Review May 7 th, 8 th 2009 Model Overview Presented by Walter Short Stochastic Energy Deployment System (SEDS)

SEDS Objectives Explicit treatment of primary uncertainties –Technology development –Fuel prices –Policies Transparency: “not another black box” Quick-turn-around analysis by others Use in DOE planning

General Approach Simulation model –Focused on major drivers and their uncertainty –Long-term planning model –Simulation, not optimization –Seeks equilibrium over time –U.S. only Designed for use by others –Analytica – modular software package –Relatively quick run times Team development with modeling experts for each sector – ANL, LBNL, NETL, NREL, ORNL, PNNL, Lumina, OnLocation

SEDS Modules Macroeconomics Biomass Coal Natural Gas Oil Biofuels Electricity Hydrogen Liquid Fuels Buildings Heavy Transportation Industry Light Vehicles Macroeconomics Converted Energy Primary Energy End-Use

SEDS: Current Status Alpha version of SEDS completed; hope is that this review will identify future directions Includes both deterministic and stochastic modeling capability; many more uncertainties will be added Focused on assessing value of current R&D efforts Limited distribution to-date of the model to alpha testers; user-friendliness to be substantially improved More detailed testing by independent organization planned

R&D and LBD Module –Probabilistic treatment of improvements due to R&D –Probabilistic treatment of learning-by-doing Common Elements

Common Elements (Cont’d) LCOE Fuel Prices Capital Costs O&M Efficiency Market Share Capacity Additions Familiarity Projected Growth in Service Demand Stock Vintag e 1.. Total Stock Age / Economic Retirements Stock Vintag e 2 Stock Vintag e n Avg. Characteristics of Vintage 1 Avg. Characteristics of Vintage 2 Avg. Characteristics of Vintage n (t-1) Energy Demand CO2 Emissions Tracking Stocks of Capital Actual Service Demand

Common Elements (Cont’d) Market Share Calculation –Logit market share using LCOE and familiarity Familiarity based on Bass diffusion model Market Share (TechA) Price Ratio = PriceB/PriceA Alpha = 10 Alpha = 1

Overall Results Cases run deterministically and stochastically: –Base –Extreme – 4000 MMTC02, $200-$500/Bbl oil, $50/MMBtu gas, –Policies – e.g. carbon cap, RPS, Nuclear Overall outputs: –Energy and capacity over time by sector –Carbon by sector –Oil use –Prices

Base Case – Illustrative only Deterministic –Technology: sources and rate of improvement vary by sector –Fuel prices: endogenous – similar to AEO 2008 –Policy: No carbon cap/tax, no RPS, No PTC extension, no RFS, CAFÉ Stochastic –Technology: PDS where available –Fuel prices: endogenous uncertainties –Policies: No carbon caps, no RPS, no nuclear availability

Delivered Energy by Demand Sectors - Deterministic Base Case Quads/year

CO2 Emissions by Demand Sector - Deterministic Base Case Million metric tons CO2/year

Primary Energy Demand - Deterministic Base Case Quads/year

Delivered Petroleum Fuel by Sector – Deterministic Base Case Quads/year

Energy Prices - Deterministic Base Case Note: 2007 Dollars

Delivered Renewable Energy by Type – Deterministic Base Case Quads/year

2050 Deltas between Deterministic High Oil Price Case and Deterministic Base Case Energy Prices ($/MMBtu) ($/MWh) ($/bbl) Cumulative Fuel Consumption (quads) Cumulative CO2 Emissions (million metric tons) Cumulative Delivered Renewable Energy (quads) Note: 2007 Dollars

2050 Deltas between Deterministic High Natural Gas Price Case and Deterministic Base Case Energy Prices ($/MMBtu) ($/MWh) ($/bbl) Cumulative Fuel Consumption (quads) Cumulative CO2 Emissions (million metric tons) Cumulative Delivered Renewable Energy (quads) Note: 2007 Dollars

2050 Deltas between Deterministic Carbon Cap Case and Deterministic Base Case Energy Prices ($/MMBtu) ($/MWh) ($/bbl) Cumulative Fuel Consumption (quads) Cumulative CO2 Emissions (million metric tons) Cumulative Delivered Renewable Energy (quads) Note: 2007 Dollars

Energy Prices ($/MMBtu) ($/MWh) ($/bbl) Cumulative Delivered Energy (quads) Cumulative Fuel Consumption (quads) Cumulative CO2 Emissions (million metric tons) 2050 Deltas between Stochastic and Deterministic Base Cases Note: 2007 Dollars

Energy Prices ($/MMBtu) ($/MWh) ($/bbl) Cumulative Delivered Energy (quads) Cumulative Fuel Consumption (quads) Cumulative CO2 Emissions (million metric tons) 2050 Deltas between Stochastic Policy Case and Deterministic Base Case Note: 2007 Dollars

Probability Distributions Comparing Stochastic Base Case and Policy Case Cumulative Renewable Energy Quads Probability Quads Probability Cumulative Natural Gas Consumption

Improving Confidence Levels through Increased Funding 50% 18% SEDS shows how increased funding can improve the likelihood of a desired outcome

General Issues and Future Plans Limited-technology-detail limits responsiveness in extreme scenarios Additional uncertainties to be included; uncertainty distributions need expert input and review Macro-economic feedback not operational yet Treatment of regional differences Uncertainty in commodity prices