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Presentation on theme: "POWERING EV GROWTH IN SANTA DELANO VALLEY"— Presentation transcript:

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 Technology & Policy Group

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 Technology & Policy Group Nissan Leaf Images: and

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 Technology & Policy Group

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 Technology & Policy Group

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 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 Technology & Policy Group

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 Technology & Policy Group

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

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 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

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 Technology & Policy Group

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 Technology & Policy Group

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 Technology & Policy Group

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 Technology & Policy Group

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 Technology & Policy Group

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 Technology & Policy Group

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

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. Technology & Policy Group

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 Technology & Policy Group

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) Technology & Policy Group

31 Demand Growth Projection Details
Technology & Policy Group

32 Temporal Distribution of Added Demand
Technology & Policy Group

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 Technology & Policy Group

37 T&D Expansion Costs 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 Technology & Policy Group

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

40 DR Household Response Technology & Policy Group


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