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

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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

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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

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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

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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

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

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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

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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

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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

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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

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

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

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Primary Energy Demand - Deterministic Base Case Quads/year

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

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Energy Prices - Deterministic Base Case Note: 2007 Dollars

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

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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

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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

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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

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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

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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

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Probability Distributions Comparing Stochastic Base Case and Policy Case Cumulative Renewable Energy 2010 2030 2050 Quads Probability Quads Probability 2010 2030 2050 Cumulative Natural Gas Consumption

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

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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

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