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POWERING EV GROWTH IN SANTA DELANO VALLEY The Technology & Policy Group Ash Bharatkumar, Michael Craig, Dan Cross-Call, & Michael Davidson Prepared for.

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Presentation on theme: "POWERING EV GROWTH IN SANTA DELANO VALLEY The Technology & Policy Group Ash Bharatkumar, Michael Craig, Dan Cross-Call, & Michael Davidson Prepared for."— Presentation transcript:

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 Technology & Policy Group 2  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

3 The Challenge Technology & Policy Group 3  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 Leaf Tesla Roadster Images: and

4 Electric Vehicle Projections 4  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, % 10% 1%

5 5 Demand Growth Projections

6 Technology & Policy Group 6  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

7 BAU: T&D Expansion Costs 7 Technology & Policy Group *Note: Costs will vary with network topology, terrain, selected line voltage, distance of transmission, and reactive power profile of load

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 8 Technology & Policy Group

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 9 Technology & Policy Group

10 Alternative 1: Energy Storage Technology & Policy Group 10  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

11 Technology & Policy Group 11 Alternative 1: Energy Storage – NaS Installations (7 MWh/1 MW)

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 12 Technology & Policy Group

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 13

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 14

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: 15

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 16

17 Alternative 3: Controlled Charging Technology & Policy Group 17  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

18 Alternative 3: Controlled Charging – Model Technology & Policy Group 18  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)

19 19 No Control, Medium EV Growth Scenario Alternative 3: Controlled Charging – Load Shifting

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

21 Technology & Policy Group 21  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 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 Costs for Medium Growth Scenario Alternative 3: Controlled Charging – Costs

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 22 Technology & Policy Group

23 EV Growth Scenario LowMediumHigh EV Fleet Size46,796447,3151,307,855 % of Vehicle Fleet 1%10%30% Total (millions) Per EVTotal (millions)Per EVTotal (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 23 Summary of Alternatives

24 Tariff Structure – Essential Considerations Technology & Policy Group 24  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

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 25

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 26 Technology & Policy Group

27 Thank You for Your Attention Questions? Technology & Policy Group 27

28 Bass Diffusion Model Technology & Policy Group 28  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)

29 Proportions of EV Types in Fleet Technology & Policy Group 29 EV Type Percentage in Fleet PHEV 4.5kWh0.26 PHEV 16kWh0.30 EV 24kWh0.15 EV 40kWh0.16 EV 60kWh0.09 EV 85kWh0.04

30 Demand Growth Projection Details Technology & Policy Group 30  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

31 Demand Growth Projection Details 31 Technology & Policy Group

32 Temporal Distribution of Added Demand Technology & Policy Group 32

33 Delayed Charging 33

34 Controlled Charging under High EV Growth Scenario 34

35 Controlled Charging Model Formulation 35 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 Technology & Policy Group 36 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

37 T&D Expansion Costs 37 Technology & Policy Group

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 38 Technology & Policy Group

39 LMP Variation During Pilot Period 39 Technology & Policy Group  Only 10 hours during six months with LMP above five times average of $85 / MWh

40 DR Household Response 40 Technology & Policy Group

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