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POWERING EV GROWTH IN SANTA DELANO VALLEY
The Technology & Policy GroupAsh Bharatkumar, Michael Craig, Dan Cross-Call, & Michael Davidson
Prepared for the USAEE Case Competition 2013Anchorage, AK, July 29
Outline
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Summary of challenge EV and demand growth projections BAU: Transmission and distribution
expansion Alternatives
Energy storage Demand response Controlled charging
Tariff design for equitable allocation of EV costs
The Challenge
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Growth in electric vehicles (EVs) poses challenges for Santa Delano Electric Company (SDEC) Accommodate new electricity load Maintain affordable and reliable electricity Ensure equitable distribution system upgrade
costs While encouraging growth in EV ownership
Options and opportunities for a 15 year planning horizon
Nissan LeafTesla Roadster
Images: thecarconnection.com and proetools.com
Electric Vehicle Projections4
Projected growth with Bass diffusion model Used elsewhere to model EV growth
Low, medium and high growth scenarios Split fleet projections proportionally into EV
models
Fleet Penetration, 2027
30%
10%
1%
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Demand Growth Projections
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Distribution and transmission network expansion required to serve increased demand from EVs over 15 year horizon
Distribution expansion for each 1% increase in load relative to 2012 load Increase substation capacity (transformers +
feeders) Transmission expansion for each 5%
increase in load relative to 2012 load Add lines
BAU: T&D Expansion
BAU: T&D Expansion Costs7
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*Note: Costs will vary with network topology, terrain, selected line voltage, distance of transmission, and reactive power profile of load
BAU: T&D Expansion – Findings T&D network build-out can
accommodate projected EV growth Medium Growth cost: $1,460 per EV Not the recommended course of
action
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Summary of Alternatives
Energy storage Energy storage is not a viable option Costlier than T&D upgrades, not suitably
mature Demand response
Real time prices are not reliable alternative to T&D upgrades
Controlled charging Controlled charging is preferred solution to
accommodate EVs
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Alternative 1: Energy Storage
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Meet additional peak load from EVs with many small installations on distribution network Shifts electricity from off-peak to peak hours
Limited technologies are viable for distributed applications Sodium-sulfur (NaS) batteries – commercially
available, chosen as a representative battery chemistry
Modeled build-out per annual power and energy needs
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Alternative 1: Energy Storage –NaS Installations (7 MWh/1 MW)
Alternative 1: Energy Storage – Findings Costs much more than T&D upgrades
Medium Growth: $5,133 per EV Not suitably mature for near-term
application Energy storage is not a viable
option
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Alternative 2: Demand Response Engage households in reducing peak load
through tariffs that vary with system conditions
SDEC pilot used locational marginal price (LMP)
Peak demand will be shifted only if: Size of price incentive is sufficiently
large (>5x) Households are open and responsive to
price signals Price reflects peak system demand
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Alternative 2: Demand Response – Analysis of Pilot Weak price incentive: only 10 hours with
large differential Low opt-in rate (24%) Wide variation/unpredictability in customer
response
Average Load Reduction
Reduction of Peak Household
Demand (%)
95% CI Average Load Reduction
Reduction of Peak Household
Demand (%)
95% CI
Number of Hrs
Single-family 55.5 W 2.25% (6.4 , 104.5) 34.0 W 1.38% (14 , 53.9)Multi-family 42.1 W 1.84% (-3.2 , 87.3) 23.3 W 1.02% (4.6 , 42.1)
Peak LMP Hours Peak System Demand Hours
305 439
(Top 5% of Year) (Top 5% of Year)
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Alternative 2: Demand Response – Disadvantages of LMPs
LMP vs. Total System Load During 5% Peak Hours
0.00
100.00
200.00
300.00
400.00
500.00
600.00
6700 6800 6900 7000 7100 7200 7300 7400 7500 7600
MW
$ /
MW
h
Avg yearly LMP: $84.33 / MWh
LMP reflects mostly California wholesale prices: Congestion < 10% of LMP cost in CAISO in 2012
SDEC peak does not align with LMP peak:
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Alternative 2: Demand Response – Findings SDEC’s DR pilot using real-time prices
(RTP) led to small, inconsistent reductions in peak demand
The standard price signal – locational marginal price – does not accurately reflect distribution-level congestion
RTP is not reliable alternative to T&D upgrades
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Alternative 3: Controlled Charging
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Two options considered: Utility has full control over charging Delayed charging (4 hours after plug in)
Shift EV loads to off-peak hours But at the expense of consumer control
Alternative 3: Controlled Charging – Model
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Modeled load-shifting capability with GAMS
Cost-minimization optimization Assumed 90% EV fleet participation Guaranteed all EVs fully charge overnight Minimized total system cost (demand times
price)
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No Control, Medium EV Growth Scenario
Alternative 3: Controlled Charging – Load Shifting
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Controlled Charging, Medium EV Growth Scenario, 90% of EVs
Alternative 3: Controlled Charging – Load Shifting
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Costs of program: Smart meters IT and communications
infrastructure Annual IT costs Annual savings from less
“dumb” meter reading T&D upgrades from EVs
not in program
ItemNPV of Cost
(Savings)
Reading Old Meters ($6,040,084)
Smart Meters $29,601,080
Communications Infrastruc. $465,160
IT Infrastruc. $225,532
T&D Expansion $29,833,157
Total $54,084,845
Total Per EV $125
Costs for Medium Growth Scenario
Alternative 3: Controlled Charging – Costs
Alternative 3: Controlled Charging – Findings
Off-peak night hours can fully absorb demand from EVs under all growth scenarios
Costs less than T&D upgrades Medium Growth: $125 per EV
Preferred solution to accommodate EVs
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EV Growth Scenario
Low Medium High
EV Fleet Size
46,796 447,315 1,307,855
% of Vehicle Fleet
1% 10% 30%
Total (millions
)
Per EV Total (millions)
Per EV Total (millions)
Per EV
BAU T&D $41 $883 $644 $1,463 $1,852 $1,443Controlled Charging
$2 $53 $54 $125 $173 $139
Energy Storage -
NaS Batteries
$229 $4,893 $2,296 $5,133 $7,022 $5,474
Demand Response
No reliable load reduction
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Summary of Alternatives
Tariff Structure – Essential Considerations
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Goals of differentiated tariffs: Pursue lowest total system cost Allocate costs of system upgrades equitably
(avoid cross-subsidization) Demand (capacity) charges more precise
than energy charges from T&D perspective
Controlled charging infrastructure (e.g., smart meters) furthers other SDEC objectives
Tariff Structure - Recommendations Monthly EV charger fee of $8, effective for
15 years (approx. cost per EV of BAU T&D upgrades)
Fee waived if enrolled in controlled charging program
Program participants face higher rate when override charging schedule
Smart meters paid for by rate base Periodically review tariff (e.g., every 2
years) to ensure accurate cost accounting
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Conclusion
Large but uncertain demand growth expected from EVs Ideally accommodate load cost-effectively
and equitably while encouraging further EV growth
BAU T&D expansion costly Of alternatives, only controlled charging
accommodates load at reasonable cost Proposed tariff allocates cost equitably
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Thank You for Your Attention
Questions?
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Bass Diffusion Model
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Three key parameters (low, medium and high): Maximum potential market (m=0.03, 0.25,
0.7) Fraction of purchasers who make decisions
independent of others and network externalities (“coefficient of innovation”) (p=0.01, 0.015, 0.02)
Fraction of purchasers who are swayed by decisions of others and network effects (“coefficient of imitation”) (q=0.3, 0.35, 0.4)
Proportions of EV Types in Fleet
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EV TypePercentage in
FleetPHEV 4.5kWh 0.26PHEV 16kWh 0.30
EV 24kWh 0.15EV 40kWh 0.16EV 60kWh 0.09EV 85kWh 0.04
Demand Growth Projection Details
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Split fleet projections proportionally into EV types
Accounted for: Fraction of EVs that plug in during peak
hours at home Temporal distribution of when EVs plug in Charger level (Level 2 for EVs >40kWh) Daily travel distance (high value (52 mi.)
for EVs >40kWh) Duration of charge
Demand Growth Projection Details
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Temporal Distribution of Added Demand
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Delayed Charging33
Controlled Charging under High EV Growth Scenario
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Controlled Charging Model Formulation
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• Cost minimization:
• z = total cost, p(h) = price, B(h) = base demand,
D(h) = aggregate EV demand, h = hour, v = vehicle
• Must fully charge overnight:
• C(v,h) = charging, d(v) = hours required for full charge
• Charging and plug-in relationship:
• L(v,h) = plugs in
Controlled Charging Model Formulation
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• Charge status:
• C(v,h) = charging, L(v,h) = plug-in, U(v,h) = unplug
• Demand from EVs:
• D(h) = aggregate EV demand, P(v) = charging power
• Limit number of EVs that plug-in per hour:
• M = max number of EVs that can plug-in per hour
T&D Expansion Costs37
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T&D Expansion
Transmission expansion – add lines Line loadability governed by St. Clair Curve –
line loadability vs. line length Capacity of shorter lines limited by conductor
thermal capacity, longer lines governed by SIL and voltage stability limits
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LMP Variation During Pilot Period
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Only 10 hours during six months with LMP above five times average of $85 / MWh
DR Household Response40
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