ENERGY 2020 Model Overview

1. 1 ENERGY 2020 Model Overview Massoud Jourabchi & Jeff Amlin (Systematic Solutions Inc.) June 26th 2007

2. 2 Full Model Overview Model Overview Demand and Supply Sectors Economic Feedback GHG and CAC Emissions Policy Scenarios Major Inputs and Outputs

3. 3

4. 4 Model Overview Energy Model Energy Demand (Currently being used by Council) Energy Supply Energy Prices Economic Forecast/Model Emissions as Outputs

5. 5 ENERGY 2020 Sector Relationships

6. 6 Energy Demand Methodology

7. 7 Energy Demand Methodology Major data sources, Inputs and outputs Fuel Categories Sector Categories Approach Structure Current status

8. 8 Historical Data Sources SEDS � State Energy Demands from EIA SEPER � State Energy Prices from EIA FERC Form 1 - Electric Company Data from EIA AP 42 � Emissions Data from EPA RECS - Residential data from EIA CECS � Commercial data from EIA MECS � Manufacturing data from EIA Council�s existing models (long-term forecast, short-term forecast, conservation potential model Procost)

9. 9 NW Regional Detail Residential (saturation rates, energy intensity by enduse, regional codes, standards) Commercial (saturation rates, energy intensity by enduse, regional codes, standards) Industrial ( Energy use/employee, DSI, large industrial) Irrigation

10. 10 Demand Sector Major Inputs Economic Activity Energy Prices Technological Efficiency Improvements Industrial Process Changes Device Saturations Weather impact Policies Taxes, Standards, etc.

11. 11 Economic Drivers 2008-2030 forecasting horizon Initial economic drivers from Global Insight Modified by state forecasts Residential income Commercial output Industrial output

12. 12 Energy Prices Fossil fuel prices from Council�s forecast Wholesale Electricity market clearing prices from Council�s long-term forecast. Retail electricity prices are calculated in 2020.

13. 13 Technology Efficiency Curves The technology efficiency curves are developed using Qualitative Choice Theory where the �choice� is between capital cost and efficiency (the higher the capital cost the higher the efficiency). The consumer trades front-end cost (capital cost) for operating cost (efficiency).

14. 14 Enduse Device Saturation rates Currently device saturation rates are set exogenously. Saturations as defined in ENERGY 2020 is the percent of customers which have a particular enduse, not the percent of customers which have an electrical device. ENERGY 2020 saturations are generally a historical trend which asymptotically approaches a maximum value. ENERGY 2020 market share of a given fuel for an enduse is determined endogenous.

15. 15 Weather inputs Currently model is producing weather normalized loads for each state. Model can be provided deviations from normal temperature to simulate impact of climate change on load Area of future development ( linkage of short-term model and 2020)

16. 16 Policy variables Tax policies, state or regional and national Codes Standards

17. 17 Demand Sector Outputs Fuel Usage for All Fuels Enduse Cogeneration Feedstock (non-combustion) Fuel Market Shares Device and Process Efficiency Device and Process Investments Emissions Number of units (residence, commercial space)

18. 18 Energy Demand Methodology Major Inputs and Outputs Fuel Categories Sector Categories Approach Structure Current status

19. 19 Fuel Demands Asphalt Aviation Fuel Biomass Coal Coke Coke Oven Gas Diesel Electric Ethanol Geothermal Heavy Fuel Oil Hydro Hydrogen Kerosene Landfill Gases/Waste Light Fuel Oil LPG Lubricants Motor Gasoline Naphtha specialties Natural Gas Nuclear Other Non-Energy Products Oil, Unspecified Petrochemical Feedstock Petroleum Coke Solar Steam Still Gas Wave Wind

20. 20 Residential Energy Demands Economic Categories - Single Family, Multi-family, Manufactured/mobile homes Enduse � Space Heating, Water Heating, Cooking, Dishwashing, Clothes Washing, Drying, Refrigeration, Freezing, Lighting, Air Conditioning, Entertainment (TV, computers), Other plug loads Technologies � Electric, Gas, Coal, Oil, Biomass, Solar, LPG, Steam

21. 21 Commercial Economic Categories Large Office Medium Office Small Office Big Box-Retail Small Box-Retail High End-Retail Anchor-Retail K-12 University Warehouse Supermarket Mini-Mart Restaurant Lodging Hospital Other-Health Assembly Other

22. 22 Commercial Demands Enduse � Space Heating, Water Heating, Cooking, Refrigeration, Lighting, Air Conditioning, Ventilation, Plug-loads Technologies/fuels � Electric, Gas, Coal, Oil, Biomass, Solar, LPG, Steam

23. 23 Industrial Economic Categories Food & Tobacco Textiles Apparel Lumber Furniture Paper Printing Chemicals Petroleum Products Rubber

24. 24 Industrial Demands Enduse � Process Heat, Motors, Other Substitutable, Misc. Cogeneration Feedstocks Technologies/fuels � Electric, Gas, Coal, Oil, Biomass, Solar, LPG, Steam

25. 25 Transportation Demands Economic Categories Passenger Freight Off Road Enduse � Transport

26. 26 Transportation Technologies Light Gasoline Light Diesel Medium Gasoline Medium Diesel Heavy Gasoline Heavy Diesel

27. 27 Transportation Technologies Light Propane Light CNG Light Electric Light Ethanol Light Gasoline-electric hybrids Light Hybrid Diesel Light Fuel Cell Gasoline Light Fuel Cell CNG Light Fuel Cell Hydrogen Medium Propane Medium CNG Medium Ethanol Medium Hybrid Gasoline Medium Hybrid Diesel Medium Fuel Cell Gasoline Medium Fuel Cell CNG Medium Fuel Cell Hydrogen Heavy Propane Heavy CNG Heavy Ethanol Heavy Hybrid Gasoline Heavy Hybrid Diesel Heavy Fuel Cell Gasoline Heavy Fuel Cell CNG Heavy Fuel Cell Hydrogen

28. 28 Transportation Technologies Motorcycle Bus Gasoline Bus Diesel Bus Propane Bus CNG Bus Fuel Cell Gasoline Bus Fuel Cell Hydrogen Bus Fuel Cell Ethanol Train Plane Marine Off Road

29. 29 Energy Demand Methodology Major Inputs and Outputs Fuel Categories Sector Categories Approach Structure Current status

30. 30 Modeling Approach Two conceptual linchpins form the theoretical perspective used in the model to determine energy demand: First, a �Stocks and Flow� simulation captures the physical aspects of the process, specifically the physical flow of entities within a system (For example, new investments increase the number of energy using devices, and retirements reduce the number of energy using devices). Second, the qualitative choice theory (QCT) as put forth by the Nobel Laureate Daniel McFadden determines how consumers make their energy decisions (i.e., Accounting of the factors such as tastes and preferences in making decisions to choosing energy devices and processes).

31. 31 Energy Demand Structure

32. 32 Demand Overview In Demand Overview, the top left hand corner of the diagram contains Investments in Production Capacity - the economic component of the model. The changes in the economy come in terms of these investments. The bottom left hand corner has the fuel price term. From the arrows drawn it is clear that production capacity and prices together in some fashion determine energy demand. So this portion of the model looks at the relationship between the economy, energy prices and energy demand. The way these relationships are represented is called causal modeling where the structure and relationships between prices, the economy and energy demand are defined. All of the structure of the demand model is represented in econometrics by income and price elasticities. ENERGY 2020 attempts to define what is creating these elasticities and to go beyond them. Static price elasticity alone cannot capture all the effects modeled in ENERGY 2020. The impact of price on demand (price elasticity) depends on efficiencies - both device and process - as well as fuel market share and the growth rate of the economy. If prices are high and the economy is growing, there will be a quick turnover of capital stock. Efficiency (assuming the efficiency of new stock is greater than the old) is going to increase more quickly as well. If the economic growth is low, there will be less investment and a smaller turnover in capital stock and fewer changes in energy efficiency and other variables. These dynamics cannot be completely captured in a single price elasticity term. ENERGY 2020 breaks simulates all these dynamics. Elasticities are used at the �edges� of the model. The model incorporates all the structure and detail necessary to capture the interactions between the economy, energy prices and energy demand. Econometric equations are used to pick up the rest - the �outside the model� parameters that bound the structure. ENERGY 2020 combines both the dynamic structure of the energy demand market and the econometrically estimated parameters guiding this structure. In Demand Overview, the top left hand corner of the diagram contains Investments in Production Capacity - the economic component of the model. The changes in the economy come in terms of these investments. The bottom left hand corner has the fuel price term. From the arrows drawn it is clear that production capacity and prices together in some fashion determine energy demand. So this portion of the model looks at the relationship between the economy, energy prices and energy demand. The way these relationships are represented is called causal modeling where the structure and relationships between prices, the economy and energy demand are defined. All of the structure of the demand model is represented in econometrics by income and price elasticities. ENERGY 2020 attempts to define what is creating these elasticities and to go beyond them. Static price elasticity alone cannot capture all the effects modeled in ENERGY 2020. The impact of price on demand (price elasticity) depends on efficiencies - both device and process - as well as fuel market share and the growth rate of the economy. If prices are high and the economy is growing, there will be a quick turnover of capital stock. Efficiency (assuming the efficiency of new stock is greater than the old) is going to increase more quickly as well. If the economic growth is low, there will be less investment and a smaller turnover in capital stock and fewer changes in energy efficiency and other variables. These dynamics cannot be completely captured in a single price elasticity term. ENERGY 2020 breaks simulates all these dynamics. Elasticities are used at the �edges� of the model. The model incorporates all the structure and detail necessary to capture the interactions between the economy, energy prices and energy demand. Econometric equations are used to pick up the rest - the �outside the model� parameters that bound the structure. ENERGY 2020 combines both the dynamic structure of the energy demand market and the econometrically estimated parameters guiding this structure.

33. 33 Energy Demand Mechanisms

34. 34 Energy Demand Price Effects

35. 35 Energy Demand Overview

36. 36 Price/Utility Distributions

37. 37 Market Share from Uncertainty Distribution Market Share Mechanics These curves illustrates the process of fuel choice - trading off one fuel for another on the basis of relative prices. If consumers behaved with perfect economic rationality and had perfect information, the market share curve would look like the share with perfect knowledge illustrated in the diagram. On the horizontal axis is the ratio of the price of fuels. As long as the price of �1� is less than the price of �2�, the fraction will be less than one, and economically driven consumers will choose all fuel �1� making the market share of �2� equal to zero. However, as soon as the price of �1� exceeds the price of �2�, then the converse occurs - �2� grabs the entire market. In reality, fuel choice is a less clear cut process. As the price of one fuel rises relative to another, there will be a gradual shift to the cheaper fuel based on consumer perceptions of the relative prices (often made with imperfect information) as well as the influence of non-price factors. The curve formed by these decisions resembles the S-shaped curve in the diagram - even if price �1� is higher than price �2� some consumers will still choose the more expensive fuel. This can be the result of imperfect information or indifference (if fuel costs are a very small part of the budget) or because of a non-price related factor. For instance, some people choose gas stoves because they prefer to cook with them, not because of price differentials. Market Share Mechanics These curves illustrates the process of fuel choice - trading off one fuel for another on the basis of relative prices. If consumers behaved with perfect economic rationality and had perfect information, the market share curve would look like the share with perfect knowledge illustrated in the diagram. On the horizontal axis is the ratio of the price of fuels. As long as the price of �1� is less than the price of �2�, the fraction will be less than one, and economically driven consumers will choose all fuel �1� making the market share of �2� equal to zero. However, as soon as the price of �1� exceeds the price of �2�, then the converse occurs - �2� grabs the entire market. In reality, fuel choice is a less clear cut process. As the price of one fuel rises relative to another, there will be a gradual shift to the cheaper fuel based on consumer perceptions of the relative prices (often made with imperfect information) as well as the influence of non-price factors. The curve formed by these decisions resembles the S-shaped curve in the diagram - even if price �1� is higher than price �2� some consumers will still choose the more expensive fuel. This can be the result of imperfect information or indifference (if fuel costs are a very small part of the budget) or because of a non-price related factor. For instance, some people choose gas stoves because they prefer to cook with them, not because of price differentials.

38. 38 Efficiency Trade-Off (Infinite Tech Dist.) The trade-off curves are only estimated once when the raw historical data on historical efficiency, capital cost, and fuel prices are entered into the ENERGY 2020 databases. The binomial logit is a two parameter curve. Therefore, the two (binomial choices) can be thought of as two equations (both a function of energy prices) with two unknowns. These equations are solved by simple point estimates. The trade-off curves are only estimated once when the raw historical data on historical efficiency, capital cost, and fuel prices are entered into the ENERGY 2020 databases. The binomial logit is a two parameter curve. Therefore, the two (binomial choices) can be thought of as two equations (both a function of energy prices) with two unknowns. These equations are solved by simple point estimates.

39. 39 (Complementary) Capital Cost Trade-Off

40. 40 Discrete vs. Distributive Curve Policy Responsive Supply Curves Dynamic to interest rates, grants, tax rates, depreciation, tax credits, risk, etc. Behavioral Acceptance Is QCT Process. There Is a Distribution of Projects and Costs. Specific Project Costs Are Uncertain. Point Estimates Become Distributions. Distributions Become Acceptance Curves.

41. 41 From Cost Data to Acceptance Response

42. 42 Engineering vs. Preference

43. 43 Production Capacity

44. 44 Process

45. 45 Devices

46. 46 Utilization Factors

47. 47 Energy Demand Methodology Major Inputs and Outputs Fuel Categories Sector Categories Approach Structure Current status

48. 48 Current Status Model is currently is being calibrated Calibration horizon is 1986-2003 Calibrating to States total sectoral energy from SEDS Will report on the results in our next meeting

49. 49 Updates Council�s existing models were used as a starting point for inputs Residential model (added new end-uses) Commercial (added new building types) Industrial models (updated Sector shares) Sector and end-use load-shapes Calibration to system load (regional and state)

50. 50 ISSUES OF INTEREST VINTAGE OF INPUT DATA COMMERCIAL sector characteristics by state INDUSTRIAL sector characteristics (drop in loads and recovery post 2001) LOADSHAPE for new end-uses Residential ENTERTAINMENT Load (TV,VCR,DVD,COMPUTERS,�) ELECTRIC VEHICLE penetration rates Capacity adequacy (summer) Residential AC penetration rates Incorporating IMPACT OF CLIMATE CHANGE Temperature sensitive loads Economic impact

51. 51 Areas for review State economic forecasts (medium term) Residential end-uses energy use Commercial end-uses energy use Industrial energy by sector Natural Gas consumption

52. 52 Preliminary Time-line for the 6th Plan Complete preparation of the Demand forecasting model by Jan 2008. Prepare Assumptions for Preliminary forecast Q1- 2008 Prepare preliminary draft forecast- Q2 2008 Review of preliminary forecast Finalize load forecast