Anintegratedtrafficandpowergridsimulator
enablingtheassessmentofe-mobilityimpactonthegrid
LucianoBononiAlbertoBorghe2CarloAlbertoNucciTullioSalmonCino2
UniversityofBologna–DEI–DISI
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.
1.Introduc=on2.Aimoftheproposedco-simulator3.DescripBonoftheco-simulator4.Testcaseresults5.Conclusiveremarks
OutlineofpresnetaBon
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!
Power Distribution simulator: EMTP-RV
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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