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

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

3. ENERGY 2020 Background
Widely used in the Europe, South America, US and Canada
US DOE FOSSIL2/IDEAS [Early E2020]: Used for all National Energy Plans since between 1978 and 1998
State of Illinois (1986): Assess electric deregulation to avoid rate shock
Cambridge University/Cambridge Econometrics (1995/1995): Dynamics of EU/UK electric deregulation and climate change
New England ISO: Analysis of market-rules, capacity-expansion procedure, and market-dynamics
DOE and US Congress: Overly-accurate, deregulated-market dynamics
Bonneville Power Administration: Electric-market impact dynamics of climate change, load-control, and deregulation uncertainty.
Analyzed deregulation and climate-change policy in all fifty states, all Canadian provinces, and over a dozen countries.

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

5. ENERGY 2020 Sector Relationships
POWERWORLD
DEMAND
4 states and region
SUPPLY
Generation Location
POWER FLOWS
CONGESTION
Electric
Demands
Capacity
Bids
Generation
Prices
ENERGY 2020
SUPPLY
Electric Utility/IPPs
Gas Supply
Oil Supply
Coal Supply
International Supply
International Trade
DEMAND
Residential
Commercial
Industrial
Transportation
Demand
Prices
Prices
Policy Costs
Gross Investments
Gross Output
Utilization
Tax Rates, Inflation
Interest Rates
Tax Rates
Inflation
Interest Rates
Energy
Investments
MACROECONOMIC
MODEL( Global Insight)

6. Energy Demand Methodology

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

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

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

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

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

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

19. Fuel Demands LPG Asphalt Lubricants Aviation Fuel Motor Gasoline
Naphtha specialties
Natural Gas
Nuclear
Other Non-Energy Products
Oil, Unspecified
Petrochemical Feedstock
Petroleum Coke
Solar
Steam
Still Gas
Wave
Wind
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

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. 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. 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. Industrial Economic Categories
Food & Tobacco
Textiles
Apparel
Lumber
Furniture
Paper
Printing
Chemicals
Petroleum Products
Rubber
Leather
Stone, Clay, etc.
Primary Metals
Primary metals (DSI aluminum)
Fabricated Metals
Machines & Computer
Electric Equipment
Transport Equipment
Other Manufacturing
Mining
Agriculture (Irrigation)

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

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

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

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

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. Energy Demand Structure

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 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. Energy Demand Mechanisms

34. Energy Demand Price Effects

35. Energy Demand Overview
Fuel
Prices
(oil)
(gas)
Capital Stocks
(electric)
Retirements
Stock Energy
Requirements
Capacity Utilization
Socio-demographic
Weather
Energy Use
(by enduse)
New Capital
Additions
(by fuel)
New Energy
Energy
Efficiency
Investments
Technology Mix
Through QCT
Total Energy Cost
O&M
Costs
Capital

36. Price/Utility Distributions

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.

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.

39. (Complementary) Capital Cost Trade-Off

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. From Cost Data to Acceptance Response

42. Engineering vs. Preference
Engineering Curve
Acceptance Curve

43. Production Capacity

44. Process

45. Devices

46. Utilization Factors

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

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

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