The Impact of Electric Vehicles on the Grid · Electric Vehicle Research Project: Goals • Study...
Transcript of The Impact of Electric Vehicles on the Grid · Electric Vehicle Research Project: Goals • Study...
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The Impact of Electric Vehicles on the Grid
Dr.JuliandeHoog
PresentationtoEconomyandInfrastructureCommittee,StateParliamentofVictoria,Australia
9November2017
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Feb 2012 – Feb 2015 University of Melbourne Electric Vehicle Research Project: Goals
• Study impact of electric vehicles on the grid – at distribution level
• Design an optimal charging policy, informed by:– Electricity market spot price– State of charge in every battery– Present and anticipated constraints of the distribution network
Ø Maximal uptake of electric vehicles with minimal capacity upgrade requirements
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Simulation Approach
Networks
• Models of several neighbourhoods (Melbourne, Townsville)• Real line and transformer specs• Accurate phase allocation (although not for all)
House on Phase AHouse on Phase BHouse on Phase C
Pole
Transformer
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House on Phase AHouse on Phase BHouse on Phase C
Pole
Transformer
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Simulation Approach
Networks
Household Demand
• Household demand as measured at distribution transformer / or at individual houses
• Range of months
0 2 4 6 8 10 12 14 16 18 20 220
20
40
60
80
Time of Day
Tota
l Dem
and
(kVA
)
Phase APhase BPhase C
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Simulation Approach
Networks
Household Demand
Travel Patterns and Charging Needs
• 2009 VISTA Travel Survey (Victoria Department of Transport)• 13,000 records of 24-hour vehicle travel profiles• Know when vehicles home, and how far they have travelled
10 20 30 40 50 60 70 80 90 100 >1000
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10
15
20
Daily maximum travel distance (km)
% o
f tot
al
0 2 4 6 8 10 12 14 16 18 20 22 240
5
10
Time of day
Hom
e ar
rival
s, %
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Simulation Approach
Networks
Household Demand
Travel Patterns and Charging Needs
Load Flow and Simulation
• Load flow using MATLAB SimPowerSystems• Optimisation using MATLAB Optimisation Toolbox• All else using POSSIM (POwer Systems SIMulator)
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Simulation Approach
Networks
Household Demand
Travel Patterns and Charging Needs
Load Flow and Simulation
Validation
• Extensive comparison of simulator outputs to real measurements• Voltages within 1.5 V on average (0.6%)• Currents within 6 A on average (7%)
0.6
0.9
1.2
V B (pu)
RealSimulated
0 2 4 6 8 10 12 14 16 18 20 22 240
0.3
0.6
I B (pu)
Time of day
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Early Observations 1: Network Impacts
Peak Demand
8:00 12:00 16:00 20:00 0:00 4:00 8:000
0.5
1
1.5
2
Dem
and
(pu)
VehicleHousehold
• Thermal overload• Increased ageing / failure of network assets
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Early Observations 1: Network Impacts
Peak Demand
Voltage Drop
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1.1Vo
ltage
(pu)
• Voltage below minimum required levels at certain houses in the network
• Appliances may run inefficiently / reduced lifetimes
Transformer234V 230V 226V
231V 224V 220V
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Early Observations 1: Network Impacts
Peak Demand
Voltage Drop
Phase Unbalance
IA
IB
IC IA • Neutral current• More losses• Increased voltage drop
8:00 12:00 16:00 20:00 0:00 4:00 8:000
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4
Unb
alan
ce (%
)
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Early Observation 2: Importance of Location
A: 50B: 43C: 21
House on Phase AHouse on Phase BHouse on Phase C
Pole
Most sensitive!
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Early Observation 2: Importance of Location
• Adding a single vehicle at the red house has the same impact on minimum voltage as adding vehicles at all (45) green houses!
• Reason: voltage drop and unbalance in the network
• Vulnerable: houses distant from the transformer on highly loaded phases
• Significant implications for fairness of charging. Should all customers have equal rights?
House on Phase AHouse on Phase BHouse on Phase C
Pole
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Early Observation 3: Flexibility
8:00 12:00 16:00 20:00 0:00 4:00 8:00Time of day
Indi
vidu
al v
ehic
les
Home, fully charged Home, charging Away
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Early Observation 3: Flexibility
8:00 12:00 16:00 20:00 0:00 4:00 8:00Time of day
Indi
vidu
al v
ehic
les
Home, fully charged Home, charging Away
Coincident charging
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Early Observation 3: Flexibility
8:00 12:00 16:00 20:00 0:00 4:00 8:00Time of day
Indi
vidu
al v
ehic
les
Home, fully charged Home, charging Away
Coincident charging
Flexibility
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Load Shifting for Electric Vehicles
UtilityOperator SmartMeter ChargePoint ElectricVehicle
Z.Angelovski and K.Handberg (DiUS Computing), together with Raman Jegatheesan (United Energy)Demand management of electric vehicle charging using Victoria’s Smart Grid. Technical report, DiUS Computing (May 2013).
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By controlling timing of charging, negative impacts can be avoided
: current supplied to vehicle k at time t
Provide as much current to vehicle charging as the network will allowmaxxk ,t k=1
K
∑ xk,tt=0
T
∑xk,t
Uncontrolled Controlled
Total Demand:
Voltage:
Charging Profiles:
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2D
eman
d (p
u)
VehicleHousehold
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Volta
ge (p
u)
8:00 12:00 16:00 20:00 0:00 4:00 8:0050
100
Time of Day
SOC
(%)
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100
Time of Day
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VehicleHousehold
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Conclusions
• Uncontrolled chargingallowsEVpenetrationofonly10-15%;typicallythefirst“pointoffailure”isvoltagedrop
• Optimal chargingallowsEVpenetrationof80%ormoreinthenetworkswestudied,usingonlyexistinginfrastructure
• Installinganewtransformercosts~$100,000- $150,000…Savings(oratleastdelayedupgrades)of$1000- $2000perhousehold.
• Price-basedoptimisation leadstosavingsof10-20%(andconsiderablymoreondayswithgreaterfluctuationsinprice)
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Distributed Charge Control (Lu Xia)
Local voltage is a good indicator of total network demand
Use local voltage as a demand response signal
0
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Tota
l dem
and
(per
uni
t)
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l Vol
tage
(per
uni
t)
Total DemandLocal Voltage
Measure local voltage
Increase/Decrease
Charge Rate
Apply for discrete interval
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Distributed DSM solutions
Uncontrolled Centralised Distributed
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and
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ge (p
u)
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Time of Day
SOC
(%)
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Time of Day
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Time of Day
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VehicleHousehold
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© Copyright The University of Melbourne 2017
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