# Presentation: 483929

## Overview and Special Features of SAGESystem for the Analysis of Global Energy markets 

- Pia Hartman & Barry Kapilow-Cohen
- Office of Integrated Analysis and Forecasting
- Energy Information Administration
- Prepared for the NEMS Conference
- March 12, 2002

## Overview of International Energy Modeling Activity at EIA

**World Energy Projection System (WEPS)**
- Integrated set of pc-based spreadsheets
- Incorporates projections from independently documented models, and assumptions regarding future energy intensity (E/GDP) trends & the rate of incremental energy requirements met by each fuel type over time
- Used to develop forecasts for the *International Energy Outlook (IEO):*
- Total energy consumption, by fuel  (oil, natural gas, coal, nuclear, renewables)
- Net electricity consumption
- Energy consumption for transportation sector, by fuel
- Energy-related carbon dioxide emissions, by fuel

**Overview of International Energy Modeling Activity at EIA**

## Overview of International Energy Modeling Activity at EIA  (cont’d)

**System for the Analysis of Global Energy Markets (SAGE)**
- 15-region, pc-based, linear programming model of world energy markets
- Developed using MARKAL modeling framework
- User-friendly input structure & output reporting capability
- Projection horizon up to 50 years, in 5-year steps
- Inter-region trade in energy and/or emissions
- Two operational modes: dynamic LP or time-stepped
- Intended use: 1) develop set of *IEO* forecasts, 2) tool for policy/scenario analysis involving international energy markets

**Overview of International Energy Modeling Activity at EIA  (cont’d)**

**Notes:**
MARKAL - large international infrastructure knowledge base
Captures int’l trading in energy and/or emissions – flexible multilateral relationships built in 
Dynamic LP – assumes perfect foresight - answers “What’s best”
Time stepped - Sequential Period Solving - approximates market simulation
150,000 rows - quickly solved -- less than 20 minutes

## 15 SAGE Country/Region Models

- USA
- Canada
- Mexico
- Central & South America
- Western Europe
- Eastern Europe
- Former Soviet Union
- China
- India
- Other Developing Asia
- South Korea
- Japan
- Africa
- Middle East
- Australia & New Zealand

_**15 SAGE Country/Region Models**_

## Each SAGE Regional Model...

- Employs common naming conventions & units
- Draws from comparable energy and economic/demographic data to develop the energy service demand estimates
*	**Energy Statistics and Balances*  (International Energy Agency)
*	**International Energy Annual*  (Energy Information Administration)
*	**World Economic Outlook, World Industry Monitor * (DRI/WEFA)
*	**World Populations, Global Report on Human Settlements* (United Nations)** **

**Each SAGE Regional Model...**

**Comprehensive Inputs & Assumptions Templates**

## RES Building Blocks 

- 1.  **Energy Carriers**
-       e.g.   crude oil
-                liquefied petroleum gas
-                gasoline
-                distillate
-                jet fuel
- 2.  **Technologies**
-        e.g.   extraction
-                 import
-                 refining
-                 electricity generation
-                 light duty vehicles
-                 airplanes
-                 cookstoves
- 3.  **End-Use Energy Service Demands**
**     ****categorized by sector****  **

- Resource Technologies

- Process Technologies

- Conversion Technologies

- Demand Technologies

- Multiple End-Use
- Energy Service Demands

_**RES Building Blocks**_

## End-Use Energy Services Represented in SAGE

**Residential Sector  **(11)		**Commercial Sector** (8)		**Transportation Sector** (13)
- Space Heating 		Space Heating			Personal Automobiles
- Space Cooling			Space Cooling			Personal Light Duty Trucks
- Refrigeration 			Refrigeration 			Buses
- Water Heating			Water Heating			Commercial Heavy Trucks
- Cooking			Cooking			Commercial Medium Trucks
- Lighting			Lighting			Commercial Light Trucks
- Clothes Washing		Electric Office Equipment		Two & Three Wheeled Vehicles
- Clothes Drying		Other Commercial Energy Services	Domestic Aviation
- Dishwashing						International Aviation
- Residential Electric Appliances				Passenger Rail
- Other Residential Energy Services	 **Industrial Sector** (6) 		Freight Rail
- 			 Iron & Steel 			Internal Navigation
**	****	****	**** **Non-Ferrous Metals** **		International Water Shipping
- 			 Chemicals
- 			 Non-Metallic Minerals		**Agriculture Sector  **(1)
- 			 Pulp & Paper			Total Agriculture Energy Demand
- 			 Other Industries

- .

## Technology Representation in SAGE

- Each technology is described by a set of parameters, including:
**Technical Parameters**
- Efficiency 			Energy Carrier Input & Output Mix
- Capacity Factor			Availability Factor
- Physical Lifetime		Bound on activity levels or capacity
**Economic Parameters**
- Investment Costs		Discount Rate
- 	Fixed O&M Costs		Bound on Investment
- Variable O&M Costs
**Environmental Parameters **
- Emissions factors		Bound on Emissions

## SAGE Solutions

- Demand-driven
- Objective function minimizes discounted energy system costs
- Identify least-cost combinations of technologies and fuels for each 5-year solution period, employing either fixed energy service demands, or price-elastic energy service demands*
- Various modes of operation:
- Single Region, or Multiple Region* runs
- Energy & Emissions Trading*
- Dynamic LP (perfect foresight), or  Time-Stepped Approach* (imperfect expectations)
- 							*See last slide for status of these features

**Notes:**
Time-stepped Version Allows 
Technology learning for many technologies (as opposed to dynamic LP) (implementation is underway)
Modeling structure to avoid “all-or-nothing” or “knife-edge” solutions (implementation is underway)
Much less costly integration of economic feedback (to be done)

## Learning-By-DoingReturns to Adoption

- _Background_
- Learning-by-doing--well documented since 1930’s
- Wright (1936) -- direct labor costs of manufacturing an airframe fell by 20% with every doubling of cumulative output
- Subsequent authors broadened analysis of learning to other costs and showed costs declined with experience
- _Application in SAGE_
- Costs decline as the market accumulates operational and manufacturing experience
- Cumulative capacity built is a surrogate for “market experience”
- Credit for international builds

**Notes:**
“Learning by doing” is the process by which the market gains operational and manufacturing experience.  Cumulative capacity is surrogate for experience.

This feature can illustrate the “market lock-out” phenomenon.

## Learning Curves – Electricity Sector

- Three piece-wise non-linear curves for overnight costs, one for each learning interval
- Overnight Cost (C) is a function of Cumulative Capacity (Q)
- C(Q) = a * Q -b
- Progress Ratio (pr) defines the speed of learning:    f =  % cost decline with doubling of cumulative capacity
- pr = 2- b = ( 1- f)
- b  = - ln(1-f)/ln(2);   a = C(Q0)/Q0-b

**Notes:**

In the NEMS formulation,  we are not convinced that the progress ratio remains unchanged as a function of cumulative production.   

We divide technological phases and progress into three groups,  revolutionary,  evolutionary,  and mature  each with its own progress ratio (learning factor) -- current estimated shown in a later slide.

## Slide 12

## Market Adjustment/Penetration Algorithm

- Select “noise” (“close-enough”) parameter
- Select the fraction of each market segment that should be reallocated to marginally uncompetitive technologies
- Solve SAGE for the “reduced costs”
- Use “reduced costs” and “noise” parameter to determine “close” technologies within each market group
- Recalculate market shares for technologies that were “close enough” based on their relative economics in their markets

**Notes:**
The “Noise” Parameter is User selectable.  It is intended to correct for uncertainties, regional labor and material cost variations or other aggregation biases.  The actual value is yet to be determined but we feel something between 10 – 20 % of the incremental market is appropriate.

We set tiny lower bounds for all technologies that we want to consider in the market adjustment algorithm,  usually all commercially available technologies.

The recalculation of market shares for technologies ends up setting new lower bounds for those that were close enough to earn a market share.  The rest of the lower bounds are reset to zero. Only a small portion of the capacity is reallocated.  

The market share formula currently being investigated is

Share of technology I  =   P –a / [Sum over all technologies in the group that were close enough of (Pk –a)

## Status of SAGE Development

- Proof of Concept has been completed and tested for 15 regions
- User interface software for front-end (inputs & assumptions) and back-end (results & analysis) has been developed
- Internal review and adjustment of inputs and assumptions is underway (transportation sector for all regions is close to final for all 15 regions; other sectors are being enhanced simultaneously) – expected completion by this fall
- Initial market share algorithm for the time-stepped version has been implemented and will be extensively tested over next few months
- Energy and emissions trading to be implemented and tested over the next few months
- Learning Curve structure to be implemented and tested by this fall
- Anticipate using SAGE for *IEO2003* and policy analysis thereafter

**Notes:**
Proof of concept stage - with place holder data (IEO2002 has been replicated)