1. POWERING EV GROWTH IN SANTA DELANO VALLEY
The Technology & Policy Group
Ash Bharatkumar, Michael Craig, Dan Cross-Call, & Michael Davidson
Prepared for the USAEE Case Competition 2013
Anchorage, AK, July 29

2. Outline 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
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3. The Challenge
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
Increasing penetration of electric vehicles (EVs) in Santa Delano Valley
How can Santa Delano Electric Company (SDEC) serve the new load introduced by EVs and continue to deliver electricity reliably and affordably to all of its customers?
Options and opportunities for a 15 year planning horizon
Tesla Roadster
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Nissan Leaf
Images: thecarconnection.com and proetools.com

4. Electric Vehicle Projections
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
30%
Bass diffusion model - Forecasting method for emerging technologies
Parameters for Bass diffusion: total market size (%age of fleet), coefficient of innovation, coefficient of imitation
Near market saturation in 2027
2027 fleet penetrations (total all types): 1%, 10% and 30%
Fleet Penetration, 2027
10% 1%

5. Demand Growth Projections
Taking into account fraction of EVs that plug in during peak hours at home, at what hour they plug in, what charger level, daily distance traveled, and duratoin of charge.
Demand curve shown is from stringing together the highest demand for each hour of the day. So highest demand at 9am, 10am, etc. Regardless when of occur.

6. BAU: T&D Expansion
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
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7. BAU: T&D Expansion Costs
Low growth
Medium growth
High growth
$40.9 m
$644.5 m
$1,800 m
$880/EV
$1,460/EV
$1,440/EV
Note caveat about different costs associated with different network topologies
*Note: Costs will vary with network topology, terrain, selected line voltage, distance of transmission, and reactive power profile of load
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8. 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
Provides a benchmark against which to assess other alternatives
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9. 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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10. Alternative 1: Energy Storage
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
Amount of NaS batteries needed for lal EV demand in medium growth scenario is > total NaS installations worldwide today. (EPRI)
----- Meeting Notes (7/28/13 20:22) -----
Why did we choose NaS batteries and why did we choose adiabatic CAES
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11. Alternative 1: Energy Storage – NaS Installations (7 MWh/1 MW)
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12. 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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13. 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

14. 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

15. Alternative 2: Demand Response – Disadvantages of LMPs
LMP reflects mostly California wholesale prices: Congestion < 10% of LMP cost in CAISO in 2012
SDEC peak does not align with LMP peak:

16. 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

17. Alternative 3: Controlled Charging
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
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18. Alternative 3: Controlled Charging – Model
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)
Total sytsem cost = Sum of hourly product of demand and price
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19. Alternative 3: Controlled Charging – Load Shifting
No Control, Medium EV Growth Scenario

20. Alternative 3: Controlled Charging – Load Shifting
Controlled Charging, Medium EV Growth Scenario, 90% of EVs

21. Alternative 3: Controlled Charging – Costs
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
Costs for Medium Growth Scenario
Item
NPV 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
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22. 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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23. Summary of Alternatives
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
BAU T&D
$41
$883
$644
$1,463
$1,852
$1,443
Controlled 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

24. Tariff Structure – Essential Considerations
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
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25. 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

26. 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
Equitably meaning by avoiding cross-subsidization
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27. Thank You for Your Attention Questions?
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28. Bass Diffusion Model 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)
Typical observed values for p and q: and m, max potential market, is %age of total fleet size. Population growth projected with CA Dept of Finance state growth projections. Applied projections to given # households, and assumed 2 cars/household for total potential fleet size. So max market is m * total cars.
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29. Proportions of EV Types in Fleet
Percentage in Fleet
PHEV 4.5kWh
0.26
PHEV 16kWh
0.30
EV 24kWh
0.15
EV 40kWh
0.16
EV 60kWh
0.09
EV 85kWh
0.04
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30. Demand Growth Projection Details
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
%age plug in: (26% at 5PM, 48% at 6PM, 20% at 7PM, 6% at 8PM)
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31. Demand Growth Projection Details
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32. Temporal Distribution of Added Demand
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33. Delayed Charging

34. Controlled Charging under High EV Growth Scenario

35. Controlled Charging Model Formulation
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

36. Controlled Charging Model Formulation
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
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37. T&D Expansion Costs
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38. 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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39. LMP Variation During Pilot Period
Only 10 hours during six months with LMP above five times average of $85 / MWh
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40. DR Household Response
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