POWERING EV GROWTH IN SANTA DELANO VALLEY

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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