hydive (hydrogen dynamic infrastructure and vehicle ...€¦ · hydive (hydrogen dynamic...

HyDIVE™(Hydrogen Dynamic Infrastructure and

Vehicle Evolution) model analysis

Cory Welch

Hydrogen Analysis Workshop, August 9-10

Washington, D.C.

Disclaimer and Government License

This work has been authored by Midwest Research Institute (MRI) under Contract No. DE-AC36-99GO10337 with the U.S. Department of Energy (the “DOE”). The United States Government (the “Government”) retains and the publisher, by accepting the work for publication, acknowledges that the Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for Government purposes.

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Overview

• Brief overview of spatial, dynamic model

• Example intermediate static calculations

• Key unknowns, next step, and insights

Main Feedback Structure: Initial Model (1)

Vehicle Incentives (2)

Fuel Production/ Purchase Incentives

Station Incentives

(1) See Struben, 2006a for model exposition and analysis

(2) to be incorporated (research at both MIT and NREL is envisioned to b e part of a larger model including additional relevant dynamics.)

Spatial, dynamic interdependence

From Struben, Welch, Sterman (2006). See Struben (2006a) for detailed discussion of initial model.

Vehicle Utility

• Utility of vehicle for driving, which affects vehicle sales, will be affected by:– added drive time to limited refueling stations– time to refuel vehicle: f (refueling rate, “queue” time)– probability, cost of running out of fuel

• Above are endogenously calculated dynamically and spatially for different “trip lengths” (next 2 slides)

• Other vehicle attributes (e.g., price, performance, etc.) to be incorporated with future development.

Trip distribution – a driver of spatial diffusion

• GPS data from southern CA also being sought from SCAG. NHTS data to be inspected/reviewed.

Trip Distribution Frequency*

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* Data still needs to be inspected and reviewed for proper context. <http://nhts.ornl.gov/2001/html_files/download_directory.shtml>

642,292 trips from National Household Transportation Survey, V4.0, July 2005.

Desired Annual Driver Miles vs. Trip Length

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Gasoline 10 H2 Stations


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Limited station coverage reduces driving convenience, which will affect sales & miles driven


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Gasoline 600 H2 Stations 100 H2 Stations20 H2 Stations 10 H2 Stations

Vehicle utility is reduced due to additional effort/risk. The effect on potential sales needs to be better understood.

calculated for each location and each time step in model

Insights and Questions

• Consumer sensitivity to reduced vehicle “utility”(due to limited coverage) is a major driver of dynamics, but not yet well understood.

• Spatial “diffusion” beyond urban areas may be difficult.

• The following slides illustrate how we are beginning to improve understanding the above.

Directional trip distribution now modeled

Station profitability/growth are calculated based on endogenously calculated vehicle demand and trip distribution

Example trip distribution for one driver living here

Additional drive time, refueling time, “out of fuel” risk are calculated based on level of H2 station coverage

Trip distribution affects station profitability

L.A.

Trip destinations and refueling events are skewed largely, on average, toward drivers’ home location.

N

Fraction of trips

Profitability difficult for stations far from vehicle owners

Advantage: large % of refuelings are “covered” by stations close to drivers

Disadvantage: stations far from drivers’home location may be required for adequate utility, but may be very unprofitable.

Expected Distance to Nearest Gasoline Station

44 m

i.

Example: Expected Distance to Nearest H2 Station (20 Stations)

44 m

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Example: Extra Distance to Nearest H2 Station (20 Stations)

44 m

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Extra “time” to station readily calculable assuming an average speed of travel (currently use a constant value, but could relax if spatial data are obtained)

Expected Distance to Nearest Station

"Expected" Distance to Nearest Station*(for most "station dense" cell in LA -- 16x22 mi. cells)

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H2 Lighthouse Stations

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iles

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* Probabilistic distance to nearest station, which is a function of station density within each cell and across adjacent cells.

Gasoline: ~1/3 mile

Potential calibration data – Hydrogen Learning Demonstration Project

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Radius Histogram: Vehicle EcoCar1

Distance from nearest refueling station

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Created: Jul-11-06 9:59 AM

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1Range Histogram: EcoCars

Fraction of theoretical range b/w refueling

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Created: Jul-11-06 10:14 AM

Theoretical Range = 260 miles.

Total detected1 refuelings = 674

1. Due to data noise, some refueling events are not detected.

Analysis of learning demo driving behavior could be useful for calibration (if future industry approval is obtained), particularly for low levels of station coverage. 0 10 20 30 40 50 60 700

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70Trip Length Histogram: All Vehicles

Trip Length (miles)

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ber o

f Trip

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% of non-idle trips < 1 mile = 17%Drivers DO tend to drive vehicles

less/differently with reduced station

coverage.

Unknowns and next steps

• Unknown: purchasing sensitivity to reduced vehicle utility

• Next Steps:– discrete choice analysis techniques envisioned to be used for

quantification of this sensitivity (collaborate with auto OEMs)– broaden model boundary, include additional relevant dynamics– policy/strategy analysis

f (additional drive time, additional refueling time, “out of fuel” risk)


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river

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Gasoline 600 H2 Stations 100 H2 Stations20 H2 Stations 10 H2 Stations



Insights and Questions

• Incentives may need to differ for urban, rural, intercity, or interstate stations to avoid over/under subsidization.

• Difficult to satisfy simultaneously high % “coverage” for vehicle owners and profitability for station owners.

• Consumer sensitivity to reduced vehicle “utility” (due to limited coverage) is a major driver of dynamics, but not yet well understood.

• Spatial “diffusion” beyond urban areas may be difficult ... could require continued gov’t/market “seeding” in differentcities/states (e.g., intercity/interstate/regional networks).

• Early in model development process. Additional development and analysis of consumer sensitivity to driving convenience measures will shed more light.

Related References

Struben, J. (2006a). “Identifying Challenges for Sustained Adoption ofAlternative Fuel Vehicles and Infrastructure.” MIT Sloan School ofManagement Working Paper (abstract at http://web.mit.edu/jjrs/www/Current%20Research.htm, paper soon available)

Struben, J. (2006b). “Transitions towards Alternative Fuel Vehicles:Learning and Technology Spillover Trade-Offs.” Under Preparation.

Struben, Jeroen and Sterman, John, (2006c) “Transition Challenges forAlternative Fuel Vehicle and Transportation Systems.” MIT SloanResearch Paper No. 4587-06 http://ssrn.com/abstract=881800

Struben J. Welch C. and Sterman J. (2006) “Modeling the Spatial Co-Evolutionary Dynamics of Hydrogen Vehicles and Refueling Stations.” Poster presentation at the National Hydrogen Association Conference. Long Beach, CA. http://www.nrel.gov/hydrogen/pdfs/nha_poster_welch.pdf

Welch, C. (2006) Lessons Learned from Alternative Transportation Fuels:Modeling Transition Dynamics. National Renewable Energy Laboratory.NREL/TP-540-39446. www.nrel.gov/docs/fy06osti/39446.pdf

http://web.mit.edu/jjrs/www/Current Research.htm

http://ssrn.com/abstract=881800

http://www.nrel.gov/hydrogen/pdfs/nha_poster_welch.pdf

http://www.nrel.gov/docs/fy06osti/39446.pdf

Backup Slides

44 m

i.44

mi.

44 m

i.

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