Energy Information Administration NPC Demand Task Group June 8, 2010 John Maples U.S. Energy Information Administration Transportation Modeling

John Maples, NPC FTF Demand Task Group Meeting, June 8, Overview Transportation models VISION NEMS Questions

John Maples, NPC FTF Demand Task Group Meeting, June 8, What service will the model perform? Analyze and develop input assumptions Develop and assess scenario analysis Project the impact of input assumptions

John Maples, NPC FTF Demand Task Group Meeting, June 8, Considerations Context –Fuel economy, consumer behavior, travel, energy demand, and emissions Modeling techniques –Economic, statistical, econometric, engineering, optimization, GIS/network, and accounting Geographic representation –Local, state, regional, national, and international Model structure –Level of disaggregation Model platform and availability –Software requirements, coding, etc.

John Maples, NPC FTF Demand Task Group Meeting, June 8, Technology evaluation models Full System Simulation (FSS) –Engineering model –Subsystem/component analysis –Light, medium, and heavy vehicles –PSAT/AUTONOMIE, GT-Power, AMESIM Partial Discrete Approximation (PDA) –Mathematical simulation/engineering model –Subsystem/component analysis –Light, medium, and heavy vehicles –NEMS, EPA, and NHTSA

John Maples, NPC FTF Demand Task Group Meeting, June 8, Consumer Choice Models NEMS –Discrete choice nested multinomial logit (NMNL) Vehicle Type –Nests represent vehicles with similar attributes –Attributes (price, cost of driving, driving range, fuel availability, luggage space, acceleration, maintenance cost, battery replacement cost, etc) Fuel Economy Regulatory Analysis Model (FERAM) –NMNL –Non-linear optimization –Nameplate representation Market Adoption of Advanced Automotive Technologies (MA 3 T) –1,458 market segments –Regional specification (Census division, urban, suburban, rural) –Consumer groups (early adopter, early majority, late majority) –Distribution by daily VMT –Housing types and access to home refueling/recharging –Access to work recharging

John Maples, NPC FTF Demand Task Group Meeting, June 8, Travel demand models Economic –Elasticity and cross price elasticity Econometric –Travel metric per capita –Race, gender, and/or age –NEMS, DOT, and others GIS/Network –Travel demand within a system –Bike/walk lane, HOV/Hot lanes, mass transit, zoning, and land use planning –States and MPOs Other –Household travel budget Trip chaining, telecommuting, mode shifting (DOT) –Origin/Destination Freight Analysis Framework (DOT) –43 commodity classes –7 modes (truck, rail, marine, air, pipeline, and 2 intermodal) –114 domestic regions, 17 international gateways, 7 international regions Air travel demand

John Maples, NPC FTF Demand Task Group Meeting, June 8, Emissions models Greenhouse Gases, Regulated Emissions and Energy Use in Transportation (GREET) model –Estimates fuel full cycle greenhouse gas emission rates for highway vehicles (cars and trucks) –13 fuel types, 7 propulsion systems Motor Vehicle Emission Simulator (MOVES) –Estimates and projects criteria and GHG emissions for highway vehicles –4 fuel types (gasoline, diesel, CNG, electricity) –Vehicle emissions only –National, state, county, and MPO –Sales, stocks, travel speed, vehicle load, I/M programs, and VMT demand

John Maples, NPC FTF Demand Task Group Meeting, June 8, Energy systems models Projected impact –Captures agent behavior Travel demand, consumer behavior, supplier behavior, infrastructure constraints –Complex and resource intensive –NEMS and MARKAL Scenario analysis –Accounting framework that captures impacts of assumptions –Little (or no) internal feedback for agent behavior –Transparent and easily run –VISION and Mobility Model

John Maples, NPC FTF Demand Task Group Meeting, June 8, VISION Model Developed for DOE Office of Energy Efficiency and Renewable Energy and maintained by Argonne National Laboratory Model runs to 2100 and is calibrated to the latest AEO reference case (currently AEO2009) Assumption driven accounting model that measures energy and GHG emissions impacts of scenarios relative to a base case projection GHG coefficients represent full fuel cycle emissions and were developed using the GREET model Designed to provide quick turn around analyses of assumptions regarding increased fuel economy, advanced technology penetration, travel demand, and alternative fuel use for highway vehicles

John Maples, NPC FTF Demand Task Group Meeting, June 8, VISION light-duty vehicle parameters 11 vehicle technology types for cars and light trucks –Conventional gasoline, diesel, FFV, CNG, HEV (3), PHEV (2), BEV, and FCV Fuel options –HEV: gasoline, diesel, E85 or hydrogen –PHEV: diesel and gasoline or E85 or hydrogen –FCV: gasoline, diesel, natural gas, E100, hydrogen, or methanol Market penetration of new vehicle sales by technology type New vehicle fuel economy ratio relative to the base gasoline vehicle New vehicle price ratio relative to the base gasoline vehicle Light truck sales as a percent of total new LDV sales Fuel use parameters –FFV: percent of fuel demand met by E85 –PHEV: all electric driving range – model calculates percent of annual travel met in EV mode of operation

John Maples, NPC FTF Demand Task Group Meeting, June 8, VISION light-duty vehicle parameters (2) Travel demand –Exponential function (base or user defined) –User defined –Combination of exponential function and user defined –AEO based growth rate

John Maples, NPC FTF Demand Task Group Meeting, June 8, VISION heavy-duty vehicle parameters Technology types –Class 3-6: gasoline, diesel, HEV diesel –Class 7&8 single unit: gasoline, diesel, diesel HEV –Class 7&8 combination: diesel New vehicle fuel economy relative to the base case Alternative fuel use: share F-T diesel, share bio- diesel Growth rate in vehicle miles traveled

John Maples, NPC FTF Demand Task Group Meeting, June 8, VISION fuel parameters Fuel prices –Gasoline, diesel, natural gas, E85, hydrogen, F-T diesel, bio- diesel, methanol, electricity Fuel blending –Share of ethanol in gasoline –Share of F-T diesel –Share of Bio-diesel Other fuel parameters –Ethanol feedstock (corn, corn stover, switchgrass, woody biomass, and forest residue) –Hydrogen production At station: natural gas, electrolysis Central: natural gas, electrolysis, coal (with sequestration), high temperature electrolysis, biomass, solar, and thermo-chemical –Utility Mix (coal, natural gas, petroleum, nuclear, renewables)

John Maples, NPC FTF Demand Task Group Meeting, June 8, VISION output Energy use by vehicle type by fuel type Percent reduction in oil use Full fuel cycle carbon and GHG (CO 2 equivalent) emissions Vehicle and fuel expenditures All metrics provided with a comparison to the base case

John Maples, NPC FTF Demand Task Group Meeting, June 8, National Energy Modeling System (NEMS)

John Maples, NPC FTF Demand Task Group Meeting, June 8, NEMS Transportation Demand Module

John Maples, NPC FTF Demand Task Group Meeting, June 8, NEMS configuration by mode of travel Light duty vehicles –Cars and light trucks by size class –16 vehicle types (fuel and propulsion system combinations) –Household –Fleet (business, government, fuel provider) –Class 2b trucks (gasoline and diesel) Heavy duty vehicles –Trucks 3 size classes (Class 3, Class 4-6, Class 7&8) 4 fuel types (gasoline, diesel, CNG, LPG) Fleet and non-fleet –Busses School, transit, intercity (gasoline, diesel, CNG, LPG) Rail –Freight (diesel) –Passenger Intercity, commuter, transit (diesel and electricity)

John Maples, NPC FTF Demand Task Group Meeting, June 8, NEMS configuration by mode of travel (con’t) Aircraft –3 size classes (regional, narrow body, and wide body) –Passenger and freight –Domestic, US origin/destination international, general aviation, non-US –Jet fuel, gasoline Marine –Freight Domestic and International (diesel and residual) –Recreational (gasoline and diesel) Pipeline Military (jet fuel, diesel, residual)

John Maples, NPC FTF Demand Task Group Meeting, June 8, NEMS LDV travel demand Household vehicles –Econometric forecast based on cost of driving, disposable income, population growth Fleet vehicles –Travel static per vehicle by fleet and vehicle type Commercial light trucks –Travel forecast based on personal travel and output growth from 5 industrial sectors (agriculture, mining, construction, utility, and manufacturing)

John Maples, NPC FTF Demand Task Group Meeting, June 8, NEMS freight travel demand Truck, rail, and marine travel demand based on industrial output from 10 aggregate sectors: chemicals, primary metals, processed food, paper products, petroleum products, stone/concrete, metal durables, other manufacturing, agriculture, and mining Truck VMT based on industrial output from 12 sectors Truck travel also captures growth in government and utility sectors Rail travel also includes coal production Truck travel measured in VMT, while rail and marine are measured in ton-miles For all freight modes, freight allocation coefficients (FACs) used to capture relationship between travel demand and industrial output Mode choice is not explicitly model –Mode shifts occur as industrial output changes over time

John Maples, NPC FTF Demand Task Group Meeting, June 8, NEMS air travel demand Passenger travel modeled by aircraft size class and based on population growth, disposable income, and ticket price (yield) Domestic ton-mile travel based on value of merchandise imports and exports Maximum potential travel limited by infrastructure growth

John Maples, NPC FTF Demand Task Group Meeting, June 8, NEMS light duty vehicle technology adoption Models manufacturer and consumer behavior –9 manufacturer groups and 12 market segments Manufacturers build 16 vehicle types based on cost effectiveness of 63 subsystem technologies –8 fuel types and 6 propulsion systems –3 year payback at 15 percent –Technology attributes include cost, fuel economy improvement, horsepower adjustment, and weight adjustment Captures engineering relationships and constraints –Technology based: co-related, synergistic, supercedes, mandated –Minimum and maximum horsepower Technology learning captured for advanced technologies (including batteries) –Probability of R&D success –Increased production (economies of scale) –Design cycle (cumulative production)

John Maples, NPC FTF Demand Task Group Meeting, June 8, Policy impacts on fuel economy Model designed to capture effects of fuel economy policy Iterates around technology adoption and consumer preference to solve CAFE compliance –Modifies vehicle attributes once all technology options have been exhausted Captures impact of fuel economy credits provided for alternative fuel vehicles Captures impact of CAFE credits banked for over compliance or spent for under compliance Policies implemented to incentivize sales of advanced technology or alternative fuel vehicles have marginal impacts on projected fuel economy

John Maples, NPC FTF Demand Task Group Meeting, June 8, NEMS heavy duty vehicle technology adoption Economic decision to adopt fuel economy technology based on initial cost versus expected fuel savings –Distribution of 4 payback periods (1 to 4 years) that varies by technology –Discount rate of 10 percent –Rate of adoption based on logistic curve derived from historic data Technology adoption calculated for 24 market segments –Fleet type, fuel type, and size class 37 technologies by size class –Technologies represent fuel economy improvement and emission control Captures engineering relationships and constraints –Technology based: co-related, synergistic, supercedes, mandated –Emission control technologies are mandated but compete on cost Does not currently model fuel economy standards

John Maples, NPC FTF Demand Task Group Meeting, June 8, Stock models Light duty vehicles represented by 20 year vintage model Heavy duty vehicles represented by a 37 year vintage model –Bus stocks not modeled Aircraft represented by a 45 year vintage model –Algorithm also parks and transfers aircraft to other markets Older aircraft transferred to cargo movement or passenger travel in developing countries Rail and marine equipment stocks not modeled

John Maples, NPC FTF Demand Task Group Meeting, June 8, Hydrogen modeling in NEMS Analyzes interactions of hydrogen market with other energy markets –Focus on the economic, not engineering, aspects of hydrogen production, delivery, and consumption Hydrogen represent in 3 market segments within the 9 Census Divisions –Large city, small city, and rural In the Hydrogen Market Module (HMM) supply options compete on delivered cost in each market –Multiple production and deliver pathways Central, city-gate, and forecourt

John Maples, NPC FTF Demand Task Group Meeting, June 8, John Maples VISION: NEMS: Thank you U.S. Energy Information Administration