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Intermediate Future

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NATIONAL BUREAU OF STANDARDS

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Intermediate Future Forecasting System

Proceedings of a Symposium

held at The Department of Energy

Washington, DC

August 19, 1982

Edited by:

Saul I. Gass

University of Maryland and

National Bureau of Standards

Frederic H. Murphy

Temple University and

Energy Information Administration

Susan H. Shaw

Energy Information Administration

Sponsored by:

Energy Information Administration

Department of Energy

Washington, DC 20585

Operations Research Division

National Bureau of Standards

Washington, DC 20234

U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director

Issued December 1983

and

PREFACE

The Intermediate Future Forecasting System (IFFS) was developed

in the Longer-Term Information Division, Office of Energy Markets

and End Use of the Energy Information Administration. Its

purpose is to provide an integrated representation of the energy

system, using information from the fuel analysis divisions of

EIA, in the eight- to twelve-year time horizon.

This symposium was held to provide a forum for presentation of

the system and its methodologies and for critical review by

external experts. In particular, critiques of the model were

invited before the first published use of the model's projections

in the 1982 Annual Energy Outlook (AEO) . These proceedings

reflect the state of the IFFS model as of July 31, 1982.

Considerable modification and enhancements were made before the

1982 AEO, which was published in May 1983. For more current

documentation, the IFFS Model Documentation Report, Volume I:

IFFS Executive Summary will be published in August 1983. The

papers in this document were prepared by the speakers using the

transcription of their presentations. As such, they reflect the

speakers' opinions. Questions concerning IFFS should be

addressed to John D. Pearson, Director, Longer-Term Information

Division (202/252-5206) , and questions concerning the symposium

should be directed to Susan H. Shaw, Operations Branch,

Longer-Term Information Division (202/252-1469)

v

Contents

Page

Introductory Remarks — Wray Smith 1

Welcome — J. Erich Evered 3

An Introduction to the Intermediate Future Forecasting

System — Frederic H. Murphy 4

System Overview: A Comment — William W. Hogan 18

System Implementation and Software Design — Susan H. Shaw 27

Introduction to the Oil and Gas Supply

Subsystem — Richard P. O'Neill 30

Oil and Natural Gas Supply Subsystem:

A Critique — William R. Finger and David Nissen 34

Introduction to the Oil Subsystem — John Conti 44

Refineries Subsystem: A Critique — Neil J. Cleary 52

Introduction to the Afternoon Session — John E. Todd 60

Introduction to the Electric Utilities

Subsystem — Reginald Sanders 61

Electric Utilities Subsystem: A Critique — Lewis J. Rubin 71

Introduction to the Coal Subsystem — Mary J. Hutzler 82

IFFS Coal Supply Model — Richard L. Gordon 93

Coal Subsystem: A Critique — Michael Elliot-Jones 96

Introduction to the Energy-Economy

Interactions — Dale W. Jorgenson 100

Energy-Economy Interactions: A Critique — Robert E. Hall 106

IFFS: An Overview — Jerry A. Hausman 115

Symposium Program 139

vii

INTRODUCTORY REMARKS

Wray Smith

Director

Energy Markets and End Use

Department of Energy

I want to welcome you on behalf of my office, The Office of Energy

Markets and End Use, and also on behalf of the Energy Information

Administration. I would like to take this opportunity to say that the project we are reviewing today goes much beyond the boundaries of my own office. The

development has depended for its success on the efforts of staff members in

the Office of Oil and Gas, and the Office of Coal, Nuclear, and Alternate

Fuels. People like Dick O'Neill and Mary Hutzler have contributed heavily to

this. I'm very appreciative of the cooperation we have received across office

boundaries

The purpose of the conference is really two fold. We wanted to have an occasion in which we could set forth to a wider audience of persons who are intimately involved in the kind of work that we do, to let them know what we are up to and the nature of the forecasting methodology we are planning to use in the near future. And, in the process, we wanted to convene a panel of

recognized experts to examine and comment on the work to date.

We feel that it is necessary and desirable to have the commentary at this

point while the system is still clearly developmental. In this way, the

discussions and the comments of the reviewers can have a genuine impact on further model development and, hopefully, would have a lot to do with the way

we approach the preparation of the next annual report to Congress, the

projections portion of the annual report.

The system under review is called the Intermediate Future Forecasting

System. It is the new energy market model that will be used for major

analyses that require intergrated forecasting approaches, such as the annual

report to Congress. But, it also will be used to forecast across fields,

future energy supply and demand, and compare effects of energy policy and

trends

Just a word on the reasons for undertaking the project in its present

form. The first was to provide a year by year methodology for examining

transitional effects of energy policies on energy markets, such as the study

of alternative regulatory policies toward electric utilities, or things that are going on in the natural gas markets.

The second reason was the need for a forecasting tool that was managerially and computationally simpler than prior models. We are all faced

with diminishing and constrained resources. It is important for us to have a system that can be maintained, operated and improved by a much smaller

cadre of analysts and specialists than we have had in the past; that

conditions a great deal of our strategy.

1
The Intermediate Future Forecasting System does include all the major

fuels, fuel conversion technologies, and consuming sectors, as several other

modeling systems do. It can examine energy policies that impact several

fuels, or, it can analyze both the direct effect of a policy influencing a consuming sector and the indirect effects on other sectors.

Other models have had such properties and, as you will see when

Fred Murphy presents the description, the Intermediate Future Forecasting

System has its own distinctions. We have brought in reviewers to provide a candid assessment of the project. We expect to get that. And, by having you

in to review our progress before producing an analysis and set of projections,

in an official way, on behalf of EIA, we hope to make use of your constructive

comments, improve the quality of our first product, which we hope will be in

the next annual report to Congress.

2
WELCOME

J. Erich Evered

Administrator

Energy Information Administration

Department of Energy

We are pursuing this effort because we perceive the need to do our job more effectively and more efficiently. We are just as subject to resource reductions, dollars and people, as every other component of this Department,

as well as elsewhere in the government.

We are putting a lot of eggs in this basket. Because of resource constraints, we do not have the luxury of pursuing a suite of alternative

approaches simultaneously. We have every confidence in the Intermediate

Future Forecasting System. We see this as an essential element of EIA's

mission over the coming years. We earnestly solicit your comments. We are looking forward to the input we are going to receive from the program

participants. I am very pleased by the broad participation and attendance we have today. I welcome you all, and encourage you to critique mercilessly.

3
AN INTRODUCTION TO THE INTERMEDIATE

FUTURE FORECASTING SYSTEM

Frederic H. Murphy

Temple University

Philadelphia, Pennsylvania

and

Energy Information Administration

Department of Energy

Washington, D.C.

Introduction

The Energy Information Administration is completing a new energy

market model, the Intermediate Future Forecasting System (IFFS)

.

The model will be used to study issues on energy markets. Broadly

stated, the model is designed to study government policies affecting

energy markets and transitions within energy markets.

This paper presents an overview of IFFS. There are two important

aspects to the model: how the individual sectors and energy forms

are represented and how the individual pieces are combined to provide a unified representation of energy markets. The system organization and an overview of the individual fuel market representations

are presented here. The subsequent papers in this symposium describe in more detail how each fuel market is represented.

4
The Need for an Energy Market Perspective

Since the early 1970 's the nation has suffered several severe

economic shocks because of rapid changes in energy markets. The

1973-1974 embargo, during which oil prices increased several-fold,

was followed by domestic natural gas shortages in the middle 1970 's.

Continued Middle East instability led to a further doubling of oil

prices during 1978 and 1979.

Looking back, the original energy problem of this century was the

difficulty of coping with abundance. Significant oil finds had, on

occasion, forced the price per barrel down to $.20. Gas reserves

were continually added as a byproduct of the search for oil, and

often wasted as a nuisance instead of treated as a national

resource. Coal was, and still is, readily available. The major

concerns of Government were to control wasteful development of oil,

assist the expanding market for gas, and soften the dislocations

from decreasing coal use.

By the 1950 's, the picture had changed: oil started to be imported

in larger and larger quantities and the Nation's gas transmission

system matured. The energy concerns of the 1950' s and 1960 's were

to protect domestic oil producers from cheap foreign oil while

limiting America's dependence on imports and protecting consumers

from natural gas price increases.

The regulatory machinery for oil and gas, which lasted through most

of the 1970 's, was put in place in this period. There was another

dimension added: an awareness of the effects of energy production

and consumption on the environment. The Government developed new

programs to improve air and water quality. One consequence of the

environmental regulations was a simultaneous increase in oil imports

and decrease in coal use. Although the State and Federal governments had been regulating aspects of individual fuel markets for

years, there was no comprehensive view of the program impacts.

Prior to the 1973-1974 embargo there seemed to be no need. Two

earlier embargos had no serious effects on the Nation. After the

1973-1974 embargo, there was a clearly understood need for a comprehensive view of Government policy impacts and an understanding of

the energy future of the Nation.

The original tools used to analyze energy markets were the Project

Independence Evaluation System (PIES) and its successor, the Midterm

Energy Forecasting System (MEFS) . The Energy Information

Administration (EIA) has replaced these models with a new model, the

Intermediate Future Forecasting System (IFFS) . This new model is

designed to expand EIA's analytical capabilities in the 2 to 10-year

time frame while actually reducing the resources required for

analysis

.

5
The purpose of policy analysis is to understand the likely

consequences of a policy change. Invariably, there are losers as

well as winners. The policy formulation process balances competing

interests and is not fundamentally quantitative because different

people weigh consequences differently. The role of energy analysis

is to measure the consequences so that sound decisions can be made.

IFFS is an equilibrium model; it relies on economic theory to

establish the relationships necessary for measuring the

repercussions of a given policy change. It, therefore, falls into

the class of simulation models.

An equilibrium model is one which, when solved, results in supply

equalling demand at a given set of prices. The word equilibrium in

this case does not mean the model treats energy markets as static

and unchanging. Far from it; the model represents the changes in

domestic prices, consumption, capacity utilization and resource

availability. Equilibrium simply means supplies equal demands under

economic market forces.

IFFS is a partial equilibrium model, which is distinguished from a general equilibrium model in that a partial equilibrium model

incorporates selected sectors of the economy, assuming all others

remain unchanged, while a general equilibrium model represents the

interactions of all sectors of the economy. The distinction can be

illustrated in terms of the energy model discussed here. IFFS does

not contain a representation of the iron and steel industry for

example. Metallurgical coal demand is represented; however, the

impact of energy prices on steel costs and, therefore, on the costs

of drilling rigs and pipelines is not represented, unlike in a general equilibrium model.

Model Capabilities

IFFS is designed to show trends in energy markets and their

underlying causes, such as trends in oil production and imports;

regional shifts in coal production and consumption; price movements

in oil products, natural gas, coal and electricity; and changing

financial requirements of electric utilities. That is, IFFS can

illustrate the directions energy is taking in the domestic economy,

highlighting problems that may arise in the future or current

problems that may dissipate without requiring any action.

Projections with current energy policies in place are presented in

the Energy Information Administration Annual Energy Outlook

(formerly part of the Annual Report to Congress )

.

Starting with the Annual Outlook as a baseline, representations of

Government policies within IFFS may be altered and new projections

made for estimating the effects of these new Government policies.

As no model is capable of representing all energy related policies,

the capabilities of IFFS are best understood through examples of

appropriate and inappropriate uses of the model.

6
One appropriate use is to estimate the effects of the Natural Gas

Policy Act (NGPA) on energy markets. One can compare, for example,

the impacts of deregulation versus the NGPA. Another use would be

an estimation of the effects of a crude oil price change on oil

imports. This would be a comparison of different market circumstances and their ramifications for the whole energy system. The

model is also capable of estimating the effects of high railroad

rates on the penetration of coal in energy markets. This would be

an example of comparing different nonenergy policies on energy

markets. A last example of an appropriate use is an estimation of

the effects of a change in the rules concerning the inclusion of

construction work in progress in the electric utility rate base.

This issue is currently under discussion within the Federal Energy

Regulatory Commission. The model could be a vehicle for comparing

different regulatory philosophies.

No model is appropriate for meeting all analytical needs. An

example of a use for which the model is not appropriate is in issues

where seasonalities are important, because the model does not

contain a representation of changing oil inventories or changes of

gas in storage. It is also inappropriate for estimating the

immediate effects of an oil embargo because that is a disequilibrium

situation and IFFS is an economic equilibrium model. Finally, it is

also inappropriate for estimating the local effects of Government

programs. The model has regional detail, for example, Federal

region detail on energy consumption, but the model is intended for

producing estimates of national impacts and not local effects.

An Abstraction of Energy Markets

To understand the workings of IFFS one needs to have a conceptual

view of the energy system. Energy markets, as represented in this

model, are illustrated in Figure 1. The model has a representation

of energy supply, conversion and demand. The model does include

some new technologies, but it emphasizes the major fuels, incorporating domestic supplies of and demands for oil, gas and coal, and

imports of crude oil, refined petroleum products and natural gas.

It contains a representation of the conversion activities:

refineries converting crude oil into petroleum products and electric

utilities taking in fossil fuels, nuclear power, hydropower, and

some of the new technologies to produce electricity.

Demand is measured for natural gas, electricity, coal, distillate

oil, residual oil, gasoline, jet fuel, liquified petroleum gases,

petrochemical feedstocks, and other petroleum products.

7
Demand models translate fuel and electricity prices, the level of

economic activity, and the regulations affecting consumption into

estimates of demands for fuel and products. That is, they measure

the levels of energy consumption plus the competition among fuels.

In the residential sector, for example, coal, oil, gas, electrical

resistance heat and heat pumps compete for meeting home heating

needs. The demand models are organized by end-use sector as

follows: residential, commercial, raw material, industrial, and

transportation

.

The Structure of IFFS

The Department of Energy (DOE) has used several models to study

energy markets: the Brookhaven Energy Systems Optimization Model

(BESOM), MEFS, the Long-Term Energy Analysis Program (LEAP), and

F0SSIL2. These models represent a wide range of methodologies for

modeling energy markets and they predominate in domestic energy

policy analyses.

The budget cuts faced by DOE and the need for large support staffs

to maintain these models have led to the closing of MEFS and LEAP.

Rather than abandon longer term forecasting entirely, EIA decided to

extend the forecast horizon of a model under development. IFFS was

originally designed to fill the forecast gap between the EIA

short-term forecasts and MEFS. The reason for developing IFFS was

to have a relatively inexpensive model providing year-by-year

forecasts of energy markets for the time span most relevant for

policies oriented to influencing a transition from oil and gas to

other conventional fuels and to conservation.

Much of what makes MEFS and LEAP too expensive to maintain is the

organizational structure of EIA. EIA has had, since its inception,

problems in creating an organization consistent with both meeting

customer requests for analysis and being efficient in producing the

analyses. Customer requests are usually for analyses that are fuelspecific, e.g., natural gas regulatory alternatives, and analyst

expertise is also fuel-specific: one needs to be a student of

natural gas markets with all of their institutional features to

produce an adequate study. EIA is organized along fuel lines for

these reasons. The current market models, however, have strong

integrating model cores, requiring a team of analysts to maintain

software and submodel interfaces. The massive coordination required

between fuel divisions running supply models, demand divisions

running demand models and the integrating group led to meetings

regularly attended by 30 people several times per week. Producing

an Annual Report consumed up to 40-50 staff years.

8
A good part of the problem is that each supply or demand division

found it easier to test model changes by running the whole modeling

system as opposed to instituting internal validation of the results

beforehand. A large model must be structured to fit into the

organization that supports it. In the case of EIA, the model needs

to be organized along fuel lines.

IFFS consists of a central integrating procedure, an electricity

market module, an oil market module, a gas market module, and a coal

market module. The current operation of the model is depicted in

Figure 2. The model operates iteratively to balance supply and

demand for all fuels at market clearing prices. That is, the

central integrating routine calls all of the modules in turn,

repeating the sequence until all prices and quantities converge to

an equilibrium. When called, a fuel module computes a supply/demand

balance for that fuel and then returns to the integrating routine.

This means, for example, that when a trial equilibrium is being

determined for electricity markets, the prices of competing fuels

are fixed. When each module finds a new trial equilibrium, the new

prices and quantities replace previous estimates. Before

initializing the next module, the integrating routine re-evaluates

all demands, incorporating the effects of the most recent estimated

prices of all fuels. This captures the effect of substitutable

fuels on each module-specific fuel. Consequently, the most recent

trial estimates of prices initialize, the demand equations when the

integrating routine passes the computation to a new fuel module.

Using detailed demand models each time new trial demands are needed

is prohibitively expensive. A simple analytical device is used to

gain operational efficiency. A single equation is used to

approximate the detailed models for each of the 10 Federal Region

and sector combinations. The detailed demand models are revisited

after all of the fuel modules have produced a new trial solution.

This means, that once a trial equilibrium is reach, it is consistent

with the fuel modules and with the detailed demand models.

9
To summarize each of the specific fuel or energy form modules simulates the economic forces that work within that particular fuel

market, while the overall integrating routine represents the linkages among the different fuel markets within the economy.

The Individual Fuel Modules

As part of the overall logic, the model solves for an equilibrium in

each year before moving on to the next. The year-by-year nature of

the solution process leads to a natural organization of the

individual fuel modules. Each module simulates the operating and

planning activities of firms in that business. Operating activities

are the day-to-day functions such as the dispatching of existing

powerplants, while planning activities involve adding capacity that

will be used in later time periods. Operating decisions use only

the prices of the moment, and planning decisions are made based on

past and future prices. "Past" prices are either historical data or

forecast years where an equilibrium has already been found.

"Future" prices are extrapolations of past prices, generated within

the planning component. This approach allows the model to solve 1 year at a time, stepping forward to solve a new year only after the

solution for the previous year is complete.

When future prices are generated in planning components, they are

unlikely to be the same as the prices generated by the whole model

when that future year is estimated. That is, decisions are made in

the planning components using information different from the model

solution. The reasons for choosing this approach are two-fold.

First, achieving consistency would add another dimension of

complexity to the model. Second, the gains from the added effort

are unclear. A perfect foresight model would lead to over

optimization of planning decisions, since in reality planners do not

have perfect foresight and have to plan for a range of

contingencies. The model operates under the assumption that energy

producers are cost minimizers. This is consistent with the

conclusion that no single coal, oil, or natural gas firm represents

a significant market force. Electric utilities are modeled as cost

minimizers as well. Even though electric utilities are regulated

monopolies, their planning models are explicitly cost minimizing.

At the same time, there is no alternative theory of economic

behavior that has a broad base of support.

The search for the equilibrium within each module consists of the

interaction of the operations component and the demand curves. The

operations component uses the existing capacity, or what is

projected to be existing capacity, to meet a trial demand estimate

that comes from the demand curves. This in turn produces a new set

of consumer prices which are then input to the demand curves,

producing new quantity estimates. The internal cycling continues

until the individual fuel module converges.

10
Within this overall organization, a wide range of modeling

techniques are used: econometric techniques and process modeling

techniques. Also, some decomposition techniques simplify the operation of the models. The econometric techniques are used, for

example, to determine refinery capacities and the demand estimates.

For estimating refinery capacity there are time series on petroleum

product prices and capacity additions for downstream units and a simple reduced-form model to project refinery capacity. In the

demand models there are structural representations of various elements of demand, such as home heating.

Process models are used to simulate decisions in industries where

there is enough information to do this, or it is important to

capture the extra detail. For example, linear programming is used

to represent refinery operations, because this is one of the

standard techniques in the industry for guiding the operations of

refineries. The linear program translates estimates of capacities

and demands into product prices, incorporating the effects of crude

quality

Individual submodels can grow quite large. Incorporating large

models directly into IFFS would result in a cumbersome system. The

overall system is streamlined through a decomposition strategy.

This is known as using pseudodata. A complex model is run many

times, generating many solutions. In running the refinery linear

program, the solutions are known as extreme points. A simple model

that expresses the relationships between the inputs and outputs of

the larger model is fit to the model solutions as a way of

approximating the larger model. The simplified model is then

incorporated into the larger system. Refineries are modeled this

way. Some of the demand models are brought into the system directly

because they are simple enough, but for the more complex ones, this

technique is used. Finally, this approach is also used for

representing oil and gas supply.

Electricity Market Module

The electricity market module is called first as an operational

convenience. Electric utilities demand oil, natural gas and coal,

and these estimates are needed when finding an equilibrium within

the other modules. The electric utility representation, given the

initial conditions that it obtains from the central module,

estimates the future capacity expansion necessary for the electric

utility industry and then simulates the operation of utilities.

Thus equipment is purchased, scheduled, maintained, and dispatched

on the basis of minimum cost.

The electricity module finds an equilibrium in electricity markets

by starting with trial demands, simulating the dispatching of an

inventory of powerplants to meet those demands and costing the

electricity delivered. Using a combination of the new costs and

previous prices, new, end-use demands for electricity are estimated,

and the costing is repeated. The process continues until two

successive sets of prices and quantities are within a

11
close tolerance. Once internal convergence is achieved, the

integrating module regains control and initializes the next module.

Prices, or rates, are set by an iterative computation, which

includes considerations of the revenues the utilities will obtain

and of the costs and constraints that they will face in financing

new expansion. Because the electric utility module estimates how

each class of generating equipment is dispatched (e.g., coal,

nuclear, natural gas) , the model derives the utility requirements

for coal, petroleum products (primarily residual and distillate

fuels), and natural gas.

Oil Market Module

The oil market module consists of a representation of refineries and

refined product demand. Since the world price of oil is a scenario

input, the costs of refining the product mix demanded determine the

prices for refined products. The demands for petroleum products are

the demands for the end-use sectors at the refined product prices,

plus the demands from the electric utilities module. The refinery

representation provides estimates of refined product prices, the

utilization of existing domestic refineries, the needs for

investment in new downstream refining equipment, and the total

requirement for crude oil that must be supplied from domestic

production or imports.

As with the utility model, the representation of refineries is based

on the models used in corporate planning. In the case of

refineries, this means using linear programming. As these types of

models are too complex to incorporate directly, the model used is

run many times and log-linear equations are fitted to the results.

Refineries consume a portion of their output ard some natural gas in

their operations. Thus, the natural gas requirement for the

operation of refineries is provided to the central integrating

module for inclusion in the gas market module. Internal convergence

within the refineries module consists of balancing supply and

demand, incorporating the effects of each oil product production

level on every other product.

Natural Gas Market Module

The gas market module contains the oil and gas supply

representation, gas demands, and a mechanism for balancing supply

and demand. This module also provides the domestic oil supply

estimates because oil and gas are produced by the same industry,

using the same equipment. Consequently, the supply representation

has to be kept as a unit in either the oil or gas modules. Since

the world oil price is an input assumption, the price of crude oil

to domestic producers and refiners is fixed. The gas price,

however, is highly variable, making the gas module the logical place

for the oil and gas supply representation.

12
The oil and gas supply representation simulates the amount of

domestic drilling that takes place, given the prices of oil and gas

and the availability of drilling equipment. This drilling is then

translated into reserve additions and, subsequently, production.

The fuels produced consist of crude oil, plus associated and

dissolved natural gas and nonassociated natural gas. Natural gas

contains natural gas liquids, and the quantity of natural gas

liquids is computed as a fixed proportion of gas production.

Demands for natural gas come from end-use consuming sectors and from

electric utilities and refineries. The mechanism for finding a supply/demand balance is more complex than with electric utilities

and oil because of the regulations on wellhead and delivered prices

and the regulations on transportation costs.

Coal Market Module

Once the gas market solution has been found, the coal module is

called. This module emphasizes coal supply and transportation.

Supply is estimated using the Resource and Mine Costing component of

the National Coal Model. Because of the importance of

transportation costs in delivered coal prices, the module contains

an elaborate description of regional coal movements. Rather than

using fixed transportation costs, the model increases the tariffs as

increasing flows create congestion.

When the coal module has converged, the central integrating

procedure reevaluates the detailed demand models, returns to the

electric utilities module and proceeds through the entire model

again

A Mathematical Statement of the Solution Procedure

The problem of finding a market equilibrium is one of solving a set

of simultaneous nonlinear equations. IFFS may be abstracted into a set of equations as follows:

Let i = l,...,n index the fuels,

j = l,...,m index the consuming sectors including the conversion

activities

p. = price of fuel i

p = vector of prices

S. (p) = Supply of product i as a function of all fuel prices

D . (p) = Demand for product i in sector j as a function of

all fuel prices

Set

e. (p) = r D. . (p)-S. (p)

1 * j 13 e 1 ^ Now e. (p) is known as the excess demand function for fuel i.

The equilibrium problem may be stated as follows:

find p = (p

x

, . . . ,pn

)

13
such that

e

1 (p) = 0

* c • e (p) = 0 n

^

k k k k Let p = (p^ ,P2 i

• •

f Pn

) be the trial solution from iteration k.

The k+1 iteration of the algorithm proceeds as follows:

Solve k k

e

l

P 1P 2

'

* *

*

Pn * =0 * Then solve

e

2 (P 1 ,P 2 /P 3

i

• • • #Pn

) = 0

repeating until the last equation is solved

, k+1 k+1 k+1 . n e (Pi /Po , . . . ,p . ,p ) =0

n ^1 2 ^n-1 /£ n The algorithm continues until all equations are within tolerance for

some p . In terms of IFFS, e, is the electricity module. Next,

is the oil module, and it contains an estimate of utility oil

consumption. Most importantly, e^ is the natural gas module and two

of the • (p) are the derived demand curves for natural gas in

utilities-' and refineries. Finally, e^ is coal. This mapping of

modules to equations is not precise because each module contains

more than one supply and demand curve, but this is incidental for

the purposes of understanding how the model works.

The algorithm stated above is the Gauss-Seidel algorithm for solving

simultaneous equations. This algorithm has the reputation for being

relatively slow and has a linear convergence rate in the limit.

However, it is very effective in this setting, taking eight to ten

iterations in each year to reach a 1 percent convergence tolerance.

The solution is efficient here because the structure of the fuel

modules reduces the problem to the equivalent of solving four

nonlinear equations. At the same time, the model contains thousands

of equations. The equations with the greatest dependencies are

solved within a fuel module, leaving relatively independent

equations to be solved by the Gauss-Seidel algorithm.

Concluding Comments

IFFS provides a new approach for building energy market models. By

providing a fuel market organization, the model fits more readily

into the EIA organizational structure, and it is significantly

faster, computationally, than MEFS and LEAP. The less structured

model organization also allows for more natural formulations of the

individual fuel markets. That is not to say tnis model is the

unitormly dominant approach to energy modeling. The optimization

14
elements of BESOM and MEFS can provide special insights by being

explicit about cost minimization and having a large body of theory

for the analyst to draw upon. LEAP is also useful for its

representation of market dynamics and market penetration of new

technologies. And, F0SSIL2 remains the fastest of the energy

models

There is more work that needs to be done to improve IFFS and the

other energy models. These tasks involve some of the basic,

unanswered questions of economics: How should one represent

expectations in structural planning models? How should one

represent decisionmaking in regulated utilities? The tasks also

involve issues specific to energy markets: What is a good aggregate

representation of forced outages and dispatching in electric

utilities? What can be done to represent decisions of who buys what

natural gas, given that price ceilings restrict what purchasers can

pay? Also, more work can be done to improve the computational

features of the model.

15
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16

SYSTEM OVERVIEW: A COMMENT

William W. Hogan

John F. Kennedy School of Government

Harvard University

Introduction

Thank you and good morning. I extend my special thanks to Saul Gass

for the opportunity to be here today. I look" forward as always to these

Energy Information Administration (EIA) review sessions and learning more

about energy modeling and its use in the Department of Energy (DOE). The EIA

staff has accumulated a great deal of expertise and experience in these

matters and it is a privilege to participate in these general discussions with

the wider interest group of model builders and model users. Saul Gass asked me to make a few introductory remarks about the

Intermediate Future Forecasting System (IFFS) design to provide at least the

beginnings of a critique of the structure with an eye to understanding its

uses and improving its applications. I intend to take advantage of my

position as the first commentator to raise questions more than answer them.

These questions might be answered today, or at least will establish a context

for understanding the current state of the modeling exercise and the potential

of the existing tools.

My first observation, of course, is to express appreciation for the

work completed here in the DOE. I am favorably impressed with the ambitious

project underway. The IFFS is a state-of-the-art system that meets many of

the most pressing needs identified in energy policy analysis. IFFS is a powerful tool that deserves our attention and the investment required for

further application.

In addition to being impressed by a model as sophisticated as IFFS, I am fascinated with its intricacies and nuances. Of course, we should admonish

ourselves to avoid being too fascinated with the modeling alone. Part of our challenge here is to think more about how the model might be used, to identify

the applications which justify the resources devoted to this system.

Nonetheless, as someone interested in technique, as well as the results of

building and using such models, I find the EIA modeling system a fascinating

as well as useful tool. The EIA analysts have utilized a balanced mix of

theory and pragmatism in preparing this aid for analyzing and educating us about energy policy problems.

The comments in the documentation reflect the emphasis on manageability and realism which has guided the design of much of the IFFS.

Almost every twist and turn in the description of the model follows from

pragmatic judgments about what is realistic and what is manageable. The

authors have given attention to both objectives. For instance, most of the

documentation chapters contain extensive tutorials on how to think about the

sectors being modeled and the energy modeling problems faced. The modelers

then confront judgments about what is manageable in the context of the kind of

problems to be solved. This is the right objective. And for those of you who

have not read that documentation, you will find this realism in the

description of the sectors and the realistic appraisal of what can be modeled

to be both instructive and helpful. In the recurring descriptions of

particularly knotty problems, the reader can enjoy the candor of the EIA

authors. Repeatedly we read the common characterization of the approach

18
adopted as inadequate compared with the canons of pure theory, but at least as good as every other approach known, and far better than assuming away the

modeling difficulties in a convenient simplification.

I was happy to see this candor and pragmatism. It is not uncommon in

modeling documentation to see a model described as "state-of-the-art." The

EIA model, in many of its dimensions, is at the boundaries of the state-ofthe-art in energy model design. But the EIA analysts have surpassed their

counterparts in the candid recognition of the ignorance and approximation

which define the state-of-the-art.

From my perspective, and given my charge, I will address briefly three

different elements of the system design. My first comments touch on the use

of a network of models, including superficial observations about the

individual component models. Work has been proceeding to improve the realism

of the individual component models: the oil and gas supply models, the

regulatory analysis description, electric utility pricing and so on. The use

of the network approach addresses the 'manageability' challenge, taking models

which are becoming more realistic descriptions of the components and

preserving the tractability of the combined system.

The second point that interests me, particularly in comparison to the

IFFS model's predecessors, is its characterization of dynamics. The IFFS time

step model, solving a year at a time into the future, raises other questions

which we should address.

In passing, I will indulge the modeling technician in me and make a few observations about the integrating algorithm. Finally, I will touch on

the policy questions or implications raised by a review of the system.

A Network of Models

As Fred Murphy described it, IFFS is a collection models where there

is an individual component to describe each relevant sector. For each, the

EIA analysts use what they see to be the best approach in detail, and then

link these component models together to obtain a consistent equilibrium

solution in the overall system. In concept, it is a familiar approach well

established in both theory and practice as a convenient organizing device.

The interesting innovations, in comparison with other models, appear in the

details.

For refineries, the underlying models appear as linear programming

models. Who can argue with this approach, followed so often? As we now know,

the argument will come from those faced with examining a future with price

relationships dramatically different from those of the past. For these

circumstances, the linear programming model may be correct in theory, but can

be flawed in practice.

The difficulties arise when, as we must, we use a simple

representation by generating modes or extreme points for such a model. These

extreme points, a grid approximation of the feasible set, will be inadequately

descriptive of our a priori judgments. For example, we may believe that

gasoline will always be more expensive than residual oil, but the empirically

generated extreme points will not guarantee this relationship. Without

further precautions, when we solve for periods in the future when oil prices

are quite different, we will find under certain circumstances that residual

oil is very expensive, and our model will see gasoline as a by-product to be

given away. In short, we can and do move outside the range approximated by

the extreme points.

19
How should the modeler deal with such an anomaly? The answer is that

nobody knows the best way to solve this problem. I know of no better way than

followed in EIA. What they have done is to develop a "pseudo-data" approach

to impose a structure on the system of relative prices. They fit these price

relationships through cases in which they have confidence, and then use the

extrapolations, through reduced form models, to calculate the final prices.

This is a sensible approach as long as we examine the assumptions under which

those pseudo-data were generated. Particularly, if the fuel mix—-the relative

composition of the outputs of refineries—changes dramatically, then we would

want to look back again at some of the pricing assumptions. And it might be

helpful to remind the user that the familiar pattern of prices is primarily an assumption, only slightly affected by the change in the relative demand for

the products.

I didn't follow completely the organization of the calculations for

oil ang gas. For instance, there was a comment regarding an assumption that

product imports maintain their 1979 proportions to domestic production. I don't understand why this is assumed. Perhaps we can investigate this later.

The interesting part of the oil and gas effort is the elaboration of the

reservoir modeling and the description of the exploration, development and

production processes in oil and gas supply. The present model is similar in

spirit to early versions of the oil and gas supply model, but is much more

extensive in detailed characterization of reservoirs, concern with uncertainty

in the estimation of reservoir size, and so on. One problem of concern is that the new model separates many

calculations from the rest of the IFFS by taking as an input the prices of oil

and gas and other key variables, such as royalty rates, that determine the

economic rents of producers. In the previous versions of the EIA system, the

model determined the marginal cost of a given level of production and

estimates prices and rents as endogenous variables with the remainder of the

energy system. In principle, there is no reason why the new model could not

do the same, but now we would have to iterate across royalty rates as well as iterating across prices. Hence, as a practical matter the increase in detail

may force a sacrifice of an element of economic sophistication in the previous

system. I noticed a schizophrenic response to this problem: the royalty rate

is exogenous but the optimal price for a particular lease is endogenous. I am pleased to see the latter, but I am nervous about the former. There is no problem in principle, only in how the results are used.

The regulatory detail in the natural gas models is essential in

describing this sector over the next decade. I don't understand completely

how we go from the regional production to the regional demands, although there

is some discussion about allocation of demands to regions. It would be useful

to understand better how we obtain the regional equilibrium in this model.

Such calculations will be especially important for the gas side where

transportation costs are high, but probably somewhat less crucial for the oil

supply model.

The electric utility chapter presents a beautiful tutorial on how

different modelers approach characterizations of electric utilities. The

authors review several hypotheses about objective functions of utility

managers and recommend cost minimization as a good approximation to reality,

at least in summarizing decisions related to the development of capacity and

the operation of the system. I concur that cost minimization is the best

approximation that could be incorporated in such a model.

20
An aggregation issue arises in the dispatch component of the model.

Again, there are many different approaches for modeling this process. The

biggest difference between approaches, as I understand it, is whether or not

one explicitly considers uncertainty in determining the availability of

different kinds of plants. If this uncertainty receives explicit attention, we need a Booth-Balereaux type convolution of load curves to estimate the full

peak capacity requirement. This stochastic approach is to be preferred, in

principle, over the deterministic approximation in derated dispatch.

However, the derated dispatch gives a good approximation, particularly

at the national or regional level, thanks to the law of large numbers. In an individual utility, where you have a single plant modeled as interdependent

segments (if the plant is out, all the segments are out), we must recognize

interaction across different load curve components. But in an aggregation of

utilities, this interaction presents less of a problem and the derated

approach as used here should be quite satisfactory.

Compared to previous approaches, the present EIA model, through the

National Utility Financial System (NUFS) model, offers the innovation of

explicit accounting for utility costs, regulatory lag and the treatment of

pricing. Utility pricing is an idiosyncratic process. The challenge is in

getting the financial calculations about right in determining the revenue

requirements. I am not familiar with the detail of the NUFS model, but the

description sounds like it has all the essential elements. Hence the overall

utility financial sector looks appealing as a model and an impressive

improvement over previous approximations applied in the EIA.

Regarding capacity planning, there appear to be annual additions to

capacity based on annual expansions of demand using a myopic rule for electric

utilities. There may be a naive forecast of demand, but the model is

dominated by the myopic assumption. For those who might be concerned with

this short-term approximation of an inherently long-term decision, there has

been interesting recent work by Sherali and his coauthors that reinforces this

approach to capacity expansion modeling. In short, the myopic rule, under

"reasonable" conditions, is not a bad approximation to a more sophisticated

model with complete foresight.

The reasonable conditions have to do with how fast demand is growing.

Intuitively, one would think that if demand is growing relatively fast, net of

the depreciation rate of the equipment, then in every year one would have to

add new capacity. Hence, the single year myopic approach—add capacity to

meet each years demand increment—wouldn't be a bad approximation. There

would be little interaction over time; we would seldom defer construction of a new plant. Hence the myopic rule is probably not a bad assumption for a national or regional model when aggregate demand is growing, as over the

horizon considered in the EIA model.

The coal sector of the model is in an earlier stage of development.

I found only a brief description of intended developments. In concept, the

approach is similar to that being followed in the other component models. The

problems in modeling coal would be in capturing the impact of long-term

contracts and the regional detail of differences in both quality and costs of

coal supplies. These issues have been dealt with before in other EIA models,

along with a careful description of transportation, a major component of coal

costs. Given the sophistication of previous EIA models of the coal economy, I look for a satisfactory module in the present system.

21
On the demand and macroeconomic side, I was unable to understand the

details from the available documentation. However, I infer that it is the old

demand model which has been in use for a few years. I am not familiar with

the current details, but my guess is that the model includes a collection of

several different models, one for each major energy consuming sector. The

integration with the DGEM, the dynamic general equilibrium model, is a good

idea which will require care and cleverness in implementation. The chapter

discussing that integration raises the major issues to be considered, such as the overlap of the definitions of the accounts, and I am confident that the

EIA analysts can work out the connections.

Dynamics

The one problem that I don't know how to address, and it may be just

my lack of understanding of how DGEM works, is the lag adjustment in the

responses of energy users in determining the intertemporal transition of

energy demand. I am not sure how this is handled in the current demand model.

However, the speed of adjustment is well known to be of importance in the

energy system over a forecast horizon of ten years.

Apparently, the characterization of dynamics is different from the old

EIA models. In the old system, in a procedure that was both ad hoc and hidden

from view, there was judgmental estimate of what the time path of the

trajectory of prices. With this assumption, we would obtain the trajectory of

quantities of demand. A few models followed an optimization over the

trajectory of prices to obtain the quantities of demand. For others, there

was an econometric estimate of the speed of the lagged demand response. Given

the initial trajectory, we varied the prices and quantities to trace out

static demand and supply curves for each representative year. By so controlling the trajectories, we gave the modelers control over the dynamics

in order to insure a sensible profile of demand to balance against supply in

the integrated solution.

We know a good deal about the sensitivity of demand projections to the

dynamic structure of response. For example, in systems dynamics models

without adequate look ahead, there can be too much feedback and explosive

model behavior results. Modeling dynamics continues to be a technical and

conceptual challenge in a system with any tractable response structure.

In the new EIA models we find a different approach to the

representation of the dynamics of demand responses. The new approach is also

ad hoc and hidden from view. Modeling dynamics continues to be at the stateof-the-art, where theory gives us the least help. There are different options

discussed in the documentation and that will be addressed today. The use of

myopic rules—one year look ahead or current period decision making—can be a good approximation. I don't know the details of the experience at Brookhaven,

with their time step version of a linear programming system, but this myopic

approach has been used there for a few years and would be a source of

practical experience.

As I mentioned, theoretical results of Sherali et al. . in the study of

electric utility expansion models, present sufficient conditions for the

myopic rules to replicate perfect foresight optimization. As long as the

overall system is growing, the myopic rule performs rather well.

Another approach to dynamics is through the use of naive forecasting.

This is applied, for example, in the well-known Baughman-Joskow model. In

essence, we model expansion plans as optimal choices against a naive forecast

22
of the growth in demand. This attack is included here, for example, in the

EIA oil and gas supply models and the Alaskan oil supply model. For these

models, one of the inputs is the trajectory of prices for all periods of the

future. This price trajectory can be interpreted as a naive forecast; a rule

is specified by the user determining the growth rate of prices. We could

study different rules to improve the performance of the model. For instance,

in one review of the Baughman-Joskow model, the MIT Energy Lab (in report

#1071) applied alternative forecasting rules. They found that the eventual

solution produced by the model could be quite sensitive to the forecasting

rule imposed, that the supply-demand equilibrium solution depended on which

naive model was used for forecasting. Apparently a similar test would be

appropriate for examining the models being used by EIA.

But in any event there would remain a conceptual dilemma in relating

this naive forecast to the output of the integrated system of models. Do we

assume that the subjects modeled do not have access to the information

presented in the IFFS model? The modeling option at the other extreme is a perfect foresight model in which we assume that the model used by the subjects

modeled is the model itself. In effect, we operate as though the market

decision makers have IFFS and they run it to obtain the exact forecast we

produce. Hence, once we produce our forecast, announcing the result would not cause any actor to change his plans.

The outcome is the same as the assumption of rational expectations.

We know how to implement this type of computation; it appears in many models.

However, this foresight model imposes a computational cost. And with the

added burden of regional detail, a degree of elaboration found in many such

models, there will be practical limits on the ability to include rational

expectations or perfect foresight calculations.

The authors of the EIA documentation assert that it would be difficult

to attempt the extensions necessary to include perfect foresight. In

addition, they address the conceptual problems of the perfect foresight model.

Do people have perfect foresight? Even accepting the perfect foresight

assumption, we have to face the problem of dynamic inconsistency: we

(especially the government) do not have the capability to write binding

intertemporal contracts. In effect, we can't rely on the government to stick

to its word. Yet the perfect foresight models implicitly assume such

contracts are in place. Without such contracts we enter a situation where the

optimal solution is a particular trajectory of prices and quantities which

cannot come to pass. If we start along the optimal solution and step ahead

one year, changes the circumstances and conditions can arise where it is

optimal for the government to renege. This creates a new optimal solution,

and so forth. There is a dynamic inconsistency, an inherent instability in

the solution.

If we introduce uncertainty, we enter the rational expectations

debate. Many analysts present today know more than I about the revisions of

macroeconomics to accomodate foresight under uncertainty. Given the intensity

of that debate, and the sharp disagreements over theory and data, I find a great deal of appeal in the EIA approach of sidestepping the complexity by

adapting the ad hoc myopic model under development.

23
Model Integration

The EIA integration algorithm is an example of an attempt to deal with

the model manageability problem. Accepting the realism of the component

models, we could put the detailed component models together in a complete

system and, in principle, solve for a market equilibrium. But using the full

component models would require a "computational budget of staggering

proportions" to quote the authors of the documentation of the oil and gas

models.

For example, the Alaskan model is articulated to deal with different

regions, the lumpiness of pipeline investments, the optimization of

exploration expenditures, and so on. Solving that problem many times,

embedded in a larger system, is not a realistic objective for any system that

must be used on a daily basis. Hence the authors apply the pseudo-data

approach and construct simple summary models that connect to obtain an equilibrium solution. In practice, the modelers check to make sure that final

solution is consistent with the more detailed model. But it is only the

summary model that serves to represent the component in the larger system.

Fortunately in the integration algorithm, the nature of the summary models is

irrelevant to the characterization of the final solution. They are only part

of an interim computational step and so it is not important exactly how any

summary model is designed. What is important is that the procedure works

well.

The algorithm is equivalent to solving a system of equations. Under

conditions guaranteed by the demand model, the full integration is a contraction mapping. Hence, one can start with an estimate of the solution

and, comparing the error between the estimate and the ultimate answer, at each

iteration the error is reduced. We always move closer to the final solution.

We can obtain a bound on how fast we move and this bound is related to the

pseudo-data approximation in the diagonal demand model. If the own-price

effects are the most important in the model, which is almost always the case,

then that bound guarantees a rapid convergence of the contraction mapping.

And if we start with a good guess for the answer, then the contraction mapping

can arrive quickly at a 1? error-approximation of the true solution. The

experience is that only eight to ten iterations will be required to obtain a full equilibrium solution.

All of this has been discussed in detail elsewhere. Since the

algorithm works and works well, and since the final answer does not depend on

the algorithm used, the EIA model integration approach is an attractively

feasible and practical approach for integrating the tremendous detail of the

component models.

Model Applications

These few issues address primarily technical problems. Fred Murphy

did a good job and I compliment him on preparing us to consider these

technical issues. But before the relinquishing the podium, I want to remind

us to return later today to the policy questions and help set the boundaries

of application of the EIA models. The customers, as a practical matter, will

be the decision makers in the executive branch and from Capitol Hill. What

kind of questions should they ask? What answers can they reasonably expect?

I hope that the architecture in software design, which we will hear

about more today, maintains the flexibility to allow relatively quick changes

and adjustments. Flexibility will be important for making the system useful

and used by people who are concerned with policy questions.

24
Given the size of this model, I would be worried more if I knew less

about the successful uses in the past. However, it is important to preserve a concern about the trade-off between detail and insight. The particular

numbers produced will not be as important as the education that flows from

examining the details of energy choices and energy policies. The danger lies

in losing ourselves in the detail.

For the broader public, interest must be limited to the reports

produced for Congress. I have been pleased with the progress of the Energy

Information Administration in producing readable reports. They've done a fine

job, each year has shown an improvement. I hope that the operation of EIA

will include a steady stream of policy studies applying the models to typical

problems. Through these studies we will learn if the model has value, if it

helps us understand energy problems and energy solutions.

In regard to the technical information provided in the documentation,

I recommend using the approach followed in Energy Modeling Forum Studies; that

is, to include reduced-form summaries of the characterization of the model.

Much of that information needed is available because these reduced-form models

play a central role in the computation algorithm. It would help the model

reviewer to see the aggregate elasticities and other summaries of the

component models in order to describe the output solutions and evaluate the

plausibility of the conclusions.

My list of preferences and personal requests is longer, but my time is

short and other speakers have been charged with the attention to further

detail. In closing, let me repeat my compliments at this impressive step in

the evolution of this network of models in EIA. There are many technical

criticisms that we can discuss today. But we should recognize the high value

of the tools before us. And we must remember how the models will be used to

extract information and understand better our energy policy choices.

DISCUSSION

DR. MURPHY: Thank you very much, Bill. Since Bill has been so

complimentary, it is very hard to make any general statements in

response. There are two things I would like to mention. One is

the questions of dynamics. Dealing with the expectations is a terrible problem.

That is, in part, one of the reasons why we are only going to

1990 right now. The question of expectations is not significant

in this period, since the capacity additions to electric

utilities, for example, are almost entirely determined by what is

in the construction pipeline now, simplifying our problem. The

reason for having the planning module in electric utilities is to

project beyond 1990 after the model has matured. In preparation,

we can start studying the problem now. It is a very very

difficult issue.

25
Another activity that we are doing, as part of this modelling

exercise, is backcasting selected sub-models. The electric

utilities model, which is a process model and so not estimated

off historical time series, is, at this moment, being backcast to

see how well it would have forecasted current events starting

from 1960 data. So, we are trying to do our own validation

studies on these models to see how well they actually do work

over periods in which we know something.

As a final comment, one of the features of this model is that the

solution time is quite rapid in comparison to other models of

this sort. The solution time varies substantially depending on

when we insert a new piece, which usually causes the model to

blow up. Our worst solution time was 100 seconds per solution

year. Most of our solution times have been around 30 seconds per

solution year and we are somewhere in between there and heading

back down to 3 0 seconds in our work.

MR. RUDY HABERMAN, General Electric: I want to get positioned

about where this system fits into the scheme of things. Is this

replacing the short-term and the midterm and the long-term? Are

we just going to have one model until 1990?

DR. MURPHY: What has happened is that the original purpose

behind the model was to fill the gap between the short-term and

the longer-term models. As part of the budget cutbacks, the

longer-term models, the MEFS and LEAP systems, have been retired.

We have retained the short-term model so this model will step off

from the Short-Term Integrated Forecasting System solution.

Mr. Warren Langley, RDA: Would you contrast managing this

model and operating this model in terms of resources required

by other models?

DR. MURPHY: The resources are substantially less than we have

had in the past because of the budget cuts. We have achieved a certain amount of efficiency with this modeling system because,

by having individual fuel modules, we are able to allocate staff

to individual pieces that can be developed and operated

separately

One of the problems that we always had in operating the MEFS

system in a policy analysis environment is that there was always

one person who had to be the central focus of everything. And,

that person was usually worn out by the end of any single

analysis. The decentralization by fuel module has balanced the

work load and actually reduced the staffing requirements because

there is less coordination necessary.

26
System Implementation and Software Design

Susan H. Shaw

Energy Information Administration

Department of Energy

Washington, D.C.

Introduction

To understand the system implementation, it is useful to review the overall system design. The first design decision made in this model development was its organization by fuel types. That is, one sector of the model follows the particular fuel through its production, processing, transportation, and end-use consumption. This is unlike

some other integrated energy systems which are organized around a large central integrating model.

Solution Process

Figure 1 illustrates the overall system solution of IFFS. Each

module continues to solve until it has converged to an equilibrium within its own fuel market. This convergence is measured

with the end-use prices of each product in the other modules

remaining fixed at their most recently computed values.

Because of the effects of one fuel's price upon other fuels'

end-use demands and prices, the overall sequence of fuel module

solutions is repeated until the global convergence is achieved.

This convergence is measured by comparing the product prices and

quantities on entering a fuel module to the prices and quantities

after the module solution and computing the differences between

them. Once the differences are within the specified tolerance

for each module the system has converged. This convergence

occurs for each year in sequence and there is no feedback from a later year's solution to an earlier year's, a technique that

would imply perfect foresight.

Fuel Module Organization

Each fuel module can be regarded as a stand-alone system with its

own unique organization. The oil market module, however, can be used to illustrate how a fuel module is organized. The Refinery and Petrochemical Modeling System and its associated database is

27
executed independently of IFFS to generate a set of pseudo-data

which can be used to estimate the response of the RPMS refinery

model to variations in product demand levels. The information

flow moves from a detailed refinery model and its database,

outside of IFFS, to a set of pseudo data that are estimated to

produce the parameters that are retained in the IFFS refinery

module. How this is done is detailed in the refinery discussion.

These parameters as well as other information contained within

the refinery module are combined with inputs from the small

central database to produce a market equilibrium for petroleum

products

.

Databases

Because the model represents a decentralized competitive economy,

the system is designed for the minimal interfaces between the

fuel modules. The key pieces of information that must be passed

between fuel modules, and therefore must be in a central database, are the end-use prices and quantities of each of the

products in each sector and region. In this way, each fuel

module can solve to a market equilibrium knowing the prices of

all competitive fuels.

The central database also contains general input assumptions and

parameters, such as Btu contents and price deflators, and specific

run parameters. Most other data is fuel-specific and maintained

in the fuel sector databases.

In order for the fuel modules to come to a supply/demand balance,

the central database also stores the own-price elasticity of each

product and a term for each product that combines the effects of

the product's lag term and of all the cross-elasticities and

prices from other products. In this way the fuel module has a simplified demand curve for its internal solution. Each time a module completes its solution, the central integrating procedure

recomputes all the cross-elasticity terms using the recently

computed prices and stores them in the central database.

Modularity

The fuel-sector modularity has several major advantages. One key

advantage is the flexibility for each module to use the data

organization and solution algorithms that appear most suitable.

This variation of methodologies becomes apparent in the individual fuel module presentations.

The arrangement by fuel module has substantially simplified the

solution process. The central database is initialized for each

module every time the model is run. Any module or subset of

modules can be executed, thus creating a small, single fuel-model

from the larger one. This allows development and debugging of

each module to occur quickly and independently of other modules,

greatly simplifying the tasks of parallel development across

fuels

.

28
r

i

i

i

i

iSL . ri

START FIRST FORECAST YEAR

INPUT INITIAL DATA

INPUT PRICE ELASTICITIES AND

INITIAL PRICES AND QUANTITIES

ITERATION COUNT

ELECTRICITY MARKET MODULE

OIL MARKET MODULE

NATURAL GAS MARKET MODULE

& OIL/GAS SUPPLY

COAL MARKET MODULE

ALL MODULESNj^

.CONVERGED?,

WRITE SUMMARY REPORTS

STOP

TEST FOR CONVERGENCE

TEST FOR CONVERGENCE

TEST FOR CONVERGENCE

TEST FOR CONVERGENCE

SET NEXT YEAR

Figure 1. Model Solution

29
INTRODUCTION TO THE OIL AND GAS SUPPLY SUBSYSTEM

Richard P. O'Neill

Energy Information Administration

Department of Energy

Washington, D.C.

I have been asked to talk about the oil and gas supply models, as

they will be used eventually in IFFS. There are three basic

supply models, one of which I am going to spend some time on with

you today because it hasn't been in the public domain for very

long. The other two have been around for quite a while so I will

say a lot less about them.

The newest model in the suite of oil and gas models is the

Lower-4 8 onshore model, PROLOG. It has two very important

orientations. First of all, it is very data intensive because it

deals with the Lower-48 where there has been a lot of historical

information. An example of the data intensive nature is that,

instead of using the classic API/AAPE drilling statistics, we

have obtained the API well ticket tapes. We have reoriented the

data so that we are analyzing information by completion year of

the well as opposed to the report year of the well, which is what

most people generally use.

Also, we have chosen to use the API well tickets over things like

the census data because the census data, although it does report

drilling, has had major inconsistencies in the past with the API

well ticket data, and that is confounded by the fact that the

census data is reported by some accounting standards that are put

forth in the Financial Accounting Standards Board opinion on oil

and gas.

Also, we did try to stay away from over dependence on one

resource estimate and tried to stay with data on reserves that we

have available to us. Secondly, we have tried our very best to

document exactly what we have done in great detail. And, I think

I am correct in saying that this is the first EIA model to be

documented before it has been used in forecasting. If anybody is

interested, you can walk up to the National Energy Information

Center and ask for EIA-0345 and that will be the data and

methodology documentation for the Lower-48 model. I will say a little more about that after I talk about the other two models

that are used as input.

The Outer Continental Shelf (OCS) model has been around for a while. It was used in the last annual report and its basic

features that differentiate it from the Lower-48 model and the

Alaskan model is that its exploration process is a Monte Carlo

simulation of a prospect level data base. After the discovery,

the development process is modelled with a detailed

representation of the platform in gathering economics necessary

in the OCS.

30
The Alaskan model has been around for quite a while and this year

we are using the ultimate in reduced form for this model. That

is, it is not used in the 1990 horizon. Essentially through the

1990 horizon, we simply do an offline analysis of TAPS capacity

and exactly what can run through the line. And now, the late

1990 's is the earliest completion date for the natural gas

pipeline (ANGTS) system. With that as a general overall

introduction, I would like to talk a little bit more about the

Lower-4 8 onshore model, PROLOG.

Rigs are used both to explore for and to develop oil and gas, to

put resources into inventory, and to take things out of

inventory. Exploration deals with issues of federal land

availability and private land availability feeding into questions

of leases and bonuses which Bill Hogan talked about earlier,

which are still very perplexing in terms of the way you are

supposed to model them.

As you acquire land, you drill and find reserves. Reserves are

put into inventory and then development generates production,

production generates funds; funds also come and go in other ways,

from the proverbial doctors and dentists in drilling funds,

sometimes from banks and obviously, there is a cash flow

component. One of the most difficult processes that we are

trying to deal with now is the recent change in the tax

structure, which apparently has driven people out of drilling

funds to the extent that only momentum is carrying the drilling

funds at this point in time. They appear to be off about 50

percent in 1982 over 1981.

Well, that is a simple representation. Let me say that we do not

know very much about federal land availability or resources on

federal lands. We thought that there was going to be information

on that coming out in the past year but it is not yet available.

So, we have not separated out federal from privately owned lands.

First of all, let me talk about the way we are modelling the

discovery process. Although everybody has their problems and

dislikes this approach, it seems to us to be the only one

available to us in the Lower-48, at the present time. First of

all, we use statistically estimated finding rates. We let the

data tell us about the finding rates and not some judgement about

what is there.

If you look at the basic functional form that we use, we have

what a lot of purists would object to; in the finding rate

equation we have an asymptote that is not zero. Now, the purists

would argue that that will give you an infinite resource if you

integrate to infinity, but we are going to 1990.

When we discover resources via finding rates, through exploratory

drilling, we evaluate what we found. We calculate the present

31
value of discovery using an expected development schedule, that

is, how the discovery would be produced over time after finding.

There are obviously two approaches to foresight. One is perfect,

and one we have come to call the banker's foresight, which is

what you could get if you wanted to mortgage your reserves at the

bank; that is, one or two years of foresight and then flat

thereafter

.

One of the things we have done that is new to the modelling

approach in the Lower-48 is that we have separated out the

development process. We have separated out existing reserves and

sliced them up by their windfall profit tax tier, if oil, and by

their NGPA title I category, if they are gas. This gives us a rather detailed set of information on the existing reserves, and

we develop them based on the pricing for the tiers or the NGPA

category

.

Then we have a second set which are the development of the

reserves discovered by the model, which, in our case, is going to

be tier 3 for oil and sections 102 and 107 for gas. Each year,

and this is somewhat important, the development options are

reevaluated on the basis of current prices and projected future

prices which have the same foresight as in the exploration

process. And essentially you calculate how the reserves would be

produced if you did not do any development. You look at

incremental steps of development and then look at the difference

in the cash flow that you would get from that development, and

that is essentially how the development options are evaluated.

Both the discovery and the exploration process are based on these

kinds of cash flow calculations which involve revenues, excise

taxes and royalty payments (we have essentially assumed the

traditional royalty payments) . We have also included co-product

revenues in the case of oil and gas evaluations. We have a whole

plethora of other costs, both expensed and depreciated and lots

of taxes and also tax credits.

All this information is presented in a context of a linear

program, with some regional drilling constraints which do not

allow drilling to get terribly away from historical patterns in

the supply regions of the model. We have rig availability

constraints that are based on historical expenditures and

revenues, short term cost inflation for drilling, and merit order

selection of the drilling activities that is based on their

present value.

Each year the system is solved and restarted over again with a new set of activities. The model is up and running and the most

fascinating part about the current base case is that the model

starts with 1980 data and has captured the rig peak in 1981 and

it has a trough in 1982, meaning that the rigs' utilization will

go back up again. I am not sure I would go to the bank with this

kind of information or go to the stock market, but it is rather

32
interesting that the model has, in some sense, caught the peak

and the possibility of a trough in '81 and '82.

We have also looked at sensitivity analyses with respect to world

oil prices, marginal gas prices, discount rates, cost, and

finding rates; and we have found that the first partial

derivative, that is the response to the model to these various

changes, has the expected sign and pretty close to what we would

expect is the magnitude of response. We are currently in a phase

where we are putting in minor enhancements and, of course in

later times, there may be major enhancements to the system.

33
OIL AND NATURAL GAS SUPPLY SUBSYSTEM: A CRITIQUE

William R. Finger

Exxon Company, U.S.A.

Houston, Texas

David Nissen

Exxon Corporation

New York, N.Y.

The oil and natural gas supply subsystem within the Intermediate Future

Forecasting System (IFFS) is a substantial enhancement of DOE's forecasting

methodology in hydrocarbon supply modeling. For frontier areas, the new supply

model attempts to represent in much more explicit detail the geology, processes, and information entailed in hydrocarbon discovery and development. The

profitability module of the onshore Lower-48 sub-model is essentially statistical, but is developed and presented in much greater detail than previous

versions. For these reasons, the present system is better structured, and more readily understood and evaluated, when used for forecasting and policy

analysis

The supply model comprises three sub-models:

o the Alaskan Hydrocarbons Model (AHM) -- representing onshore and

offshore oil and gas production in Alaska,

o the Outer Continental Shelf Model (OCS) — representing offshore oil

and gas production for the lower 48 states, and

o the Production of Onshore Lower-48 Oil and Gas Model (PROLOG) — representing onshore production.

We discuss the first two of these sub-models rather briefly, since they

are mature models, by now fully discussed in the open literature [1,5].

PROLOG, recently developed inhouse in the Energy Information Administration

(DOE), is examined more extensively. Finally, we examine the simple,

parametrically-f itted minimodel of the aggregate price response of oil and gas

supply used to represent these models in IFFS.

These sub-models differ in their representation of the geological unit to

be explored (geologic structures or geographical regions) and in the level of

detail of their treatment of the exploration and development process. These

differences are to some extent dictated by the availability of data. (In the

Federally-owned regions, Alaska and Lower-48 offshore, geological information

is publicly available to a much greater extent and there are vastly fewer

wells.) They are also dictated by inherent differences in the hydrocarbon

supply process among these regions. In the frontier regions, the investment

process and the accrual of information is lengthier and more complex.

34
The trend represented in these developments — aggressive exploitation of

disaggregated data and detailed representation of geology, processes, costs,

and finances — is the right direction for the development of public sector

modeling in support of forecasting and policy analysis. The closer that

modeling gets to the data and problem constructs used by industry, the more clearly and completely can technical and policy issues be separated and

resolved in the policy evaluation process.

ALASKAN HYDROCARBONS MODEL

The AHM has two modules, a resource module and an integrating framework.

The resource module describes hydrocarbon resources and the cost of their

exploration and development. This establishes a feasible set of hydrocarbon

production possibilities. Given this set, the integrating framework (an

inter- temporal , mixed-integer-linear program) chooses, on an economic basis,

which of these exploration/development possibilities and, jointly, which transportation facilities (oil and gas pipeline capacities) to implement.

Within the resource module, resources are distinguished by geologic

structure and the degree of geologic knowledge. For producing structures

(presently only the Sadlerochit reservoir), production possibilities are

furnished to AHM from an external reservoir model.

For structures with proven but undeveloped resources, production possibilities are developed with volumetric/decline curve methods. For unexplored

areas, a Monte Carlo technique is used to generate distributions of resource

base characteristics and production costs. A fairly detailed level of resource description (structure number, area, pay, depth, fill, drive mechanism,

recovery factor, permeability, and viscosity) permits a fairly detailed

description of the exploration, development and operating facilities required

for production.

Thus the geological and engineering aspects of production can be calibrated to empirical or hypothesized experience. Profiles of feasible resource

exploration and development costs and production volumes are then furnished to

integrating framework.

Modeling at this level of detail is attractive in principle, but

expensive. In practice, the full-fledged exploration/development/ transportation optimization is designed for analysis of policy (taxation and resource

access) and has not been extensively used in forecasting.

OUTER CONTINENTAL SHELF MODEL

In the OCS model, the representation of geology is prospect-specific, as in AHM. Existing reserves data are taken from the API/AGA Joint Survey.

Inferred reserves are characterized through a Hubbert-type analysis inferring

total oil-in-place from the relationship of cumulative reserve additions to

cumulative drilling.

35
The novel feature of this model is its explicit treatment of uncertainty

and information-development in the characterization of the economic quantity of

undiscovered reserves. The economics of prospective exploration are specified

through a detailed, prospect-by-prospect simulation of the drillers' decisionmaking. For each prospect, the process costs of exploration and development

are modeled. A prior distribution of the geological factors affecting reservoir

behavior and costs is specified. The exploration decision is made on the basis

of the expected value of the exploration/development/production commitment.

After exploration, there is a probabilistic assessment of the hydrocarbon

show/no show outcome for each prospect. The geological prior for each prospect

is updated on the basis of information gained throughout all prospects in the

OCS . For explored prospects with hydrocarbon shows the decision to develop or

not is made. For prospects yielding dry holes the decision to explore (again)

or not is made. (The underlying distributions on which this sampling-withoutreplacement/ decision-making process operates are generated in such a way that

the consequences of alternative economic parameters can be modeled and evaluated on a given structural characterization of the underlying geology.) Thus

the model contains an explicit representation of the process by which information is generated and decisions are made in the hydrocarbon exploration and

development process, rather than depending on some "average" representation of

the underlying geological uncertainty as the input for decision-making.

The representation of the exploration and development technology in the

OCS model is also highly explicit, so that the impacts of geology, technology,

and input costs on exploration and development costs can be distinguished. Over

time, as each of these cost factors changes substantially, this provides an explicit basis for cost projecting.

PRODUCTION OF ONSHORE LOWER-48 OIL AND GAS MODEL

PROLOG dif fers from the previous models in its representation of the

geology, the supply process technology and costs, and the drillers' economic

motivation.

The onshore lower-48 states are divided into six geographical regions

(rather than geologic prospects). In each region, the profitability of

exploration and development drilling opportunities distinguished by product

(oil or gas), type (exploratory or developmental), and regulatory regime

(old/new oil, old/new and inter/intrastate gas) is projected based on statistical relationships. This profitability module comprises the interaction of

geology, technology, and costs. Then national drilling capacity is projected

and allocated down the profitability seriatum subject to regional growth

constraints. This projection and allocation of national drilling capacity

constitutes the specification of drilling factor supply and drillers' economic

behavior

For conventional oil and gas, reserve additions and revisions for oil and

gas are modeled through statistically-fitted functions of drilling and remaining resources. (Unconventional oil and gas supplies are exogenous.) In each

region, costs of exploration, development, and production are modeled through

statistically-fitted functions for five cost categories:

36
o geological and geophysical (G&G) — cost per well is trended;

o lease acquisition — costs are input at model execution time and do

not depend on endogenously determined resource exhaustion rents;

o drilling — costs distinguished by well type (oil, gas or dry) and

drilling type (exploratory or developmental) are specified as functions of depth and are trended;

o lease equipment — cost per well is specified as a quadratic function

of depth and is trended; and

o operating costs -- cost per well is estimated as a trended function

either of depth or production. (Both functions are estimated and the

choice is specified at model execution time.)

These relationships are inputted to a discounted cash flow evaluation of

drilling profitability distinguished by region, product, type, and regulatory

regime. Product prices are combined with finding rates and pro forma development/production profiles to give the revenue side. Costs are provided the

appropriate tax treatment and combined with royalties, depreciation, and taxes

to project cash outlay.

Drilling prospects are then selected in order of their profitability

subject to two sets of constraints:

o an overall constraint on drilling, made up of rig availability

(which, in turn, depends on a distributed lag function of overall

industry revenue), and an exogenous specification of rig productivity

(feet-per-year rig); and

o regional historical limits on the maximum and minimum change in

drilling activity, in order "to keep the model from unreasonable

changes in drilling levels over the forecast horizon" ([2, p. 14],

emphasis added).

Within this framework, the critical determinant of hydrocarbon supply over

time is the supply of drilling capacity (specified as the rig supply times the

productivity of rigs):

o The increment to the stock of rigs depends only on industry revenue . Apparently, this is to proxy for profits or cash flow, but though

expenses, taxes, and depreciation (as well as revenues) are treated

in the profitability module, no attempt is made to relate the income

and f low-of-funds statement of the industry to rig demand.

o Profitability of rig use does not affect overall drilling. Profitability affects only the regional allocation and, in fact, the model

will invest in drilling prospects with negative profitability when

minimum drilling constraints are binding.

o Rig productivity is specified exogenously. Rig costs are not treated

explicitly and, in fact, drilling costs generally depend only on the

conditions of their use, not their supply.

37
This model of aggregate drilling activity is wholly inadequate. It fails

to identify past and future profitability as the key determinants of the demand

for drilling. It fails to model the interaction of rig capacity, utilization,

productivity, and costs in determining the both conditions of supply and the

profitability of drilling. There is no place in this framework to incorporate

detailed information about the costs of drilling factors of production.

In fact, recognizing recent changes in oil price prospects, the cor- relation among changes in prospective profitability, utilization, and drilling

productivity in the last four years is striking:

Onshore Lower-48 Drilling Statistics

1979 1980 1981 1982

Wells (no.) 49938 63187 79167 84805

Feet (M ft.) 230.1 280.9 353.8 389.1

Rigs (no.) 1961 2667 3694 2834

Wells/Rig 25.5 23.7 21.4 29.9

Feet/Rig (no.) 117.3 105.3 95.8 137.3

Feet/Well (K ft.) 4608 4446 4469 4588

Average Cost/

Foot (1982$) 74.54

a 76.44a 86.67a 77.75-92.38

Sources: Wells, rigs, and feet: Oil & Gas Journal, various issues.

Cost/foot, a. Joint Association Survey, b. Reprint of the Cost

Study Committee, Independent Petroleum Association of

America, Mid-Year Meeting (May 24-27, 1983).

The rig count grew by two-thirds in the two years between 1979 and 1981 and

then declined by about one-fourth in the next year. Rig productivity moved in

an inverse pattern. Wells per rig declined mildly and feet per rig dropped

sharply through '81, as shallower wells were drilled, but then productivity

rebounded by more than 25% in one year as the oil-field service market went

slack. (Reporting lags in well completion may somewhat amplify the actual

swing in productivity.) At the mid-year meeting of the Independent Petroleum

Association of America Cost Study, a range of 1982 cost indices were developed

as indicated in the above table (1982 Joint Association Survey data are not yet

available). The low end of the range is thought to represent actual 1982

"field experience" whereas the upper end of the range includes all experience

which includes contracts negotiated prior to 1982 but executed in 1982. Over

the period shown, costs moved more than proportionally to productivity as rents

came and went in the changing market. It is these considerations which must be

modeled in the supply and demand for drilling.

As a final comment on the drilling allocation module, the imposition of

regional bounds on drilling growth (which, we understand, largely determine the

behavior of the model), in order to achieve "reasonable" behavior, finesses the

38
whole question for which the model is built, namely, "What is reasonable

behavior? " (Some idea of the extremely simple character of this allocation

mechanism can be gained by realizing that, though cast as a linear program, it can actually be solved with a two-key sort of the drilling prospect profitability file).

In sum, in its present state, PROLOG'S aggregate hydrocarbon supply model

rests on a formulation of the market for drilling factors which is misspecified

on the demand side and unspecified on the supply side; the regional allocation

of drilling activity with historical bounds on drilling rate changes comes

close to "dialing in" the answer.

The potentially more valuable part of PROLOG is the drilling profitability

module. To assess the success of this module in statistically projecting

profitability, the results of this module should be made available independently (not just simply inputted to the allocation module). Pending this, we

have some technical comments on its components:

o Generally, the lack of explicit sequential treatment of exploration/development decisions at the prospect level, makes this

treatment harder to compare with industry calculations, in contrast

to the previous models. Further, the imposition of a pro forma

development/production schedule blurs the variety of intensive/extensive margin decisions made in the hydrocarbon supply process. (See

Stitt [7], for a discussion of the margins at choice in hydrocarbon

supply.)

o Trending G&G costs fails to reflect the market for factors entailed

in G&G.

o The role of exhaustion rents in onshore lease acquisition costs

should be investigated.

o The finding rate equations are specified with a functional form which

implies infinite resources. This is defended as a statistical

convenience, but there is strong precedent for the value of taking

physical constraints seriously in hydrocarbon supply forecasting (M.

King Hubbert, who did so [4], is the only energy analyst we know of

who today can use, to effect, charts he prepared over 35 years ago).

o The constant-elasticity form for the drilling cost equation is not

motivated in terms of the underlying technology. In any case, it

fails to separate technological and factor cost considerations. The

market for drilling factors must be treated much more explicitly

here.

o Lease equipment costs appear to decrease with depth in four of the

six regional equations — an apparently anomolous result.

Again, an explicit presentation of the profitability results is required for

assessment of this module.

39
IFFS MINIMODEL

The aggregate response of oil and gas supply to prices is represented in

IFFS by a simple pair of equations fitted to results of model runs exercised on

a variety of price paths.

The fitted model can be written as:

.oil = K + B*

.oil

>gas + Loil

'-1

gas

-1

where the Qs and Ps represent, respectively, the logarithm of quantity and

price relative to a reference case and the subscript, -1, indicates a value

lagged one year.

The coefficient matrix

B = 0.069 -0.009

0.047 0.095

gives the short-term (one year) elasticities of oil and gas supply (rows) with

respect to the oil and gas price (columns). The coefficient matrix

L = 0.951 -0.132

-0.645 0.690

gives the proportional impact on oil and gas supply (rows) of changes in lagged

oil and gas supply (columns).

Looking first at the B-matrix, the on-diagonal elements show small,

positive, short-run own-price supply elasticities, which look reasonable.

These positive price effects on supply arise from two sources. First, increased

industry revenue induces greater rig availability. Second, increased product

profitability induces a larger allocation of drilling resources to that

product. In interpreting the of f -diagonal , cross-price elasticities, the

revenue effect offsets the allocation effect with the net effect being qualitatively indeterminate. Since oil has a larger revenue share, it is reasonable

that an oil price increase raises gas supply (with an elasticity of 0.047), but

a gas price increase lowers oil supply (with an elasticity of -0.009).

In the L-matrix, the substantial positive on-diagonal terms indicate that

a supply response, once started, gains momentum. The negative off-diagonal

terms indicate this momentum takes drilling resources from the competing

product.

40
To identify the net result of these forces over time, we calculate the

long-run price elasticities of supply from the equation (familiar, say, in

input-output analysis)

-0.09 -0.13

-0.65 0.69

giving the long-run elasticities of oil and gas supply (rows) with respect to

oil and gas prices (columns).

The gas price effects here (second column) are perhaps reasonable. The

on-diagonal element (0.69), is a substantial, positive long-run own-price

supply elasticity for gas. The negative off-diagonal element (-0.13) implies

that with the oil price constant, increased gas supply takes resources from oil

supply (i.e., the increased rig effect doesn't compensate the reallocation of

drilling resources to gas).

The oil-price effects (first column), however, are not acceptable. The

negative own-price response of oil supply (-0.09), and the large negative

cross-price response of gas supply to the oil price (-0.65), are simply anomalous in the light of the literature and the evidence.

Two lessons emerge. First, examination of their minimodels, or simply

their aggregate response to shocks, is an important part of the evaluation of

large models. Second, the price response behavior of the oil and gas supply

subsystem in IFFS, as presently constituted, is not satisfactory for forecasting or policy analysis.

-1

B- (1-L) =

CONCLUSIONS

The development of the oil and gas supply subsystem in IFFS dispels much

of the mystery that surrounded prior public sector analysis in hydrocarbon

supply and, as such, is a welcome advance.

Methodologies in the various components of the model differ sharply. The

frontier area models (AHM and OCS) are process and prospect-oriented. Thus

they are highly explicit, but at the same time expensive to operate. The

onshore Lower-48 model (PROLOG), essentially statistically-based, is a cheaper

and less cumbersome way of dealing with a much larger and more varied collection of activities.

If modeling is to be useful in the public policy debate, this may

represent the right set of methodological choices, but the returns are not yet

in. Specifically, PROLOG needs further development before it can be used

seriously:

o the whole structure for modeling the supply and demand for drilling

factors needs to be rethought; and

o the results of the profitability module need to be examined explicitly, with attention to the micro-foundations of the functional

forms to be estimated.

41
Finally, the aggregate behavior of the model must be carefully assessed before

it is incorporated in large models like IFFS.

ACKNOWLEDGEMENTS

This paper is intended as a contribution to understanding public sector

energy forecasting and policy analysis. It represents our personal, professional views, not necessarily those of our employer. We are grateful for aid

and advice to Frank Morra (Lewin and Associates), Fred Murphy (DOE), Dick

O'Neill (DOE), John Pearson (DOE), and Bill Stitt (ICF, Inc.), but, again, the

views expressed are entirely ours.

REFERENCES

[1] Brashear , J.P. , F. Morra, C. Everett, F.H. Murphy, W. Hery, and R. Ciliano,

"A Prospect Specific Simulation Model of Oil and Gas Exploration in the

Outer Continental Shelf: Methodology," [3, pp. 688-717]

[2] Carlson,M., W.G. Kurator, B. Mariner-Volpe, R.D. O'Neill, and W. Trapmann,

Production of Onshore Lower-48 Oil and Gas Model Methodology and Data

Description , EIA/DOE, June 1982.

[3] Gass, Saul 1., ed., Oil and Gas Supply Modeling , Proceedings of a Symposium

held at the Department of Commerce, June 18-20, T980 , NBS , May 1982.

[4] Hubbert, M. King, "Techniques of Prediction as Applied to the Production

of Oil and Gas," [3, pp. 116-141].

[5] Murphy, Frederick and William Trapmann, "An Evaluation of the Alaskan

Hydrocarbon Supply Model," [3, pp. 661-687].

[6] Pearson, John D. "A Simple Representation of the PROLOG Model," EIA,

undated.

[7] Stitt, William, "Current Problems in Oil and Gas Modeling," [3,

pp. 142-170].

42
DISCUSSION

DR. O'NEILL: I guess what I would like to say is that we agree

with you completely in terms of the direction. We did not have

the information to take the direction before. We may be on a path to have more detailed information including field size

distributions and things of that nature. The argument about

having a finite asymptote being intuitively unappealing was

initially unappealing to me also. But after looking at other

people's work and realizing these asymptotes are fairly low, and

may be close to some kind of an economic limit, or net energy

limit, they did not bother me that much. In other words, I am

not sure that they could effect the outcome significantly.

Secondly, it is not clear, but King Hubbert is here today, so I think he could tell us himself; it was my impression that King

took the historical data and fit it without any intuitive notion

of what was left and came out with these prediction turning

points, although his models consisted of a finite ultimate

resource base.

MR. HUBBERT: Hi, I am the King Hubbert who is being mentioned.

The approach I have taken to a very important aspect of this

problem is not the economic approach as is dominant here today,

but, simply, what are the constraints in its production and,

therefore, a means of predicting about what the complete cycle

would be.

Instead of taking the short-term view of 5 years or 10 years, I

find it much more useful to predict the entire cycle first,

because we know a future point on this production curve, namely

zero. We know where we are now and the problem, then, is to

interpret between that future point zero and where we are now.

Now, there was a conference sponsored by the DOE and the Bureau

of Standards 2 years ago last June, and I gave a more detailed

analysis of the methods that I have developed over the years. It

is presented in Bureau of Standards report number 631, June 1980.

DR. GASS: Thank you. For those of you who would like a copy of

that report, if you would write to me or to Fred, we will send

copies out until the supply is depleted. Dick has some copies he

would like to give away.

43
INTRODUCTION TO THE OIL SUBSYSTEM

John Conti

Energy Information Administration

Department of Energy

Washington , D.C.

Introduction

The function of the oil market module is to determine supply

prices of petroleum products given a supply/demand equilibrium in

the petroleum market. Because this is a model of domestic energy

markets, the price of crude oil is determined exogenously for

each of the forecast years using the international models.

What the model then tries to capture are the differentials

between the price of crude oil and the prices of refined

products. The size of these differentials depends on: (1) the

downstream processing units that are needed to produce this slate

of petroleum products, (2) the quality of the crude oils

available, and (3) legislation that affects fuel standards. To

state it differently, the model tries to capture the costs

associated with changing the mix of hydrocarbons found in a barrel of distilled crude oil into the mix of hydrocarbons that

are demanded by petroleum consumers.

Some of the processes associated with these costs include changing the molecular structure of heavier hydrocarbons into lighter

ones, extracting sulfur to produce lower-sulfur products,

and changing the gasoline yield from lower to higher octane

levels. An imbalance exists between the fractions that are

derived from a barrel of distilled crude and fractions demanded

by the petroleum product market. This imbalance will continue

to grow over the long run, because the quality of available

crude oils is declining and the market for heavier products is

shrinking more rapidly than the market for lighter products.

With the decline in crude quality, an increasing percentage of an

average barrel of distilled crude becomes heavier and the amount

of contaminating metals found in the oil increases. Both of

these results exacerbate the need for downstream capacities. The

yield of lighter products from a barrel of crude can be increased

by expanding downstream capacities or by increasing the severity

at which they are operated; but, the marginal cost of

transformation increases more rapidly. That is, for example, as

one tries to change an increasing percentage of a barrel from

heavier products into lighter products, the price per barrel of

the lighter products will increase. Similarly, if one tries to

lower the average sulfur content found in the oil, the marginal

cost of transformation also increases.

44
Estimating Refined Product Prices

Representing refined product prices in IFFS is a three-step task.

First, a model of refinery operations is built, incorporating the

same techniques as refiners use in managing their operations.

Second, econometric equations representing changes in capital

stock determine the capacities for the following aggregate unit

types: cracking units, octane boosting units, and

desulfurization units. The availability of these units

determines how much flexibility refineries have, influencing the

product prices. Third, the capacity equations are combined with

the operations model to produce the estimates of product prices.

Since this model is too large to fit into the overall modelling

system, results from the larger model are used to produce

price/quantity vectors, known as pseudo-data (see Adams and

Griffen [1]), that are used to estimate a simple, aggregate

model, which is incorporated in the overall IFFS modelling

system.

The modelling is performed in terms of the ten Federal regions

used throughout the IFFS model. Demands for eight distinct

petroleum products are modelled. They are gasoline, distillate

fuel oil, high-sulfur residual fuel oil, low-sulfur residual fuel

oil, jet fuel, liquefied petroleum gases, petrochemical

feedstocks, and a category designated as other, which includes

asphalt, lubricants, waxes, and petroleum coke. The production

of still gas by refineries is also modelled, but still gas is

considered to be used exclusively for refinery fuel. A feature

of the sub-model is that it determines internally the refinery

sector demand for specific petroleum products that it uses as

fuel

.

Refiners have long used linear programs to model their

operations. In fact, the first major computer codes to solve

linear programs were built to solve refinery models. Refiners

use linear programs to calculate the value of a given quality of

crude oil, the least cost way of meeting a given level of demand,

and the maximum profit product mix for given product prices.

Refinery operations are modelled by using a linear program of an

aggregate refinery, which combines the unit capacities within a region as if they were in a single modern refinery.

In every important refinery area in the country, there are many

refineries and not all of them are large, modern, and efficient

refineries. This technique for modelling refineries, however,

has had a history of success. Alan Manne [3] was the first to

use this process modelling approach, and he was able to replicate

refinery product prices for the United States. This aggregate

approach is reasonable apparently because the larger, modern

refineries are the price setters. This approach was also used by

the National Petroleum Council on a study of the U.S. refinery

industry [4]

.

45
The objective of the linear program (LP) is to minimize cost

subject to meeting given product demand levels. Part of the

solution to the linear program is the marginal cost of producing

another barrel of each product. These values are used as the

market prices associated with the given demand level, consistent

with the IFFS assumption of industry competitiveness.

The LP used in generating the pseudo-data is based on the

Refinery and Petrochemical Modelling System (RPMS) , a proprietary

software system developed by Bonner and Moore, Management

Consultants, Inc. RPMS provides a data base containing crude

qualities, operating characteristics of refinery units, product

qualities, and software to simplify the generation of a refinery

process. The RPMS-based LP models are widely used in the

refining industry and have been used for several years by EIA in

its midterm forecasts.

The columns of the LP represent purchases of different crude oils

and other inputs such as electricity, distillation of each crude,

modes of operation of downstream units (such as varying

temperature/pressure specifications) , blending of intermediate

streams, sales of refined products and construction of new

capacity. The rows represent internal balances, unit capacity

limits, quality specifications of products, a variety of

engineering limits restricting modes of operation, and yields of

refinery units. Full documentation of the RPMS system is

available from Bonner and Moore.

In building an energy market model, one has to control the size

of the model for each sector to have an overall, solvable energy

model. A linear program of a refinery cannot fit directly into

IFFS because of the model size that is necessary for adequately

representing refinery behavior [2] . In order to circumvent this

obstacle a pseudo-data approach is used. This is done by solving

RPMS for a base solution value. A parametric analysis is then

performed on individual petroleum product quantities and

downstream capacities. For example, the quantity of gasoline is

varied in 2-percent increments, 20 percent above and below the

base solution value. Products are varied alone and in

combination with other variables to create a series of

observations on petroleum product prices as a function of how the

refinery is operated. The prices are the dual variables from the

linear program. These observations can be considered to be a data base showing all of the different ways in which a refinery

could operate and the resulting product prices. These

observations are used to perform regressions that relate

quantities to prices.

Equations for the price of gasoline are of form:

LOG(PGASO) = B0 + Bl*LOG(CRDP) + B2*LOG (UNLQ/OCTANE) + B3*LOG (LEADQ/ OCTANE) + B4*LOG (GASQ/CRACK) + B5*LOG (DSTQ) + B6*LOG (RESQ)

46
Where

:

PGASO is the price of gasoline at the refinery gate in

dollars per barrel

CRDP is the Refinery Acquisition Cost of crude in dollars

per barrel

GASQ is refinery production of gasoline in millions of

barrels per day

UNLQ is refinery production of unleaded gasoline in millions

of barrels per day

LEADQ is refinery production of leaded gasoline in millions

of barrels per day

DSTQ is refinery production of distillate in millions of

barrels per day

RESQ is refinery production of residual in millions of

barrels per day

CRACK is the capacity of cracking units in millions of

barrels per day

OCTANE is the capacity of octane-boosting units in millions

of barrels per day

and B0, Bl , B2 , B3 , B4 , B5 , B6 are estimated parameters.

The B's in the equation are estimated coefficients from the

regressed pseudo-data. The other terms are model inputs. In the

pricing equations, two variables besides quantity shares and

crude price are significant factors, octane boosting and cracking

capacity. As represented here, these are not actually refinery

processes but rather aggregations of processes that can be

categorized into either breaking hydrocarbon molecules or raising

the octane level.

Equations for the price of distillate are of form:

LOG(PDIST) = B0 + B1*L0G (CRDP) + B2* LOG (GASQ/ OCTANE) + B3*L0G (GASQ/CRACK) + B4*L0G (DSTQ) + B5*L0G (RESQ)

Where

PDIST is the price of distillate at the refinery gate in

dollars per barrel

CRDP is the Refinery Acquisition Cost of crude in dollars

per barrel

GASQ is refinery production of gasoline in millions of

barrels per day

DSTQ is refinery production of distillate in millions of

barrels per day

RESQ is refinery production of residual in millions of

barrels per day

CRACK is the capacity of cracking units in millions of

barrels per day

OCTANE is the capacity of octane-boosting units in millions

of barrels per day

and B0, Bl , B2 , B3 , B4 , B5 are estimated parameters.

47
Equations for the price of jet fuel are of form:

LOG(PJTF) = B0 + B1*L0G (CRDP) + B2 *LOG (GASQ/OCTANE ) + B3*L0G (GASQ/CRACK) + B4*L0G (DSTQ)

Where

:

PJTF is the price of jet fuel at the refinery gate in

dollars per barrel

CRDP is the Refinery Acquisition Cost of crude in dollars

per barrel

GASQ is refinery production of gasoline in millions of

barrels per day

CRACK is the capacity of cracking units in millions of

barrels per day

OCTANE is the capacity of octane-boosting units in millions

of barrels per day

and B0, Bl , B2 , B3 , B4 are estimated parameters.

Equations for the price of high-sulfur residual are of form:

L0G(PHS6) = B0 + B1*L0G (CRDP) + B2*L0G (GASQ/OCTANE) + B3*L0G (GASQ/CRACK) + B4*L0G (DSTQ) + B5*LOG(HS6Q) + B6*L0G (LS6Q)

Where

:

PHS6 is the price of high-sulfur residual at the refinery

gate in dollars per barrel

CRDP is the Refinery Acquisition Cost of crude in dollars

per barrel

GASQ is refinery production of gasoline in millions of

barrels per day

DSTQ is refinery production of distillate in millions of

barrels per day

HS6Q is refinery production of high-sulfur residual in

millions of barrels per day

LS6Q is refinery production of low-sulfur residual in

millions of barrels per day

CRACK is the capacity of cracking units in millions of

barrels per day

OCTANE is the capacity of octane-boosting units in millions

of barrels per day

and B0, Bl , B2 , B3 , B4 , B5 , B6 are estimated parameters.

Equations for the price of low-sulfur residual are of form:

L0G(PLS6) = B0 + B1*L0G(CRDP) + B2*L0G (GASQ/OCTANE) + B3*L0G (GASQ/CRACK) + B4*L0G (DSTQ) + B5*LOG(HS6Q)

+ B6*LOG(LS6Q)

48
Where

:

PLS6 is the price of low-sulfur residual at the refinerygate in dollars per barrel

CRDP is the Refinery Acquisition Cost of crude in dollars

per barrel

GASQ is refinery production of gasoline in millions of

barrels per day

DSTQ is refinery production of distillate in millions of

barrels per day

HS6Q is refinery production of high-sulfur residual in

millions of barrels per day

LS6Q is refinery production of low-sulfur residual in

millions of barrels per day

CRACK is the capacity of cracking units in millions of

barrels per day

OCTANE is the capacity of octane-boosting units in millions

of barrels per day

and B0, Bl, B2 , B3 , B4 , B5 , B6 are estimated parameters.

Estimating Downstream Unit Capacities

The downstream unit capacities available in the forecast year are

calculated within the refinery module. The capacity of

octane-boosting units in a given year is generated as the

weighted sum of the capacities of the alkylation and reformation

units. The weights are based on the number of octane-barrels of

output obtained by processing one barrel of input through the

unit.

The capacity of cracking units is the weighted sum of the

capacities of thermal crackers, catalytic crackers, and

hydrocrackers . The weights are based on the percentage of a

barrel of residual feed that is converted to lighter products by

the unit. Cokers, which are included in thermal . cracking

capacity, convert all of their input to lighter products. Other

cracking units crack only part of their feed. The exact

percentage of residual output from these units depends on the

temperature and pressure at which they are operated. The weights

for these units correspond to typical operating severities used

in determining operable capacity.

The equation for determining octane-boosting capacity in year t

takes the form:

OCTANE (t) = B0 + B 1 * OCTANE ( t- 1 ) + B2*LDPG(t-2)

+ B3* (GASQ (t-2) -GASQ (t-3)

)

Where

:

OCTANE is the octane-boosting capacity in millions of

barrels per day

LDPG is the difference in refinery-gate price between

premium and regular gasoline

GASQ is refinery production of gasoline in millions of

barrels per day

and BO, Bl , B2 , B3 are estimated parameters.

49
The equation for determining cracking capacity in year t takes

the form:

CRACK (t) = B0 + B1*CRACK (t-1) + B2*LDPGR ( t-2

)

+ B3* (QGAS (t-2) -QGAS (t-3) ) + B4* (RESQ ( t-2

)

- RESQ (t-3)

)

Where

:

CRACK is the cracking capacity in millions of barrels per day

LDPGR is the difference in refinery-gate price between

regular gasoline and low-sulfur residual

GASQ is refinery production of gasoline in millions of

barrels per day

RESQ is refinery production of residual in millions of

barrels per day

and BO, Bl , B2 , B3 , B4 are estimated parameters.

Octane capacity is based on octane capacity in some previous year

plus a decision that was made by refiners to create new capacity

approximately two years ago. In this equation we determined that

the significant variable for adding on octane capacity was the

difference that existed between the price of premium and the

price of regular two years ago and the difference between the

quantity of gasoline between two and three years ago, basically

the change in gasoline production.

Referring back to the Introduction to IFFS, the modules are

categorized into two pieces, a planning component and an

operations component. The time series estimated equations fall

into the planning component of the module, while the pricing

equations fall into the operations part of the module.

Model Solution

A quick overview of the module algorithm shows how the individual

pieces fall into place. First, capacities are based on the time

series estimated capacity equations. The solution process begins

with a slate of demands for petroleum products. From this slate

of demands, net imports and natural gas liquids from processing

plants are removed. Petroleum product prices are determined from

the pseudo-data-estimated pricing equations. Refinery fuel

consumption is based on product production levels and historical

patterns. The final wholesale and retail prices include

transportation and wholesale to retail markups. Demands are then

re-estimated with these new end-use prices and the calculations

are continued until there is a supply/demand balance in the oil

market module.

The development of the oil market module is still in progress.

Among some of the minor tasks to be accomplished is estimation of

the pricing equations on a regional basis versus a national

basis, the way it is presently being done. Alternative

50
functional forms of the pricing equations need to be examined,

and the time series used for estimating the capacity equations

should be based on EIA data series.

Another major task which should be studied is the question of

approximating the surface of the LP directly, eliminating the

need for the pseudo-data approach presently in use; but, still

yielding a simple set of equations that can be used in the

module. Some of the aspects of the module of concern are whether

the general approach used is valid, the functional forms

presently being used are acceptable, and whether or not some of

the basic assumptions that were made are correct.

References

1. Adams, F. Gerard, James M. Griffen, "An Economic-Linear

Programming Model of the U.S. Petroleum Refining Industry,"

Journal of the American Statistical Association (Sept.

1972) . 2. Linear Programming, Inc., PIES Refinery Model Aggregation

Contract C-01-6364 (Sept. 1979) . 3. Manne, Alan S., "A Linear Programming Model of the U.S.

Petroleum Refining Industry" Econometrics (Jan. 1958) . 4. National Petroleum Council, "U.S. Refinery Flexibility"

(1980) .

51
REFINERIES SUBSYSTEM: A CRITIQUE

Neil J. Cleary

Manager of Operations Research Support Services

Standard Oil Company (Indiana)

Chicago, Illinois

First, I would like to say that the opinions you hear are mine and not

Standard Oil of Indiana's.

I would like to start out by saying that the overall approach taken by the

Intermediate Future Forecasting System (IFFS) is very good, including the

refinery submodel approach. At Standard Oil, we have successfully used the

approach that is being used for the refinery modeling subsystem called

pseudo data —we call it response surface modeling—on several occasions with

considerable success.

The documentation provided to me was extremely poor, and I'm glad to hear

that you plan to do more work on the refinery model subsystem because there

is a lot more work that needs to be done. I suggest that you take out the

basic organic chemistry lesson and the description of all the refinery

processing models. Either remove them completely or put them into an appendix, because they are unnecessary. I also found that there was no discussion about how the cost equations were determined. I don't know

whether or not a large number of different regressions and models were developed and the best ones selected. I also don't know where the

functional form came from. They appeared sort of by magic.

I have some serious reservations about the functional form of the cost

equations. Since they all are in logarithmic terms, they imply that a multiplicative form was used; I would suggest that a linear form would be more appropriate. If you look at a refinery process, part of the cost of

gasoline is the cost of the crude oil used in a distillation unit; another

part of it is the cost of the crude unit, the cost of the alkylation unit,

the cost of the reformer, and the cost of the cat cracker. Thus the cost

should be an additive function. Another reason for considering an additive

form is that you are using an LP to get the basic underlying data.

Bonner and Moore's Refining and Petrochemical Modeling System (RPMS) is a very useful modeling tool. We've used it for over 10 years, with

considerable success. I'm glad to hear that you do intend to look at those

functional forms of the cost equations and hopefully will come up with

somewhat better forms.

I don't understand why some of the variables were lagged two years and some

of them were lagged three years. If this was the result of a statistical

analysis or due to construction time lags, I have no objection, but this is

not clear from the report.

In some of your equations, there were typographical errors. In the cat

cracking capacity equation, one of the variables is the price difference

between regular and premium gasoline; the question is, is that leaded or unleaded gasoline or did you average them. This equation has as one of its

variables low sulfur resid. It would be more reasonable to have distillate

demand as a variable. In the equation for the cracking capacity as a function of time, the last term (B4) is positive according to the

documentation which, in effect, says that as the resid demand increases, cat

cracking capacity increases, and that is not logical; it should be the other

way around.

In the beta coefficients that are reported, there is no test or indication

of statistical significance. T test statistics are not given. Some of

52
those coefficients are pretty small and maybe could just as easily be left

out.

In making the RPMS runs, you mentioned something like 540 runs were made.

You said there were 18 RPMS input variables that were modified. It would be

nice to know what the variables were. Also, when you changed the demands,

the way the documentation reads, it indicates that you increased all

products in 20 per cent increments. I think this would introduce a serious

error because that would mean that you kept the gasoline to distillate ratio

constant. The gasoline to distillate ratio has a very important effect on

the cost of gasoline and distillate, a very strong effect. And that

certainly would make the results suspect. I guess you are aware there is a typo on page 316. It should read 1 barrel; gives 1.59 barrels.

I also would like to know when you made the RPMS runs; did you use the build

option, and therefore, are you calculating the incremental costs, including

capacity expansion? It would also be nice to mention what the base crude

was and what its properties were. What was the API and sulfur content of

the crude? In our modeling efforts we have found it necessary to use a number of crudes to reflect reality in meeting low-sulfur resid demand.

The ratio of leaded to unleaded gasoline is not stated. This brings up

another question. One of the products is premium leaded gasoline. My

understanding is that that represents less than 2 per cent of the total

gasoline market and in the future is going to be even less. I know our company doesn't even sell it anymore, and a lot of the oil companies don't,

so that product might just be dropped out of consideration.

Another important variable that we found in our modeling work is how sulfur

is handled. What sulfur crudes did you use? Did you use a crude with just

one sulfur content? The product prices are very dependent upon the sulfur

level of the crude. And that's not balanced, incidentally, by the

difference in the cost of the crude. High sulfur crude is not selling at

that much of a differential to low sulfur crude at present. A lot of the

oil companies, including our own, are making a major investment in

desulfur ization equipment to handle high sulfur crude.

Some of the variables in the cost equations don't make sense. I see you use

the same variables in all the equations, and maybe that is why these terms

appear in all of the equations.

For example, the octane and cracking capacity should not have much of an effect on jet fuel prices, yet it is used in the equation for jet fuel

prices.

If the gasoline equation is differentiated with respect to the price of

crude, you find that gasoline prices will not go up as fast as the price of

crude. I don't think that's a reasonable result.

I also assume you use the same demand equations for each demand region but

plan to look at that in the future.

Standard Oil has participated in an NPC study, which was referenced in your

document, on refinery flexibility. It took us about one month to build a model, about a two man-month effort; it took us seven months and two man- years of effort to validate it, and I'm not sure we did validate it

completely. Model validation is a very difficult task.

53
We also found in that study that in order to get a realistic representation

of the U.S. refining industry it was necessary to separate PAD 5, which is

California and the West Coast, from PADs 1 through 4. Refineries in PAD 5 are so different in design and operate differently because of the low demand

for resid and distillate. The West Coast refineries are designed

differently than those in the rest of the U.S., and you can't represent the

oil refining industry in the U.S. in an acceptable manner in one model,

We also had a problem in trying to validate our model for each of the

geographical areas. We had to look at refineries with different complexity.

I don't know the effect they are going to have on your overall big energy

model. The effect of not considering refinery complexity may not be

significant enough to affect it, but we ended up using three different

complexity levels. A simple refinery which has only distillation and

reforming capacity and then a more complex refinery that has cat crackers

and then finally refineries that can handle the bottom of the barrel.

Incidentally, I have with me microfiche output describing that model and can

leave you a copy if you'd like.

There are a lot more specific points which I don't think I want to take up

everybody's time with; I've got a list of them that I can give to you. I do

want to mention, however, that what we did was take the NPC model that we had generated and reran it for 1978 and 1982 using your numbers. We also

put the same numbers in your cost equations and compared results.

Our initial runs used a crude cost of $12.05 a barrel, so the results were

definitely wrong. We reran it at the $33 a barrel used in your study, and

our first attempt showed that there is reasonable agreement except for the

gasoline prices. We subsequently discovered that one of the factors in your

equation (the octane capacity) was in error by a factor of 10.

So we divided by 10 and then made some additional runs. The comparison

between the prices obtained from your model and ours are given in Table 1

attached.

I don't know how significant these differences are. We will be willing to

work further with you and continue to evaluate your models. Thank you.

54
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55
DISCUSSION

MR. CONTI: I would first like to thank Mr. Cleary and all the

people of Standard of Indiana for the work they did. We hope to

keep in contact with them and try and put into the model as many

of their suggestions as we can. I think what I first should do

is talk about the documentation, since that is what many of the

comments are concerned with.

In the documentation, I believe the units are not carried through

correctly. But, in the model itself, for example with respect to

that octane capacity, the equations are kept consistent. So, we

have to reexamine the documentation for its accuracy.

Secondly, more should be said about how the pseudo-data was

generated, since you had a few questions on that. All petroleum

product quantities were not changed by the same percentages

simultaneously. First, they were varied separately, then they

were changed in combination with other variables. Secondly, the

downstream capacities were changed alone and also in combination

with other variables, by 2-percent increments, 20 percent above

and 20 percent below the base solution value. I think that deals

with most of the comments you have made. As far as the variables

that were used, I probably could not name them all, but I could

tell you that it was all four types of gasoline, two types of jet

fuel, distillate, low- and high-sulfur residual, and a few other

minor products. In some of the perturbations to the model the

products were aggregated, but the identities of the individual

products were kept available in the model.

As far as the estimation of the equations is concerned, a

stepwise approximation was performed. You would examine

statistics for alternative functional forms for many different

variables, depending on the particular petroleum product.

Returning for a second to the problem with the documentation, the

documentation says that all the variables, with associated

coefficients, were used in every equation. That is not true,

only coefficients with t-statistics that were significant were

used

.

Regarding the assumptions about qualities of the crude slate, I could not tell you what the gravities were, but what was done was

to take a weighted average of the qualities of crude that are

believed to be available in the future. The average API gravity

for the crudes was 32.55. So, I am assured that we do not have

the component characteristics of only one type of oil, but rather

a weighted sum of a few different types.

We want to investigate in greater depth the regional issue which

could clarify some present difficulties. We also hope to

estimate linear as opposed to log-linear equations, but, there is

a problem in the limitations of nonlinear regression packages.

56
Your note about the comparison of PADD 5 model results to data I am afraid is valid; however, as we try to estimate these

equations regionally, we hope to eliminate that problem. I am

not exactly sure how we should deal with the complexity of

refineries that you suggested, but I will be in touch with you

about generating some possibilities. I do not know whether I said this already, but, the way that the gasoline quantity is

determined is a weighted average of all the different types:

premium leaded, premium unleaded, regular and unleaded regular.

In the octane equation, you asked how we determined the price of

premium and the price of regular. Premium price is a weighted

average of the unleaded and leaded premium and the regular price

is a weighted average of the unleaded and leaded regular. I hope

I answered all your questions. I know a problem you brought up

to us previously was with vis-breaking being included with the

thermal processing units in the data base. As we re-estimate the

capacity equations with EIA data series, we will revise the

definition of thermal processes and examine alternative

functional forms.

MR. CLEARY: I suggest you change the terminology of thermal

cracking. It is an obsolete process.

MR. CONTI: OK. I think that covers just about everything. Did

I leave out something that you could think of off the top of your

head?

MR. CLEARY: No. I think that when you clear up the

documentation, most of these problems will be cleared up.

I might also mention that if you are looking at different crude

slates by region, you look at the NPC study. We put a great deal

of effort into determining appropriate crude slates.

MR. CONTI: I would like to thank you again very much for the

effort you have put into this. I am sure it will be a fruitful

experience in working with you and putting these changes that we

do not already have in there in the model. Thank you.

DR. GASS: Thank you, John. I would like to open it up for

general questions, answers, and discussion. I think all the

speakers from this morning are here and so if anybody has any

particular question to address to a particular person, or a question in general that could be answered by any of the EIA

people, I welcome any questions. Yes, please. Identify yourself

in the microphone, thank you.

MR. SHEEHAN: I am Tom Sheehan from the National Bureau of

Standards and as background my question is motivated by the fact

that a study has been done by the Congress's Office of Technology

Assessment, looking at the effects of various legislative options

such as tax credits, fuel price increases and so forth, on the

demand side of oil and gas. Now, Richard O'Neill, in his

57
presentation, pointed out that it is a little tough to keep up

with some of the things Congress is doing and also observed that

the doctors and dentists were fleeing from the oil drilling rig

business, presumably because they are now only 50 percent tax

brackets instead of 70 percent tax bracket. What I am wondering

is, is there anything in any of EIA models that is capable of

handling congressional requests or other inputs from changes in

the law and seeing how they affect the projected prices or

demands or supplies out to the vicinity of 1990 or so?

DR. MURPHY: The answer is yes because one of the chief purposes

of this system is to make information available on the impacts of

alternative congressional policies on energy markets. The only

reason for generating any information about the future is because

you want to make some decisions. Even though we have not talked

much beyond my first slide on the possible uses of the model for

policy analysis, that is the main reason for going into this

activity.

MR. SHEEHAN: Just a minor clarification. Maybe I did not phrase

the question right but the real question was not is policy

analysis involved, but how sensitive are the models or how fine

is the granularity you find in changes produced by Congress?

DR. MURPHY: It depends on how significant the changes. It is

sector specific; for example, when you see the electric utility

presentation, you will see more detail there. And, as was

described by Dick O'Neill, the oil and gas supply models have the

necessary cash flow equations for studying the tax structure.

The models clearly cannot handle every policy. There is no

explicit representation of changing highway programs, for

example, on transportation fuel demand.

We have oriented the model development toward the kinds of issues

that we have had to treat in the past, hopefully we have not done

the equivalent of going into World War II with a horse cavalry on

new questions. I think we have designed a system that can adapt

to new issues, but, we do not know what they are yet. So, we

have designed the models around the typical issues we have been

treating in the past. They do not seem to go away.

MR. ALEXANDER: Mark Alexander of the Department of Interior. To

what extent does your analysis system incorporate international

models such as PAL?

DR. MURPHY: There are a whole group of international models in

EIA. PAL is one of them. Another one is the Oil Market

Simulation Model. These models are used to determine the world

price of crude oil, which we use as an input in this model.

There have been some preliminary discussions in terms of linking

OMS to this system so that we get the feedback from changing U.S.

oil import requirements on the world price of oil as

characterized in OMS. Our models are in the same division within

EIA, so, although they are not physically connected, analyses are

linked at this point.

58
DR. GASS: Are there any comments from the EIA speakers to

clarify any points?

DR. MURPHY: I would like to clarify two points. One is that the

reason why we did not have, say, a linear term for the crude

price and then a log-linear term for other oil product prices is

that when you mix linear and loglinear terms you have a problem

in finding statistical estimation packages that work for any

moderate size nonlinear systems. And there is another whole

question associated with the use of pseudo-data that has not

really been resolved. The data are not independent random

observations; we are talking about formally generated material

used to estimate an approximating function.

What does this mean? The statistical tests are based on an

underlying notion of randomness that does not exist here. We are

approximating a model with a model and there is a lot of work

that needs to be done in understanding the bounds on the

usefulness of this procedure and the meanings of the results.

We have chosen it for pragmatic reasons as have other people,

but, it is a subject that needs quite a bit further study. It is

really not so much a statistical process as a technique of

numerical analysis in function approximation. So, we have some

people investigating this for us and we hope to have some

reasonable results this fall.

59
INTRODUCTION TO THE AFTERNOON SESSION

John E. Todd

Director

Short-Term Information Division

Energy Information Administration

Department of Energy

Washington, D.C.

I would like to welcome you back to the afternoon session on the

Intermediate Future Forecasting System. My name is John Todd and

I am Director of the Short-Term Information Division, which has

responsibility for short-term domestic forecasting and

international forecasting. I assumed responsibility for the

longer-term division last December.

The virtues of having all these activities under one roof is that

we have a better sense of what everybody else is doing and it is

a little more efficient to allocate manpower. Some of the

development agendas for the future include trying to capitalize

on that communication by making the short-term system, the STIFS

system, more consistent with the longer-term system, the IFFS

system. This is one of the things we will be working on over the

next year. We are going to get as much consistency as we can

this year. Also, on the international models, the quality of the

crude runs and the world oil prices that we use in the IFFS

system come as well from the Oil Market Simulation Model which is

operated in the International Branch.

The difficulty, of course, of having all these activities under

one roof is that there is an awful lot to do, and it is very

difficult to give detailed supervision. Fortunately, I am

blessed by having a group of very fine managers, and I am

particularly grateful for both the creativity and leadership that

Fred Murphy has shown in helping to manage what Bill Hogan called

this ambitious and impressive venture and, also for Julie Zalkind

and John Pearson, who have joined us and are helping to manage

this operation.

We have additional copies of the Oil and Gas Supply Modelling

proceedings of the 1980 symposium that was mentioned earlier this

morning; some right outside and, if we run out there, we even

have a few more available. So, those of you who would like to

get a copy of this, you probably can do so before you leave

today

As you can see from the afternoon schedule, we have three pairs

of presentations of electric utilities, coal, and the

macroeconomic interactions, followed by a summary presentation.

The remaining time will be allotted to a panel discussion and

questions, by which we mean an opportunity for all of the

presenters today to offer any last minute comments that have come

to their attention and for all of you to have a chance to ask

questions, not only of EIA personnel but of the other people who

have made presentations from outside DOE as well.

60
INTRODUCTION TO THE

ELECTRIC UTILITIES SUBSYSTEM

Reginald Sanders

Energy Information Administration

Department of Energy

Washington, D.C.

Introduction

As emphasized in the overview, modularity is a design feature for

the overall IFFS system. In the electricity market module this

concept is extended to include a division of the module into four

basic components. As illustrated in Figure 1, there is a planning component, a production costing or supply component, a pricing component, and a demand component. The interaction of

these components can best be illustrated by following the

information flows through the module.

Input from other IFFS modules consists of electricity demand

estimates and fuel prices. The planning module provides capital

accounts to the pricing module and determines available capacity

in the current forecast year for the production costing

component. The production costing component allocates available

generation capacity to satisfy electricity demands and tabulates

the resulting operating cost and fuel consumption patterns. The

pricing component combines the financial accounts from the

planning component and the operating costs from the production

costing component to produce an electricity price. The demand

component adjusts the electricity demand to be consistent with

the current electricity price estimate.

Thus, an iterative cycle is established between the production

costing, pricing, and demand components. This cycle terminates

when electricity price and demand estimates remain constant

through one full cycle. Output to the other IFFS modules consist

of resulting fuel consumption patterns and electricity prices.

The Planning Component

The planning component is unique in that it is executed only once

each forecast year. Planning decisions are based on previous

year's solutions or historical data. First-order exponential

smoothing is used to determine an expected growth rate for

electricity demand and fuel prices for planning purposes. There

are a number of planning functions that take place in this

component, which include simulating the capital outlays necessary

to build and maintain the transmission and distribution network,

as well as short-term concerns for maintenance scheduling.

However, the emphasis is on formulating an expansion profile for

generation capacity.

This component determines how much and what type of capacity must

be initiated in the current forecast year such that an "optimal

61
capacity mix" is maintained over the planning horizon. Thus

construction lags for each capacity type are explicitly dealt

with in this planning framework. For example, the decision as to

how much coal steam capacity to initiate in the current year is

made by solving the static capacity mix problem in the first year

that construction could be completed. This procedure is repeated

for each capacity type.

A breakeven analysis is the basis of the solution technique used

to solve the capacity mix problem. However, to understand this

procedure, it is necessary to introduce the concept of a load

duration curve.

The need for the concept of a load duration curve results from

the fact that electricity cannot be inventoried; and, therefore,

sufficient capacity must be available to meet instantaneous

demand. Figure 2 shows a typical daily load curve which plots

demand as a function of time. The area under the curve gives

total demand and the height of the curve provides a measure of

capacity requirements. A load duration curve is created by

reordering the load curve from highest to lowest demand levels,

as shown in Figure 3. Like the load curve, the load duration

curve provides total demand information and more clearly

illustrates capacity utilization requirements.

Illustrated in Figure 4 is a simplified representation of the

planning methodology. The bottom half of Figure 4 is an annual

load duration curve. The top half is a breakeven chart. This

chart plots annual operating costs for three competing capacity

types as a function of capacity utilization. The chart shows

that for utilization less than the first breakeven point the oil

turbine is the best choice. For utilization between the two

breakpoints, the natural gas steam capacity is best. The coal

steam capacity is best for utilization greater than the second

breakpoint. If these breakpoints are projected down to the load

duration curve, the optimal mix can be read from the vertical

axis. Oil turbines are selected to meet load at the top of the

load curve, coal steam at the bottom, and natural gas steam the

remainder. The utilization of the equipment is the area in the

load duration curve covered by each plant type. This methodology

can be easily modified to deal with existing capacity. Since the

capital cost for existing capacity is already sunk, this cost is

eliminated from the breakeven analysis and the total cost curve

for existing capacity begins at the origin. However, with these

cost curves, the model selects a capacity mix which includes more

existing capacity than is available. The remedy is to slide the

cost curve up the breakeven chart, without altering its slope.

At some point, the model will select a mix which includes the

precise amount of existing capacity. This is the optimal mix.

It is important to note that within the time frame considered by

this model, most capacity expansion decisions have already been

made. This committed capacity is captured by the model through

62
the actual electric utility plans as reported in the Generation

Unit Reference File (GURF) as maintained by the Energy

Information Administration (EIA) . Thus the importance of the

planning component is in projecting a construction profile used

within the pricing component since few of the expansion decisions

made by the model come to fruition in the forecast horizon.

However, this is sufficient cause to be concerned about the

reasonableness of the expansion plans. Two important steps can

aid in insuring this. First, financial feasibility is being made

a factor in the expansion decision. The model incorporates the

effect of balance sheet weakness on the cost of capital and the

economics of new equipment. The higher the cost of capital, the

less desirable the capital-intensive plants become. Second,

electric utilities have, in the past, tended to order more

capacity than necessary. This is because it is more costly to be

caught short in the face of increasing demand than to have some

excess capacity. Also, demand growth has been below some of the

most pessimistic expectations.

What is needed is a way of including in the capacity expansion

component a method for delaying the additions of new capacity,

slipping the construction schedules the way utilities do when

demand growth expectations are not met. The latter task may not

be completed in time for the Annual Energy Outlook . The Production Costing Component

Available electricity generation capacity by seasonal period for

each forecast year is determined by the planning component and

passed to the production costing component. The preparation of

this data is a two-step process. First, new and existing

generation assets are sorted and combined by capacity type.

Second a maintenance schedule is developed in the following

manner. Peak load requirements are constructed for four seasonal

groupings: summer, winter, spring, and fall. Electricity demand

is generally lowest in the fall and spring periods. The base

load units are scheduled for maintenance during these periods to

the extent there is an adequate reserve margin. Plants are then

scheduled for maintenance in either the summer or winter,

whichever has the lowest peak demand. If there is any more need

for maintenance downtime, it is scheduled in the remaining

period. Given the maintenance schedule, the equipment available

in each seasonal period for dispatching is now known. The

capacities are fixed for the current forecast year, therefore,

capital costs are of little concern in making the dispatch

decisions. The only concern is with operating costs.

The production costing methodology is relatively simple. For

each capacity type the variable cost of producing one unit of

electricity is calculated, the capacity types are sorted

according to these costs and stacked under the seasonal load

duration curve until sufficient capacity has been allocated to

fully meet demand. In Figure 5, for example, nuclear is at the

bottom of load curve with coal steam, oil steam and oil turbines

63
stacked on top. The area under the load curve in each of the

four strips represents the electricity generation required of

each capacity type. Fuel consumption and operating costs for

each capacity type can be determined from their assigned

generation requirements. The operating costs are summed and

passed to the pricing component, and the fuel consumption

patterns are sent to the other IFFS supply modules.

To better emulate actual dispatch decisions, this methodology is

modified to reflect additional constraints on the electricity

generation network. For example, hydroelectric capacity is

limited by water flow and thus has both a capacity and energy

constraint. The production costing component allocates

hydroelectric capacity to that unique position under the load

curve such that both constraints are exactly met. Other concerns

include: utilization constraints on steam and turbine capacity

(turbines cannot operate on a continuous basis and it is

impractical to operate steam units to satisfy peak demand)

determination of availability rates by capacity type; and the

development of load following rates for base load equipment.

Load following refers to the practice of lowering utilization of

base load and intermediate load capacity to some critical level

during off peak-periods to avoid start up costs on intermediate

load capacity when demand again rises.

The Pricing and Demand Components

The pricing component simulates the financial flows and status of

electric utilities. It produces income statements and balance

sheets, for private and public utilities, treating all of the

utilities of each type in each Federal region as two integrated

systems. By following utility accounting practices, the rate

base and the attendant revenue necessary to meet the allowed rate

of return is estimated while covering depreciation, fuel, and

other operating costs. Given the total quantity of electricity

demanded, the price is the required revenue divided by demand.

The demand component consists of the load duration curves and

equations that relate the price of electricity in each sector to

the demand for electricity. The sectoral demands are summed and

the function that defines the load curve, a fifth degree

polynomial, is multiplied by a constant so that the area under

the load curve is the kilowatt-hours demanded.

The supply/demand balance is found by iterating between the

production costing, pricing, and demand components until the

price from the pricing component is consistent with the demands

produced using those prices in the demand component. Once an

equilibrium is reached in electricity markets, IFFS moves to find

an equilibrium in the other market modules.

64
Conclusions

The status of the system is that there is something in place for

every component of the utility module. Given a working module,

individual pieces are being upgraded with improved data and

occasionally with structure, as experience is gained and the

model is calibrated to historical experience. Given the number

of simplifications and approximations in this kind of enterprise,

we would like to solicit comments as to the appropriateness of

the methodology and, perhaps, some of the pitfalls that may occur

once the model is used.

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70
ELECTRIC UTILITIES SUBSYSTEM: A CRITIQUE

Lewis J. Rubin

Electric Power Research Institute

Palo Alto, California

I. Introduction

The Intermediate Future Forecasting System (IFFS) is an

integrated energy system model being developed by the Energy

Information Administration (EIA) of DOE to use in forecasting and

analysis over a medium-term horizon (8-10 years). Specific

pieces of the energy system have been modeled individually and

then linked together to form the integrated representation. This

paper focuses exclusively on the electric utility module of the

system, and attempts to review and critique the design and

philosophy underlying it.

This critique is meant to be constructive, in every sense of the

term. It is hoped that the observations and comments made in

this paper will be helpful to the modelers in their continuing

efforts in development, refinement and eventual use of the model

in the analytical support of energy policy-making.

For the most part, an understanding of the module design

decisions and the principles underlying those decisions has been

developed from the documentation provided by the modelers.

Sample output reports have also been provided by the modelers,

but these have been less useful as they are quite preliminary and

the results are not particularly insightful. Also the modelers

themselves have been questioned on certain specific issues, and

their comments have helped to complete the information base

necessary to this critique.

Section II makes several general observations about the module,

including underlying philosophy, purpose, and uses. Section III

comments in depth on the underlying principle of decision-making - that of cost minimization - implicit in the module. Section IV

addresses the production costing issue and its treatment in this

module. Section V comments on the two principles of demand

uncertainty and foresight in capacity planning, and attempts to

point out potential difficulties with respect to their treatment

in the design of the module. Finally, Section VI concludes by

consolidating and summarizing the specific cautions and recommendations suggested to the modelers.

II. General Comments

Several important considerations of context and usage underlie

the design decisions made by the modelers. These considerations

are presented in order to define the environment in which the

module will be used and thus to help explain the rationale for

specific design decisions. These considerations include the

following:

1. The time-frame of the module and of the entire IFFS

system is medium-term. The system will run and produce

results over an eight- to ten-year forecasting period.

71
2. The module is designed to simulate electric utilityoperations in an entire region, rather than in an

individual company.

3. The module must be structured in such a way as to

sensibly interface with other modules in the IFFS

system.

4. In order to be responsive and valuable in the policyoriented environment in which it exists, the module

must avoid overly complex, slow or dif f icult-to-use

algorithms and procedures.

In subsequent sections of this paper, these considerations will

be referenced again as direct motivation for many of the design

decisions made by the developers.

The documentation of this module also deserves comment. The

chapter on the electric utilities module is excellent, both in

terms of structure and information content. It is quite clear

and well-written.

Further, it is very well organized. It is essentially divided

into three sections: The first section presents a comprehensive

discussion on the nature of the electric utility industry, including planning, technology options and regulatory constraints;

the second section discusses a number of different approaches to

modeling the industry, including state-of-the-art attempts to

incorporate such difficult issues as demand uncertainty and

contingency planning; finally, the third section discusses the

current model design and the motivation behind it. With the

first two sections of the chapter as motivating background, the

reader is well prepared for the discussion of the IFFS electric

utility module, and understands clearly the context and rationale

of its design.

Finally, the output reports of the system deserve a short

comment. At this stage in the development process they are not

very useful. There are no summary-type reports yet developed;

and thus there is no way to develop any summary insights into any

particular scenario or run of the model.

Currently the output reports of the system produce nothing but

long lists of prices and quantities, by region and year. More

sophisticated and insightful reports will have to evolve as the

model is used to support real-world analyses.

III. The Cost-Minimization Principle

In simulating the planning and operations of any firm or

business, a modeler must be guided by some underlying principle

of behavior. In this electric utility module development, the

modelers have chosen, not surprisingly, to be guided by the costminimization principle of neoclassical economics.

72
In essence, this principle states that the utility, in order to

produce electricity, will use its factors of production (capital,

labor and fuel, for example) in some combination so as to minimize total costs across all factors. This is a standard and

sensible principle in almost all business environments, but one

area where it has had serious competition from an alternative

principle is among firms under rate of return regulation.

The alternative principle is the Averch-Johnson (A-J) hypothesis

(see references 1 and 2.). A-J states simply that a firm under

rate of return regulation will tend to choose a factor mix which

is more capital-intensive than the optimal, cost-minimizing mix

if its regulated rate of return is greater than its cost of

capital. In the era when this hypothesis was originally

developed - the early 1960s - it was generally considered to be

true that the rate of return allowed to most regulated firms,

electric utilities included, was greater than their cost of

capital

.

The hypothesis was thus offered in part to help explain such

observed phenomena in the utility industry as redundancy of

eguipment and "gold-plating" in order to ensure maximum

reliability.

The A-J hypothesis, as a guide to and predictor of managerial

behavior in regulated industries, has been quite controversial.

The original Averch-Johnson article has spawned an enormous

number of apologies and refutations, both theoretical and

empirical, over the last two decades. It is now generally

acknowledged that A-J is not a particularly good model of utility

behavior; thus the cost-minimization principle is a reasonable

and relatively simple substitute to use in this modeling effort.

However, in recent years still another alternative model has

surfaced, and its rationale is at least qualitatively compelling.

The so-called "reverse Averch-Johnson" hypothesis questions what

kind of behavior will ensue in a regulated firm if the allowed

rate of return is less than the cost of capital. This question

is compelling, of course, because many observers of the industry

feel that in recent years the allowed rate of return for electric

utilities has fallen below the cost of capital. Recent trends in

the market value of utility common stocks tend to corroborate

this ( Figure 1 )

.

Analogous to the original A-J hypothesis, the reverse A-J

predicts a mix of productive factors containing less than the

cost-minimizing share of capital. Once again, observers of the

utility industry in the late 1970s feel that this condition may

become increasingly prevalent due to severe financial difficulties within the industry. (See reference 3 for a fuller

discussion of these phenomena.

)

73
Utility executives are publicly stating more and more often their

reluctance to undertake or even finish large capital projects due

to problems of equity dilution and the difficulties of raising

money. Instead, a host of other, less capital-intensive means of

satisfying load growth are being discussed and experimented with,

both by utilities themselves and also by regulators and intervenors. It is not yet clear whether such techniques as conservation, load management, dispersed sources and cogeneration

represent a least-cost alternative overall; what is generally

true is that they are less capital-intensive, so that they may in

fact be chosen as the capital stock of the future even though

they do not represent a most efficient utilization of resources.

The preceding discussion is speculative; no one knows how the mix

of capital equipment available to utilities will evolve, or how

utility executives will make capacity addition decisions. There

is the potential however, if "reverse A-J" has any predictive

power, for the world to evolve quite differently than the IFFS

model will predict. In particular, the asset bases of many

utilities may be much smaller, and contain much higher amounts of

demand-side equipment and old, refurbished plants than IFFS

projections. It is a phenomenon which the modelers should be

aware of and be prepared to adjust to according to circumstances.

IV. Production Costing

Another area of choice for the modelers was the algorithm by

which production costing, or dispatch, was treated. In general,

two techniques, one deterministic and the other probabilistic,

are available. The modelers chose the deterministic technique in

this context, and their choice appears to be proper.

Any simulation of electric utility operations must specify a method to determine which plants to operate - or dispatch - at

what capacity levels at any given point in time. These decisions

are complicated by several factors:

o Load - electricity demanded by the system - fluctuates

from instant to instant over the course of any arbitrary

time period as time-of-day, season, and weather change.

Further, load must be satisfied through production as it

is demanded as electricity is not generally storable.

o At any given instant one or more of the power plants

available to the system may be out of service, either

because of a planned, maintenance outage or because of

an unplanned, forced outage.

Typically, in long-term utility planning and operating studies,

the load fluctuation problem is dealt with in an aggregate way.

Rather than attempting to dispatch plants in operating order at

each instant, sequentially, over the time period, such aggregate

methods rely on a load duration curve (Figure 2) as a representation of demand. The load duration curve represents a set of

Ik
estimates of demand levels at each instant over the time period -

a season or a year, typically - reordered, for convenience, from

highest to lowest. It is, in effect, a cumulative probability

distribution of load over the period.

Aggregate methods then dispatch against the load duration curve - in effect, treating the entire time period instantaneously - by

simply filling in the area under the curve with the energy capability of each plant, from the cheapest to the most expensive.

Analogously, the treatment of plant outages instant-by-instant is

also dealt with in an aggregate manner. Typically, an outage

rate is specified for each plant, representing some estimate of

the average fraction of time that plant is expected to be

unavailable over the time period. The outage rate is typically

an enginering parameter associated with a specific plant type and

technology

In a utility system, outage rates of individual plants are not

independent of one another. At any given instant, the fact that

a particular plant is unavailable might influence the availabilities of other plants because it will place increased burdens

on the other plants to pick up the slack. Further, the availability of a given plant is at least partially random and can

thus be described by a probability distribution. The availability of the system, then, can be thought of as a combination

of the interdependent probability distributions of the plants,

and can also be described by a probability distribution.

Probabilistic production costing techniques, then, are fundamentally motivated by the above complexities. Specifically, they

attempt to incorporate: a) the interdependence among the outages

of individual plants; and, b) the interaction between the probability distribution of system availability and the probability

distribution of load across a given time period, into their

simulation algorithm.

On the other hand, deterministic techniques are much simpler and

more straightforward. They essentially abstract from the

complexities of interacting probability distributions and treat

both load and individual plant capacities independently and

determinist ically . A fuller but still readable discussion of

these issues can be found in the electric utility chapter of the

IFFS documentation itself. Other sources include references 4

and 5

Clearly, probabilistic techniques of production costing are more

accurate and more rigorous as simulators of the way an individual

utility system actually operates. These techniques were in fact

developed in the mid-1960s in direct response to inaccuracies

which were experienced in the use of deterministic techniques,

particularly in describing system behavior at the high, "peak"

end of the load duration curve. On the other hand, deterministic

techniques are much simpler, faster, and computationally less

burdensome

75
Simplicity and speed are quite important in the IFFS system, but

the primary reason the modelers chose a deterministic technique

for their production costing was because they lost very little

accuracy in using it. This in turn is because they are modeling

an entire region, not an individual utility system.

In effect, a region is a set of interconnected utility systems.

It is much larger and more heterogeneous than an individual

system, and the natural diversity factor among customer loads is

greater. Further, the ratio of typical plant size to system size

is much lower; therefore, the interdependence among individual

plants is much weaker.

Essentially the interdependence among all probability distributions within the system, both load and plant outages, is much

weaker; thus, the need for probabilistic methods is also much

weaker. The degree of error introduced by the use of deterministic methods is much smaller at the regional level, and the

increased speed and simplicity achieved further help to justify

the modelers' decision on this issue.

V. Demand Uncertainty and Myopic Capacity Planning

Two final areas where the modelers were forced to make explicit

design decisions were:

o the treatment of demand uncertainty

o the treatment of foresight in capacity planning

In both of these areas the simpler, more straightforward path was

chosen, for several reasons which will be discussed. Again,

these decisions appear to be generaly correct, but under certain

circumstances problems could arise.

The treatment of demand uncertainty is an issue which has arisen

only recently in utility capacity planning (see reference 6). In

essence, two approaches exist. The traditional method involves

utilizing a point estimate load forecast, and planning a sequence

of plant additions over time to meet it. An alternative approach

is to estimate an explicit probability distribution of load in

any given future year, and to then build contingencies into the

system plan, allowing for and minimizing the costs of having

either excess or deficient capacity.

Treating foresight in capacity planning is also a fairly recent

phenomenon (see reference 7 as an example). The issue here is

one of dynamic vs. static optimization. In simulation, a capacity planning model is faced with the task of specifying an

optimal sequence of plant additions over time, subject to some

forecasts of load requirements and prices. Traditionally, the

load forecast is derived independently from the capacity plan;

there is no interaction between the two and thus no need to

76
reconcile them over time. This describes, in essence, the static

optimization approach, in which each year the capacity planning

algorithm has available to it only past information about demand

and prices, and some simple forecasting rules. The technique

simulates imperfect foresight, and allows for the possibility of

surprises

Dynamic optimization, on the other hand, explicitly attempts to

reconcile capacity planning decisions with the future behavior of

demand and prices, iteratively, until a convergence is achieved.

This technique simulates perfect foresight of demand and prices

on the part of the capacity planners. Planning is optimized

dynamically as well as statically.

In the electric utilities module of IFFS, the modelers have

decided to specify the simpler representation on both of these

questions. This is, generally speaking, the most reasonable

decision to make because of the following:

o Both dynamic optimization and the explicit recognition

of demand uncertainty require complex and time-consuming

algorithms. In the interest of keeping IFFS simple and

fast, these techniques were rejected.

o Dynamic optimization and demand uncertainty both require

complexity in the electric utilities module; furthermore, they would require additional complexity in other

modules of the IFFS system as well. Specifically,

demand uncertainty would require a range of forecasts,

along with associated probabilities, to be produced by

the demand module. Also, dynamic optimization would

require the entire IFFS system to converge to a solution

dynamically as well as statically, presumably causing

many more iterations in order to reach a solution.

These additional requirements on the entire system were

rightly judged to be prohibitive.

o Given the relatively short time frame over which IFFS

must operate (8-10 years), the majority of system plans

are already known in advance from commitment and

construction schedule data from the National Electric

Reliability Council (NERC) and other sources. The

system planning algorithm is really only responsible for

incremental additions and changes, so it need not be so

sophisticated.

o At least in the case of dynamic optimization, the

simpler approach is generally a more realistic

simulation of the way plans are actually made.

There is still one additional aspect of the system planning

algorithm of the IFFS utility module which should be noted. This

is the fact that the algorithm always makes capacity additions

continuously and smoothly. It does not consider such things as

77
minimum unit sizes, minimum construction lead times or the

inflexibilities inherent in modifying construction plans on short

notice. This fact, when combined with point estimate load forecasts and static optimization techniques, can lead to difficulties in certain circumstances. In particular:

o Use of point estimate load forecasts does not allow the

simulation of contingency planning, i.e., the building

of excess capacity in case the forecast turns out to be

low

.

o Forecasts can in fact turn out to be low (or high)

because foresight is imperfect and optimization is

stat ic

.

o If mistakes are made, however, the ability to add

capacity smoothly and quickly precludes any ability to

simulate either a deficiency or an excess of capacity.

Adjustment in this model is virtually instantaneous.

o As a consequence, there is no ability to simulate either

delays or accelerations in construction schedules, and

their attendant financial and reliability consequences.

As previously stated, in most instances the above issues will not

cause problems because of the largely fixed nature of capacity

plans over a ten-year horizon. However, in some instances, such

as the simulation of radically different scenarios, misleading

results are possible. The modelers should be aware of the potential problems in this area, and make appropriate adjustments.

VI. Concl usions

In this critique of the IFFS electric utility module, three major

issues in design philosophy have been addresssed. These are:

1 . The Cost Minimization Principle -

This is clearly the simplest underlying philosophy of behavior

which the modelers could use. As such, it is probably the

best. However, in an era of serious financial difficulty in the

utility industry and of the popularity of many "soft-path,"

capital non-intensive options, the "reverse A-J" theory and the

type of behavior which it might predict deserve scrutiny.

2. Deterministic Production Costing -

Again, this decision is the simplest option and therefore the

best under the circumstances. Furthermore this is probably the

most robust of the three major design decisions. Given the

stated uses of the model, particularly its use at a regional

level, it is difficult to conceive of a situation where this

simplification will create problems.

78
3 . Demand Uncertainty/Capacity Planning -

Given the largely determined nature of capacity plans over a 10-year horizon, the simple options appear to be the best options

in these areas also. However, it should be recognized that there

are potentially serious limitations in this approach, in terms of

simulating a) contingency planning and b) surprises caused by

inaccurate forecasting.

Finally, it should be noted that this critique is based entirely

on an evaluation of the module in the context of its stated

purpose and uses. Most of the cautions raised in this paper have

to do with nonstandard situations and uses outside of the stated

ones. Inevitably a model like IFFS, existing as it does in a volatile, policy-making environment, will be used for purposes it

is not really equipped to handle. When that happens, the

cautions pointed out in this critique may become even more useful

to the modelers than they will be in normal usage.

VII. Reference

1. "Behavior of the Firm Under Regulatory Constraint," Averch & Johnson, AER, December 1962.

2. "Input Choices and Rate of Return Regulation: An Overview of

the Discussion," Baumol & Klevorick, Bell Journal of

Economics, 1970.

3. "Electric Utility Capacity Expansion: Its Implications for

Customers and Stockholders," S. C. Peck, submitted to Public

Utilities Fortnightly, 1982.

4. "The A-B-C of Probabilistic Simulation," R. R. Booth, State

Electricity Commission of Victoria, Australia, 1971.

5. "Production Costing Using the Cumulant Method of Representing

Equivalent Load Curve," Stremel, Jenkins, Bolt & Boyless,

Presented at IEEE PES Summer Meeting, 1979.

6. "Costs and Benefits of Over/Under Capacity in Electric Power

System Planning," EPRI EA-927, Decision Focus, Inc., 1978.

7. "Evaluating R&D Options Under Uncertainty - Volume 3: An

Electric Utility Generation Expansion Planning Model," EPRI

EA-1964, Applied Decision Analysis, 1981.

79
120

40 I I I I I 1960 1965 1970 1975 1980

Source: EEI Statistical Yearbooks, Section VIII, Table 595.

Figure 1. Moody's 24 utility common stocks (weighted average).

80
8760

Number of Hours in the Year

Figure 2. A typical load duration curve for an annual time period.

81
INTRODUCTION TO THE COAL SUBSYSTEM

Mary J. Hutzler

Department of Energy

Washington, D.C.

The IFFS coal market module is currently under development. The purpose

of the coal module is twofold. First, it is to be a policy tool with which

the Coal Division can examine legislation that affects the coal industry

(e.g., the proposed legislation on coal slurry pipelines that is currently

being considered). Second it is to be used to develop integrated projections

of coal supply that are consistent throughout EIA. There are three major

components of the module (coal supply, coal transportation, and the demand

interface component) and they are in different stages of completion. I'll

mention their status as I describe them.

Figure 1 is a flow diagram of the coal module. It begins with a component that produces annual short-run coal supply curves. These are given

for 31 supply regions and for 30 coal types—5 Btu categories and 6 sulfar

categories. The curves generated by the coal supply component are piecewise

linear and are used in conjunction with the transportation component of the

system. The purpose of the coal transportation module is to capture the

distribution patterns of coal movements from supply regions to demand regions

and to determine the supplies of coal that can be delivered to the demand

regions at the least cost.

The transportation module feeds the demand interface program with

delivered prices of coal to each of the consuming sectors within 44 demand

regions. These prices are aggregated within the demand interface program to

the 10 demand region levels considered by IFFS and the 9 coal types considered

by the IFFS utility module. A backward flow also exists where the main module

of IFFS supplies demands to the demand interface module, which disaggregates

them and feeds them to the coal transportation module.

There are two levels of iteration between the coal market module and the

IFFS integrating program. The first level is between the coal transportation

module and the demand interface module. The demand interface module contains

a condensed demand equation that has a constant term reflecting the

cross-elasticity effects of other fuels. These two components are solved

until the delivered price form the transportation component results in no change in demand from the demand interface component. Once the coal module is

solved, the second level of iteration is with the IFFS main module where the

constant cross-elasticity term is updated.

For each forecast year, the supply curve component is reentered to obtain

the supply curve for that year. EIA has traditionally used long-run supply curves in modeling coal. Developing short-run supply curves ia a nontrivial

82
task. Price fluctuations caused by the interactions of the spot and contract

markets and the planning decision of coal companies must be captured in the

short term. That is, the curves must capture how coal companies decide to

increase capacity, through expansion of existing mines or by opening new

mines. Richard Gordon will discuss the methodology and the status of the

supply curve component of the coal module in a later section of this report.

Figure 2 shows the major coal fields in the United States. There are three major coal-producing regions: the Appalachian region, the Interior

region, and the Western region. The Appalachian region contains mostly

bituminous coals. Moving from the eastern to the western United States, the

trend is towards lower Btu coal and lower sulfur content in the coal. The

Interior region contains bituminous coal fields and also some lignite seams in

Texas, Louisiana, Arkansas, Mississippi and Alabama. The Western region

contains mostly subbituminous coal with some lignite and bituminous coal.

We would like to superimpose a transportation network over these coal

supply fields. Figure 3 shows the major rail lines in the United States. The

rail lines are fairly dense in the East and quite sparse in the West,

reflecting less competition among railroads in the West and the potential for

exceeding rail capacity as Western coal production grows.

The major barge routes are represented by Figure 4. They are on the

Great Lakes and the Ohio, Tennessee, Mississippi and Tombigbee Rivers. There

are also barge routes going from New Orleans, Louisiana to Florida and collier

routes supplying New England from East coast ports.

The coal transportation module of the IFFS model contains a detailed

network of about 725 links representing all mainline railroad routes and major

barge routes (Figure 5). Oceangoing routes for the coal export market are also represented. The rail routes represent movements by unit trains for the

utility market, multi-car movements for the industrial market, trainloads for

the metallurgical market, and single carloads for the residential and

commercial markets. There are about 285 nodes in the model representing

loading points, unloading points, rail intersections and rail barge

transshipment points.

The rail costs are a function of (1) distance on the link, (2) terrain,

(flat or mountainous), (3) congestion, (bottlenecks in cities or congestion of

barges on rivers), and (4) competition of parallel lines (rail versus rail or rail versus barge) (Figure 6). The base rates are also calibrated to

historical data. Currently, 1982 rates are used in an attempt to capture the

initial effects of the Staggers Rail Act of 1980, which provided the first

step towards deregulation of railroads.

83
The base rates represent both coal and non-coal movements. They are escalated for congestion by a function that depends on the tonnage moved and

also the capacity of the line. The congestion factor serves to divert coal

shipments to longer routes as the flow increases.

The transportation module is complete. Two improvements are anticipated.

The first is to add coal slurry pipelines to the network. Currently, there is one pipeline in operation—the Black Mesa. The second improvement is to imbed

a network of long-term contracts as lower bounds in the module.

The demand interface module has two purposes (Figure 7). First, it

aggregates and disaggregates regional demands and coal types. Second, it

contains a condensed demand equation that allows the coal module to be solved

independently from the rest of IFFS. The demand interface module is not

complete.

In case the sophistication of the coal module dramatically increases the

running time of IFFS, two alternative methodologies have been proposed (Figure

8). One is to use a function that can be calibrated to the entire coal

module—sort of a pseudo data approach. The other is to use a combination of

the two approaches where the entire module will be used for, perhaps, the

first iteration and a simplified representation will be used for the other

iterations

.

I've asked the reviewer to focus on three questions. First, which

methodology should be used and what other alternatives are there? Second,

have we considered the major factors affecting the coal industry in the short

term for development of the annual supply curve component? Third, should we represent the spot and contract markets explicitly in IFFS? This would mean a major change, not only to the supply module but to the demand module of IFFS

as well. I would like to encourage comments on the coal module since it is

under development and major changes can still be made.

84
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92
IFFS COAL SUPPLY MODEL

Richard L. Gordon

The Pennsylvania State University

University Park, Pennsylvania

This paper sketches our long-run goals for coal supply analysis. The

actual work was at a preliminary stage at the time of the conference and

subsequently it was decided to concentrate on simpler first approximation

analyses in the present work and delay implementation of the more elaborate

approach noted here until later in 1983. To suggest what we are attempting

to do, let me sketch, very briefly, coal supply analysis as it presently

exists

IFFS, and for that matter, all forms of mineral supply analysis have

to involve two processes. The first is devising the relationship between

mining characteristics in the broadest possible sense and the supply price

of coal (or whatever other mineral is being treated). Thus, in existing

models such factors as seam thickness, the size of the mine, the depth of

the mine, and the method of mining are included as determinants of the

required supply price.

The second step in the analysis is to estimate the amount of coal

available that possesses given combinations of mining characteristics. The

usual starting point for coal supply analysis is the Demonstrated Reserve

Base —an estimate of the magnitude of the best delineated, most readily

mineable portion of U.S. coal resources. Reserve base data were originally

reported in several detailed U.S. Bureau of Mines reports. EIA has

provided annual updates. The Reserve Base provides little information

about the mining characteristics of the reserves. Analysts, therefore,

have tried to piece together ways to deduce ways to assign reserves to

characteristic classes.

By taking these two steps, we have a supply function. Over the years

I have said much, often highly critical, about this supply estimation

process. However, I would say that generally speaking it has been sensibly

done, cleverly executed, and gives plausible results despite its faults.

This appraisal applies, for example, to the work done in the original

National Coal Model and its supply curve component RAMC and to the various

modifications of RAMC, both by EIA and its contractors on the one hand and

ICF Incorporated, on the other.

A quite different but equally well-done approach was taken by Martin

B. Zimmerman of the Massachusetts Institute of Technology. I am less

impressed with the originality of most of the other studies that have

become publicly available up to the end of 1982. Most are imitations,

often admittedly, of RAMC or Zimmerman. A more impressive contribution has

been made in studies to develop mine costing rules for use in appraising a specific mining project. Bonner and Moore did one such study on surface

93
mining for ERDA; NUS did another for EPRI covering both surface and

underground mining. These studies provide more complex analyses of the

determinants of cost than do RAMC or Zimmerman. In particular, NUS and

Bonner and Moore try to incorporate all the major cost influences. The NUS

model, for example, deals with such factors as roof and floor conditions in

underground mines. The costing rules are more realistic but require even

more data than are readily available.

With all the virtues of the existing systems, they have one characteristic that becomes problematic when you move to a forecasting

system such as IFFS. IFFS involves moving forward year by year from the

present. There is little or no tie to the present in the existing models.

In particular, there is limited use in coal supply analysis of the great mass of available data on historical performance of the coal industry.

Data, such as all the EIA coal and energy data systems, EIA's utility fuel

purchase reporting system, the MSHA data on individual mines, the detailed

reports by the individual states, and finally, the various information that

people in the private sector (such as Coal Outlook and McGraw Hill's

various services) make available.

The neglect is understandable. The data systems contain an overwhelming amount of material that, under the best of circumstances,

would be difficult to handle. The mass of inconsistencies and gaps and the

inaccessibility of much of the material are further barriers to use. Conversely, the time emphasis in existing models has been on 10, 20, or more years into the future, a period in which the details of the past are rather irrelevant. Thus, it has not seemed worthwhile to plow through the

indigestible mass of available historical material.

As I indicated, this is not the case with IFFS and that is why we are proposing an alternative, more data-driven approach to a supply estimation.

As with most of the people who have been talking about supply development

work, we make the distinction between a supply expansion module, on the one

hand, and a mine operating module, on the other hand.

For both modules, we will build on principles established in prior

work, particularly in our approach to costing. We believe existing methods

such as the various versions of RAMC and the NUS model provides a framework on which we can rely. We are particularly interested in seeing the

adaptability of the NUS methods for our purposes, but we will also work

with the latest DOE version of RAMC.

The critical problem in our work is in developing a more realistic

characterization of the attributes of the coal industry as it presently

exists and as it will expand, incrementally year by year, over the next 10

years. This characteristic is most relevant to our assignment of

quantities given prices over this 10 year period.

It is here that we want to draw upon as much of the available data as we possibly can. Those of you familiar with the data base and its great

94
magnitude and fragmentation will realize that we could not, at this stage

of the game, effect the incorporation of all the data that I have

mentioned. We, therefore, will focus on the more readily available

information on the characteristics of existing mining operation.

Where the mines are large and visible, we will, in fact, try

explicitly to model them. Where there are a large number of mines in a category, such as the smaller mines in states such as Kentucky, Tennessee,

West Virginia, and Pennsylvania, then we will model generic mine types.

One way or the other, we will get, from readily available sources,

descriptions of the characteristics of these mines in terms of their size, seam thickness, depth, methods of raining and similar information as a basis

for data to go into a costing model such as that of NUS. Initially, we are going to assume that coal is produced as a composite commodity and has a composite price.

We have already done extensive work analyzing the data reported to the

Federal Energy Regulatory Commission on the cost of fuels to electric

utilities. This has produced estimates of how coal prices vary with coal

characteristics. The effort to simulate the evolution of the coal industry

by use of detailed data would produce estimates of the price of a coal

having a Btu and sulfur content equal to the average for the supply area. The work we have done on rules showing how the prices in the region vary

with Btu and sulfur content then would be used to produce the estimates for

the different coal types actually included, as Ms. Hutzler has noted, in

IFFS.

We obviously have much work to do to implement these goals. We expect

that as this full proposal is implemented important contributions will be

made in the form of an interesting alternative way to approaching coal

supply in this project for EIA.

95
COAL SUBSYSTEM: A CRITIQUE

Michael Elliot-Jones

Chase Econometrics

Bala Cynwyd , Pennsylvania

This critique addresses design options of the Coal Subsystem. In

particular, the following issues are discussed:

1. Does the coal subsystem contain too much or too little detail?

2. Should the subsystem include an explicit representation of the

contract spot market differentiation?

3. Should the supply curves be integrated as stepwise vectors or represented by equations?

4. The "excess capacity" question.

5. The migration of coals among categories as a result of preparation.

Level of De t a i.1 As constituted, the subsystem contains 30 types of coal, 31 supply

regions and 44 demand regions. With regard to the level of detail in types of

coal, the greater the number of categories, the greater the difficulity in

acquiring and processing data, and in calibrating the system. For the U.S.

coal industry, with over 2000 mines in Appalachia alone, a very large number

of seams, and considering the inherently difficult process of measuring

reserves, the raw data from the Bureau of Mines (BOM) may contain significant

errors

.

Similarly, a complaint often heard from industrial sources is that the

BOM data for reserves do not contain sufficient information on the other

important characteristics of coals such as grindability , fusion temperature,

trace elements, etc. Thus, the detail is built solely on the dimensions of

BTU and sulfur content.

Having substantial degrees of detail also opens the problem identified as issue No. 5 above: the migration of coals across categories. If, for

example, Southern Appalachian coal, normally mined for coking purposes, can also be sold in compliance markets, how does one represent such a practice?

Probably more serious is the movement of coals among steam coal categories.

It is, according to Schmidt, quite feasible to take a moderate sulfur coal and

clean it to compliance standards, and, in the process, raise the BTU content

by the reduction of ash. The degree to which sulfur can be removed depends

upon the mix of pyritic and elemental sulfur, information which is not

normally available. In the relatively short term, the ability to do so depends upon existing capacity for deep cleaning and upon the cost of the

process relative to the price of coal. As a result, it will be necessary to

obtain information on capacity and costs for coal preparation of various

degrees of cleaning. Thus, if the purpose of greater detail is to obtain

greater accuracy, there are serious questions as to whether the lack of

necessary information to characterize coals will lead to errors. It may be more efficient to use fewer coal categories: e.g., three heat content and

three sulfur content ranges, reducing the necessary data by two thirds and the

computation by a significant amount.

With regard to geographic detail, the use of 31 supply and 44 demand

regions appears quite reasonable.

96
Contract and Spot Markets

Contracts between domestic coal buyers and sellers are enforceable.

These contracts carry prices which are normally based on a negotiated base

price and escalators tied to mining costs. As a result, transactions tend to

be rigid and the physical flows in the system may be expected to have

substantial inertia. When relative prices change, reallocation is highly

damped. Thus, even if the system is at an optimal solution in one year, given

a change in relative prices, it is highly unlikely to remain in an optimal

position in the following periods. Any model system that represents the coal

markets must include a representation of the contract stratum of the market.

If a system is used without such a dampening mechanism, the model will tend to

show much more rapid shifting among supply sources than actually occurs.

It may be useful to understand why this inertia comes to exist. Briefly

one may point to three causes:

- Boilers are normally designed for relatively narrow ranges of coal

variation, especially boilers in large power generating units. Thus,

continuous supplies from the same seam reduce the fuel use problems

faced by the buyer.

- In utilities and large captives, reliability of supply is a paramount

consideration, in many cases overshadowing price.

- Habit.

In the future, we may expect an increasing tendency towards captive

mines. In metallurgical coal, captive mines have long been predominant.

However, the regulated nature of utilities has tended to provide an obstacle

to electrics owning mines. We expect a slow emergence of the utility holding

company which can effectively remove the mine from the regulatory commission's

aegis and, considering the dominance of supply reliability, captive mines will

increase. In a modeling system, one way to represent such a mine-utility

relationship is by a very long contract. As more mines are owned, we may

expect the average life of contracts for steam coal to lengthen.

On the other hand, the spot market, which represents a relatively small

proportion of the steam coal market and a very small proportion of met coal,

tends to show a highly volatile price behavior.

Implications of the Market Structure

Average prices are of little value in analysis, especially short term.

Contract prices tend to be tied to mine costs and therefore do not, on the

whole, reflect market movements. Spot prices react to market movements, and

the ability to obtain spot prices information on a consistent basis appears

difficult.

These results stem from the fact that the elasticity of supply with

respect to price appears to be significantly higher than the demand

elasticity. Also, the rigidity in demand is institutionalized by the contract

system.

Problems in Modeling

If one is to introduce contract/spot strata into a coal modeling system,

a number of issues need to be considered.

97
Contracts should be recognized as containing two basics — price and

volume. To characterize a contract segment of the market, it is necessary to

obtain empirical information on (1) the average volume lives of contracts;

(2) how much coal is contracted in various areas? (3) how "hard" are the

contract terms i.e. , are there min-max offtake bands and how wide are they?

Obtaining such data tends to be a considerable task, especially as the

regional detail increases.

Supply Curves: Numeric vs. Analytic

The choice of numeric, stepwise supply curves derived (from geologic and

engineering information) versus an analytic equation approach is by no means

clear. It appears necessary to derive the former so as to be able to estimate

an equation. Numeric supply curves exhibit complex shapes which may be

difficult to estimate. Moreover, the equations are not easily understood by

industry analysts — there being, of course, the exceptions. However, the

information required in computation is greatly reduced by use of an equation,

and the modeling process can be simplified.

Thus, if computational capacity is not a constraint, and if the model is

to be discussed outside of the immediate circle of its architects, numeric

methods are likely to be preferred over analytic. Numeric methods also allow

explicit consideration of depletion.

Short Run vs. Long Run Curves

The issue here is the feasibility and desirability of developing

short-run cost (SRC) curves. A common source of concern is the observed

behavior in market contractions where there is a shift down a much steeper SRC

curve, a behavior consistent with standard micro theory.

How then can a SRC curve be estimated? It appears that a picture of the

existing debt structure of the industry in different regions could be obtained

by survey, and at least rough estimates of the cost structure of mines could

be represented. It would then be necessary to determine if spot market

suppliers face the same cost structure as the representative mines. Thus, the

construction of SRC curves appears feasible, but probably expensive.

The desirability question is another issue. Development of SRC curves for 31 regions and several types of coal is a significant task, and

integrating a set of curves into a fully described coal model, as DOE

presently envisions, would complicate the system substantially. Moreover, it

is not clear as to what information would be gained that could not be gained

from a separate analysis of the financial structure of the industry.

One aspect which the SRC curves and their construction would shed light on is the excess capacity issue. Indeed, capacity in any natural resource extraction industry is a hazy measure at best. However, one suspects, on the

basis of information for individual mines, that maximum capacity and optimum

capacity are significantly different. While excess capacity in the true sense

of redundant capacity at optimum mine operating levels probable exists in the

high sulfur Illinois Basincoals (due to demand shift caused by the Clean Air

Act and subsequent amendments), it appears that most of what is viewed as excess is simply the difference between optima and maxima. Thus, it is not a temporary phenomenon depressing prices, but is probably a permanent one which

has little to do with prices.

98
Discussion

Mr. Todd: Mary, do you have anything you would like to add to the reviewer's

comments?

Ms. Hutzler: I would like to respond to two issues raised by the reviewer.

First, we do consider the metallurgical coal market. The scaricity of

documentation that was provided to Michael must have misled him on that point.

The other issue concerns the spot versus the contract market. I do agree that we should look more closely at representing them explicitly in the coal

module. However, as I said previously, it is not a simple task because the

IFFS demand module would need to represent stocks explicitly, particularly

within the utility component. It would mean a major overhaul of the present

structure of IFFS.

99
INTRODUCTION TO THE ENERGY- ECONOMY INTERACTIONS

Dale W. Jorgenson

Harvard University

Cambridge, Massachusetts

Let me start out by refreshing your memory about the purpose of

this modeling effort. One possible purpose for building a modeling system like this would be to provide input to the very important considerations of the Treasury Department, the Council of Economic Advisors, the

Federal Reserve Board, the Department of Commerce, and so on, as well as the Department of Energy. That is precisely the opposite of the orientation that we have chosen.

Our intention here is to analyze the impact of alternative energy

policies with other policies fixed, in other words, with monetary and

fiscal policy in place. That is not to say that monetary and fiscal

policy is irrelevant to the evaluation of alternative energy policies.

But the policy levers that people who are consuming the output of this

system manipulate are not those of monetary and fiscal policy. They are those of energy policy alone. The problem we face is to obtain a realistic assessment of what monetary and fiscal policy will be. We will then

attempt to analyze the impact of different energy policies against that

background.

Our point of view is that what is relevant to the analysis of energy

policy is relative, rather than nominal or absolute prices. What matters

from the point of view of energy policy impacts, given monetary and fiscal policy, is what happens to the price of energy relative to other prices — for example, energy prices relative to the wage rate, energy prices

relative to the price of capital, and so on. What consequences does this

have for the growth of the output of the economy and for the composition

of its output?

The strategy that has been adopted in order to achieve these objectives is portrayed in the view graph* where it is appropriately labeled

Systems Design. The basic design is a modular design which is intended

to provide self contained models that can be used either by themselves

or in cooperation with other models to produce analytical results. You

have already heard a great deal about some of these models today. You

have heard a detailed description of IFFS. You have heard references,

at least, to the demand system. I am going to focus on the economic

models. The purpose of this module of the complete system is to analyze

the impact of energy on the economy and to analyze the feedback from the

economy through demands for energy into the composition of energy supply.

*See Figure 1

100
The modular structure I have mentioned plays a very essential role

in the design. The purpose of this structure is to make it possible to

manage the individual model components in an efficient way and to use only those components that are relevant to a particular analysis. Each

of the components, as you can see, is developed by those that are, hopefully at least, expert in a particular area. Economists can take res- ponsibility for the economic models. Energy analysts can take responsibility for the demand system, and so on.

In terms of flexibility of application, it is possible to select

the models, or modeling system, that are appropriate to a particular kind

of analysis. For global analysis, involving a complete energy analysis

of the type that would be characteristic of the annual Administrator's

Report required from the Energy Information Administration by statute,

it is necessary to use the whole modeling system. Given a global analysis, which would not be done frequently, the modeling system provides

the capability of doing analyses that are smaller in scope. In these

smaller analyses, fewer inputs are changed and only part of the total

output of the system is recomputed.

One mode of analysis illustrated by the view graph is the mode of

economic impacts, where one would attempt to focus on the economic implications of a particular change in energy policy. It may turn out that some of those economic impacts have to be analyzed by tomorrow morning

because the Assistant Secretary is due for a meeting with the Secretary,

or maybe the relevant officer of OMB, or perhaps, someone in the Congress,

and needs a quick response. Of course, one has to be in a position to

respond quickly.

The flexibility of this modular approach is that one can select the

appropriate tool for each task. This requires that the managers of the

system must preserve flexibility by having links among the different

models. These links must work in a fairly automatic way and must provide

for the capability of combining the models for different purposes in an effective and efficient way. For this purpose it is necessary to link

the models in a way that provides a picture of the basic feedbacks among

the different components of the model. The linkage has been discussed

in detail in the paper which is being written for this symposium on energy-economic modeling.

With regard to the general philosophy of linking models of this

type, I would be remiss if I fail to mention my paper with Kenneth Hoffman, as a classical reference. Even those who haven't read it might

find it beneficial to do so. A much more recent version of the story

that is more extensive and more informative, I think, because it covers

a wider scope, is the paper by Hogan and Weyant that discusses linking

different models. In the system that we're dealing with here, we can

see that the basic purpose of the energy part of the modeling system,

101
which consists of IFFS and DAS, is to produce, as outputs, world oil

prices, oil imports quantities, prices of delivered fuels, and quantities, and levels of operation of new energy technologies and their costs.

The purpose of the economy part of the system, is to take the output of the energy part of the system to produce impacts on the real GNP

and on changes in relative prices — prices, for example, of GNP versus

wages, or prices of energy versus capital, and so on. The economy part

of the system produces a breakdown by different industries where that is

required, produces interest rates or rates of return and generates disposable income. Those outputs are fed back into the energy system; hopefully, the two can be brought into some kind of consistency.

I would like to turn next to the economic models themselves. There

are two models in the complete modeling system. The two models that are used for the economic module of our system are models that differ in one fundamental dimension, that is, whether or not it's necessary to break

down energy into its components and to deal with the price of electricity

relative to natural gas, or the price of oil relative to coal, or, in

general, the price of any specific fuel relative to another fuel. If

that is an issue in the analysis in question, then the model of choice

would be DGEM, the Dynamic General Equilibrium Model, which is a nine

sector model that distinguishes among five different energy sectors that

include the four primary fuels and electricity.

Let me mention the capabilities of the DGEM modeling framework. If

the word had not been used for a completely different purpose, you might

think of DGEM as a supply side model. But, it is not a Laffer supply

side model. It covers non-energy production and breaks it down into four

components —- manufacturing, services and trade, agriculture and transportation. It includes a household sector so that it contains an endogenous explanation of the breakdown of personal consumption and the final

demand patterns that result, and, also, analyzes saving patterns and

finally, it provides a way of incorporating energy effects, like those

that I just mentioned, on the prices, inputs and outputs and spending on

the non-energy sectors of the economy.

Mini-macro is a small model which has only about 20 equations. It

is a relatively small system and is an attempt to deal with issues, as

I mentioned before, that focus on energy prices as opposed to non-energy

prices. Mini-macro attempts to deal with the issues in which the impact

of energy can be adequately summarized by a single energy price and

quantity as opposed to the breakdown of the type provided in DGEM. Minimacro is like DGEM in the sense that it is a neoclassical supply side

general equilibrium model. It's relationships are modeled on those in

DGEM as well as other well known relationships.

102
Next, I would like to describe very briefly the modeling framework

that is appropriate where relative prices of different fuels, say, electricity versus primary fuels, are germane to the analysis in terms of

the economic impact. I would like to talk about DGEM very briefly and

to describe its critical features. As I say, it has four non-energy

producing sectors, five energy supplying sectors and a feature that you

may not recognize from our earlier work, five new energy technology

groups, one for each of the energy supplying sectors. These are essentially activity analysis representations of new technologies that substitute for existing fuels — for example, synthetic oil in place of oil,

synthetic gas in place of gas and so on.

There are four categories of final demand in DGEM and three primary

inputs — capital, labor and imports. These are analyzed in terms of a supply-demand framework. The nine products and the three factors of

production are determined by markets that have to be cleared by means of

prices. That means that we can only determine, for example, employment.

We can't determine unemployment. It is also that case that we can determine the amount of capital which is actually used in terms of the capital

service flows. There is no concept here that you would think of as capital utilization.

DGEM is a neoclassical general equilibrium system which acquires a dynamic character through the equilibration of saving and investment by means of the rate of return. In general, increases in the rate of return

will increase saving as a proportion of wealth. That creates additional

capacity for the following period. You should bear in mind that the

time structure of a growth path in a neoclassical model is characterized

by fixed capacity in the short run. The amount of capital on hand is

unchangeable. It's an immutable constant of nature at the time that an analyst enters the scene. If you want to use economic terminology, you

have a vertical supply function for capital in the short run.

By contrast, you have a horizontal supply function for capital in

the long run since we have a saving function that represents optimization over time. The rate of return appropriate for saving behavior

always reestablishes itself in a sufficiently long run. In between the

behavior of the model is determined by this gradual shift from a vertical supply function to a horizontal supply function, which takes from

10 to 12 years. So there is a process of dynamic adjustment that is

built into this highly neoclassical story.

The other features that are extremely important to the qualitative

character of the model output are those that involve productivity. Output per unit of input is an endogenous variable in the model and is

dependent on relative prices. Of course, the quantities of the inputs

themselves result from substitution among the different fuels and substitution between energy and other things in terms of the relative prices

of those inputs.

103
The message I would like to have you carry away is that the advantages from using DGEM accrue from the fact that the energy information

that can be produced by the IFFS system and by DAS is very complete.

In particular, it provides separate prices and quantities for the individual fuels. To make use of all that detail, it is essential to use

DGEM. On the other hand, if that detail is unessential, or if you don't

have time to take account of the detail because the Secretary wants an answer by tomorrow morning, then it is necessary to make use of a more

aggregative approach. The aggregative approach has the advantage of

being a lot cheaper and easier to use.

You can think of the basic framework of mini-macro as one in which

nominal GNP depends on the money supply. That is a very common point of

view in Washington these days. Nominal GNP is divided between the price

changes and quantity changes in a way that is responsive to energy. When

we determine the rate of inflation and the level of real GNP, we can then

break it down into components that reflect the production structure of

the economy at a very highly aggregated level. That, of course, includes

determination of investment which determines future productive capacity.

The rate of inflation itself determines the momentum, so to speak, in the

price system that then becomes part of the future inflation.

My overall conclusion is, then, that we have a very highly modular

system. It is possible to look at the energy sub-system of the economy

in isolation by focusing on the IFFS modeling system on the supply side

and the DAS modeling system on the demand side. The new feature of the

system is the addition of an economic module that is part of the same

system. This module can be operated and managed within the same apparatus here at EIA. This makes it possible to augment the energy modeling

system by providing feedbacks from energy to the economy and from the

economy to energy. This reflects one of two different kinds of information that would be relevant to an economic analysis.

One mode of analysis would involve the energy prices and quantities

in total. These totals can be used to structure a macroeconomic scenario.

The other mode of analysis involves a more detailed story about the role

of specific fuels. The relative prices of different fuels would come out

of the use of IFFS and DAS. For that purpose we have a model, DGEM, that

absorbs that detail, spins out an econmic story that corresponds. The

results can be fed back into a complete system. The complete system provides a supply-demand picture for the economy in which prices are determined along with the quantities. The prices and quantities are determined not only for energy and for all the non-energy commodities, but

also for the factors of production — capital and labor.

Of course, we can exit here to economic impact analysis and we can

take the detailed output of the economic models and spin out a story about

welfare impacts. That is to say, we can find out how good a particular

outcome is from the point of view of the national objectives that have

to be served. Economic impact analysis, of course, is the final goal,

the bottom line, of the modeling system that we described here.

104
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105
ENERGY -ECONOMY INTER ACTIONS-- A CRITIQUE

Robert

Stanford

Stanford

,

E. Hall

University

Cali f or nia

You may well ask, what am I doing here? It is fair to say

I am an outsider compared to everyone else on this program. I am

not now, nor have I ever been, a contractor for the Energy

Department or its predecessors. I opposed the creation of the

Energy Department and I support its abolition. I am a free-market economist when it comes to energy issues and an

opponent of federal intervention in energy markets. That much I have in common with quite a few other economists in this room.

What may set me apart is that I am a little concerned about

federal surveillance of energy markets. There is a question in

my mind, for example, whether we should even have an EIA.

Let me try to give an outside macroeconomis t 's perspective

about how you would go from thinking about energy issues in

particular to thinking about the macroeconomy in general. I think it's a good idea to look at these issues at this point in

the conference. So far, we have had presentations where energy

economists are making their way into the tough issues of

macroeconomics and it's probably good to hear from a macroeconomis t who has done the opposite, namely invade the

territory of energy economists.

I want to start by discussing what kinds of questions might

be answered with the type of analysis we are looking at today.

Bill Hogan and others have mentioned that it's easy to get

wrapped up in the modeling details and to fail to keep in mind

why we build these models and what type of advice we would like

to derive from them for policy makers and the public. It seems

to me that there are two rather specific classes of questions we

would like to answer in the area of energy-economy interactions.

The first one is, what happens to the domestic economy when world

energy prices change dramatically, as they have in the past

decade? In particular, what can we say about the effects of

those changes in energy prices on output and income?

The second class of questions has to do with energy policy.

What happens to domestic economic performance as we change energy

policy? What happens to output, income, interest rates,

employment, investment, and all the other variables that we care

about, as the different knobs of energy policy are turned?

Free-market ideas have dominated energy policy under

Presidents Carter and Reagan. I applaud this liberalization, but

it raises questions about what will happen to the macroeconomy as

the result of shocks that come from changes in policy.

106
I am going to depart from the format of other speakers and

say a little about what I think are the answers to some of these

questions. I will speak as a mae r oec onomis t who has thought

about energy issues but I will not be giving results from a particular quantitative model. I have dabbled with models but

have never reached the point where I would want to use those

models to speak to policy questions in public.

To take the first question, suppose that the world oil

price rises. The first effect on the U.S. is obvious, though its

magnitude is sometimes mis und er stood --U . S . real income falls when

the world oil price rises. The reason is simply that the U.S. is

a small importer of energy. Higher world oil prices have an

adverse effect because we are an importer. Were we an exporter,

the situation would be the oppos i te --re al income would rise as

world energy prices rose. The point I want to stress is that the

effect on U.S. real income is not very large because we are close

to self-sufficient in energy, so changes in energy prices have

close to a neutral effect.

Of course, the net effect

very important redistributional

resources become better off and

of close to zero carries with it

effects. The owners of energy

the consumers become worse off.

The second point I want to make is one where complete

misunderstanding has reigned. Full employment output, measured

specifically as real GNP, should not be affected to a first

approximation by world energy prices. Prices don't affect

production functions. The capacity of the U.S. economy to

produce is not hurt by higher energy prices and it's not helped

by lower energy prices. Furthermore, a characteristic of the

concept of real GNP is that even when more expensive energy

causes the U.S. to substitute away from imported energy and

towards capital and labor as ways of producing, that substitution

should not affect real GNP, again to a first approximation.

An immediate implication of the small theoretical effects

of energy prices on U.S. mac r oec onomi c performance is that the

very poor performance of the economy in the 1970s can't be blamed

on higher energy prices in any obvious way. If poor performance

has something to do with energy, as many mac roec onomis ts

including myself suspect, it is not a first-order effect. A

straightforward neoclassical analysis of the type embodied in the

most respectable work on energy-economy interactions is probably

not going to give the answer. We can anticipate that

disappointment without doing the research.

Where should we turn in building models that give sensible

answers about the impact of energy prices on total output? The

crucial message I want to convey is that full employment is not a good way to think about the economy with respect to energy

issues. We especially are aware of that point today with the

unemployment rate close to 10 percent. Unemployment has been a

107
severe problem in the U.S. economy since the first increase in

energy prices in 1073-74. We have failed to achieve consistent

full employment ever since.

The obvious conclusion is that higher energy prices induce

economic slack. If that's true, it has a very important

implication for energy-macro modeling. Any model that assumes

full employment is going to miss the biggest point. A

market-clearing, neoclassical model can't handle the dominant way

that energy prices influence real GNP.

I would guess that the costs of the short-run dislocation,

increase in unemployment, decrease in real output, and other

effects of energy prices not comprehended by a neoclassical model

far exceed the long-run costs from the terms of trade effect and

other effects that are included in a neoclassical model. The

hundreds of billions of dollars in real GNP we seem to have lost

in the past decade from economic slack far exceed the terms of

trade effect. Even though the recessions brought on by energy

shocks are short-run phenomena, they are tremendously expensive.

Therefore, I would plead for consideration of the short-run macro

issues on the same level as the longer run issues that have

occupied most of the attention in this area so far.

Why does the economy undergo recession in the face of an

energy price increase instead of behaving in a neoclassical way

and remaining at full employment? The finger points to wage and

price rigidity. That's the key macro issue. The role of energy

prices seems reasonably clear. Higher energy prices drive up the

prices of all products containing energy directly or indirectly.

In a neoclassial f ull -employmen t model, higher prices of

energy-intensive products would occur, but prices of other

products would fall correspondingly, and, in particular, wages

would fall to keep the overall price level about the same. With

exogenous monetary policy, the overall price level would remain

at its full -employmen t level.

Flexibility of non-energy prices, especially wages, is not

a good description of the U.S. economy. Because wages and other

prices don't fall, higher energy prices raise the overall price

level. The most striking examples were in 1Q74 and 1979-80:

Sharply higher energy prices brought general inflation, not the

deflation in wages and other prices predicted by a neoclassical

model. With a higher overall price level and unresponsive

monetary policy, it was simply inevitable that real output and

employment had to decline.

I think there is pretty substantial agreement among a reasonably wide class of macroeconomis ts on chat basic mechanism.

Different mac r oec onomi s ts have different stories to tell about

exactly why it is that the economy functions in that way, but

there is good agreement that price shocks for important inputs

like energy bring on recessions.

108
Price rigidity is not an unfamiliar issue to

macroeconomics. It has been at center stage in the field since

Keynes. And, although the word Keynesian is associated with

almost a splinter group in the profession today, in the sense of

emphasis on fiscal measures to offset recessions, the thoughts of

Keynes on price rigidity are dominant. Many of us are Keynesians

in the sense of worrying seriously about price and wage rigidity,

even though we are very much an ti-Keynesian in our

recommendations about economic policy.

Once price rigidity enters a model, many more things become

important that weren't important in the f ull -employmen t model.

The money stock and financial markets become central. In

full -employmen t , neoclassical energy-macro models, money and

finance are correctly viewed as footnotes. But once the models

venture into disequilibrium and price rigidity, the money stock

takes on an important role and deserves much more attention. I am not making a structural criticism of the models on their own

grounds. Rather, I am pointing out what a large change will

occur when the models are opened up to serious consideration of

the recession that an energy price increase brings.

I don't have a strong message about how to proceed at this

point, simply because there isn't a professional consensus on why

price and wage rigidity is a feature of the economy and how we might go about building models of it. Some mac roec onomis ts today

would stick to the idea of a Phillips curve. In some respects

it's a discredited idea, yet no empirical regularity has been

demonstrated that can replace the Phillips curve as a statement

about how the wage and price process operates. There is, of

course, the highly developed rational expectations school which

says that the Phillips curve is a generalization from particular

experience and may not hold in the future if the economic

situation changes in other respects. At a minimum, the school

has undermined any faith in the idea that a simple Phillips curve

is an unchanging feature of the economy. I am a very careful

reader about rational expectations and a participant in debates

on the subject, but I have not seen anything emerge from the

school that would commend itself to be plugged into a model that

would deal with the issues that we would want to deal with.

Thousands of pages have been published on the subject in

professional journals, yet we haven't come to a good answer.

Another issue that matters a lot for the energy-economy

interaction model is productivity. Again, productivity has been

a colossal disappointment in the past decade. In the postwar era

through 1973, productivity grew at quite a satisfactory pace.

Since 1973, and especially through the two major oil price

increases, productivty has been a disaster. Surely, the price

increases had something to do with the disaster. But no simple

theory of productivity growth explains the relation.

Productivity is a feature of the production function, and, again,

factor prices don't shift production functions. We have only

just begun to sort out this central issue, but it will be hard to

109
get sensible results from models until we do. If productivity

growth has been cut from almost 3 percent per year to below one

percent and that situation is going to remain for the next decade

or two, that's a much more important fact than almost anything

that can come out of a f ull -employmen t i n te r sec tor a 1 model of the

type that has been under discussion in energy work to date.

There has been some work on this topic — Dale Jorgenson has been a leader in the area. I don't think we've reached a clear

conclusion at this stage but it certainly is an issue that we

ought to be thinking about very hard.

Now let me talk about energy policy. First, there is a whole set of policy questions that can be dealt with quite

outside of any model of the type that's under construction in the

Energy Department. If energy prices are set fundamentally in

world energy markets and we do have a U.S. energy market that is

small relative to the world market, then we can make a very

simple assumption: Policy does not affect price.

For many policies, for example, for anything that has to do

with the details of U.S. oil production, we can just take the

price as given. Then there are virtually none of the

interactions that create all the difficulties in these models.

We can price out synthetic fuels, it seems to me, and reach the

conclusion that nobody in their right mind would have anything to

do with synthetic fuels, without having to do any of the kind of

research or build the kind of model we are talking about here.

Policy analysis can have a life of its own quite apart from the

construction of elaborate models.

On the other hand, some issues require the general

equilibrium approach. Natural gas deregulation is an example.

Detailed work is required to assure the President when he signs

the bill for total cold-turkey deregulation that there won't be a mac r oec onomi c disaster. I'm certainly not persuaded that the

models we have built to date are of much value on an issue like

natural gas deregulation.

It's important to recognize the intellectual force behind

the simple free-market proposition that all we have to do in

energy policy is to let free markets do their job. That force is

overwhelming compared to anything that is associated with

research or modeling acitivty. The appeal of free-market

principles in the Carter administration and, perhaps a little

less, in the Reagan administration, is very, very strong. The

head of steam that free-market policies have today is, I think, a continuing one and will dominate whatever one gets out of models

that are under construction in the Energy Department.

Now let me talk a bit about macro modeling issues. In the

first place, I think we are on pretty firm ground in talking

about the full -employmen t production side of an analysis of the

economy. The work of Hudson and Jorgenson, in its many

manifestations today, is widely respected and used by economists

110
and by energy modelers. It's disappointing that even at this

date, it doesn't seem to be a part of the EIA's model, but it

certainly could be.

The thing that is new, which I read with great interest in

the material for this meeting, is that the wage-price rigidity

issue seems to be entering the discussion for the first time.

But, it has entered at a very primitive level. The remarks in

Section 5, the paper on energy-macro interactions, is pretty

naive by professional standards. The movement of energy

economists into macroeconomics is the mirror image of something

that first happened nine years ago, namely the invasion of energy

economics by mac r oec ono mi s t s . We suddenly discovered that energy

existed and made equally naive statements about energy. Herbert

Stein has been quoted as saying that all you have to know to be

an energy economist is that there are 42 gallons in a barrel,

that Kaddafi is a man, and Qatar is a place.

There is somewhat the same flavor in the energy economists'

discussions of what it takes to understand wage and price

rigidity. Some of the brightest mac roec onomis ts , Keynes and

since, have struggled hard with the issue. It's going to take

more than the relatively small amount of attention than it has

received so far from energy economists to make progress.

The most highly developed thinking on macro energy issues

within a framework of wage and price rigidity is in the quarterly

econometric models, which now reside almost entirely in the

commercial sector. DRI, Chase, Wharton, and so on, are the only

places where energy and price-wage rigidity and the possibility

of fluctuating unemployment and departure from neoclassical full

employment are taken seriously and put to work. I don't

personally have much faith in what comes out of their

calculations, but I have a lot of respect for the amount of work

that went into the effort. The fact that the effort has been

going on for decades and has only reached this stage illustrates

how tought the issues are.

Let me mention just briefly that I have done a little work

in these ares with Knut Mork, written up in two papers in the

Energy Journal . I don't think this research really provides a practical alternative to the EI A models and the quarterly

econometric models, but it does illustrate another way to go

about thinking about the issue of departures from full

employment. What we found was that a whole set of issues beyond

the obvious one of wage-price rigidity mattered. The interest

elasticity of money demand is central because of the importance

of money demand in a disequilibrium model. Short-run

fluctuations in productivity are another issue of first-order

importance. So are investment lags. The role of the rest of the

world, beyond just trade in oil, is critical. Consumption

decisions of households matter a great deal, and there is a serious question whether the full neoclassical treatment of

consumption is right, or whether we need to recognize some

111
practical limitations on consumption and saving.

I want to close by asking: How will the President be

advised on the macro effects of energy? Are we moving in the

direction of having a model in the EI A which is sufficiently

respected by economists elsewhere to let the model have a central

role in policy advice? I would guess not. I think that

back-of-the-envelope economics will continue to dominate policy

advice. Even though I believe very strongly in the notion of

modeling based on serious research, for the moment, I am more

comfortable about the destiny of the nation to think that it's

the back of the envelope types who still have the President's

attention. We are very, very far from having any kind of an

energy model in the area of macro interactions that has a real

claim to providing usable answers on the central issues of what

happens when world energy prices change or energy policy changes.

DISCUSSION

DR. JORGENSON:

Well, let me be slightly unfair to Bob Hall by caricaturing his

remarks. First of all, his point is that energy cannot effect the real

GNP, at least not to the first order. So it is a great mystery as to

what is going on. That is a point that was actually first popularized

by Ed Denison and it goes something like this: Energy cancels out in

the GNP accounts since it is produced by oil refineries and electric

generating plants, but it is used up by other producers who produce automobiles and other things that are delivered to final demand. Since

energy essentially disappears when you look at the GNP — deliveries to

the GNP are four percent of the total value added — it must cancel out.

The basic presupposition of Denison' s analysis and implicitly of

Hall's, is an assumption that Hall himself analyzes in his earlier incarnation as a neoclassical economist. That is, that production functions

everywhere in the whole economy, all 20,000 of them, have to be identical up to a scalar multiple and must be, in fact, value added separable.

That assumption, of course, is discarded in the multisectoral approach

that I alluded to earlier.

As to whether wage-price rigidity is the key to the problem, that

is, the problem of how energy affects the economy, let me simply point

to some obvious facts. Since 1973, real energy prices have increased

by four-fold. Therefore, the whole key to the analysis cannot be wageprice rigidity. This amounts to using weapons that were built for the

Great Depression of the 1930 's to solve current problems. What is really

critical to energy analysis and to economic impact analysis is the change

in relative prices. It is not only the price of energy relative to other

prices which has changed dramatically, but it is also wage rates relative

to other prices.

112
For example, real wages in the United States have risen until about

1973. Since that time real wages have remained stable or have fallen,

so that we are now at something like 1967 levels. There is no such thing

as wage-price rigidity over a period of time even as long as the one that

has elapsed since 1973. There is really nothing to be learned from the

macroeconomic discussion summarized by Bob Hall that is relevant to economic impacts of energy policy and world energy prices.

Let me just illustrate my conclusion by the productivity conundrum.

What is it that generates productivity growth? It is not good theory,

Bob tells us, to confuse substitution with technical change. Of course,

I couldn't agree more. In the modeling framework that I described earlier

productivity is a function of relative prices. This makes the productivity growth rate itself a function of relative prices. As an empirical

fact, higher energy prices are associated with lower productivity growth.

That explains a very sizable amount of the productivity slowdown, but not

by any means all of it. The point is that our explanation of the productivity slowdown runs in terms of relative prices.

Bob's second point is that there is no point in trying to build any

great analytical apparatus since the President only wants to know, what

is the free market answer? He doesn't want to fool around with all the

details. He doesn't even want to know if the economy is going to collapse

He wants to know what the free market answer is and then we'll let free

markets to their job. I don't think that any of us would disagree about

that.

One of the most positive contributions of analysis that has taken

place within EIA and elsewhere is toward thinking how to undo the mess

that we got into when people attempted to use depression- type economics

to deal with the energy problem, as they did in the great lamented days

of Richard Nixon. You remember that we had a system of price controls

that led to energy price controls that persisted for a very very long

period of time. Now, of course, Milton Friedman would have said, I could

have advised the President immediately that, all he had to do was just

get rid of those controls. But, it took Jimmy Carter to start doing it

and Ronald Reagan to finish the job in the case of petroleum prices.

A very important role was played, not be depression- type thinking

and depression economics, but by the economics of relative prices which

showed precisely what the impacts of price controls were — unsuspected

impacts like stimulating the growth of energy imports, distorting relative prices in the economy, leading to greater inefficiency, slowing productivity growth and all the rest. The calculations, in fact, preceded

the Carter decision and demonstrated precisely what those economic loses were and put forward a very convincing case. This is reported in the

famous EIA study, Energy Markets - Energy Policies, which was a case for

free market economics.

So, the conclusion is, that indeed, the President doesn't need an economist, ever. He doesn't need a chairman of the Council of Economic

Advisors. He doesn't need Bob Hall. He doesn't need me. He doesn't

need any economists who are here in the group. He knows what he wants

and I think that is probably true of many presidents. But, in terms of

responding to what happens when energy policy changes, if you think

about trying to do it with the economic tools that were appropriate for

analysis of the Great Depression we are in for a real depression.

113
DR. TODD: Let me just take a moment to emphasize one thing that

Fred mentioned in the beginning of this, but which we may have

been remiss in not making clear to people; which is, that the

determination of the world oil price is done in a separate system — a system also in my division, but independent of this. This

system, IFFS, assumes the world oil price to be given and worries

about some of the domestic interactions issues such as the rate

base and electric utilities and things of that sort.

The macroeconomic affects of petroleum supply interruption may

also be poorly modelled but, at any rate, are not part of this

particular modelling enterprise. This particular enterprise

takes the world oil price and the results for initial

macroeconomic variables from an independent interaction of a set

of models including the DRI model and the OMS model spoken of

earlier, and a little bit of envelopes on occasion. That then

becomes the input to IFFS which is not responsible for modelling

the macroeconomic effects of changes in the world oil price. So,

that may escape us periodically as we go through.

114
IFFS: AN OVERVIEW

by

Jerry A. Hausman

Department of Economics

Massachusetts Institute of Technology

I. Overall Structure ; The IFFS model is basically a year-by-year

partial equilibrium model of the domestic energy markets, designed to

provide forecasts over a 5-10 year horizon. IFFS is designed to provide

forecasts for the period between the Short-Term Integrated Forecasting

System (STIFS) and the Midterm Energy Forecasting System (MEFS). STIFS

is used for forecasts of one or two years. MEFS has been used to provide

forecasts 5 years in the future, although its major use is for forecasts

of 10-15 years. Therefore, substantial overlap exists between IFFS and

MEFS. The first question to be asked is whether a new model like IFFS is

needed? It seems to me that two arguments can be made in favor of IFFS:

(1) MEFS is a quite large and to some extent an unwieldy model to use. IFFS promises to be considerably smaller and easier to operate. 1 However, the simplified structure of IFFS is gained at some cost in

loss of regional detail with respect to MEFS. (2) MEFS is an optimization model designed around the solution of an integrating

(transportation) model. While some dynamics, especially in the demand

sectors, are included in the structure of MEFS, it really is not designed

to provide yearly forecasts. IFFS is explicitly designed to provide

yearly forecasts. This feature might be quite important in assessing the

adjustment of energy markets to changes in regulations like the Natural

Gas Policy Act (NGPA) and the Fuel Use Act (FUA). In fact, early

versions of IFFS have been used in just this type of policy analysis. To

the extent that year-by-year forecasts are useful for policy analysis, a strong argument can be made for a new model. Thus, the IFFS model may

well have an important role in analysis of intermediate-run response to

changes in energy policy. Macroeconometric models which IFFS resembles

in many respects have achieved their greatest success in an intermediate

range forecasting interval. Yet a possible weakness of the IFFS

framework is its failure to integrate long-run effects of changes in

regulations and energy markets on intermediate-run outcomes. It is not

clear that a basically myopic model like IFFS will capture successfully

the operation of energy markets because it ignores the role of

expectations which can have an important effect on current activity. I will return to this point subsequently.

IFFS is a partial equilibrium energy market model which takes the

path of the crude oil price as given and attempts to solve for the

1 However recent developments of IFFS with respect to domestic oil and

natural gas supply and integration with a macro model seem to point in

the direction of a more complicated structure than the earlier structure

of IFFS, cf. Hausman (1982). Whether the computationally convenient

properties of IFFS will remain when these extensions are made remains to

be investigated.

115
equilibrium prices in each year of refined oil products, the various

types of natural gas, electricity, and coal.1 Since oil, coal, and

natural gas are used to produce electricity and oil and natural gas are used to produce refined oil products and all the energy sources are substitutes for each other in certain demand sectors, it is clear that an equilibrium exists to find prices such that supply and demand are equated

in all the energy markets. A simplified diagram of the interdependencies

(excluding transportation) is given in Figure 1. In terms of supply, oil

and natural gas often compete for the same resources, e.g., drilling

rigs, and are also to some extent produced together with associated

products. Overall, the model is driven by the yearly forecasts of a macro-model. Macro models have a limited capability of utilizing the

output from IFFS so that a complete integration may be difficult to

perform in terms of overall supply effects on the economy.

The solution procedure used for IFFS is to vary one price at a time, holding all other prices constant to find an equilibrium in the

market under consideration. When an equilibrium has been found, the

solution procedure moves onto the next market, taking as fixed the price

in the previous market. This solution procedure corresponds to the

Gauss-Seidel procedure for the solution of nonlinear equations. It is

often used to solve for forecasts in macroeconometric models. An

equilibrium is claimed when prices do not change in any market, i.e.,

demand equals supply in all markets at current prices. To date the

algorithm appears to have worked well, although its performance may

degrade as the model size increases.

I see the following problems associated with the partial equilibrium

year-by-year approach adopted for the IFFS model structure:

(1) Given the severe shocks and the presence of government

regulations we may not observe the energy markets in equilibrium on a year to year basis. An example may be the reported shortage of drilling

rigs after a large price jump in the world oil market. Of course, a respected school of economic thought argues that we are always in

equilibrium, but it suffices to point out that the reported prices of

drilling rigs were unlikely to have cleared the market in the mid-1 970' s. Therefore, the historical data on which the model forecasts are based may

well not represent an equilibrium situation. It is unclear how good a guide to a posited equilibrium future is provided. Also, (unexpected)

government regulations or deregulations can have a similar effect on market equilibrium. The effect of natural gas curtailments during the

shortage of a few years ago has generated a pronounced effect on current

industrial use which may not be well represented in a equilibrium model.

(2) A lack of expectations in the model structure together with its

myopic approach to year-by-year equilibrium lead to the absence of an intertemporal equilibrium for the model. For instance, under the current

1 Some general equilibrium aspects will be included when successful

integration is performed with the macro model 9DGEM. But even here,

apart from the energy sector, the rest of the economy is treated in a highly aggregated form.

116
NGPA significant price incentives exist for production of deep (107) gas

due to its unregulated status and the average cost pricing of natural

gas. Current drillers of non-deep gas have an incentive to shut-in their

gas if they expect that the price rise in 1985 (or sooner) exceeds the

rate of interest, after taxes, of investing current proceeds from

production. Or producers may not sign long term contracts without

provisions for price escalation. Clearly, the expectations of natural

gas producers could have a significant impact on current gas supply.

Similarly, on the demand side for natural gas, price expectations form an important component of conversion decisions which have led to increased

consumption, especially in the Northeast. Unfortunately, the year by

year solution approach does not build in these price expectations.*

Within an equilibrium market model these expectations can be included

although the model structure is complicated considerably. However, this

lack of expectations and intertemporal equilibrium in the model is an important potential shortcoming.

(3) As currently constructed, equilibrium in the model is found via

the price of unregulated deep gas. However, 'shortages' of natural gas are handled by the existence of supplemental gas to which the "price of

crude oil effectively places a ceiling on the price of 107 gas in the

model". I believe this assumption is contrary to fact because of the

average cost pricing and the existence of old low price gas contracts,

the price of deep gas can be much higher than the price of crude oil. In

fact, I believe that the 1981 price of deep gas was about $8-$9 which is over 50% higher than the crude oil price.2 It is in the best interests

of the gas pipelines to 'roll-in' this expensive gas because of the

nature of their contracts. It is not clear how well the model would

perform if the fiction of supplemental gas were discarded. Yet even if

this discrepancy were corrected, it points up an important structural

defect of the model. Since the price of oil is taken as given, it is

essentially the equilibrium price in the natural gas market which drives

the model since electricity production represents a derived demand for

all three energy sources. To simplify, we can identify three types of

natural gas: old gas subject to long term contracts, new gas subject to

the NGPA up through 1985, and deep gas which is unregulated. At least

through 1985 equilibrium is essentially found in the deep gas market.

But, we have very incomplete evidence on the quantities of gas supply in

each category. Educated guesses are made at the amount of old gas under

long term contracts, but little hard data exist. Yet the amount of old

1 Quantity expectations may also be important with regard to electricity

capacity expansion and transportation of coal.

2 The current (August 1982) price of 107 gas is now reported in the Wall

Street Journal, Aug. 3, 1982, to be closer to $6 more in line with the

crude oil price. These sharp fluctuations in the price of 107 gas when

the price of crude oil has fluctuated about 10-15$ illustrate the dangers

of basing model equilibration on 107 gas and a price ceiling on "supplemental gas".

117
gas is a crucial determinant for prices and supplies of new gas "because

of average cost pricing. It seems to me to be an extremely problematical

choice to base a model on a market of which our knowledge is veryincomplete.

Along these same lines, up through 1985 at least, it is the price of

deep gas that drives the natural gas market. But up until 1978 deep gas

did not exist as a separate category. It is only the perverse

consequences of government regulation that have created the ine fficient

path of resource development where more costly deposits are developed

before existing less costly deposits. Since the historical experience

can offer little guidance about exploration costs, finding rates, and

other facts of deep gas production it seems unwise to place such a heavy

reliance on this segment of the natural gas market which comprises less

than 10$ of total natural gas production.

I have identified three areas where the equilibrium structure of

IFFS seem questionable: the disequilibrium nature of some of our recent

experience in energy markets, * the lack of expectations and an intertemporal equilibrium, and excessive reliance on deep gas prices in

determining the equilibrium solution. The latter two shortcomings are amenable to improvement by further development of IFFS. The

disequilibrium problem would require a substantially different model

structure which is well beyond the techniques of current models and data

requirements. But disequilibrium is a potentially important problem

which should be kept in mind especially because of its potential

interaction with government regulation.

II. Supply Analysis : An attractive feature of the IFFS model is the

treatment of the separate energy markets. To a large extent, the

interdependence of energy supply is captured by the equilibrium

conditions that prevail when a solution is reached. Yet, at the same

time the markets can be studied independently, at least for small

perturbations, so that model development should be possible in a segmented framework. Also, since an optimizing framework is not used as in MEFS, it seems very much easier to model the effects of government

regulations such as the NGPA. Within the market setup average cost

pricing is straightforward to represent. For instance the representation

of the natural gas supply curve is given in Figure 2. S^ is the marginal

cost curve which would be the supply curve under deregulation if

competition exists in the market. S2 is the average cost supply curve where the quantity of old gas, at price P 0 i(j> is averaged together

with the unregulated gas. Other categories of partially unregulated gas can also be included. With this type of supply curve analysis the effect

of changes in government regulations can also be included. I find this

1 Some notice is taken that natural gas consumption in a given year may

not equal natural gas demand at the historical prices, i.e., quantity

constraints exist. However, these quantity constraints do not seem to

have been incorporated into the econometric procedures as of yet.

118
flexibility which exists in the model structure to be a very attractive

feature of IFFS.

It is now proposed (July 1982) to abandon the time series

econometric approach for the supply module used in an earlier version of

IFFS, c.f. Hausman (1982), which had severe problems. Instead, a discounted cash flow (DCF) approach similar to that used in PIES-MEFS is

to be adopted. I encourage this change as an improvement in model

structure although the approach is subject to potential problems, Hausman

( 1 975 ) • While I cannot go into detail on the oil and gas supply models

and since evaluation would be difficult given that they have not yet been

integrated with IFFS, I would like to point out the following potential

problems

(1) The quantity constraint on rigs which determines total drilling

through a merit order approach seems overly rigid. During the 1970's we saw a significant supply response of rigs to economic forces. A problem

arises of how to incorporate such a supply response into the model

without greatly increasing its size and complexity.

(2) The production equation has a very simple decline relationship.

Has the key formula been verified empirically? A potentially serious

problem exists because of uncertainty in the size of reserves, especially

for natural gas.

(3) BTU parity prices are assumed for deep gas with respect to oil.

As I mentioned previously, this assumption is incorrect because of

average cost pricing. Arguments can be made against a BTU equilibrium on either the positive or negative side and 1985 may be too soon for the

equilibrating processes to proceed very much.

(4) Only current prices are used in the drilling module for 102

gas. This assumption seems incorrect because of forthcoming

deregulation.

(5) The deep (107) gas module is based on very shaky assumptions

given the lack of historical data. Yet, it plays an essential role in

the determination of equilibrium as I discussed previously. Sensitivity

analysis with respect to the assumptions seems an important short term

goal.

(6) Most importantly, a fixed level of development is assumed for

107-deep gas. Because of the crucial equilibrium role assigned to 107

gas this fixed development assumption could have a significant effect on model results. Development should depend crucially on the current oil

price, expectations of the future oil price, and the tax treatment of

natural gas. Recent experience in 1982 demonstrates how important thse

factors can be.

Lastly, to fit the oil and gas models within IFFS, a pseudo-data

approach will be used. I discuss the pseudo-data approach subsequently,

but it is not clear that the approach offers a satisfactory resolution to

to a diffficult modelling problem.

119
III. Energy Transformation Activities, : In the last section I considered

the supply models for three primary sources of energy - coal, oil, and

natural gas. In this section the two major energy transformation

activities are investigated - oil refining and electricity supply.

Here, activity analysis type of models which depend on cost minimization

through linear or non-linear programming techniques have been used.

These activitv models are usually constructed on a microeconomic or even plant-level basis. They offer a detailed description of the technology

and the possibility of substitution in inputs and outputs. The models

are in actual use in both operations and planning activities by industry.

Their engineering detail can provide a considerably more accurate

representation of the process under study than can econometric models

which must depend on price and quantity variation observed in the time

series data. Yet, the amount of engineering detail can make the use of

the models unwieldy in the context of a larger equilibrium or optimization model. Thus, often simplifications of the activity model

are made in their actual use, e.g., the model of electricity generation

in PIES as described by Hausman (1975). The IFFS modellers have taken an interesting approach to the conflicting properties of the activity models

accurate technological detail together with reasonably quick execution.

I will attempt to assess the likely success of this approach.

The approach taken in PIES, MEMM, and MPS to model the oil refining

process has been to use an approximation to a larger optimization model

through a mixture of the extreme points generated by the larger model.

However, it was found that an excessive number of extreme points were required to represent the solution space adequately. An alternative

approach of a mixture of a set of extreme points and historical data has

been unable to model the changes in product mix adequately. This problem

may be especially important if coal is substituted for residual oil in

boiler use. The expected product mix may be quite different from recent

historical experience.

The IFFS model takes the pseudo-data approach to the problem

originally advocated by Adams and Griffin (1972). The pseudo-data

approach uses the activity analysis optimization model to solve for costs

and factor demands for a large number of output price points. The

approach there was a statistical cost function summarize the input-output

and elasticity relationships which is estimated by econometric

techniques. Since the data generation process is not constrained by

limited historical variability on price, inputs, and outputs, in general

an accurate fit of the econometric model can be obtained. Griffin has

used the pseudo data approach to model electricity generation (1977) and

petrochemicals (1978) in addition to the original Adams-Griffin model of

refineries

The particular econometric specification used in IFFS is basically a log linear ( Cobb-Douglas) approach to the technology. The equations take

the form

120
(5-1. a)

log(price of gasoline) =ailog(price or crude) + j3-|(gas quality)

+ y 1 (quality of resid) + 6 ^ (percentage of gas).

(3-1 -b)

log(price of other product) = a2log(price of crude) + ^(product demand)

+ 6 2(p r ice of gasoline)

These equations can be assumed to arise from a (restricted) profit

function or a cost function where output prices arise from marginal cost

relationships. Three possible improvements might be made in the IFFS

pseudo-data approach: (1) A more general approach than the simple CobbDouglas should be tried. Griffin has tended to use flexible function

forms such as the translog in his recent work. While the Cobb-Douglas

representation is easier to use within IFFS, its unitary elasticity

assumptions may be unreliable for the refining industry. In fact, the

implied separability assumptions of the Cobb-Douglas function are likely

to be invalid. Instead a joint cost function approach of the form

C(y;Pc;Png) where y is the vector of outputs, Pc is the price of crude,

and Png is the price of natural gas should be taken. The form of C should

permit jointness of production, e.g., the translog.1 The joint product

nature of the refining process seems especially important with respect to

the question of output substitution. (2) The question of cost markups

to determine prices is not considered adequately. Currently, IFFS

assumes a constant markup in real terms. Especially in gasoline the

markups seem to have varied considerably over the last decade. At the

current time a variation in gasoline prices among retail stations of ^0%

is not uncommon. The markup relationship in wholesale prices should be

included in the price equations. (3) Little or no research into the

effect of industry capacity utilization has been done. Currently,

capacity utilization in the refinery industry is below 10%. A vintage

effect might be important here which neither the activity analysis model

nor econometric equation is taking account of.

A more substantive question exists about the overall validity of the

pseudo data approach. Maddala and Roberts (1980) have recently cast

considerable doubt on the accuracy of the approach although Griffin

(1980) does not accept their critique. In a simple example Maddala and

Roberts claim that even though the statistical fit of the econometric

equation may be excellent, i.e., very high R's, that they found the

elasticity estimate to vary significantly with the functional form and to

1 Caves, Christensen and Swanson (1980) and Caves, Christensen and

Trethewey (1981 ) use translog forms to model the joint product case.

121
not give accurate estimates of the true underlying elasticities. Nor did

they find that the translog model offered a significant improvement over

a Cobb-Douglas model. Furthermore, the change in factor demands was not

captured accurately by the pseudo-data cost function. Griffin, in his

reply, critized the simple model of Maddala and Roberts and the extreme

points at which they evaluated the elasticities. My evaluation of the

controversy is that the pseudo-data approach has not been validated

adequately to date. However, an improvement I would suggest that merits

investigation is not to attempt to fit the same translog or Cobb-Douglas

function over the entire price and quantity space. The reason that

econometric cost functions work adequately on historical data is that the

limited variation in the data can be suitably approximated by a first or second order approximation. Investigators who use pseudo data are unconstrained by historical data and therefore use a wide variation to

improve the fit of the equation. I believe this procedure is usually a mistake because the econometric functions cannot approximate the actual

technological relationship over a wide range of variation. Instead, I would attempt to choose a limited number of points and to generate

pseudo-data around them. I would then fit separate econometric

functions at each point. For use in IFFS interpolations among the points

could be made. The required number of estimates and the optimal spacing

of the reference points would need to be determined with further

research. I feel the current pseudo-data aproach of "distilling the

complex process analysis representation into a single equation", Griffin

(1977), is beyond the approximation capabilities of the econometric

equations. However, the use of more than one set of estimates may well

improve the accuracy of the econometric representation.

I have significantly less expertise with respect to electricity

generation models. Therefore my remarks here will be relatively brief.

A simplification of utility planning models such as WASP and OGP is used

in a static and deterministic framework. The documentation here was not

adequate for me to assess how well the approach was carried out. The

model works on a year by year basis with an exponential smoothing of

future prices for capacity planning in the manner of the Baughman- Joskow

model. This approach has the shortcoming of the omission of

considerations of intertemporal equilibrium . That is, at the quantities

being planned for, the prices may need to be higher than the smoothed

forecasts. Thus the expectations can be incorrect within the context of

the planning model. Financial and regulatory issues are handled by the

National Utility Financial Statement (NUFS). NUFS does seem to include

the important factors which will affect utility performance in the

future. The main shortcoming of NUFS which I can see is its failure to

represent adequately the interaction of the tax treatment of utilities

with the cost of capital as generated by financial markets. For instance

the altered tax treatment of electric utilities in the 1981 tax

legislation is meant to decrease the cost of capital for electric

utilities. The cost of capital is now one of the major factors in

capacity expansion plans. Furthermore, this output needs to be

integrated with the planning models. Currently, expansion plans which

122
have already begun are being cancelled due to the high cost of capital.

This aspect of planning, regulation, and the tax treatment of utilities

should be represented better in IFFS.

A deterministic merit order dispatch is used to generate the derived

demand curves by fuel type. A certainly equivalent approach is used

instead of a probabilistic approach which may bias downward the predicted

operation of peaking capacity and consequently oil and gas demand. A

further source of uncertainty is the amount of conversion to coal use by

base load plants from current oil consumption. My general impression

here is that the deterministic approach may well be adequate, especially

if some adjustments were made for peaking operation in line with the

historical experience. However, greater uncertainty due to conversion to

coal and possible adoption of Time of Use (TOU) pricing and the continued

effects of a fall in the demand growth rate need to be studied. Further

data gathering efforts in these areas might be quite helpful in

decreasing this current uncertainty.

IV. Demand : The information provided on demand models is not extensive.

As I understand matters, the log linear representation of demand used in

the MEFS demand system is being used in IFFS. These demand equations are

in turn based on the engineering approach of the Oak Ridge models:

(1) the Hirst-Carney model for residential demand, (2) the Jackson model

for commercial demand, (3) the Oak Ridge industrial model (ORIM) for

industrial demand. Lastly, the Sweeney model is used for transportation

demand. I have some familiarity with all of the demand models except

ORIM. However, an in-depth review of the models is beyond the scope of

this paper since each demand model is a proper subject subject for an individual review. To the best of my knowledge the Hirst-Carney model

has undergone such a review by Freedman, Rothenberg, and Sutch (1980),

and McFadden (1981). I do not know of intensive reviews of the other

three models although they may well exist. Certainly the Hirst-Carney

model has certain questionable features, e.g., Hausman and Joskow (1981),

and I would not be surprised if review of the other models did not lead

to an improvement in their structure after a careful analysis was undertaken.

I do have four points about the structure of the demand model which

may merit further examination. (1) the log linear approximation to a demand curve at a vector of prices typically takes the form for fuel i

(4.1) 1it-b i<li,t-1 " I a ij l0 S Pjt = a

i lQ g Pit

5*3

where q is quantity, p is price and the a's and b's are estimated

coefficients. However, within the operation of an individual fuel model

the terms b

iq i -

£ a^log p^ t are put into a constant term when price

123
is is varied to find the equilibrium price. It is my understanding,

which may be inaccurate, that this constant term is not changed across iterations of IFFS when an equilibrium is searched for. If my described

procedure is correct, serious errors may result. Since b^ is often

estimated to be near one then t>±Q± t-1 an^ ^e equilibrium is

reached via equality of -

I a^jlog pj-j. to a^log P-j_-f But if the correct

prices are not used in the p^ the supposed equilibrium solution may be

quite inaccurate. (2) No expectations occur in the demand models. In

the Northeast knowledge that natural gas prices may sharply rise in 1 985

has certainly affected conversions from oil for home heating. It would

take a substantial revision of the demand methodology to include

expectations in the model, but such revision may be called for.

Similary, it is not clear how accurate the lagged endogenous demand curve representation will be if a sharp rise in natural gas prices does occur

in 1985- In intertemporal models, it is usually the role of expectations

to smooth out response variables. But since expectations are not

included in IFFS, it is possible to have a large jump in quantities of

natural gas which is probably not a realistic forecast of what we expect

to occur. (3) Industrial demand currently comprises about 40% of

natural gas use of which manufacturing consumes 80$. Therefore, the

particular macroeconomic forecasts used may be extremely important since

the forecasts are used to drive the demand model. IFFS model runs under

different macro forecast assumption should be useful to ascertain the

sensitivity of the models results to this factor. I would not be

surprised if the IFFS results are sensitive to the macroeconomic

assumptions which would emphasize the importance of the macro forecasts

in results derived from the model.

V. Results : The IFFS model was run for eight years, 1980-1988, without

any change in federal regulatory policy assumed beyond existing

legislation. Other model assumptions were: (l) The base supply-base

demand case of ARC81 is used in all cases. (2) For the baseline run the

price of oil is set exogeneously at 31.20 for 1981 (in 1979 dollars) and

the average world oil price from the ARC81 report is used for subsequent

years. (3) For the 'high price' run the oil price is set at 10% higher

for all years. The oil price is 34. 32 for 1981. (4) For the 'low price' run the oil price is set at 10% lower than the base case. The oil price

is 28.08 for 1981. The three model runs correspond to a change in the

oil price of plus or minus 10%. They should be able to give a reasonable

idea about the price and demand and supply elasticities of the model.

Since natural gas and petroleum prices and quantities are the central

part of the IFFS equilibrium process, I will mainly consider them in

discussing the results.

First, we consider petroleum products. Since the price of crude oil

is set exogeneously to IFFS here we are basically looking at the

124
interaction of the refinery model and the demand models in terms of the

output. In Table 1 I present the results for the baseline run for 5 years; 1980, 1981, and 1983, 1985, and 1988. For comparison purposes I include actual U.S. prices (in 1979 dollars) as reported in the Monthly

Energy Review . Dec. 1981. Note that the IFFS results have both tne

wholesale and retail price of distillate decreasing by about 8.1 over the

first four year period while the wholesale and retail price of gasoline

are forecast to increase by about 5*3$ over the period 1980-1983. The

divergence in price paths is somewhat puzzling. Furthermore, from

1985-88 the exogenously set world oil price increases by 19*3$ while the

wholesale gasoline price decreases by 4«9$. The relationship of the

products price with the crude price seems to merit further investigation.

Overall, IFFS results do seem to have the price differentials among the

various petroleum products approximately correct. Gasoline price seems

to be the only remaining problem.

I now turn to the high price and low price oil runs for comparison

purposes in Table 2. The price pattern is quite similar to the baseline

run. Distillate prices fall by about 6$ over the five year period 1981-

1985 while gasoline prices rise by about 5$« Again, the latter finding

seems quite surprising.

I now consider the IFFS model results with respect to the natural

gas price in Table 3* Since the Monthly Energy Review only reports an average delivered price and average wellhead price for natural gas

,

comparison to the actual prices is quite difficult. Information

on current deep gas (unregulated) prices would be very helpful. Prices

are given in 1979 dollars per MMBTU.

An anomaly in the table is the low price of deep gas in 1981 which

cannot rise above a ceiling set by the IFFS program. It is not clear to me how well the program would work if this ceiling were removed.

Subsequently, the price of deep gas falls sharply in 1983 and thereafter

remains at the basic price of unallocated gas. It is not clear why this

path would occur prior to deregulation in 1985. Also the decline in the

price of delivered natural gas between 1981 and 1983 and 1981 and 1985 seems puzzling. Given the vintage contract structure of 102 gas, this

outcome seems very unlikely if not impossible.

With respect to the possibility of a 'BTU equilibirum' after

deregulation, the results have the price of natural gas at the wellhead

significantly below the crude oil price in 1985 and 1988 by about 75$-

Given significant delivery costs for natural gas, we expect this result.

Residential gas remains cheaper than distillate by about 50$. The price

for industrial gas is about 33$ lower than the high sulphur residual

price. Lastly, for electric utilities the price difference is 37$ in

1985, but it falls to 17$ in 1988. Therefore, at all margins gas is

still a bargain after 1985 although some convergence of prices certainly

occurs. But to my way of thinking, natural gas remains too cheap

relative to oil in the model during the entire period up through 1988.

125
I next consider prices of natural gas in the high oil price and low

oil price cases. Results are presented in Table 4» Again the deep gas

prices are set at the ceiling imposed by the IFFS program in 1981 but

then decline to the unallocated gas price. The delivered price of gas

continues to take a surprising fall of about 33$ between 1981 and 1983.

I do not see what set of events could create this decline in price.

Since old gas contracts are being replaced by higher priced new

contracts, I would expect a price rise over the 1981-85 period. Lastly,

almost no price response in natural gas occurs over the base, high, and

low oil scenarios even though the oil price varies by 20$. The level of

price response in natural gas to changes in the oil price certainly bears

further investigation.

The last table that I present considers quantities consumed in

1981 to 1985 under the three price scenarios. These results are presented in Table 5 to give some notion of overall price elasticities.

The change in consumption seem surprisingly small. For instance, total

consumption does not vary at all in 1983 despite a 20$ difference in the

oil price. Since the delivered natural gas price does not vary over the

period, it seems that the natural gas model is 'uncoupled' from the oil

model at the expected level of demands. Further rises in the price of

oil need to be considered to find at what point the natural gas market

is affected by changes in oil prices. At least for changes of ± 10$, no effect is forthcoming. Lastly, in the industrial sector which might be

expected to be the most price sensitive sector, consumption differs by

only 0.3$ in 1983 and 0.6$ in 1985 with respect to the high and low oil

price case from the base case. This result might well call for further

investigation.

VI. Conclusion : Overall, I like the potential represented by the IFFS

model. It models the actual working of energy markets on a year-by-year

basis. Thus, its results can be compared to the actual situation in

energy markets. This feature is its best attribute and makes it much

more favorable than models like PIES, MEFS, and LEAP. The potential

also exists for a much more successful integration with a macromodel than

has occurred previously, cf. the proposed integration with the 9DGEM

discussed in Kydes, et al. (1982). I strongly feel that more emphasis

should be placed on models that can be compared to real data. This

argument is developed in Hausman (1981). IFFS has the potential for

policy analysis and forecasts well beyond other existing EIA models. It

closely resembles in its solution framework and architecture many

econometric models. Thus, I feel that the overall concept is sound.

However, as I have pointed out significant improvements still need to be

made in IFFS. My most important concern is data on natural gas

categories in the supply module. For the IFFS model to predict

successfully events up to 1985 (or when deregulation occurs) and after,

better data seem necessary. At the current time the natural gas market

model shows little responsiveness to events in the oil market. I doubt

this result is plausible. Better data on gas contracts might help

alleviate the problem since increasing amounts of deregulated natural gas

126
which enter the market will reflect, at least partly, the effects of

higher or lower oil prices.

References

Caves, D. , L. Christensen, and J. Swanson (1980), "Productivity in U.S.

Railroads, 1951-1974", Bell Journal of Economics .

Caves, D. , L. Christensen, and M. Tretheway (1981), "Flexible Cost

Functions for Multiproduct Firms", Review of Economics and

Statistics

Freedman, D. , T. Rothenberg, and R. Sutch, (1980), "Progress Report on a Review of the Hirst-Carney Model of Residential Use" , National

Bureau of Standards.

Griffin, J. (1977), "Long-run Production Modelling with Pseudo-data:

Electric Power Generation", The Bell Journal of Economics .

Griffin, J. (1978), "Joint Production Technology: The Case of

Petrochemicals", Econometrica .

Hausman, J. (1975), "Project Independence Report: An Appraisal of U.S.

Energy Needs Up to 1985", Bell Journal of Economics.

Hausman, J. (1981), "Validation and Assessment Procedures for Energy

Models", in S. Gass (ed.), Validation and Assessment of Energy

Models , (Washington; National Bureau of Standards).

Hausman, J. (1982), "A Review of IFFS" , report to EIA, February 1982.

Hausman, J. and P. Joskow (1981), "Evaluating the Costs and Benefits of

Appliance Efficiency Standards", MIT Energy Laboratory Paper, MITEL82-005WP, American Economic Review , May 1982.

Hill, L.J. et al. (1 981 )

, "An Evaluation of the National Utility

Financial Statement Model", Oak Ridge National Laboratory, Report

ORNL-5831

•

Kydes, A.S., M. Minasi, and E.A. Hudson (1982), "Energy-Economy Feedback

A Description of EIA's IFFS-9DGEM0-DAS-Linkages" , Interim Report,

July 1982.

Maddala, G.S. and R. Roberts (1980), "Alternative Function Forms and

Errors of Pseudo Data Estimation" , Review of Economics and

Statistics .

McFadden, D. (1981 ), "An Evaluation of the ORNL Residential Energy Use

Model", MIT Energy Laboratory Paper, MIT-EL 81-021.

Zimmerman, M. (1979), "Rent and Regulation in Unit-Train Rate

Determination" , Bell Journal of Economics.

127
123
129
TABLE 1

Oil Product Prices

IFFS80 IFFS81 IFFS83 IFFS85 IFFS88 MER1 98Q1 MER1981 2

1. Crude-baseline $31.10 $31-20 $29-37 $30.28 $36.71 $25-52 $30.33

2. No. 2 heating oil 36.16 36.60 33-35 34-46 41-51 31-39 37-70

(wholesale)

3- Gasoline 36.82 36.67 38.81 38.58 36.75

(wholesale)

4- No- 2 Heating Oil, 41-12 41-56 38-32 39-44 46.51 37-34 44-42

residential

( retail)

5- Gasoline (retail) 49-85 49-70 51-84 51-60 49-77 46.62 49-67

1 Average 1980 price from December 1981 issue of Monthly Energy Review changed to

1979 basis.

2 July 1981 price from December 1981 issue of Monthly Energy Review changed to

1979 basis.

130
TABLE 2

Oil Product Prices with High and Low Crude Prices Assumed

High80 High81 High83 High85 Low80 Low81 Low83 Low85

1. Crude 35-21 34-32 32.30 33-31 27-99 28.08 26.43 27-26

2. No. 2 39-72 40.20 36.58 37-86 32.60 32.99 30.02 31-05

(wholesale)

3. Gasoline 39-75 39-58 42.08 41-56 33-89 33-76 35-93 35-61

(wholesale)

4- No. 2 44.68 45-16 41-55 42.85 37-56 37-95 34-99 36.03

(retail)

5- Gasoline 52.78 52.60 55-11 54-59 46-91 46.79 48.96 48.64

(retail)

131
Category

1. Ave. Wellhead

Price Allocated

Gas

2. Unallocated

Gas

3- Deep Gas (107)

4. Ave. Wellhead

Price

5. Residential Price

(natural)

6. Elec. Utility Price

(national)

TABLE 3

Natural Gas Results

(Region 6)

IEFS81 IFFS83 IFFS85

1.19 1.34 1-42

3.84 1.46 1.78

5.87 1.46 1.78

1.41 1-37 1.81

4-45 3.29 3.68

3-22 2.50 2.93

IFFS88 MER80 MER81

1.91

3.23

3.23

2.95 1.36 1.59

4.87 3-56 4.02

4-16 1.95 2.61
TABLE 4

Natural Gas Prices with High and Low Crude Prices Assumed

(Region 6)

Category High81 High83 High85 Low81 Low83 Low85

1

.

Allocated Gas 1.19 1 .34 1 .83 1.19 1 -34 1 .83

2. Unallocated Gas 4-07 1 .50 1 .81 3-59 1 .41 1 .81

3- Deep Gas (107 6.39 1 .50 1 .81 5.31 1 .41 1 .81

4- Ave. Wellhead

Price

1 .42 1 .38 1 .82 1 .39 1 .36 1 .82

5- Residential

Price

4-59 3-29 3-68 4-29 3-28 3-71

6. Elec. Utility 3-33 2.51 2.94 3-04 2.50 2.98

Price

133
Table 5

Quantity of Natural Gas Consumed in 1981 and 1983 (quads of BTU's

Category- Base81 Base83 Base85 High.81 High83 High85 Low81 Low83 Low85

1. Total 17.84 18.14 18.82 17-81 18.13 18.91 17-79 18.07 18.75

2. Residential 4.44 4-57 4.70 4-41 4-59 4.69 4.49 4.53 4-59

3* Elec Utility- 3-99 3-57 3-57 4.03 3-56 3-64 3-85 3-47 3-52

4- Industrial 6.07 6.12 6.50 6.07 6.13 6.52 6.07 6.11 6.48

134
DISCUSSION

JULIE ZALKIND: This is not really a question, but, it seems like

I ought to explain a little bit about the missing demand models

being brought up. First, I want to say that we are not using the

Hirst-Jackson model or other models that have been used in the

past. But, we have rejected these models and started a new

effort, in part because there are new data sources to exploit

from EIA surveys. We also have fewer resources. We also seem to

need to know less about demand technologies with fewer demand

initiatives. So we can live with smaller models. These will be

used in the next annual report cycle.

DR. TODD: I will pass the remaining time to Fred for the future

agenda

.

DR. MURPHY: Before going on to the future agenda, Jerry raised

several issues that I would like to make a few comments about.

His main points were, to begin with, the need to represent

disequilibrium, the problems with myopia, and the problems with

107 gas. These are the ones that I will address at this point.

It is a question of how long you have to wait before an

equilibrium occurs in the market or in what you define as an

equilibrium. An economic equilibrium is not a static, unchanging

entity. We have tried to represent the adjustment processes in

the market place, such as the expansion of the drilling industry.

Dick O'Neill, who just left, cannot address your question about

the rig shortages. In natural gas the curtailments that we have

had in the past are compensated for by taking our curtailment

survey information and adjusting demand levels.

We currently do not feel that natural gas curtailments are a significant factor except for industries that may be discouraged

from buying natural gas boilers. Currently there seems to be a natural gas surplus. The question of disequilibria in gas

markets will be addressed again when I talk about the role of 107

gas

.

The myopia issue is a terribly important issue. The problem of

expectations is significant. The question of people's

expectations about 102 gas prices is important. However, how do

you model the response of actors in an industry to an event about

which you have no experience? I am open for suggestions, I do

not know how to deal with that. One of the possibilities is

survey data on intentions.

In the electric utilities environment, it's an overstatement to

say that the planning component is myopic. Let me start out by

saying that, if you have a demand growth rate that is

sufficiently high, even in the face of moderate fuel price growth

rates, the myopic solution is identical to the intertemporal

optimal planning solution. How do you adjust for it when you get

135
into a situation where demand growth slows and fuel price growth

accelerates? We have adjusted for this by trying to include some

compensating factors, mapping the equivalent of full, multiperiod, linear programming solution into the static problem by

adjustments in fuel prices.

One of the key points Jerry makes is the way we essentially

capped the 107 price, in solving for the natural gas market

equilibrium. Earlier this morning, I eluded to the problem of

determining shortage levels. When we were doing work with an

earlier version of the natural gas market module (a model that

was used to produce a report called The Analysis of the Economic

Effects of Deregulation of Natural Gas ) , we found that when we

solved for market equilibrium without capping the 107 gas price,

107 gas prices reached around $25 to $30 in 1984.

We felt that printing something like that would not be a

realistic result. It is ridiculous to presume that so small a

component of natural gas supply will swing the market so

thoroughly. We chose to cap the 107 gas price and measure

potential shortages rather than presume a market equilibrium.

Either, FERC would take some administration action to adjust the

NGPA, or there would be a lot of law suits by consumers. We

chose the crude oil price as the cap because that is close to the

natural gas liquids price, because there is a lot of injection of

gas liquids to make up for seasonalities in natural gas demand,

and because it was a convenient and very obvious point to cap the

gas price for measuring the potential for shortages. Shortages

could also be met by increasing imports of natural gas from

Canada or Mexico or by utilization of the synthetic gas plants

that produce natural gas from gas liquids and naphtha.

This is why we made that modelling decision as we did. Again, it

is because there is no easy solution to representing market

clearing with the potential for serious shortages. We needed a gas price for measuring where that potential existed. We chose

the crude oil price for the ceiling on the 107 gas price.

Why pseudo-data? Again, because we want a model with quick turnaround. If you can get a model to turn around within a half

hour, then you can get a lot of debugging done when you are

trying to do scenario implementation. It's not the computer time

in terms of the dollars that we are worried about here. The

machine exists and has surplus capacity with the cutbacks in DOE

activities. So, in some ways, it is a free good. But we

consider the run time important. Pseudo-data is a device to keep

the run time in control, as the means of facilitating the

analysis and not having to have our staff neve working all hours

of the night and working every weekend to produce a forecast, a

problem in the past.

136
There are a few other questions that have been raised, such as

the cost of capital in electric utilities. We view that as

significant and we have a capital supply curve in the planning

module. It was not discussed beyond a passing sentence in the

documentation, because it still needs a lot of work on the basic

theory and on its implementation.

I have given some responses to the questions you raised. I agree

with you that one of the key problems we face is the size of the

model. It is hard to keep pressure on people to go with

something simpler than something more complex. I keep trying to

emphasize the statement of Mies van der Rohe: "less is more."

With any individual sector, you should be able to pick out no

more than, say, five to ten key variables that you really need to

capture

However, the model exists within an organization, and we have to

respond to competing pressures for the individual analyses that

people wish to make. One of the ongoing issues that EIA is going

to have to face is how to keep the downward pressure on size and

complexity, while, at the same time, achieving a realistic

balance. In terms of what has gone on today, the whole

discussion has been on the trade-off between size and realism and

there is no good answer to these issues. They involve a lot of

personal judgments and are not scientific.

Let me just quickly give an overview of the development status.

Again, we have working versions of all components except for

coal. The reason why coal lags is not because anyone has been

negligent, but because of the limited resources we have had to

schedule some pieces to follow along after others. Coal started

last and that is why it is not finished yet.

The electricity market module, is, we feel, in reasonably

complete form. We do not see adding on any more structure. If

anything, we see shrinking some of the structure in electric

utilities to simplify its operation. Our main goal in electric

utilities is to continue the data development task, automating a lot of the data that feeds into the model right off the EIA

collection forms, and simplying the task to maintain the model

over the future. We are devoting a fair amount of time to

resolving inconsistencies in the data that exist, so that we can

have a good starting point based on historical experience.

In the oil market module, which is primarily refineries, we are

re-estimating some of the equations in the model with EIA data.

We very quickly got some Oil and Gas Journal data and used that

to get something working. Jerry raised the issue of the

Cobb-Douglas form with the pseudo-data. We have been testing

variants. We have tried the equivalent of a two-term Taylor

series, adding more terms to that model. We did not talk about

it today because the results have not been as good as we

expected. We are going to re-estimate the capacity expansion

equations using EIA data.

137
In the gas market module, I was as surprised as Jerry to see

those results. The reason why they are there and have not been

prettied up at all is because those are the first runs with the

new oil and gas supply pieces, versus the one we were using as a place holder. We have not done any studying of what really

happened.

The fall in the 102 price is, I think, is an unrealistic

phenomenon and we might have to build something in the module to

deal with that because the contract structure with take-or-pay

provisions can lead to strong results. The fluctuations in 107

price are something that I would expect because 107 gas is the

tail wagging the dog in a sense. Extreme price movements have a certain amount of realism because of the market-out clauses

allowing pipelines to break contracts.

We are also doing some work in reducing the complexity of the

supply models. One of the important components is the PROLOG

model that Dick O'Neill described, a linear program with about

150 to 200 rows. We are working on using something other than

linear programming that would reproduce the linear programming

solution and reduce it down to three constraints plus a linking

Lagrange multiplier. We are trying to achieve some simplicity

and regain some more control.

In the coal market module, I really will not say any more about

it, except that today you have seen the development task that is

ahead of us.

MR. TOM : Fred, you said that you are using Oil and Gas

Journal data for some purposes. Were they better than EIA?

DR. MURPHY: We do not consider it to be better than EIA data.

It was just easily accessible, and we took it because it was on

the shelf and we knew where it was.

MR. : (inaudible, too far from microphone)

DR. MURPHY: The reason for going with EIA data is that we want

to be compatible with our own internal data systems. I am not

making any quality judgment, I am just making an organizational

statement. Any other questions? Thank you all. I know I am

exhausted and I am sure you are.

DR. TODD: I just wanted to say one final thing in closing, which

was, that we are very greatful for the effort that the outside

participants put into the critiques that they presented today.

The quality of the presentations that were given today have, in

all cases, been useful to us. Thank you all for coming today.

138
SYMPOSIUM ON THE

DEPARTMENT OF ENERGY'S

INTERMEDIATE FUTURE FORECASTING SYSTEM

Sponsored by

The Energy Information Administration, Department of Energy

and

The Operations Research Division, National Bureau of Standards

Thursday, August 19, 1982

Forrestal Building Auditorium

1000 Independence Ave., S.W.

Washington, D. C.

9:00 a.m. - 9:15 a.m.:

Welcome

Wray Smith, Director

Energy Markets and

End Use/DOE

9:15 a.m. - 9:45 a.m.:

An Introduction to the New

Integrated Forecasting System Frederic H. Murphy

EIA/DOE

9:45 a.m. - 10:30 a.m.:

System Overview — A Critique William W. Hogan

Harvard University

10:30 a.m. - 10:45 a.m.:

Software Design Susan H. Shaw

EIA/DOE

10:45 a.m. - 11:00 a.m.:

Introduction to the Oil and Gas Supply

Subsystem Richard P. O'Neill

EIA/DOE

11:00 a.m. - 11:30 a.m.:

Oil and Natural Gas Supply

Subsystem — A Critique David Nissen

EXXON

William R. Finger

EXXON USA

PROGRAM

,Saul I. Gass, U. MD/NBS

Symposium Chairman

139
11:30 a.m. - 11:45 a.m.:

Introduction to the Refineries Subsystem John Conti

EIA/DOE

11:45 a.m. - 12:15 p.m.:

Refineries Subsystem A Critique — Neil J. Cleary

Standard Oil of Indiana

12:15 p.m. - 12:30 p.m.: Discussion and Questions

12:30 p.m. - 1:45 p.m.: Lunch

1:45 p.m. - 2:00 p.m.:

Introduction to the Electric Utilities

Subsystem Reginald C. Sanders

EIA/DOE

2:00 p.m. - 2:30 p.m.:

Electric Utilities Subsystem — A Critique Lewis J. Rubin

EPRI

2:30 p.m. - 2.45 p.m.:

Introduction to the Coal Subsystem Mary J. Hutzler

EIA/DOE

2:45 p.m. - 3:15 p.m.:

Coal Subsystem — A Critique Michael Elliot-Jones

Chase Econometrics

3:15 p.m. - 3:30 p.m.:

Introduction to the Energy Economy

Component ....Dale Jorgenson

Harvard University

3:30 p.m. - 4:00 p.m.:

Energy Economy Interactions — A Critique Robert Hall

Stanford University

4:00 p.m. - 4:30 p.m.:

Summation Jerry A. Hausman

M.I.T.

4:30 p.m. - 5:00 p.m.: Panel Discussion and Questions

5:00 p.m. - 5:30 p.m.:

System Use and Future Developments Frederic H. Murphy

EIA/DOE

140
NBS-T14A (rev. 2-6Q

U.S. DEPT. OF COMM. 1. PUBLICATION OR 2. Performing Organ. Report No. 3. Publication Date BIBLIOGRAPHIC DATA

REPORT NO. SHEET (See instructions) NBS SP 670 December 1983

4. TITLE AND SUBTITLE

INTERMEDIATE FUTURE FORECASTING SYSTEM

Proceedings of a Symposium held at The Department of Energy, Washington, D.C

August 19, 1982

5. Saul I. Gass, Frederic H. Murphy and Susan H. Shaw, editors

6. PERFORMING ORGANIZATION (If joint or other than NBS. see instructions)

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

7. Contract/Grant No.

8. Type of Report & Period Covered

Final

9. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street. City. State. ZIP)

Energy Information Administration Operations Research Division

Department of Energy and National Bureau of Standards

Washington, D.C. 20585 Washington, D.C. 20234

10. SUPPLEMENTARY NOTES

Library of Congress Catalog Card Number: 83-600615

~J Document describes a computer program; SF-185, FIPS Software Summary, is attached.

11. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bibliography or literature survey, mention it here)

The Symposium on the Department of Energy's Intermediate Future Forecasting

System IFFS was held in Washington, D.C. on August 19, 1982. It was funded

by the Energy Information Administration of The Department of Energy (DOE) and

organized by the Operations Research Division of the National Bureau of Standards.

The purposes of the Symposium were (1) to present to the energy community

details of DOE's new energy market model IFFS and (2) to have an open forum in

which IFFS and its major elements could be reviewed and critiqued by external

experts. DOE speakers discussed the total system, its software design, and the

modeling aspects of oil and gas supply, refineries, electric utilities, coal, and

the energy economy. Invited experts critiqued each of these topics and offered

suggestions for modifications and improvement. This volume documents the

Proceedings (papers and discussion) of the Symposium.

12. KEY WORDS (Six to twelve entries; alphabetical order; capitalize only proper names; and separate key words by semicolons)

coal; electric utilities; energy economy; energy models; energy markets;

forecasting; natural gas; oil; policy analysis

13. AVAILABILITY

[x] Unlimited

|

| For Official Distribution. Do Not Release to NTIS

[v~| Order From Superintendent of Documents, U.S. Government Printing Office, Washington, D.C.

20402.

14. NO. OF

PRINTED PAGES

147

15. Price

USCOMM-DC 6043-P80



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Technical Notes—Studies or reports which are complete in themselves but restrictive in their treatment of a subject. Analogous to monographs but not so comprehensive in scope or definitive in treatment of the subject area. Often serve as a vehicle for final reports of work performed at NBS under the sponsorship of other government agencies.

Voluntary Product Standards—Developed under procedures

published by the Department of Commerce in Part 10, Title 15, of

the Code of Federal Regulations. The standards establish nationally recognized requirements for products, and provide all concerned interests with a basis for common understanding of the

characteristics of the products. NBS administers this program as a supplement to the activities of the private sector standardizing

organizations. Consumer Information Series—Practical information, based on NBS research and experience, covering areas of interest to the con- sumer. Easily understandable language and illustrations provide

useful background knowledge for shopping in today's tech- nological marketplace. Order the above NBS publications from: Superintendent of Documents, Government Printing Office, Washington. DC 20402. Order the following NBS publications—FIPS and NBSIR's—from

the National Technical Information Service, Springfield, VA 22161.

Federal Information Processing Standards Publications (FIPS

PUB)—Publications in this series collectively constitute the Federal Information Processing Standards Register. The Register

serves as the official source of information in the Federal Govern- ment regarding standards issued by NBS pursuant to the Federal Property and Administrative Services Act of 1949 as amended.

Public Law 89-306 (79 Stat. 1127), and as implemented by Executive Order 1 1717(38 FR 12315, dated May 11, 1973) and Part 6 of Title 15 CFR (Code of Federal Regulations).

NBS Interagency Reports (NBSIR)—A special series of interim or

final reports on work performed by NBS for outside sponsors

(both government and non-government). In general, initial dis- tribution is handled by the sponsor; public distribution is by the National Technical Information Service , Springfield, VA 22161,

in paper copy or microfiche form.
U.S. Department of Commerce

National Bureau of Standards

Washington, D C. 20234

Official Business

Penalty for Private Use $300

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