asimulatorofaUMTScommunicaBonnetwork,whichhasbeendevelopedtothispurpose.

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1.Introduc=on2.Aimoftheproposedco-simulator3.DescripBonoftheco-simulator4.Testcaseresults5.Conclusiveremarks

OutlineofpresnetaBon

9(4)#!%()+,<-./&(!'($*<%&3,.1!#&%/.(R!!!,.70$123)B.#!-3)Q.(1!

Mobilitysimulator

Traffic Simulation Framework

ItisasimulatorforanalysisoftrafficincludingElectricVehicles(EV)andElectricVehiclesSupplyEquipment(EVSE)en==esinrealis=cscenarios(includingsupportforext.servicesandapps).•  BasedonVeins,Omnet++,SUMO,andOpenstreetmap

•  Accuratemodelingofcityscenariosandmul=pleeMobilityen==es

•  Modelingandcontroloftraffic(realis=cdatavs.modelassump=ons)cont’d

S.4$3$%8!0$123)%.(!

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!

The platform enables services and apps to interact with field

devices: EV and Charging

Spot SP Back-end

"**$B.#)3![&)%2(&0!

AnIntegratedSpain-Italy-Bayernrechargingecosystemdemonstrator

AddiBonalFeatures

Power Distribution simulator: EMTP-RV

•! O(<-+!(?!/1-!&00.-0&/-<!#',&+&'$-<!+(&<A!X$('A/&'/!%7@-<&'$-!k!$#..-'/!k!@(J-.Z!/1&/!%'$+#<-A!/1-!>K@>!-(.\3&0\!/1-!&7@+%/#<-!(?!&!/.%@+-/!(?!$#..-'/!A(#.$-A!%A!$('/.(++-<!,K!&!?--<B,&$H!.-0#+&/(.!%'!(.<-.!/(!%'P-$/!(.!&,A(.,!/1-!.-e#-A/-<!G&+#-!@-.B@1&A-!(?!&$=G-!&'<!.-&$=G-!@(J-.8!

PHASE 1 PHASE 2 PHASE 3

P

Q

Communication Network Simulator

ThesimulatoroftheUMTScommunica=onnetworkisbasedontheRiverbedModelerWirelesssuitev18.0.

TheRiverbedmodelincludestherepresenta=onofthemaincomponentsoftheUMTSnetwork:

theuserequipment(UE),i.e.theUMTSmoduleofeachagent,theNodeB,

theRadioNetworkController(RNC),whichmanagestheNodeBlogicalresourcesand

theUE-NodeBinterfaceresources.

Theimplementedmodelaccountsforablockerrorrate(BLER),i.e.thepercentageoftransportblockswitherrorsoverthetotalnumberoftransportblocks.Inordertotesttherobustnessofthecontrolprocedureagainstdelaysinthecommunica=onnetwork,somesimula=onsincludeabackgroundtraffic(BT)createdbyaddi=onalUserEquipment(Ues),otherthanthoseassociatedwiththeagents.ThesenewUEsandalsotheagents,generatetheBTbyusingsomedefaultmobileusertrafficprofilesdefinedbyRiverbedModeleraccordingtothe3GPPtechnicalreportTR36.822.

Communication Network Simulator

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WefocusonparkinglotsthatincludefastpublicchargingstaBonswithpowerra=ngassumedequalto50kW.

TheEVSEunitsofeachparkinglotarefedthroughaMV/LVtransformerthatrepresentthegridconnec=onpoint(GCP).

Weanalyzeamul=-agentsystemcomposedby-  someintelligentelectronicdevices(IEDs)installedattheHV/

MVsubstaBonandincorrespondenceofcri=calnetworkbranches(atthebeginningofeachfeederconnectedtothesecondarysideoftheHV/MV)thatmaybeoverloadedduetoEVschargingand

-  distributedagents,i.e.controlunitsconnectedtothesharedcommunicaBonnetwork,eachassociatedtotheclusterofEVSEunitsofaparkinglotabletoadjusttheEVSEchargingpowers.

MulBAgentSystemforDistributedControlofChargingStaBons

TheIEDattransformermeasuresthecurrentandcomparessuchavaluewiththemaximumopera=oncurrentvalueofthelineandtheoverloadstateoftheHV/MVtransformer.Suchalocalvoltageregulatorpropor=onallyreducesifthelocalvoltageattheMVsideofthetransformerislowerthanapredefinedvalue(e.g.,0.97pu)EachIEDisabletocommunicatea‘conges=on’indexovertheUMTScellularnetworkthatdenoteswhetherandhowmuchthecorrespondingpowercomponentisoverloaded.(Intheliteraturethistypeofindexesareopencalledconges=onprices).

TheagentassociatedwitheachclusterofEVSEsisabletocontrolthechargingpoweraccordingtothereceivedconges=onindex.

MulBAgentSystemforDistributedControlofChargingStaBons

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TheimplementeddistributedcontrolalgorithmààallowsthecongesBonmanagementofthenetworkwhilstthelocalregulaBonfuncBonoftheagentsàà reducesthechargingeffectsonvoltagevaria=ons.

EachagentcommunicatesalsowitheachsingleEVSEoftheclusterinordertoallocatethemaximumpowerthatcouldbeabsorbedfromtheMVnetworkamongthevariouschargingEVstakingintoaccounttheirspecificcharacteris=csandrequirements.

Weassumethatthemul=-agentsystem(MAS)usesathirdgenera=onmobilecellularnetwork,namelyaUniversalMobileTelecommunicaBonSystem(UMTS)

MulBAgentSystemforDistributedControlofChargingStaBons:INSUMMARY

1.Introduc=on2.Aimoftheproposedco-simulator3.Descrip=onoftheco-simulator4.Testcaseresults5.Conclusiveremarks

OutlineofpresnetaBon

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TestcaseresultsThetrafficsimulatorgeneratesaflowofrandomeventseachrepresen=ngaspecifictripofavehicleinthecity(SPtoFD).

ForthecaseofEVs,whentheSoCofthecorrespondingbaSeryisbelowathresholdsetto25%,thevehicledeviatesfromtheplannedjourneyandreachestheclosestEVSEunitavailable.

Thefirttestcasereferstoasimula=onthatstartswitharandomgenera=onof200events,eachoneevery5seconds.Witha50%uniformprobabilityeacheventisassociatedtoanEV,otherwiseisrepresentedbyafuelvehicle.Every=meavehicleleavesthesimula=on,i.e.reachesitsdes=na=on,aneweventisgenerated.EMTPislinkedandsynchronizedwithVeinSaper400softrafficgenera=on.

TheEMTPmodelrepresentsthetwo15kVfeedersofinterests,the132/15kVtransformeroftheHV/MVsubsta=onandtheequivalentimpedanceoftheHVnetwork.

Testcaseresults

Powerrequestedbyeachcluster:

CongesBonindexvariaBonscalculatedbytheIEDs:

CongesBonindexescalculatedbyeachagent:

3

4

5

6

7

8

9

10

400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700

N°o

f Ele

ctri

c Veh

icle

s con

nect

ed

Time (s)

EVSE_1EVSE_2EVSE_3EVSE_4

280300320340360380400420440460

400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700

Pow

er (k

W)

Time (s)

EVSE_1 - BT0 BLER0 EVSE_1 - BT1 BLER1EVSE_2 - BT0 BLER0 EVSE_2 - BT1 BLER1EVSE_3 - BT0 BLER0 EVSE_3 - BT1 BLER1EVSE_4 - BT0 BLER0 EVSE_4 - BT1 BLER1 0.E+00

5.E-03

1.E-02

2.E-02

2.E-02

3.E-02

3.E-02

400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700

Δpr

Time (s)

feeder 1 (EVSE_1, EVSE_2) - BT0 BLER0feeder 1 (EVSE_1, EVSE_2) - BT1 BLER1feeder 2 (EVSE_3, EVSE_4) - BT0 BLER0feeder 2 (EVSE_3, EVSE_4) - BT1 BLER1

11.051.1

1.151.2

1.251.3

1.351.4

1.451.5

1.551.6

400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700

Con

gest

ion

Inde

x

Time (s)

EVSE_1 - BT0 BLER0EVSE_2 - BT0 BLER0EVSE_3 - BT0 BLER0EVSE_4 - BT0 BLER0EVSE_1 - BT1 BLER1EVSE_2 - BT1 BLER1EVSE_3 - BT1 BLER1EVSE_4 - BT1 BLER1

CurrentmeasuredbyIntelligentElectricDeviceassociatedtothefirstbranchofthetwoconsideredfeeders

Δt=1sNumberofchargingEVsineachcluster:

0.998

1

1.002

1.004

1.006

1.008

1.01

1.012

1.014

400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700

Cur

rent

(p.u

.)

Time (s)

feeder 1 (EVSE_1, EVSE_2) - BT0 BLER0feeder 1 (EVSE_1, EVSE_2) - BT1 BLER1feeder 2 (EVSE_3, EVSE_4) - BT0 BLER0feeder 2 (EVSE_3, EVSE_4) - BT1 BLER1

Simulation results IEEE 37 Node Test Feeder

“IEEE PES Distribution Test Feeders.” http://www.ewh.ieee.org/soc/pes/dsacom/testfeeders/.

±500kvar compensator

OLTC with ±8 tap increments of 1.875%; tap mechanical delay = 2s; minimum variation time to initiate a tap change = 0.5 s; time to first change = 20 s; maximum delay time of the regulator = 15 s with an inverse law.

Rated voltage = 4.8 kV

agent participates to the regulation cycles of the VVO procedure but it does not directly adjust the output of any compensator

0

1

2

3

4

5

6

7

3(BA%7#+&=('!(?!;35!&'<!V(J-.!6KA/-7A!

Simulation results IEEE 123 Node Test Feeder

“IEEE PES Distribution Test Feeders.” http://www.ewh.ieee.org/soc/pes/dsacom/testfeeders/.

Simulation results IEEE 123 Node Test Feeder

±500kvar compensator Rated voltage = 4.16 kV additional agent

0 1

2 3

4

5

6

7

2nd OLTC with ±16 tap increments of 0.625%; time to first change and maximum delay time =10 s.

OLTC at the substation identical to the one of the 37 Node Feeder

3(BA%7#+&=('!(?!;35!&'<!V(J-.!6KA/-7A!

Communication by

using TCP

*CourtesyIng.R.BoSura,Prof.A.Borghe2–LISEP-DEI-Unibo

Co-simula=onofICTandPowerSystems

ExamplesofsimulaBonresultsResultsforIEEE37NodesFeeder

373941434547495153

0 10 20 30 40 50 60 70 80

PowerLo

ss(kW)

Time(s)

BT2TCP BT2UDP BT4TCP BT4UDP

Power loss variation for two different BT levels by using TCP and UDP

OLTCblockedintapposi=on=-1

Co-simula=onofICTandPowerSystems

Here,ascenarioof10000differentroutesgeneratedaccordingacertaintrafficrateassuminganaverageelectricloadprofileadoptedinthesimulatorofthepowernetwork.

Anexampleofresults,fortwodifferentpercentagesofplug-inEVsinthetrafficflow(namely,5%and10%),isshownbelowfortwofeeders.

05101520253035404550

05

101520253035404550

0 2 4 6 8 10 12 14 16 18 20 22 0

power(kW)

numbe

rofveh

icles

time(hours)

vehicles(5%EV)vehicles(10%EV)power(5%EV)power(10%EV)

2405101520253035404550

05101520253035404550

0 2 4 6 8 10 12 14 16 18 20 22 0

power(kW)

numbe

rofveh

icles

time(hours)

vehicles(5%EV)vehicles(10%EV)power(5%EV)power(10%EV)

24

Testcaseresultscont’d

Parkinglotssuppliedbyfeeder1tendtobelesspreferredbydriverscomparedthosesuppliedbyfeeder2,duetotheparkinglotclosenesstotheavenuessurroundingthecenterofBologna.Whilstinthemorning,e.g.around8AM,thedistribu=ongridsucceedstohandlethechargingdemandduetohightrafficdensity,intheeveningthemaximumpoweravailableforeachEVSEissignificantlyreducedbytheautoma=cconges=onmanagementcontrolsystem.

Testcaseresultscont’d

05101520253035404550

05

101520253035404550

0 2 4 6 8 10 12 14 16 18 20 22 0

power(kW)

numbe

rofveh

icles

time(hours)

vehicles(5%EV)vehicles(10%EV)power(5%EV)power(10%EV)

2405101520253035404550

05101520253035404550

0 2 4 6 8 10 12 14 16 18 20 22 0

power(kW)

numbe

rofveh

icles

time(hours)

vehicles(5%EV)vehicles(10%EV)power(5%EV)power(10%EV)

24

1.Introduc=on2.Aimoftheproposedco-simulator3.Descrip=onoftheco-simulator4.Testcaseresults5.Conclusiveremarks

OutlineofpresnetaBon

ConclusiveRemarks•  Tobeabletodevelop,implementandoperateEvsinsmartgrids,

oneneeds

o  goodtechnicalknowledge/skillsonelectricalpowersystemsandelectricalenergyengineeringingeneral–asbefore

o  AlargerknowledgeonICTrelateddisciplines–morethanbefore

•  Smartgridsrepresentoneofthestrongestenablersfortheimplementa=onofthesmartenergyandsmartcityconcepts

•  Toaccomplishthatamul=-layerapproachisrequired,whichrepresentsachallengeforPhDstudents

•  Collabora=onwithu=li=esandmunicipali=esiscrucial

V&6&(&#,&0!

2015 IEEE Eindhoven PowerTech

Session DER and EV integration in Smart Cities

Zilele Academiei de Stiinte Tehnice din Romania, 6-7 October at Targu Mures