DOE/EIA-0581(2009) 

The Na tional En ergy Mod el ing 

Sys tem:

An Over view 2009

October 2009

En ergy In for ma tion Ad min is tra tion

Of fice of In te grated Anal y sis and Fore cast ing

U.S. De part ment of En ergy

Wash ing ton, DC 20585

This re port was pre pared by the En ergy In for ma tion Ad min is tra tion, the in de pend ent sta tis ti cal and an a lyt i cal agency

within the U.S. De part ment of En ergy. The in for ma tion con tained herein should be at trib uted to the En ergy In for ma tion

Ad min is tra tion and should not be con strued as ad vo cat ing or re flect ing any pol icy po si tion of the De part ment of En ergy

or any other or ga ni za tion.

This pub li ca tion is on the WEB at:

www.eia.doe.gov/oiaf/aeo/over view/
The Na tional En ergy Mod el ing Sys tem: An Over view 2009 pro vides a sum mary de scrip tion of the Na tional En ergy

Mod el ing Sys tem, which was used to gen er ate the pro jec tions of en ergy pro duc tion, de mand, im ports, and prices

through the year 2030 for the An nual En ergy Out look 2009, (DOE/EIA-0383(2009)), re leased in March 2009.

AEO2009 pres ents na tional pro jec tions of en ergy mar kets for five pri mary cases—a ref er ence case and four ad di -

tional cases that as sume higher and lower eco nomic growth and higher and lower world oil prices than in the ref er -

ence case. The Over view pres ents a brief de scrip tion of the meth od ol ogy and scope of each of the com po nent

mod ules of NEMS. The model doc u men ta tion re ports listed in the ap pen dix of this doc u ment pro vide fur ther de tails. 

The Over view was pre pared by the En ergy In for ma tion Ad min is tra tion, Of fice of In te grated Anal y sis and Fore cast ing

un der the di rec tion of John J. Conti (john.conti@eia.doe.gov, 202/586-2222), Di rec tor, Of fice of In te grated Anal y sis

and Fore cast ing; Paul D. Holtberg (paul.holtberg@eia.doe.gov, 202/586-1284), Di rec tor of the De mand and In te gra -

tion Di vi sion; Jo seph A. Beamon (jbeamon@eia.doe.gov, 202/586-2025), Di rec tor of the Coal and Elec tric Power Di -

vi sion; A. Mi chael Schaal (mi chael,schaal@eia.doe.gov, 202/586-5590), Di rec tor of the Oil and Gas Di vi sion; Glen

E. Sweetnam (glen.sweetnam@eia.doe.gov, 202-586-2188), Di rec tor, In ter na tional, Eco nomic, and Green house

Gases Di vi sion; and Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Se nior Tech ni cal Ad vi sor. 

De tailed ques tions con cern ing the Na tional En ergy Mod el ing Sys tem and the An nual En ergy Out look 2009 may be

ad dressed to the fol low ing an a lysts:

AEO2009 ................. Paul D. Holtberg (paul.holtberg@eia.doe.gov, 202/586-1284)

In te grat ing Mod ule/Car bon Emis sions... Dan iel H. Skelly (dan iel.skelly@eia.doe.gov, 202-586-1722)

Mac ro eco nomic Ac tiv ity Mod ule...... Kay A. Smith (kay.smith@eia.doe.gov, 202/586-1132)

In ter na tional En ergy Mod ule........ Adrian Geagla (adrian.geagla@eia.doe.gov, 202/586-2873)

Res i den tial De mand ............ John H. Cymbalsky (john.cymbalsky@eia.doe.gov, 202/586-4815)

Com mer cial De mand ........... Erin E. Boedecker (erin.boedecker@eia.doe.gov, 202/586-4791)

In dus trial De mand ............. Amelia L. Elson (amelia.elson@eia.doe.gov, 202/586-1420)

Trans por ta tion De mand .......... John D. Ma ples (john.ma ples@eia.doe.gov, 202/586-1757)

Elec tric ity De mand Mod ule ........ Jeffrey S. Jones (jeffrey.jones@eia.doe.gov, 202/586-2038)

Re new able Fu els Mod ule ......... Chris R. Namovicz (chris.namovicz@eia.doe.gov, 202/586-7120)

Oil and Gas Sup ply Mod ule ........ Eddie L. Thomas, Jr. (eddie.thomas@eia.doe.gov, 202/586-5877)

Nat u ral Gas Trans mis sion and Dis tri bu tion 

 Mod ule ................. Jo seph G. Benneche (jo seph.benneche@eia.doe.gov, 202/586-6132)

Pe tro leum Mar ket Mod ule ......... Wil liam S. Brown (wil liam.brown@eia.doe.gov, 202/586-8181)

Coal Mar ket Mod ule ............ Di ane R. Kear ney (di ane.kear ney@eia.doe.gov, 202/586-2415)

AEO2009 is avail able on the EIA Home Page on the Internet (http://www.eia.doe.gov/oiaf/aeo/in dex.html). As sump -

tions un der ly ing the pro jec tions are avail able in As sump tions to the An nual En ergy Out look 2009 at

http://www.eia.doe.gov/oiaf/aeo/as sump tion/in dex.html. Ta bles of re gional pro jec tions and other un der ly ing de tails

of the ref er ence case are avail able at http://www.eia.doe.gov/oiaf/aeo/sup ple ment/in dex.html. Model doc u men ta tion 

re ports and The Na tional En ergy Mod el ing Sys tem: An Over view 2009 are also avail able on the Home Page at

http://tonto.eia.doe.gov/re ports/re ports_kindD.asp?type=model doc u men ta tion.

For or der ing in for ma tion and for ques tions on en ergy sta tis tics, please con tact EIA’s Na tional En ergy In for ma tion

Cen ter.

Na tional En ergy In for ma tion Cen ter, EI-30

En ergy In for ma tion Ad min is tra tion, Forrestal Build ing

Wash ing ton, DC 20585, Tele phone: 202/586-8800

FAX: 202/586-0727, TTY: 202/586-1181

9 a.m. to 5 p.m., east ern time, M-F

E-mail: infoctr@eia.doe.gov

World Wide Web Site: http://www.eia.doe.gov, FTP Site: ftp://ftp.eia.doe.gov

ii Energy Information Administration / The National Energy Modeling System: An Overview 2009

Preface
Energy Information Administration / The National Energy Modeling System: An Overview 2009 iii

Contents

Pref ace .............................................................................. ii

Introduction ........................................................................... 1

Over view of NEMS...................................................................... 6

Car bon Diox ide and Meth ane Emissions .................................................... 12

Mac ro eco nomic Activ ity Module ........................................................... 14

Inter na tional Energy Module .............................................................. 17

Res i den tial Demand Module .............................................................. 20

Com mer cial Demand Module ............................................................. 25

Indus trial Demand Module................................................................ 32

Trans por ta tion Demand Module ........................................................... 37

Elec tric ity Mar ket Module................................................................. 43

Renew able Fuels Module ................................................................ 50

Oil and Gas Sup ply Module............................................................... 54

Nat u ral Gas Trans mis sion and Dis tri bu tion Module ............................................ 59

Petro leum Mar ket Module ................................................................ 64

Coal Mar ket Module..................................................................... 71

Appen dix: Bib li og ra phy .................................................................. 77
Figures

iv Energy Information Administration / The National Energy Modeling System : An Overview 2009

Contents

1. Census Divisions .................................................................... 8

2. National Energy Modeling System ...................................................... 9

3. Macroeconomic Activity Module Structure ................................................. 15

4. International Energy Module Structure.................................................... 18

5. Residential Demand Module Structure.................................................... 21

6. Commercial Demand Module Structure ................................................... 26

7. Industrial Demand Module Structure ..................................................... 33

8. Transportation Demand Module Structure................................................. 39

9. Electricity Market Module Structure ...................................................... 44

10. Electricity Market Module Supply Regions ................................................. 45

11. Renewable Fuels Module Structure ...................................................... 51

12. Oil and Gas Supply Module Regions ..................................................... 55

13. Oil and Gas Supply Module Structure .................................................... 56

14. Natural Gas Transmission and Distribution Module Structure.................................. 60

15. Natural Gas Transmission and Distribution Module Network................................... 62

16. Petroleum Market Module Structure...................................................... 65

17. Petroleum Administration for Defense Districts ............................................. 66

18. Coal Market Module Demand Regions ................................................... 72

19. Coal Market Module Supply Regions ..................................................... 73

20. Coal Market Module Structure .......................................................... 75
Ta bles

v Energy Information Administration / The National Energy Modeling System: An Overview 2009

Contents

1. Characteristics of Selected Modules ..................................................... 6

2. NEMS Residential Module Equipment Summary............................................ 22

3. Characteristics of Selected Equipment ................................................... 23

4. Capital Cost and Efficiency Ratings of Selected Commercial Space Heating Equipment............. 29

5. Commercial End-Use Technology Types.................................................. 30

6. Economic subsectors Within the IDM..................................................... 32

7. Fuel-Consuming Activities for the Energy-Intensive Manufacturing Subsectors .................... 34

8. Selected Technology Characteristics for Automobiles........................................ 38

9. Examples of Midsize Automobile Attributes ................................................ 38

10. Example of Truck Technology Characteristics (Diesel) ....................................... 41

11. Generating Technologies .............................................................. 46

12. 2008 Overnight Capital lcosts (including Contingencies), 2008 Heat Rates, and Online Year by 

 Technology for the AEO2009 Reference Case ............................................. 47

13. Coal Export Component ............................................................... 74
Intro duc tion
The Na tional En ergy Mod el ing Sys tem (NEMS) is a com -

puter-based, en ergy-econ omy mod el ing sys tem of U.S.

through 2030. NEMS pro jects the pro duc tion, imports,

con ver sion, con sump tion, and prices of en ergy, sub ject to

as sump tions on mac ro eco nomic and financial fac tors,

world en ergy mar kets, re source availabil ity and costs,

be hav ioral and tech no log i cal choice cri te ria, cost and per -

for mance char ac ter is tics of energy technologies, and de -

mo graph ics. NEMS was designed and im ple mented by

the En ergy In for ma tion Administration (EIA) of the U.S.

De part ment of Energy (DOE).

The Na tional En ergy Mod el ing Sys tem: An Over view

2009 provides an over view of the struc ture and meth -

od ol ogy of NEMS and each of its com po nents. This

chap ter pro vides a de scrip tion of the de sign and ob jec tives 

of the sys tem, fol lowed by a chap ter on the over all mod el -

ing struc ture and so lu tion al go rithm. The re main der of

the re port sum ma rizes the methodology and scope of

the com po nent mod ules of NEMS. The model de scrip -

tions are in tended for read ers fa mil iar with ter mi nol ogy

from eco nomic, op er a tions re search, and en ergy mod el -

ing. More detailed model doc u men ta tion re ports for all

the NEMS mod ules are also avail able from EIA

(Appendix, “Bibliography”).

Purpose of NEMS

NEMS is used by EIA to pro ject the en ergy, economic,

en vi ron men tal, and se cu rity im pacts on the United

States of al ter na tive en ergy pol i cies and dif fer ent as -

sump tions about en ergy mar kets. The pro jec tion ho ri zon 

is ap prox i mately 25 years into the fu ture. The pro jec tions in 

An nual En ergy Out look 2009 (AEO2009) are from the

pres ent through 2030. This time pe riod is one in which

tech nol ogy, de mo graph ics, and eco nomic con di tions are

suf fi cientlyun der stood in or der to rep re sent en ergy mar -

kets with a rea son able de gree of con fi dence. NEMS

provides a con sis tent frame work for rep re sent ing the

com plex in ter ac tions of the U.S. en ergy sys tem and its

re sponse to a wide va ri ety of al ter na tive assumptions and 

pol i cies or pol icy ini tia tives. As an an nual model, NEMS

can also be used to ex am ine the im pact of new en ergy

pro grams and pol i cies.

En ergy re sources and prices, the de mand for spe cific en -

ergy ser vices, and other char ac ter is tics of en ergy mar -

kets vary widely across the United States. To address

these differences, NEMS is a regional model. The

regional disaggregation for each module reflects the

availability of data, the regional format typically used to

analyze trends in the specific area, geology, and other

factors, as well as the regions determined to be the most

useful for policy analysis. For example, the demand

modules (e.g., residential, commercial, industrial and

transportation) use the nine Census divisions, the

Electricity Market Module uses 15 supply regions based

on the North American Electric Reliability Council (NERC)

regions, the Oil and Gas Supply Modules use 12 supply

regions, including 3 offshore and 3 Alaskan regions, and

the Petroleum Market Module uses 5 regions based on

the Petroleum Administration for Defense Districts.

Base line pro jec tions are de vel oped with NEMS and

pub lished an nu ally in the An nual En ergy Out look

(AEO). In ac cor dance with the re quire ment that EIA re -

main pol icy-neu tral, the AEO projections are gen er ally

based on Fed eral, State, and lo cal laws and reg u la tions in

af fect at the time of the pro jec tion. The po ten tial im pacts of

pend ing or pro posed leg is la tion, reg u la tions, and stan -

dards or of sec tions of leg is la tion that have been en -

acted but that re quire im ple ment ing reg u la tions or

ap pro pri a tions of funds that have not been pro vided or

spec i fied in the leg is la tion it self are not re flected in

NEMS. The first ver sion of NEMS, com pleted in De -

cem ber 1993, was used to de velop the pro jec tions pre -

sented in the An nual En ergy Out look 1994. This re port

de scribes the ver sion of NEMS used for the

AEO2009.

1

The pro jec tions produced by NEMS are not con sid ered to 

be state ments of what will hap pen but of what might

hap pen, given the as sump tions and methodologies used.

As sump tions in clude, for ex am ple, the es ti mated size of

the eco nom i cally re cov er able re source base of fos sil fu -

els, and changes in world en ergy sup ply and de mand. 

The pro jec tions are busi ness-as-usual trend es ti mates,

given known tech no log i cal and de mo graphic trends.

Analytical Capability

NEMS can be used to an a lyze the ef fects of ex ist ing and

pro posed gov ern ment laws and reg u la tions related to

en ergy pro duc tion and use; the po ten tial impact of new

and ad vanced en ergy pro duc tion, conver sion, and con -

sump tion tech nol o gies; the im pact and cost of green -

house gas con trol; the im pact of in creased use of

re new able en ergy sources; and the po ten tial sav ings

Energy Information Administration / The National Energy Modeling System: An Overview 2009 1

Introduction

1 Energy Information Administration, Annual Energy Outlook 2009, DOE/EIA-0383(2009) (Washington, DC, March

2009)
from in creased ef fi ciency of energy use; and the im pact of

reg u la tions on the use of al ter na tive or re for mu lated 

fuels.

In ad di tion to pro duc ing the anal y ses in the AEO, NEMS

is used for one-time analytical re ports and pa pers, such

as An Up dated An nual En ergy Out look 2009 Ref er ence

Case Re flect ing Pro vi sions of the Amer i can Re cov ery

and Re in vest ment Act and Re cent Changes in the Eco -

nomic Out look,

2 which up dates the AEO2009 ref er ence

case to re flect the en act ment of the Amer i can Re cov ery

and Re in vest ment Act in Feb ru ary 2009 and to adopt a

re vised mac ro eco nomic out look for the U.S. and global

econ o mies. The re vised AEO2009 ref er ence case will be

used as the start ing point for pend ing and fu ture anal y ses

of pro posed en ergy and en vi ron mental leg is la tion. Other

an a lyt i cal pa pers, which either de scribe the

assumptions and meth od ol ogy of the NEMS or look at cur -

rent en ergy mar kets is sues, are pre pared us ing the NEMS. 

Many of these pa pers are pub lished in the Is sues In Fo cus

sec tion of the AEO. Past and cur rent anal y ses are avail -

able at http://www.eia.doe.gov/oiaf/aeo/otheranalysis/

aeo_analyes.html.

NEMS has also been used for a num ber of spe cial anal -

y ses at the re quest of the Ad min is tra tion, U.S. Con -

gress, other of fices of DOE and other gov ernment

agen cies, who spec ify the sce nar ios and assumptions

for the anal y sis. Some re cent ex am ples in clude:

• En ergy Mar ket and Eco nomic Im pacts of H.R.

2454, the Amer i can Clean En ergy and Se cu rity Act

of 2009,

3 re quested by Chair man Henry Waxman

and Chair man Ed ward Markey to an a lyze the im -

pacts of H.R. 2454, the Amer i can Clean En ergy and 

Se cu rity Act of 2009 (ACESA), which was passed

by the House of Rep re sen ta tives on June 26, 2009. 

ACESA is a com plex bill that reg u lates emis sions of 

green house gases through mar ket-based

mech a nisms, ef fi ciency pro grams, and eco nomic

in cen tives.

• Im pacts of a 25-Per cent Re new able Elec tric ity

Stan dard as Pro posed in the Amer i can Clean En -

ergy and Se cu rity Act,

4 re quested by Sen a tor

Markey to an a lyze the ef fects of a 25-per cent Fed -

eral re new able elec tric ity stan dard (RES) as in -

cluded in the dis cus sion draft of broader leg is la tion,

the Amer i can Clean En ergy and Se cu rity Act.

• Light-Duty Die sel Ve hi cles: Ef fi ciency and Emis -

sions At trib utes and Mar ket Is sues,

5 re quested by

Sen a tor Ses sions to an a lyze the en vi ron men tal and 

en ergy ef fi ciency at trib utes of die sel-fu eled

light-duty ve hi cles (LDV’s), in clud ing com par i son of 

the char ac ter is tics of the ve hi cles with those of sim i -

lar gas o line-fu eled, E85-fu eled, and hy brid ve hi -

cles, as well as a dis cus sion of any tech ni cal,

eco nomic, reg u la tory, or other ob sta cles to in creas -

ing the use of die sel-fu eled ve hi cles in the United

States.

• The Im pact of In creased Use of Hy dro gen on Pe tro -

leum Con sump tion and Car bon Di ox ide Emis -

sions,

6 re quested by Sen a tor Dorgan to an a lyze the 

im pacts on U.S. en ergy im port de pend ence and

emis sions re duc tions re sult ing from the com mer -

cial iza tion of ad vanced hy dro gen and fuel cell tech -

nol o gies in the trans por ta tion and dis trib uted

gen er a tion mar kets.

• Anal y sis of Crude Oil Pro duc tion in the Arc tic Na -

tional Wild life Ref uge,

7 re quested by Sen a tor

Stevens to ac cess the im pact of Fed eral oil and nat u -

ral gas leas ing in the coastal plain of the Arc tic Na -

tional Wild life Ref uge in Alaska.

• En ergy Mar ket and Eco nomic Im pacts of S.2191,

the Lieberman-Warner Cli mate Se cu rity Act of

2 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Introduction

2 Energy Information Administration, An Updated Annual Energy Outlook 2009 Reference Case Reflecting Provisions of 

the American Recovery and Reinvestment Act and Recent Changes in the Economic Outlook, SR/OIAF/2009-4

(Washington, DC, April 2009).

3 Energy Information Administration, Energy Market and Economic Impacts of H.R. 2454, the American Clean energy

and Security Act of 2009, SR/OIAF/2009-05 (Washington, DC, August 2009).

4 Energy Information Administration, Impacts of a 25-Percent Renewable Electricity Standard as proposed in the

American Clean Energy and Security Act Discussion, SR/OIAF/2009-03 (Washington, DC, April 2009)

5 Energy Information Administration, Light-Duty Diesel Vehicles: Efficiency and Emissions Attributes and Market Issues, 

SR/OIAF/2009-02 (Washington, DC, February 2009).

6 Energy Information Administration, The Impact of Increased Use of Hydrogen on Petroleum Consumption and Carbon

Light-Duty Diesel Vehicles: Efficiency and Emissions Attributes and Market Issues, SR/OIAF/2008-04 (Washington,

DC, September 2008).

7 Energy Information Administration, Analysis of Crude Oil Production in the Arctic National Wildlife Refuge,

SR/OIAF/2008-03 (Washington, DC, May 2008).
2007,

8 re quested by Sen a tors Lieberman, Warner,

Inhofe, Voinovich, and Barrasso to an a lyze the im -

pacts of the green house gas cap-and-trade pro gram

that would be es tab lished un der Ti tle I of S.2191.

• En ergy Mar ket and Eco nomic Im pacts of S.1766,

the Low Car bon Econ omy Act of 2007,

9 re quested

by Sen a tors Bingaman and Spec ter to an a lyze the im -

pact of the man da tory green house gas al low ance

pro gram un der S.1766 de signed to main tain cov ered

emis sions at ap prox i mately 2006 lev els in 2020, 1990 

lev els in 2030, and at least 60 per cent be low 1990

lev els by 2050.

Representations of Energy Market

Interactions

NEMS is de signed to rep re sent the im por tant interac tions 

of sup ply and de mand in U.S. en ergy markets. In the

United States, en ergy mar kets are driven pri mar ily by the 

fun da men tal eco nomic interac tions of sup ply and de -

mand. Gov ern ment regulations and pol i cies can ex ert

con sid er able in flu ence, but the ma jor ity of de ci sions af -

fect ing fuel prices and con sump tion pat terns, re source

al lo ca tion, and energy tech nol o gies are made by pri -

vate in di vid u als who value at trib utes other than life cy -

cle costs or com pa nies at tempt ing to op ti mize their own 

economic in ter ests. NEMS rep re sents the mar ket

behavior of the pro duc ers and con sum ers of en ergy at a

level of de tail that is use ful for an a lyz ing the implications of

tech no log i cal im prove ments and pol icy initiatives.

Energy Supply/Conversion/Demand Interactions

NEMS is a mod u lar sys tem. Four end-use de mand

mod ules rep re sent fuel consumption in the res i den tial,

com mer cial, trans por ta tion, and in dus trial sec tors, sub -

ject to de liv ered fuel prices, mac ro eco nomic in flu -

ences, and tech nol ogy char ac ter is tics. The pri mary fuel

sup ply and con ver sion mod ules com pute the lev els of do -

mes tic production, im ports, trans por ta tion costs, and

fuel prices that are needed to meet do mes tic and ex port 

demands for en ergy, sub ject to re source base char ac -

teristics, in dus try in fra struc ture and tech nol ogy, and

world mar ket con di tions. The mod ules in ter act to solve

for the eco nomic sup ply and de mand bal ance for each

fuel. Be cause of the mod u lar de sign, each sec tor can

be rep re sented with the meth od ol ogy and the level of

de tail, in clud ing re gional de tail, ap pro pri ate for that sec -

tor. The mod u lar ity also facilitates the anal y sis,

main te nance, and test ing of the NEMS com po nent mod -

ules in the multi-user environment.

Domestic Energy System/Economy Interactions 

The gen eral level of eco nomic ac tiv ity, rep re sented by

gross do mes tic prod uct, has tra di tion ally been used as

a key ex plan a tory vari able or driver for projections of en -

ergy con sump tion at the sec toral and re gional lev els. In

turn, en ergy prices and other energy sys tem ac tiv i ties in -

flu ence eco nomic growth and ac tiv ity. NEMS cap tures

this feed back be tween the do mes tic econ omy and the

en ergy sys tem. Thus, changes in en ergy prices af fect

the key mac ro eco nomic vari ables—such as gross do -

mes tic prod uct, dis pos able per sonal in come, in dus trial

out put, housing starts, em ploy ment, and in ter est

rates—that drive en ergy con sump tion and ca pac ity ex -

pan sion de ci sions.

Domestic/World Energy Market Interactions

World oil prices play a key role in do mes tic en ergy sup -

ply and de mand de ci sion mak ing and oil price as sump -

tions are a typ i cal start ing point for en ergy sys tem

pro jec tions. The level of oil pro duc tion and con sump -

tion in the U.S. en ergy sys tem also has a sig nif i cant in -

flu ence on world oil mar kets and prices. In NEMS, an

in ter na tional mod ule represents the re sponse of world

oil mar kets (sup ply and de mand) to as sumed world oil

prices. The re sults/out puts of the mod ule are in ter na -

tional liq uids con sump tion and pro duc tion by re gion,

and a crude oil sup ply curve rep re sent ing in ter na tional

crude oil sim i lar in qual ity to West Texas In ter me di ate

that is avail able to U.S. mar kets through the Pe tro leum

Mar ket Mod ule (PMM) of NEMS. The sup ply-curve cal -

cu la tions are based on his tor i cal mar ket data and a

world oil sup ply/de mand bal ance, which is de vel oped

from re duced-form mod els of in ter na tional liq uids sup -

ply and de mand, cur rent in vest ment trends in ex plo ra -

tion and de vel op ment, and long-term re source

eco nom ics for 221 coun tries/ter ri to ries. The oil pro duc -

tion es ti mates in clude both conventional and

unconventional supply recovery technologies.

Introduction

Energy Information Administration / The National Energy Modeling System: An Overview 2009 3

8 Energy Information Administration, Energy Market and Economic Impacts of S.2191, the Lieberan-Warner Climate

Security Act of 2007, SR/OIAF/2008-01 (Washington, DC, April 2008).

9 Energy Information Administration, Energy Market and Economic Impacts of S.1766, the Low Carbon Economy Act of

2007, SR/OIAF/2007-06 (Washington, DC, January 2008).
Economic Decision Making Over Time

The pro duc tion and con sump tion of en ergy prod ucts

to day are in flu enced by past in vest ment de ci sions to

de velop en ergy re sources and ac quire en ergy-us ing

cap i tal stock. Sim i larly, the pro duc tion and

consumption of en ergy in a fu ture time pe riod will be

influenced by de ci sions made to day and in the past.

Cur rent in vest ment de ci sions de pend on expectations

about fu ture mar kets. For ex am ple, ex pec ta tions of ris -

ing en ergy prices in the fu ture in crease the like li hood of

cur rent de ci sions to in vest in more en ergy-ef fi cient tech -

nol o gies or al ter na tive en ergy sources. A va ri ety of as -

sump tions about plan ning horizons, the for ma tion of

ex pec ta tions about the future, and the role of those ex -

pec ta tions in eco nomic de ci sion mak ing are ap plied

within the in di vid ual NEMS mod ules.

Technology Representation

A key fea ture of NEMS is the rep re sen ta tion of

technology and tech nol ogy im prove ment over time. Five

of the sec tors—res i den tial, com mer cial, transportation, 

elec tric ity gen er a tion, and re fin ing—in clude ex ten sive

treat ment of individual tech nol o gies and their char ac ter -

is tics, such as the ini tial cap i tal cost, op er at ing cost, date

of avail abil ity, ef fi ciency, and other char ac ter is tics spe -

cific to the par tic u lar tech nol ogy. For ex am ple, tech no -

log i cal prog ress in light ing tech nol o gies re sults in a

grad ual re duc tion in cost and is mod eled as a function

of time in these end-use sec tors. In ad di tion, the elec tric ity

sec tor ac counts for tech no log i cal op ti mism in the cap i tal

costs of first-of-a-kind gen er at ing technologies and for a

de cline in cost as ex pe ri ence with the tech nol o gies is

gained both do mes ti cally and internationally. In each of

these sec tors, equip ment choices are made for in di vid -

ual tech nol o gies as new equip ment is needed to meet

grow ing de mand for energy ser vices or to re place re tired

equip ment.

In the other sec tors—in dus trial, oil and gas sup ply, and

coal sup ply—the treat ment of tech nol o gies is more lim -

ited due to a lack of data on in di vid ual technologies. In the

in dus trial sec tor, only the com bined heat and power and

mo tor tech nol o gies are ex plic itly con sid ered and char ac -

ter ized. Cost re duc tions resulting from tech no log i cal

prog ress in com bined heat and power tech nol o gies are

rep re sented as a func tion of time as ex pe ri ence with the

tech nol o gies grows. Tech no log i cal prog ress is not ex -

plic itly mod eled for the in dus trial mo tor tech nol o gies.

Other technologies in the en ergy-in ten sive in dus tries

are represented by tech nol ogy bun dles, with tech nol ogy

possibility curves rep re sent ing ef fi ciency im prove ment

over time. In the oil and gas sup ply sec tor, technological

prog ress is rep re sented by econometrically es ti mated

im prove ments in find ing rates, suc cess rates, and

costs. Pro duc tiv ity im prove ments over time rep re sent

tech no log i cal prog ress in coal production.

External Availability

In ac cor dance with EIA re quire ments, NEMS is fully doc u -

mented and ar chived. EIA has been run ning NEMS on

four EIA ter mi nal serv ers and sev eral dual-pro ces sor

per sonal com put ers (PCs) us ing the Win dows XP op er -

at ing sys tem. The ar chive file pro vides the source lan -

guage, in put files, and out put files to rep li cate the

An nual En ergy Out look re ference case runs on an iden -

ti cally equipped com puter; how ever, it does not in clude

the pro pri etary por tions of the model, such as the IHS

Global In sight, Inc. (for merly DRI-WEFA) mac ro eco -

nomic model and the optimization mod el ing li brar ies.

NEMS can be run on a high-pow ered in di vid ual PC as

long as the required pro pri etary soft ware re sides on the

PC. Because of the com plex ity of NEMS, and the rel a -

tively high cost of the pro pri etary soft ware, NEMS is not

widely used out side of the De part ment of En ergy. How -

ever, NEMS, or por tions of it, is in stalled at the Law rence

Berke ley Na tional Lab o ra tory, Oak Ridge Na tional Lab o -

ra tory, the Elec tric Power Re search In sti tute, the Na -

tional En ergy Tech nol ogy Laboratory, the Na tional

Re new able En ergy Lab o ra tory, sev eral pri vate con sult -

ing firms, and a few uni ver si ties. 

4 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Introduction
Over view of NEMS
NEMS ex plic itly rep re sents do mes tic en ergy markets

by the eco nomic de ci sion mak ing in volved in the pro -

duc tion, con ver sion, and consumption of en ergy prod -

ucts. Where pos si ble, NEMS in cludes ex plicit

rep re sen ta tion of en ergy technologies and their char -

ac ter is tics. Since en ergy costs, avail abil ity, and

energy-con sum ing char ac ter is tics vary widely across

re gions, con sid er able re gional de tail is in cluded. Other

de tails of pro duc tion and con sump tion are rep re sented

to fa cil i tate pol icy anal y sis and en sure the va lid ity of the

re sults. A sum mary of the de tail pro vided in NEMS is

shown in Ta ble 1.

6 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Overview of NEMS

 

 Energy Activity Categories Regions

 Res i den tial De mand Twenty four end-use ser vices

Three hous ing types

Fifty end-use tech nol o gies

Nine Cen sus divisions

 Com mer cial demand Ten end-use ser vices

Eleven build ing types

Eleven dis trib uted gen er a tion tech nol o gies

Sixty-three end-use technologies

Nine Cen sus divisions

 In dus trial demand Seven en ergy-in ten sive in dus tries

Eight non-en ergy-in ten sive in dus tries

Six non-man u fac tur ing in dus tries

Cogeneration

Four Cen sus re gions, shared to 

 nine Cen sus di vi sions

 Trans por ta tion demand Six car sizes

Six light truck sizes

Sixty-three con ven tional fuel-sav ing tech nol o gies 

 for light-duty ve hi cles

Gas o line, die sel, and four teen al ter na tive-fuel

 ve hi cle tech nol o gies for light-duty ve hi cles

Twenty vin tages for light-duty ve hi cles

Re gional, nar row, and wide-body air craft

Six ad vanced air craft tech nol o gies

Light, me dium, and heavy freight trucks

Thirty-seven ad vanced freight truck tech nol o gies

Nine Cen sus divisions

 Elec tric ity Eleven fos sil gen er a tion tech nol o gies

Two dis trib uted gen er a tion tech nol o gies

Eight re new able gen er a tion tech nol o gies

Con ven tional and ad vanced nu clear

Stor age tech nol ogy to model load shift ing

Mar ginal and av er age cost pric ing

Gen er a tion ca pac ity ex pan sion

Seven en vi ron men tal con trol tech nol o gies

Fif teen elec tric ity sup ply re gions (in clud ing Alaska and

 Ha waii) based on the North Amer i can Elec tric Re li abil ity 

 Coun cil re gions and sub re gions

Nine Cen sus di vi sions for de mand

Fif teen elec tric ity sup ply re gions

 Renewables Two wind tech nol o gies—on shore and off shore—, 

 geo ther mal, so lar ther mal, so lar pho to vol taic,

 land fill gas, bio mass, con ven tional hydropower

 Oil supply Lower-48 on shore

Lower-48 deep and shal low off shore

Alaska on shore and off shore

Six lower 48 on shore re gions

Three lower 48 off shore re gions

Three Alaska re gions

 Nat u ral gas sup ply Con ven tional lower-48 on shore

Lower-48 deep and shal low off shore

Coalbed meth ane

Gas shales

Tight sands

Six lower 48 on shore re gions

Three lower 48 off shore re gions

Three Alaska regions

 Nat u ral gas trans mis sion and distribution Core vs. noncore de liv ered prices

Peak vs. off-peak flows and prices

Pipe line ca pac ity ex pan sion

Pipe line and dis trib u tor tar iffs

Can ada, Mex ico, and LNG im ports and ex ports

Alaska gas con sump tion and sup ply

Twelve lower 48 re gions

Ten pipe line bor der points

Eight LNG im port re gions

 Refining Five crude oil cat e go ries

Four teen prod uct cat e go ries

More than 40 dis tinct tech nol o gies

Re fin ery ca pac ity ex pan sion

Five re fin ery re gions based on the Pe tro leum

 Ad min is tra tion for De fense Dis tricts

 Coal supply Three sul fur cat e go ries

Four ther mal cat e go ries

Un der ground and sur face min ing types

Im ports and Ex ports

Four teen sup ply re gions

Four teen de mand re gions

Sev en teen ex port re gions

Twenty im port re gions

Table 1. Characteristics of Selected Modules
Ma jor As sump tions

Each mod ule of NEMS em bod ies many as sump tions and 

data to char ac ter ize the fu ture pro duc tion, conver sion, or

con sump tion of en ergy in the United States. Two of the

more im por tant fac tors in flu enc ing en ergy mar kets are

eco nomic growth and oil prices.

The AEO2009 in cludes five pri mary fully-in te grated

cases: a re ference case, high and low eco nomic growth

cases, and high and low oil price cases. The primary

de ter mi nant for dif fer ent eco nomic growth rates are as -

sump tions about growth in the la bor force and pro duc tiv -

ity, while the long-term oil price paths are based on

ac cess to and cost of oil from the non-Or ga ni za tion of 

Pe tro leum Ex port ing Coun tries (OPEC), OPEC sup ply

de ci sions, and the sup ply po ten tial of un con ven tional liq -

uids, as well as the de mand for liq uids.

In ad di tion to the five pri mary fully-in te grated cases,

AEO2009 in cludes 34 other cases that ex plore the im pact 

of vary ing key as sump tions in the individual com po nents

of NEMS. Many of these cases involve changes in the as -

sump tions that im pact the pen e tra tion of new or im -

proved tech nol o gies, which is a ma jor un cer tainty in

for mu lat ing pro jec tions of fu ture en ergy mar kets. Some

of these cases are run as fully in te grated cases (e.g., in te -

grated 2009 tech nol ogy case, in te grated high tech nol ogy 

case, low and high renewables tech nol ogy cost cases,

slow and rapid oil and gas tech nol ogy cases, and low and 

high coal cost cases). Oth ers ex ploit the mod u lar struc -

ture of NEMS by run ning only a por tion of the en tire mod -

el ing sys tem in or der to fo cus on the first-or der im pacts

of changes in the as sump tions (e.g., 2009, high, and

best avail able tech nol ogy cases in the res i den tial and

com mer cial sec tors, 2009 and high tech nol ogy cases in

the in dus trial sec tor and, low and high tech nol ogy cases in

the trans por ta tion sec tor).

NEMS Modular Structure

Over all, NEMS rep re sents the be hav ior of en ergy mar -

kets and their in ter ac tions with the U.S. economy. The

model achieves a sup ply/de mand bal ance in the

end-use de mand re gions, de fined as the nine Cen sus di -

vi sions (Fig ure 1), by solv ing for the prices of each en ergy

type that will bal ance the quantities pro duc ers are will ing

to sup ply with the quan ti ties con sum ers wish to con sume. 

The sys tem re flects mar ket eco nom ics, in dus try struc -

ture, and ex ist ing en ergy pol i cies and reg u la tions that in -

flu ence market be hav ior.

NEMS con sists of four sup ply mod ules (oil and gas, nat -

u ral gas trans mis sion and dis tri bu tion, coal mar ket, and

re new able fu els); two con ver sion mod ules (elec tric ity mar -

ketand pe tro leum mar ket); four end-use de mand mod -

ules (res i den tial de mand, com mer cial de mand,

in dus trial de mand, and trans por ta tion de mand); one

mod ule to sim u late en ergy/economy in ter ac tions (mac ro -

eco nomic ac tiv ity); one module to sim u late in ter na tional

en ergy mar kets (in ter na tional energy); and one mod ule

that pro vides the mech a nism to achieve a gen eral mar -

ket equi lib rium among all the other mod ules (in te grat ing

mod ule). Fig ure 2 depicts the high-level structure of

NEMS.

Be cause en ergy mar kets are het er o ge neous, a sin gle

meth od ol ogy does not ad e quately rep re sent all supply,

con ver sion, and end-use de mand sec tors. The mod u -

lar ity of the NEMS de sign pro vides the flexibility for each

com po nent of the U.S. en ergy sys tem to use the meth od -

ol ogy and cov er age that is most appropriate. Fur ther more, 

mod u lar ity pro vides the capability to ex e cute the mod ules

in di vid u ally or in collec tions of mod ules, which fa cil i tates

the de velopment and anal y sis of the sep a rate com po -

nent modules. The in ter ac tions among these mod ules

are controlled by the in te grat ing mod ule.

The NEMS global data struc ture is used to co or di nate

and com mu ni cate the flow of in for ma tion among the

mod ules. These data are passed through com mon in ter -

faces via the in te grat ing mod ule. The global data struc -

ture in cludes en ergy mar ket prices and con sump tion;

mac ro eco nomic vari ables; en ergy pro duc tion, trans por -

ta tion, and con ver sion information; and cen tral ized model

con trol vari ables, parameters, and as sump tions. The

global data struc ture ex cludes vari ables that are de fined

lo cally within the mod ules and are not com mu ni cated to

other modules.

A key sub set of the vari ables in the global data structure is

the end-use prices and quan ti ties of fu els that are used

to equilibrate the NEMS en ergy balance in the con ver -

gence al go rithm. These de liv ered prices of en ergy and

the quan ti ties de manded are defined by prod uct, re gion,

and sec tor. The de liv ered prices of fuel en com pass all

the ac tiv i ties nec es sary to pro duce, im port, and trans -

port fu els to the end user. The re gions used for the price

and quan tity vari ables in the global data struc ture are the

nine Cen sus di vi sions. The four Cen sus re gions (shown in

Fig ure 1 by breaks be tween State groups) and nine Cen -

sus di vi sions are a com mon, main stream level of

regionality widely used by EIA and other or ga ni za tions for

data col lec tion and analysis.

Energy Information Administration / The National Energy Modeling System: An Overview 2009 7

Overview of NEMS
8 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Overview of NEMS

Fig ure 1. Cen sus Division
Integrating Module

The NEMS integrating module controls the entire

NEMS solution process as it iterates to determine a

general market equilibrium across all the NEMS

modules. It has the following functions:

• Man ages the NEMS global data struc ture

• Ex e cutes all or any of the user-se lected mod ules

in an it er a tive con ver gence al go rithm

• Checks for con ver gence and re ports vari ables that

re main out of con ver gence

• Im ple ments con ver gence re lax ation on se lected

vari ables be tween it er a tions to ac cel er ate con ver -

gence

• Up dates ex pected val ues of the key NEMS vari -

ables.

The in te grat ing mod ule ex e cutes the de mand, con ver -

sion, and sup ply mod ules iteratively un til it achieves an

eco nomic equi lib rium of sup ply and demand in all the

con sum ing and pro duc ing sec tors. Each mod ule is

called in se quence and solved, assuming that all other

vari ables in the en ergy markets are fixed. The mod ules

are called iteratively un til the end-use prices and quan ti ties

remain constant within a specified tolerance, a con di tion

defined as convergence. Equilibration is achieved

annually throughout the projection period, cur rently

through 2030, for each of the nine Census divisions.

In ad di tion, the mac ro eco nomic ac tiv ity and in ter na -

tional en ergy mod ules are ex e cuted iteratively to in cor -

po rate the feed back on the econ omy and in ter na tional

en ergy mar kets from changes in the do mes tic en ergy

mar kets. Con ver gence tests check the sta bil ity of a set

of key mac ro eco nomic and in ter na tional trade vari ables

in re sponse to in ter ac tions with the do mes tic en ergy

system.

The NEMS al go rithm ex e cutes the sys tem of modules

un til con ver gence is reached. The so lu tion procedure for 

one it er a tion in volves the ex e cu tion of all the com po nent

mod ules, as well as the up dat ing of ex pec ta tion vari -

ables (re lated to fore sight assumptions) for use in the

next it er a tion. The sys tem is executed se quen tially for

Energy Information Administration / The National Energy Modeling System: An Overview 2009 9

Overview of NEMS

Oil and Gas

Supply Module

Natural Gas

Transmission

and Distribution 

Module

Coal Market

Module

Renewable

Fuels Module

Supply

Residential

Demand Module

Commercial

Demand

Module

Transportation

Demand Module

Industrial

Demand Module

Demand

Electricity

Market

Module

Petroleum

Market

Module

Conversion

Macroeconomic

Activity

Module

International

Energy

Module

Integrating

Module

Fig ure 2. Na tional En ergy Mod el ing Sys tem
each year in the pro jec tion period. Dur ing each it er a tion,

the mod ules are ex e cuted in turn, with in ter ven ing

convergence checks that iso late spe cific mod ules that

are not con verg ing. A con ver gence check is made for

each price and quan tity vari able to see whether the per -

cent age change in the vari able is within the assumed

tol er ance. To avoid un nec es sary it er a tions for changes in

in sig nif i cant val ues, the quan tity convergence check is

omit ted for quan ti ties less than a user-spec i fied min i -

mum level. The or der of ex e cu tion of the mod ules may af -

fect the rate of con ver gence but will gen er ally not pre vent

con ver gence to an equilibrium so lu tion or sig nif i cantly

al ter the re sults. An op tional re lax ation rou tine can be 

ex e cuted to dampen swings in so lu tion val ues be -

tween iterations. With this op tion, the cur rent it er a tion

val ues are re set part way be tween so lu tion val ues from

the cur rent and pre vi ous it er a tions. Because of the

modular structure of NEMS and the it er a tive so lu tion al -

go rithm, any sin gle mod ule or sub set of mod ules can

be ex e cuted in de pend ently. Mod ules not ex e cuted are

by passed in the call ing sequence, and the val ues they

would cal cu late and pro vide to the other mod ules are held

fixed at the val ues in the global data struc ture, which are

the so lu tion val ues from a pre vi ous run of NEMS. This

flex i bil ity is an aid to in de pend ent de vel op ment, de bug -

ging, and anal y sis.

10 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Overview of NEMS
Car bon Diox ide Emis sions
The emis sions pol icy submodule, part of the in te grat ing 

mod ule, es ti mates en ergy-re lated car bon di ox ide emis -

sions and is ca pa ble of rep re sent ing two re lated green -

house gas (GHG) emis sions pol i cies: a cap-and-trade

pro gram and a car bon dioxide emission tax. 

Car bon di ox ide emis sions are cal cu lated from fos -

sil-fuel en ergy con sump tion and fuel-spe cific emis -

sions fac tors. The es ti mates are ad justed for car bon

cap ture tech nol o gies where ap pli ca ble. Car bon di ox -

ide emis sions from en ergy use are de pend ent on the

car bon con tent of the fos sil fuel, the frac tion of the fuel

con sumed in com bus tion, and the con sump tion of that

fuel. The prod uct of the car bon con tent at full com bus -

tion and the com bus tion frac tion yields an ad justed car -

bon emis sion fac tor. The ad justed car bon emis sions

fac tors, one for each fuel and sec tor, are provided as

input to the emissions policy module. 

Data on past car bon di ox ide emis sions and emis sions

fac tors are up dated each year from the EIA’s an nual in -

ven tory, Emis sions of Green house Gases the United

States.

10 To pro vide a more com plete ac count ing of

green house gas emis sions con sis tent with that in ven -

tory, a base line emis sions pro jec tion for the non-en ergy 

car bon di ox ide and other green house gases may be

spec i fied as an exogenous input. 

To rep re sent car bon tax or cap-and-trade pol i cies, an

in cre men tal cost of us ing each fos sil fuel, on a dol -

lar-per-Btu ba sis, is cal cu lated based the car bon di ox -

ide emis sions fac tors and the per-ton car bon di ox ide 

tax or cap-and-trade al low ance cost. This in cre men tal

cost, or car bon price ad just ment, is added to the cor re -

spond ing en ergy prices as seen by the en ergy de mand

mod ules. These price ad just ments in flu ence en ergy

de mand and en ergy-re lated CO2 emis sions, as well as

macroeconomic trends. 

Un der a cap-and-trade pol icy, the al low ance or per mit

price is de ter mined in an it er a tive so lu tion pro cess such 

that the an nual cov ered emis sions match the cap each

year. If al low ance bank ing is per mit ted, a con -

stant-growth al low ance price path is found such that

cu mu la tive emis sions over the bank ing in ter val match

the cu mu la tive cov ered emis sions. To the ex tent the

pol i cies cover green house gases other than CO2, the

cov er age as sump tions and abate ment po ten tial for the

gases must be pro vided as in put. In past stud ies, EIA

has drawn on work by the En vi ron men tal Pro tec tion

Agency (EPA) to rep re sent ex og e nous es ti mates of

emis sions abate ment and the use of off sets as a func -

tion of al low ance prices. 

Rep re sent ing spe cific cap-and-trade pol i cies in NEMS

al most al ways re quires cus tom iz ation of the model. 

Among the is sues that must be ad dressed are what

gases and sec tors are cov ered, what off sets are el i gi -

ble as com pli ance mea sures, how the rev e nues raised

by the taxes or al low ance sales are used, how al low -

ances or the value of al low ances are dis trib uted, and

how the dis tri bu tion af fects en ergy pric ing or the cost of

us ing en ergy.

12 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Carbon Dioxide and Methane Emissions

10 Energy Information Administration, Emissions of Greenhouse Gases in the United States 2007, DOE/EIA-0573 (2007)

(Washington, DC, December 2008), web site www.eia.doe.gov/oiaf/1605/ggrpt/index.html.
Mac ro eco nomic Activity

Mod ule
The Mac ro eco nomic Ac tiv ity Mod ule (MAM) links

NEMS to the rest of the econ omy by pro vid ing projections

of eco nomic driver vari ables for use by the sup ply, de -

mand, and con ver sion mod ules of NEMS. The der i va tion

of the base line mac ro eco nomic pro jec tion lays a foun -

da tion for the de ter mi na tion of the en ergy de mand and

sup ply fore cast. MAM is used to pres ent al ter na tive mac ro -

eco nomic growth cases to pro vide a range of un cer tainty

about the growth po ten tial for the econ omy and its likely

con se quences for the energy sys tem. MAM is also able

to ad dress the mac ro eco nomic im pacts as so ci ated with

chang ing en ergy mar ket con di tions, such as al ter na tive

world oil price as sump tions. Out side of the AEO set ting, 

MAM rep re sents a sys tem of linked mod ules which can

as sess the po ten tial im pacts on the econ omy of

changes in en ergy events or pol icy pro pos als. These

eco nomic im pacts then feed back into NEMS for an in te -

grated so lu tion. MAM con sists of five submodules:

• Global In sight Model of the U.S. Econ omy 

• Global In sight In dus try Model

• Global In sight Em ploy ment Model

• EIA Re gional Model

• EIA Com mer cial Floorspace Model

The IHS Global In sight Model of the U.S. Econ omy

(Macroeconomic Model) is the same model used by IHS

Global In sight, Inc. to gen er ate the eco nomic pro jec -

tions be hind the com pany’s monthly as sess ment of the

U.S. econ omy. The In dus try and Em ploy ment

submodules, are de riv a tives of IHS Global In sight’s In -

dus try and Em ploy ment Mod els, and have been tai lored

to pro vide the in dus try and re gional de tail re quired by

NEMS. The Re gional and Com mer cial Floorspace

Submodules were developed by EIA to com ple ment the 

set of Global Insight mod els, pro vid ing a fully in te grated 

ap proach to pro ject ing eco nomic ac tiv ity at the na -

tional, in dus try and re gional lev els. The set of mod els is 

de signed to run in a re cur sive man ner (see Fig ure 3).

Global In sight’s Mac ro eco nomic Model de ter mines the

na tional econ omy’s growth path and fi nal demand mix.

The Global In sight Mac ro eco nomic Model pro vides pro -

jec tions of over 1300 con cepts span ning fi nal de mands,

ag gre gate sup ply, prices, in comes, in ter na tional

trade, in dus trial de tail, in ter est rates and fi nan cial flows.

The In dus try Submodule takes the fi nal de mand

projections from the Mac ro eco nomic Submodule as in -

puts to pro vide pro jec tions of out put and other key in -

dicators for 61 sec tors, cov er ing the en tire econ omy.

This is later ag gre gated to 41 sec tors to pro vide

information to NEMS. The In dus try Submodule in sures

that sup ply by in dus try is con sis tent with the fi nal

demands (con sump tion, in vest ment, gov ern ment

spending, exports and imports) generated in the

Macroeconomic Submodule.

The Em ploy ment Submodule takes the in dus try out put

pro jec tions from the In dus try Submodule and na tional

wage rates, pro duc tiv ity trends and av er age work-week 

trends from the Mac ro eco nomic Submodule to project

em ploy ment for the 41 NEMS in dus tries. The sum of

non-ag ri cul tural em ploy ment is con strained to sum to

the na tional to tal pro jected by the Macroeconomic

Submodule.

The Re gional Submodule de ter mines the level of in dus try 

out put and em ploy ment, pop u la tion, in comes, and hous -

ing ac tiv ity in each of nine Cen sus re gions. The Com mer -

cial Floorspace Submodule cal cu lates re gional

floorspace for 13 types of build ing use by Cen sus

Divi sion.

14 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Macroeconomic Activity Module

MAM Outputs Inputs from NEMS Exogenous Inputs

Gross do mes tic prod uct

Other eco nomic ac tiv ity mea sures, in clud ing hous ing

 starts, com mer cial floorspace growth, ve hi cle sales, 

 pop u la tion

Price in di ces and de fla tors

Pro duc tion and em ploy ment for man u fac tur ing

Pro duc tion and em ploy ment for nonmanufacturing

In ter est rates

Pe tro leum, nat u ral gas, coal, and

 elec tric ity prices

Oil, nat u ral gas, and coal production

Elec tric and gas in dus try out put

Re fin ery out put

End-use en ergy con sump tion by fuel

Mac ro eco nomic vari ables de fin ing al ter na tive

 eco nomic growth cases
In te grated fore casts of NEMS cen ter around es ti mat ing 

the state of the en ergy-econ omy sys tem un der a set of

al ter na tive en ergy con di tions. Typ i cally, the pro jec tions 

fall into the fol low ing four types of in te grated NEMS

sim u la tions:

• Base line Pro jec tion

• Al ter na tive World Oil Prices

• Pro posed En ergy Fees or Emis sions Per mits

• Pro posed Changes in Com bined Av er age Fuel

Econ omy (CAFE) Stan dards

In these in te grated NEMS sim u la tions, pro jec tion pe -

riod base line val ues for over 240 mac ro eco nomic and

de mo graphic vari ables from MAM are passed to NEMS 

which solves for de mand, sup ply and prices of en ergy

for the pro jec tion pe riod. These en ergy prices and

quan ti ties are passed back to MAM and solved in the

Mac ro eco nomic, In dus try, Em ploy ment, Re gional, and

Com mer cial Floorspace Submodules in the EViews en -

vi ron ment.11 

Energy Information Administration / The National Energy Modeling System: An Overview 2009 15

Macroeconomic Activity Module

National Employment

Variables

Regional Macroeconomic 

Industry Employment

and Commercial

Floorspace Variables

National Interindustry

Variables

Oil, Natural Gas and Coal 

Production; Refinery 

Activity; Electric and Gas 

Industry Output

Energy Prices and End-use 

Consumption

National Macroeconomic 

Variables

National 

Macroeconomic 

Variables & Industrial 

Shipments

Macroeconomic

Submodule

Industry

Submodule

Employment

Submodule

Regional

Submodule

Macroeconomic 

Growth Cases

Exogenous

Commercial Floorspace

Submodule

Macroeconomic Activity Module

NEMS

Fig ure 3. Mac ro eco nomic Ac tiv ity Mod ule Structure

11 Eviews is a model build ing nad op er at ing soft ware pack age main tained by QMS (Quan ti ta tive Mi cro Soft ware.)
Inter na tional Energy Mod ule
The In ter na tional En ergy Mod ule (IEM) (Fig ure 4) per -

forms the fol low ing func tions: 

• Cal cu lates the world oil price (WOP) that

equilibrates world crude-like liq uids sup ply with de -

mand for each year. The WOP is de fined as the

price of light, low sul fur crude oil de liv ered to Cush -

ing, Oklahoma. 

• Pro vides the pro jected world crude-like liq uids sup -

ply curve (for each year) used by the Pe tro leum

Mar ket Mod ule (PMM). These curves are ad justed

to re flect ex pected con di tions in in ter na tional oil

mar kets and pro jected changes in U.S. crude-like

liq uids pro duc tion and con sump tion.

• Pro vide an nual re gional (coun try) level pro duc tion

de tail for con ven tional and un con ven tional liq uids

based on ex og e nous as sump tions about ex pected

coun try-level liq uid fu els pro duc tion and pro ducer

be hav ior.

• Pro jects crude oil and light and heavy re fined prod -

uct im port quan ti ties into the U.S. by year and by

source based on ex og e nous as sump tions about fu -

ture ex plo ra tion, pro duc tion, re fin ing, and dis tri bu -

tion in vest ments world wide.

Scope of IEM 

Non-U.S. liq uid fu els mar kets are rep re sented in NEMS 

by the in ter ac tion be tween the PMM and the IEM. Us -

ing the spe cific al go rithm de scribed in the doc u men ta -

tion of this mod ule, IEM cal cu lates the WOP that

equilibrates world crude-like liq uids sup ply with de -

mand for each year. The IEM then es ti mates new world 

crude-like liq uids sup ply curves based on ex og e nous,

ex pected U.S. and world crude-like liq uids sup ply and

de mand curves and that in cor po rate any changes in

U.S. crude-like liq uids pro duc tion or con sump tion pro -

jected by other NEMS mod ules. Op er a tion ally, IEM

passes to PMM an ar ray of nine points of this sup ply

curve, with the equi lib rium point be ing the fifth point of

this ar ray.

In put data into IEM con tain the his tor i cal per cent ages

of im ports of oils, heavy and light prod ucts im ported into 

U.S. from dif fer ent re gions in the world. Us ing these

val ues and to tal im ports into the U.S. of crudes, heavy

and light prod ucts pro vided by PMM, IEM gen er ates a

re port, with im ports by source for ev ery year in the

pro jec tion.

While the IEM is in tended to be ex e cuted as a mod ule

of the NEMS sys tem, and uti liz ing its com plete ca pa bil i -

ties and fea tures re quires a NEMS in ter face, it is also

pos si ble to ex e cute the IEM mod ule on a stand-alone

ba sis. In stand-alone mode, the IEM cal cu lates the

WOP based on an ex og e nously spec i fied pro jec tion of

U.S. crude-like liq uids pro duc tion and con sump tion.

Sen si tiv ity anal y ses can be con ducted to ex am ine the

re sponse of the world oil mar ket to changes in oil price,

pro duc tion ca pac ity, and de mand. To sum ma rize, the

model searches for the WOP that equilibrates

crude-like liq uids sup ply and de mand at the world level. 

Based on the fi nal re sults for U.S. to tal liq uids pro duc -

tion and con sump tion, IEM also pro vides an In ter na -

tional Pe tro leum Sup ply and Dis po si tion Sum mary

ta ble for world con ven tional and un con ven tional liq uids

pro duc tion as well as for world liq uids de mand by re -

gion. Ex og e nous data used to build this re port is con -

tained in omsinput.wk1 file. Each sce nario has its own

ver sion of this file.

Be cause U.S. pro duc tion and con sump tion of con ven -

tional liq uids are dy namic val ues (out put from NEMS),

all other world re gions have been pro por tion ally up -

dated such that the world liq uids pro duc tion and con -

sump tion re flect the cor re spond ing value as in the

In ter na tional En ergy Out look (IEO).

Relation to Other NEMS Components

The IEM both uses in for ma tion from and pro vides in for -

ma tion to other NEMS com po nents. It pri mar ily uses in -

for ma tion about pro jected U.S and world crude-like

liq uids pro duc tion and con sump tion and pe tro leum im -

ports and pro vides in for ma tion about the world liq uid fu -

els mar kets, in clud ing global crude-like liq uids sup ply

curves and the sources of pe tro leum im ports into the

U.S. It should be noted, how ever, that the pres ent fo cus 

of the IEM is on the in ter na tional oil mar ket where the

Energy Information Administration / The National Energy Modeling System: An Overview 2009 17

International Energy Module

IEM Outputs Inputs from NEMS Exogenous Inputs

World crude-like liq uids sup ply curves 

Pro jected world liq uid fu els pro duc tion

 and con sump tion by re gion

Sources of crude oil and pe tro leum

 prod uct im ports by year

Con trol ling in for ma tion: it er a tion count, time

 ho ri zon, etc

GDP de fla tor

Pro jected U.S. and world crude-like liq uids

 pro duc tion and con sump tion

U.S. crude oil and pe tro leum prod uct imports

Ex pected US and world crude-like liq uids sup ply and

 de mand curves

Ex pected world liq uid fuel pro duc tion and con sump tion

 by region
WOP is com puted. Any in ter ac tions be tween the U.S.

and for eign re gions in fu els other than oil (for ex am ple,

coal trade) are mod eled in the par tic u lar NEMS mod ule

that deals with that fuel. 

For U.S. crude-like liq uids pro duc tion and con sump tion 

in any year of the pro jec tion pe riod, the IEM uses pro -

jec tions gen er ated by the NEMS PMM (based on sup -

ply curves pro vided by the Oil and Gas Sup ply Mod ule

(OGSM) and de mand curves from the end-use de mand 

mod ules). 

U.S. and world ex pected crude-like liq uids sup ply and

de mand curves, for any year in the pro jec tion pe riod,

are ex og e nously pro vided through data in cluded in in -

put file omsecon.txt, as de tailed in the doc u men ta tion of 

the IEM. 

International Energy Module

18 Energy Information Administration / The National Energy Modeling System: An Overview 2009

World Oil Price

Petroleum

Market Module

Macroeconomic

Activity

Module

N

E

M

S

Projected U. S. 

crude-like liquids 

production

and consumption

by year 

U. S. crude oil and 

petroleum product

imports by year

World crude-like

liquids supply

curve by year

Projected world liquid

fuels production and

consumption by 

region by year

GDP Deflator

Projected world 

crude-like liquids

production and 

consumption

by year

Sources of crude oil

and petroleum product

imports by year

Expected U. S.

 crude-like liquids

supply and demand

curves by year 

Exogenous

Expected world

 crude-like liquids

supply and demand

curves by year

Expected world

liquid fuels production

and consumption 

by region by year 

International 

Energy Module

Fig ure 4. In ter na tional En ergy Mod ule Structure
Res i den tial Demand Module
The res i den tial de mand mod ule (RDM) pro jects energy 

con sump tion by Cen sus di vi sion for seven marketed

en ergy sources plus so lar, wind, and geo ther mal

energy. RDM is a struc tural model and its de mand pro -

jec tions are built up from pro jec tions of the res i den tial

hous ing stock and en ergy-con sum ing equip ment. The

com po nents of RDM and its interactions with the NEMS 

sys tem are shown in Figure 5. NEMS pro vides pro jec -

tions of res i den tial en ergy prices, pop u la tion, dis pos -

able in come, and hous ing starts, which are used by

RDM to de velop pro jec tions of en ergy con sump tion by

end–use ser vice, fuel type, and Census division.

RDM in cor po rates the ef fects of four broadly-de fined

de ter mi nants of en ergy con sump tion: eco nomic and

de mo graphic ef fects, struc tural ef fects, tech nol ogy

turn over and ad vance ment ef fects, and en ergy mar ket

ef fects. Eco nomic and de mo graphic ef fects in clude the

num ber, dwell ing type (sin gle-fam ily, mul ti fam ily or mo -

bile homes), oc cu pants per household, dis pos able in -

come, and lo ca tion of hous ing units.Struc tural ef fects

in clude in creas ing av er age dwell ing size and changes

in the mix of de sired end-use ser vices pro vided by en -

ergy (new end uses and/or in creas ing pen e tra tion of

cur rent end uses, such as the in creas ing pop u lar ity of

elec tronic equip ment and com put ers). Tech nol ogy ef -

fects in clude changes in the stock of in stalled equip -

ment caused by nor mal turn over of old, worn out

equip ment with newer ver sions that tend to be more en -

ergy ef fi cient, the in te grated ef fects of equip ment and

build ing shell (in su la tion level) in new con struc tion, and

the pro jected avail abil ity of even more en ergy-ef fi cient

equip ment in the fu ture. En ergy mar ket ef fects in clude

the short-run ef fects of en ergy prices on en ergy de -

mands, the lon ger-run ef fects of en ergy prices on the

ef fi ciency of pur chased equip ment and the ef fi ciency of

build ing shells, and lim i ta tions on min i mum lev els of ef -

fi ciency im posed by leg is lated ef fi ciency stan dards. 

Hous ing Stock Submodule

The base hous ing stock by Cen sus di vi sion and dwell -

ing type is de rived from EIA's 2005 Res i den tial En ergy

Con sump tion Sur vey (RECS). Each el e ment of the of

the base stock is re tired on the ba sis of a con stant rate

of de cay for each dwellling type. RDM re ceives as an

input from the macroeconomic activity module pro jec -

tions of housing additions by type and Census division.

RDM supplements the surviving stocks from the previous 

year with the pro jected ad di tions by dwelling type and

Census division. The average square footage of new

construction is based on recent upward trends developed 

from the RECS and the Census Bureau’s Characteristics 

of New Housing.

Appliance Stock Submodule

The in stalled stock of ap pli ances is also taken from the

2005 RECS. The ef fi ciency of the ap pli ance stock is

derived from his tor i cal ship ments by ef fi ciency level

over a multi-year in ter val for the fol low ing equip ment:

heat pumps, gas fur naces, cen tral air con di tion ers,

room air con di tion ers, wa ter heat ers, re frig er a tors,

freez ers, stoves, dish wash ers, clothes wash ers, and

clothes dry ers. A lin ear re tire ment func tion with both

min i mum and max i mum equipment lives is used to re -

tire equip ment in sur viv ing hous ing units. For equip ment

where ship ment data are avail able, the ef fi ciency of the

re tir ing equipment var ies over the pro jec tion. In early

years, the re tir ing ef fi ciency tends to be lower as the

older, less ef fi cient equip ment in the stock turns over

first. Also, as hous ing units re tire, the as so ci ated appliances are re moved from the base ap pli ance stock as

well. Ad di tions to the base stock are tracked separately

for housing units existing in 2005 and for cumulative new

construction.

As ap pli ances are re moved from the stock, they are re -

placed by new ap pli ances with gen er ally higher

efficiencies due to tech nol ogy im prove ments,

equipment stan dards, and mar ket forces. Ap pli ances 

added due to new con struc tion are ac cu mu lated and re -

tired par al lel to ap pli ances in the ex ist ing stock. Ap pli -

ance stocks are main tained by fuel, end use, and

tech nol ogy as shown in Ta ble 2.

Technology Choice Submodule

Fuel-spe cific equip ment choices are made for both new 

con struc tion and re place ment pur chases. For new

con struc tion, ini tial heat ing sys tem shares (taken from

the most re cently avail able Cen sus Bureau sur vey data 

cov er ing new con struc tion, currently 2005) are ad justed

20 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Residential Demand Module

RDM Outputs Inputs from NEMS Exogenous Inputs

En ergy de mand by ser vice and fuel type

Changes in hous ing and ap pli ance stocks

Ap pli ance stock efficiency

En ergy prod uct prices

Hous ing starts

Population

Cur rent hous ing stocks and re tire ment rates

Cur rent ap pli ance stocks and life ex pec tancy

New ap pli ance types, efficiences, and costs

Hous ing shell ret ro fit in di ces

Unit en ergy con sump tion

Square footage
Energy Information Administration / The National Energy Modeling System: An Overview 2009 21

 Residential Demand Module

NEMS

Macroeconomic

Activity Module

Petroleum

Market

Module

Electricity

Market

Module

Housing Starts,

Population

Technology 

Choice 

Submodule

Fuel

Consumption

Submodule

Housing

Stock

Submodule

Electricity Prices

Natural Gas 

Transmission

and Distribution

Module

Coal

Market 

Module

Electricity Demand

Natural Gas Prices

Natural Gas Demand

Petroleum Product 

Prices

Petroleum Demand

Coal Prices

Coal Demand

Shell

Integrity

Submodule

Stock of Structures by Type and Vintage

Appliance

Stock 

Submodule

Building Shell Efficiencies

Surviving Stock of Appliances

Average Efficiency of Appliance Stock

Exogenous

Base Year Housing Stock and

Retirement Rates, Appliance

Stocks and Life Expectancies,

New Appliance Types, Efficiencies,

and Costs, Housing Shell

Retrofit Indices, Unit Energy

Consumption, Square Footage

Distributed

Generation

Submodule

Residential Demand Module

Fig ure 5. Res i den tial De mand Mod ule Struc ture
based on rel a tive life cy cle costs for all com pet ing tech -

nol ogy and fuel com bi na tions. Once new home heat ing

sys tem shares are es tab lished, the fuel choices for

other ser vices, such as wa ter heat ing and cook ing, are

de ter mined based on the fuel cho sen for space heat -

ing. For re place ment pur chases, fuel switch ing is al -

lowed for an as sumed per cent age of all re place ments

but is de pend ent on the es ti mated costs of fuel-switch -

ing (for ex am ple, switch ing from elec tric to gas heat ing

is as sumed to in volve the costs of run ning a new gas

line).

For both re place ment equip ment and new construction, 

a “sec ond-stage” of the equip ment choice decision re -

quires se lect ing from sev eral avail able ef fi ciency lev -

els. The efficiency range of avail able equip ment

rep re sents a “menu” of efficiency lev els and in stalled

cost com bi na tions projected to be avail able at the time

the choice is be ing made. Costs and ef fi cien cies for se -

lected ap pli ances are shown in Table 3, de rived from

the re port As sump tions to the An nual En ergy Out look

2009.

12 At the low end of the ef fi ciency range are the min -

i mum lev els re quired by leg is lated stan dards. In any

given year, higher ef fi ciency lev els are as so ci ated with

higher in stalled costs. Thus, pur chas ing higher than 

the min i mum ef fi ciency in volves a trade-off be tween

higher in stal la tion costs and future sav ings in en ergy 

ex pen di tures. In RDM, these trade-offs are cal i brated

to re cent ship ment, cost, and ef fi ciency data. Changes

in purchases by ef fi ciency level are based on changes in

either the in stalled cap i tal costs or changes in the

first-year op er at ing costs across the avail able ef fi -

ciency lev els. As en ergy prices in crease, the incentive

of greater en ergy ex pen di tures sav ings will pro mote in -

creased pur chases of higher-ef fi ciency equipment. In

some cases, due to gov ern ment pro grams or gen eral pro -

jec tions of tech nol ogy im prove ment, in creases in ef fi ciency 

or de creases in the installed costs of higher-ef fi ciency

equip ment will also pro mote purchases of

higher-efficiency equipment.

Shell Integrity Submodule

Shell in teg rity is also tracked sep a rately for the existing

hous ing stock and new con struc tion. Shell integrity for

ex ist ing con struc tion is as sumed to respond to in -

creases in real en ergy prices by be com ing more ef fi cient. 

There is no change in ex ist ing shell in teg rity when real

en ergy prices de cline. New shell ef fi cien cies are based

on the cost and per for mance of the heat ing and cool ing

equip ment as well as the shell characteristics. Sev eral

ef fi ciency lev els of shell char ac ter is tics are avail able

through out the pro jec tion pe riod and can change over

time based on changes in build ing codes. All shell ef fi -

cien cies are sub ject to a max i mum shell ef fi ciency based

on studies of cur rently avail able res i den tial con struc tion 

methods.

Distributed Generation Submodule

Dis trib uted gen er a tion equip ment with ex plicit technology

char ac ter iza tions is also mod eled for residential cus tom -

ers. Cur rently, three tech nol o gies are char ac ter ized, 

photovoltaics, wind, and fuel cells. The submodule 

in cor po rates his tor i cal es ti mates of photovoltaics

(res i den tial-sized fuel cells are not expected to be com -

mer cial ized un til af ter 2005, the base year of the model)

from its tech nol ogy char ac ter iza tion and ex og e nous

penetration in put file. Pro gram-based pho to vol taic

22 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Residential Demand Module

Space Heating Equipment: electric furnace, electric air-source 

heat pump, natural gas furnace, natural gas hydronic, kerosene

furnace, liquefied petroleum gas, distillate furnace, distillate

hydronic, wood stove, ground-source heat pump, natural gas

heat pump.

Space Cooling Equipment: room air conditioner, central air

conditioner, electric air-source heat pump, ground-source heat

pump, natural gas heat pump.

Water Heaters: solar, natural gas, electric distiallate, liquefied

petroleum gas.

Refrigerators: 18 cubic foot top-mounted freezer, 25 cubic foot

side-by-side with through-the-door features.

Freezers: chest - manual defrost, upright - manual defrost.

Lighting: incandescent, compact fluorescent, LED, halogen,

linear fluoresent.

Clothes Dryers: natural gas, electric.

Cooking: natural gas, electric, liquefied petroleum gas.

Dishwashers

Clothes Washers

Fuel Cells

Solar Photovoltaic

Wind

Table 2. NEMS Res i den tial Mod ule Equip ment Sum mary

12 Energy Information Administration, Assumptions to the Annual Energy Outlook 2009,

http://www.eia.doe.gov/oiaf/aeo/assumption/pdf/0554(2009).pdf (Washington, DC, March 2009).
estimates for the De part ment of En ergy’s Mil lion So lar

Roofs pro gram are also in put to the submodule from the

exogenous penetration portion of the input file.

Endogenous, economic purchases are based on a

penetration function driven by a cash flow model that

simulates the costs and benefits of distributed generation

purchases. The cash flow calculations are developed

from NEMS projected energy prices coupled with the

technology characterizations provided from the input file.

Po ten tial eco nomic pur chases are mod eled by Cen -

sus di vi sion and tech nol ogy for all years subsequent to

the base year. The cash flow model de vel ops a 30-year

cost-ben e fit ho ri zon for each po ten tial invest ment. It in -

cludes con sid er ations of an nual costs (down pay ments,

loan pay ments, main te nance costs and, for fuel cells, gas

costs) and an nual ben e fits (interest tax de duc tions, any

ap pli ca ble tax cred its, elec tric ity cost sav ings, and wa -

ter heat ing sav ings for fuel cells) over the en tire 30-year 

pe riod. Penetration for a po ten tial in vest ment in ei -

ther photovoltaics, wind, or fuel cells is a func tion of

whether it achieves a cu mu la tive pos i tive dis counted

cash flow, and if so, how many years it takes to achieve

it.

Once the cu mu la tive stock of dis trib uted equip ment is

pro jected, re duced res i den tial pur chases of electricity

are pro vided to NEMS. For fuel cells, increased residential nat u ral gas con sump tion is also pro vided to NEMS

based on the cal cu lated en ergy input re quire ments of the

fuel cells, par tially off set by nat u ral gas wa ter heat ing sav -

ings from the use of waste heat from the fuel cell.

Energy Consumption Submodule

The fuel con sump tion submodule mod i fies base year en -

ergy con sump tion in ten si ties in each pro jec tion year.

Base year en ergy con sump tion for each end use is de rived

from en ergy in ten sity es ti mates from the 2005 RECS. The 

base year en ergy in ten si ties are mod i fied for the fol low ing

ef fects: (1) in creases in efficiency, based on a com par i son

of the appliance stock serv ing this end use rel a tive to the

base year stock, (2) changes in shell in teg rity for space

heat ing and cool ing end uses, (3) changes in real fuel

prices—(short-run price elas tic ity ef fects), (4) changes

in square foot age, (5) changes in the num ber of oc cu pants

per house hold, (6) changes in dis pos able in come, (7)

changes in weather rel a tive to the base year, (8) ad just -

ments in uti li za tion rates caused by ef fi ciency in creases

(ef fi ciency “re bound” ef fects), and (9) re duc tions in pur -

chased elec tric ity and increases in nat u ral gas con sump -

tion from dis trib uted gen er a tion. Once these mod i fi ca tions

are made, to tal en ergy use is com puted across end uses

and hous ing types and then summed by fuel for each Cen -

sus division.

23 Energy Information Administration / The National Energy Modeling System: An Overview 2009

 Residential Demand Module

 

 Equip ment Type

Rel a tive

Per for mance1

2007

In stalled Cost

($2007)2

 

Ef fi ciency3

2020 

In stalled Cost 

 ($2007)2 Ef fi ciency3

 Ap prox i mate

 Hur dle

 Rate

 Elec tric Heat Pump

 

 Nat u ral Gas Furnace 

Min i mum

Best

Min i mum

Best

$3,800

$6,700

$1,900

$3,050

13.0

17.0

0.80

0.96

$3,800

$6,700

$1,900

$2,700

13.0

20.0

0.80

0.96

15% 

15%

 Room Air Con di tioner

 Cen tral Air Conditioner

Min i mum

Best

Min i mum

Best

$310

$925

$3,000

$5,700

9.8

11.7

13.0

21.0

$310

$875

$3,000

$5,750

9.8

12.0

13.0

23.0

140%

15%

 Re frig er a tor (23.9 cu bic ft in ad justed vol ume)

 Elec tric Wa ter Heater 

Min i mum

Best

Min i mum

Best

$550

$950

$400

$1,400

510

417

0.90

2.4

$550

$1000

$400

$1,700

510

417

0.90

2.4

19%

30%

Table 3. Characteristics of Selected Equipment

1Min i mum per for mance re fers to the low est ef fi ciency equip ment avail able. Best re fers to the high est ef fi ciency equip ment avail able.

2In stalled costs are given in 2007 dol lars in the orig i nal source doc u ment.

3Ef fi ciency mea sure ments vary by equip ment type. Elec tric heat pumps and cen tral air con di tion ers are rated for cool ing per for mance us ing the Sea sonal En ergy Ef fi -

ciency Ra tio (SEER); nat u ral gas fur naces are based on An nual Fuel Uti li za tion Ef fi ciency; room air con di tion ers are based on En ergy Ef fi ciency Ra tio (EER); re frig er a -

tors are based on ki lo watt-hours per year; and wa ter heat ers are based on En ergy Fac tor (de liv ered Btu di vided by in put Btu).

Source: Navigant Con sult ing, EIA Tech nol ogy Fore cast Up dates-Res i den tial and Com mer cial Build ings Tech nol o gies, Sep tem ber 2007.
Com mer cial Demand Module
The com mer cial de mand mod ule (CDM) pro jects

energy consumption by Census division for eight

marketed energy sources plus solar, wind, and

geothermal energy. For the three major commercial

sector fuels, electricity, natural gas and distillate oil,

CDM is a structural model and the pro jec tions are built

up from the stock of commercial floorspace and

energy-consuming equipment. For the remaining five 

marketed minor fuels, simple econometric projections

are made.

The com mer cial sec tor en com passes busi ness

establishments that are not en gaged in in dus trial or

trans por ta tion ac tiv i ties. Com mer cial sec tor en ergy is

con sumed mainly in build ings, ex cept for a relatively

small amount for ser vices such as street lights and wa ter

sup ply. CDM in cor po rates the ef fects of four

broadly-de fined de ter mi nants of en ergy consumption:

eco nomic and de mo graph ics, struc tural, tech nol ogy

turn over and change, and en ergy mar kets. De mo -

graphic ef fects in clude to tal floorspace, build ing type

and lo ca tion. Struc tural ef fects in clude changes in the mix

of de sired end-use ser vices pro vided by en ergy (such

as the pen e tra tion of telecommunications equip ment,

per sonal com put ers and other of fice equip ment). Tech -

nol ogy ef fects in clude changes in the stock of in stalled

equip ment caused by the nor mal turn over of old, worn out 

equip ment to newer ver sions that tend to be more en -

ergy ef fi cient, the in te grated ef fects of equip ment and

building shell (in su la tion level) in new con struc tion, and

the pro jected avail abil ity of equip ment with even greater 

en ergy-ef fi ciency. En ergy mar ket ef fects include the

short-run ef fects of en ergy prices on energy de mands, 

the lon ger-run ef fects of en ergy prices on the ef fi -

ciency of pur chased equip ment, and lim i ta tions on min i -

mum lev els of ef fi ciency im posed by leg is lated ef fi ciency

stan dards. The model structure car ries out a se quence

of five ba sic steps, as shown in Fig ure 6. The first step

is to pro ject commercial sec tor floorspace. The sec ond

step is to pro ject the en ergy ser vices (space heat ing,

light ing, etc.) re quired by the pro jected floorspace. The

third step is to pro ject the elec tric ity gen er a tion and wa ter

and space heat ing sup plied by dis trib uted gen eration and

com bined heat and power (CHP) technologies. The

fourth step is to se lect spe cific tech nol o gies (nat u ral gas

fur naces, flu o res cent lights, etc.) to meet the de mand for 

en ergy ser vices. The last step is to de ter mine how much

en ergy will be con sumed by the equip ment cho sen to

meet the de mand for en ergy ser vices.

Floorspace Submodule

The base stock of com mer cial floorspace by Cen sus di -

vi sion and build ing type is de rived from EIA’s 2003

Com mer cial Build ings En ergy Con sump tion Sur vey

(CBECS). CDM re ceives pro jec tions of to tal floorspace

by build ing type and Cen sus di vi sion from the

macroeconomic ac tiv ity mod ule (MAM) based on IHS

Global In sight, Inc. def i ni tions of the com mer cial sec tor.

These pro jec tions em body both economic and de mo -

graphic ef fects on com mer cial floorspace. Since the 

def i ni tion of com mer cial floorspace from IHS Global In -

sight, Inc. is not cal i brated to CBECS, CDM es ti mates the

sur viv ing floorspace from the pre vi ous year and then

cal i brates its new con struc tion so that growth in to tal 

floorspace matches that from MAM by build ing type and

Census division.

CDM mod els com mer cial floorspace for the fol low ing 11

build ing types:

• As sem bly

• Ed u ca tion

• Food sales

• Food ser vice

• Health care

• Lodg ing

• Of fice-large

• Of fice-small

• Mer can tile and ser vice

• Ware house

• Other

Energy Information Administration / The National Energy Modeling System: An Overview 2009 25

Commercial Demand Module

CDM Outputs Inputs from NEMS Exogenous Inputs

En ergy de mand by ser vice and fuel type

Changes in floorspace and ap pli ance stocks

En ergy prod uct prices

In ter est rates

Floorspace growth

Ex ist ing com mer cial floorspace

Floorspace sur vival rates

Ap pli ance stocks and sur vival

New ap pli ance types, ef fi cien cies, costs

En ergy use intensities
26 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Commercial Demand Module

NEMS

Macroeconomic

Activity Module

Petroleum

Market

Module

Electricity

Market

Module

Floorspace Additions,

Interest Rates

Equipment 

Choice 

Submodule

Floorspace

Submodule

Electricity Prices

Natural Gas 

Transmission

and Distribution

Module

Coal

Market 

Module

Electricity Demand

Natural Gas Prices

Natural Gas Demand

Petroleum Product 

Prices

Petroleum Demand

Coal Prices

Coal Demand

Energy

Consumption

Submodule

Stock of Commercial Floorspace

Energy

Service

Demand

Submodule

End-Use Service Demands

Stocks of Energy-Consuming Equipment

Exogenous

Existing Commercial Floorspace

and Survival Rates,

Appliance Stocks and Survival Rates,

New Appliance Types,

Efficiences, Costs,

Energy Use Intensities

Distributed

Generation/

Cogeneration

Submodule

Commercial Demand Module

Fig ure 6. Com mer cial De mand Mod ule Structure
Energy Service Demand Submodule

En ergy con sump tion is de rived from the de mand for en -

ergy ser vices. So the next step is to pro ject en ergy ser vice 

de mands for the pro jected floorspace. CDM mod els 

ser vice de mands for the fol low ing ten end-use

services:

• Heat ing

• Cool ing

• Ven ti la tion

• Wa ter heat ing

• Light ing

• Cook ing

• Re frig er a tion

• Of fice equip ment per sonal com puter

• Of fice equip ment other

• Other end uses.

Dif fer ent build ing types re quire unique combinations of

en ergy ser vices. A hos pi tal must have more light than a

ware house. An of fice build ing in the North east re quires

more heat ing than one in the South. To tal ser vice de -

mand for any ser vice de pends on the floorspace, type,

and lo ca tion of build ings. Base ser vice de mand by end

use by build ing type and Cen sus di vi sion is de rived from

es ti mates de vel oped from CBECS en ergy con sump tion

data. Pro jected ser vice de mands are ad justed for trends in 

new con struc tion based on CBECS data con cern ing re -

cent construction.

Distributed Generation and CHP Submodule

Com mer cial con sum ers may de cide to pur chase

equip ment to gen er ate elec tric ity (and per haps pro vide

heat as well) rather than de pend on pur chased elec tric ity

to ful fill all of their elec tric power re quirements. The third

step of the com mer cial module struc ture is to pro ject elec -

tric ity gen er a tion, fuel con sump tion, wa ter heat ing, and

space heat ing sup plied by eleven dis trib uted gen er a tion

and CHP tech nol o gies. The tech nol o gies char ac ter ized

in clude: pho to vol taic so lar sys tems, wind tur bines, nat u -

ral gas fuel cells, re cip ro cat ing en gines, tur bines and

microturbines, die selen gine, coal-fired CHP, and mu nic -

i pal solid waste, wood, and hy dro elec tric gen er a tors.

Ex ist ing elec tric ity gen er a tion by CHP tech nol o gies is de -

rived from his tor i cal data con tained in the most re cent

year’s ver sion of Form EIA-860, An nual Elec tric Gen -

er a tor Re port. The estimated units form the in stalled

base of CHP equipment that is car ried for ward into fu -

ture years and sup ple mented with any ad di tions.

Proven in stal la tions of so lar pho to vol taic systems, wind

tur bines and fuel cells are also in cluded based on

information from the De part ments of En ergy and

Defense. For years fol low ing the base year, an

endogenous pro jec tion of dis trib uted gen er a tion and

CHP is de vel oped based on the eco nomic re turns pro -

jected for dis trib uted gen er a tion tech nol o gies. A de -

tailed dis counted cash-flow ap proach is used to

es ti mate the in ter nal rate of re turn for an in vest ment. The 

cal cu la tions in clude the an nual costs (down pay ments,

loan pay ments, main te nance costs, and fuel costs) and

re turns (tax de duc tions, tax credits, and en ergy cost sav -

ings) from the in vest ment cov er ing a 30-year pe riod

from the time of the invest ment de ci sion. Pen e tra tion of

these tech nol o gies is a func tion of how quickly an in vest -

ment in a technology is es ti mated to re coup its flow of

costs. In terms of NEMS pro jec tions, in vest ments in

distributed gen er a tion re duce pur chases of elec tric ity.

Fuel con sum ing tech nol o gies also gen er ate waste heat 

that is as sumed to be par tially cap tured and used to off -

set com mer cial wa ter heat ing and space heating en ergy

use.

Equip ment Choice Submodule

Once ser vice de mands are pro jected, the next step is to

de fine the type and ef fi ciency of equip ment that will be

used to sat isfy the de mands. The bulk of equip ment re -

quired to meet ser vice de mand will carry over from the

equip ment stock of the pre vi ous model year. How ever,

equip ment must al ways be pur chased to sat isfy ser vice 

de mand for new construction. It must also be pur -

chased to re place equip ment that has ei ther worn out

(re place ment equip ment) or reached the end of its eco -

nom i cally use ful life (retrofit equip ment). For re quired

equip ment re placements, CDM uses a con stant de cay

rate based on equip ment life. A tech nol ogy will be retro fit -

ted only if the com bined an nual op er at ing and main te -

nance costs plus an nu al ized cap i tal costs of a po ten tial 

tech nol ogy are lower than the an nual operating and

maintenance costs of an existing technology.

Equip ment choices are made based on a com par i son of

an nu al ized cap i tal and op er at ing and maintenance

costs across all al low able equip ment for a particular

end-use ser vice. In or der to add in er tia to the equip ment

choices, only sub sets of the to tal menu of po ten tially

avail able equip ment may be al lowed for de fined mar ket

seg ments. For ex am ple, only 7 percent of floorspace in

large of fice build ings may consider all avail able equip -

ment us ing any fuel or technology when mak ing space

Energy Information Administration / The National Energy Modeling System: An Overview 2009 27

Commercial Demand Module
heat ing equip ment replace ment de ci sions. A sec ond

seg ment equal to 31 per cent of floorspace, must se lect

from tech nol o gies us ing the same fuel as al ready in -

stalled. A third segment, the remaining 62 percent of

floorspace, is constrained to consider only different

efficiency levels of the same fuel and technology already

installed. For light ing and refrigeration, all replacement

choices are limited to the same tech nol ogy class, where

technologies are broadly defined to encompass the prin -

ci pal competing tech nol o gies for a par tic u lar ap pli ca tion.

For ex am ple, a com mer cial ice maker may re place an -

other ice maker, but may not re place a re frig er ated vend -

ing ma chine.

When com put ing an nu al ized costs to de ter mine equip -

ment choices, com mer cial floorspace is segmented by

what are re ferred to as hur dle rates or implicit dis count

rates (to dis tin guish them from the gen er ally lower and

more com mon no tion of fi nan cial dis count rates). Seven

seg ments are used to sim u late con sumer be hav ior when

pur chas ing com mer cial equip ment. The seg ments range

from rates as low as the 10-year Trea sury bond rate to 

rates high enough to guarantee that only equipment

with the lowest capital cost (and least efficiency) is chosen. 

As real energy prices increase (decrease) there is an

incentive for all but the highest implicit discount rate

segments to purchase increased (decreased) levels of

efficiency.

The equip ment choice submodule is de signed to

choose among a dis crete set of tech nol o gies that are

char ac ter ized by a menu which de fines avail abil ity, cap i -

tal costs, main te nance costs, ef fi cien cies, and equip -

ment life. Tech nol ogy char ac ter is tics for selected space 

heat ing equip ment are shown Ta ble 4, de rived from the

re port As sump tions to the An nual En ergy 

Out look 2009.

13 This menu of equip ment in cludes tech -

no log i cal in no va tion, mar ket de vel op ments, and pol icy

interventions. For the AEO2009, the tech nol ogy types

that are in cluded for seven of the ten ser vice de mand

cat e go ries are listed in Ta ble 5.

The re main ing three end-use ser vices (PC-re lated of -

fice equip ment, other of fice equip ment, and other end

uses) are con sid ered mi nor ser vices and are pro jected us -

ing ex og e nous equip ment ef fi ciency and market pen e tra -

tion trends.

Energy Consumption Submodule

Once the re quired equip ment choices have been made, 

the to tal stock and ef fi ciency of equip ment for a par tic u lar

end use are de ter mined. En ergy consumption by fuel can

be cal cu lated from the amount of ser vice de mand sat is -

fied by each tech nol ogy and the cor re spond ing ef fi ciency

of the tech nol ogy. At this stage, ad just ments to en ergy 

con sump tion are also made. These in clude ad just ments 

for changes in real energy prices (short-run price elas -

tic ity ef fects), adjustments in uti li za tion rates caused by

ef fi ciency increases (ef fi ciency re bound ef fects), and

changes for weather rel a tive to the CBECS sur vey year. 

Once these mod i fi ca tions are made, to tal en ergy use is

com puted across end uses and build ing types for the

three ma jor fu els, for each Cen sus di vi sion. Combining

these pro jec tions with the ec ono met ric/trend pro jec tions

for the five mi nor fu els yields to tal projected com mer cial

en ergy con sump tion.

28 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Commercial Demand Module

13 Energy Information Administration, Assumptions to the Annual Energy Outlook 2009,

http://www.eia.doe.gov/oiaf/aeo/assumption/pdf/0554(2009).pdf (Washington, DC, March 2009)
Energy Information Administration / The National Energy Modeling System: An Overview 2009 29

Commercial Demand Module

 Equip ment Type Vin tage Ef fi ciency2

Cap i tal Cost

($2007 per

Mbtu/hour) 3

Main te nance Cost

($2007 per

Mbtu/hour) 3

Ser vice

Life

(Years)

 Elec tric Roof top Heat Pump 2007- typ i cal 

2007- high efficiency

3.2

3.4 

$72.78 

$96.67

$1.39 

$1.39

15 

15

2010 - typ i cal (stan dard)

2010 - high efficiency

3.3 

3.4

$76.67 

$96.67

$1.39 

$1.39

15 

15

2020 - typ i cal

2020 - high efficiency

3.3

3.4

$76.67 

$96.67

$1.39

$1.39

15 

15

 Ground-Source Heat Pump 2007 - typ i cal 

2007 - high efficiency

3.5 

4.9

$140.00 

$170.00 

$16.80 

$16.80

20

20

2010 - typ i cal

2010 - high efficiency

3.5 

4.9

$140.00 

$170.00

$16.80 

$16.80

20

20

2020 - typ i cal

2020 - high efficiency

4.0

4.9

$140.00 

$170.00 

$16.80

$16.80

20

20

 Elec tric Boiler

 Pack aged Electric

Cur rent typ i cal

Typical

0.98 

0.96

$17.53 

$16.87

$0.58 

$3.95

21 

18

 Nat u ral Gas Fur nace Cur rent Stan dard 

2007 - high efficiency

0.80 

0.82

$9.35

$9.90

$0.97 

$0.94

20 

20

2020 - typ i cal

2020 - high efficiency

0.81 

0.90

$9.23 

$11.57

$0.96 

$0.86

20 

20

2030 - typ i cal

2030 - high efficiency

0.82

0.91 

$9.12 

$11.44

$0.94 

$0.85

20 

20

 Nat u ral Gas Boiler Cur rent Stan dard

2007 - mid efficiency

0.80 

0.85

$22.42 

$25.57

$0.50 

$0.47

25 

25

2007 - high ef fi ciency

2020 - typical

0.96 

0.82

$39.96

$21.84 

$0.52 

$0.49

25 

25

 Nat u ral Gas Heat Pump 2007 - ab sorp tion

2010 - absorption

1.4 

1.4

$158.33 

$158.33

$2.50

$2.50

15 

15

2020 - ab sorp tion 1.4 $158.33 $2.50 15

 Dis til late Oil Fur nace Cur rent Stan dard 0.81 $11.14 $0.96 20 

2020 - typ i cal 0.81 $11.14 $0.96 20 

 Dis til late Oil Boiler Cur rent Stan dard

2007 - high efficiency

0.83

0.89

$17.63 

$19.84

$0.15 

$0.14

20 

20

2020 - typ i cal 0.83 $17.63 $015 20 

Table 4. Cap i tal Cost and Ef fi ciency Rat ings of Se lected Com mer cial Space Heat ing Equip ment1

1Equip ment listed is for the New Eng land Cen sus di vi sion, but is also rep re sen ta tive of the tech nol ogy data for the rest of the U.S. See the

source ref er enced be low for the com plete set of tech nol ogy data..

2Ef fi ciency mea sure ments vary by equip ment type. Elec tric roof top air-source heat pumps, ground source and nat u ral gas heat pumps are

rated for heat ing per for mance us ing co ef fi cient of per for mance; nat u ral gas and dis til late fur naces are based on Ther mal Ef fi ciency; and boil ers

are based on com bus tion ef fi ciency.

3Cap i tal and main te nance costs are given in 2007 dol lars.

Source: En ergy In for ma tion Ad min is tra tion, “EIA - Tech nol ogy Fore cast Up dates - Res i den tial and Com mer cial Build ing Tech nol o gies - Ref er -

ence Case Sec ond Edi tion (Re vised)”, Navigant Con sult ing, Inc., Ref er ence Num ber 20070831.1, Sep tem ber 2007.
30 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Commercial Demand Module

End-Use Service by Fuel Technology Types

Electric Space Heating air-source heat pump, ground-source heat pump, boiler, packaged space heating

Natural Gas Space Heating boiler, furnace, absorption heat pump

Fuel Oil Space Heating boiler, furnace

Electric Space Cooling air-source heat pump, ground-source heat pump, reciprocating chiller, centrifugal chiller, screw

chiller, scroll chiller, rooftop air conditioner, residential style central air conditioner, window unit

Natural Gas Space Cooling absorption chiller, engine-driven chiller, rooftop air conditioner, engine-driven heat pump, absorption

heat pump

Electric Water Heating electric resistance, heat pump water heater, solar water heater with electric back-up

Natural Gas Water Heating natural gas water heater

Fuel Oil Water Heating fuel oil water heater

Ventilation constant air volume (CAV) system, variable air volume (VAV) system

Electric Cooking range/oven/griddle, induction range/oven/griddle

Natural Gas Cooking range/oven/griddle, power burner range/oven/griddle

Incandescent Style Lighting incandescent, compact fluorescent, halogen, halogen-infrared, light emitting diode (LED)

Four-foot Fluorescent Lighting magnetic ballast, electronic ballast-T8 electronic w/controls, electronic w/reflectors, electronic

ballast-T5, electronic ballast-super T8, LED,

Eight-foot Fluorescent Lighting magnetic ballast, electronic ballast, electronic-high output, LED

High Intensity-Discharge Lighting metal halide, mercury vapor, high pressure sodium, electronic-T8 high output, electronic-T5 high

output, LED

Refrigeration supermarket compressor rack, suupermarket condenser, supermarket display case, walk-in cooler,

walk-in freezer, reach-in refrigerator, reach-in freezer, ice machine, beverage merchandiser,

refrigerated vending machine

Table 5. Com mer cial End-Use Tech nol ogy Types
Indus trial Demand Mod ule
The In dus trial De mand Mod ule (IDM) pro jects energy

con sump tion for fu els and feedstocks for fif teen man u -

fac tur ing in dus tries and six nonmanufacturing in dus -

tries, sub ject to de liv ered prices of en ergy and

macroeconomic variables representing the value of

shipments for each industry. The module includes

electricity generated through Com bined Heat and

Power (CHP) systems that is either used in the

industrial sector or sold to the electricity grid. The IDM

structure is shown in Figure 7.

In dus trial en ergy de mand is pro jected as a combination 

of “bot tom up” char ac ter iza tions of the en ergy-us ing

technology and “top down” econometric estimates of

behavior. The influence of energy prices on industrial

energy consumption is modeled in terms of the

efficiency of use of existing capital, the efficiency of new 

capital acquisitions, and the mix of fuels utilized, given

existing capital stocks. Energy conservation from

technological change is represented over time by

trend-based “technology possibility curves.” These

curves represent the aggregate efficiency of all new

technologies that are likely to penetrate the future

markets as well as the aggregate improvement in

efficiency of 2002 technology.

IDM in cor po rates three ma jor in dus try cat e go ries: en -

ergy-in ten sive man u fac tur ing in dus tries, non-en -

ergy-in ten sive man u fac tur ing in dus tries, and

nonmanufacturing in dus tries (see Ta ble 6). The level

and type of mod el ing and de tail is dif fer ent for each.

Man u fac tur ing disaggregation is at the 3-digit North 

Amer i can In dus trial Clas si fi ca tion Sys tem (NAICS)

level, with some fur ther disaggregation of large and en -

ergy-in ten sive in dus tries. Detailed in dus tries in clude

food, pa per, chem i cals, glass, ce ment, steel, and

aluminum. En ergy prod uct de mands are cal cu lated in -

de pend ently for each industry.

Each in dus try is mod eled (where ap pro pri ate) as three 

in ter re lated com po nents: build ings (BLD), boil -

ers/steam/cogeneration (BSC), and pro cess/as sem -

bly (PA) ac tiv i ties. Build ings are es ti mated to ac count

for 4 per cent of en ergy con sump tion in manufacturing 

in dus tries (in nonmanufacturing in dus tries, build ing

en ergy con sump tion is not cur rently cal cu lated).

Con se quently, IDM uses a sim ple mod el ing 

approach for the BLD com po nent. En ergy con sump tion 

in in dus trial build ings is as sumed to grow at the same

rate as the av er age growth rate of em ploy ment and out -

put in that in dus try. The BSC com po nent con sumes

en ergy to meet the steam de mands from and pro vide

in ter nally gen er ated elec tric ity to the other two com po -

nents. The boiler com po nent con sumes by-prod uct fu -

els and fos sil fu els to pro duce steam, which is passed

to the PA and BLD com po nents.

32 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Industrial Demand Module

IDM Outputs Inputs from NEMS Exogenous Inputs

En ergy de mand by ser vice and fuel type

Elec tric ity sales to grid

Cogeneration out put and fuel consumption

En ergy prod uct prices

Eco nomic out put by in dus try

Re fin ery fuel con sump tion

Lease and plant fuel con sump tion

Cogeneration from re fin er ies and oil and gas

 pro duc tion

Pro duc tion stages in en ergy-in ten sive in dus tries

Tech nol ogy pos si bil ity curves

Unit en ergy con sump tion of out puts

Cap i tal stock re tire ment rates

Energy-Intensive

Manufacturing

Nonmanufacturing

Industries

Food and Kindred Products 

 (NAICS 311)

Agricultural Production - Crops 

(NAICS 111)

Paper and Allied Products 

 (NAICS 322)

Other Agriculture including 

 Livestock (NAICS 112-115)

Bulk Chemicals (NAICS 325) Coal Mining (NAICS 2121)

Glass and Glass Products 

 (NAICS 3272)

Oil and Gas Extraction

 (NAICS 211)

Hydraulic Cement

 (NAICS 32731)

Metal and Other Nonmetallic 

 Mining (NAICS 2122-2123)

Blast Furnaces and Basic Steel

 (NAICS 331111)

Construction (NAICS 233-235)

Aluminum (NAICS 3313)

Nonenergy-Intensive

Manufacturing

Metals-Based Durables

 (NAICS 332-336)

Other Manufacturing

 (all remaining manufacturing

NAICS)

NAICS = North American Industry Classification System

Table 6. Eco nomic Subsectors Within the IDM
IDM mod els “tra di tional” CHP based on steam de mand

from the BLD and the PA com po nents. The “non-tra di -

tional” CHP units are rep re sented in the elec tric ity mar -

ket mod ule since these units are mainly grid-serv ing, 

elec tricity-price-driven entities.

CHP ca pac ity, gen er a tion, and fuel use are cal cu lated

from ex og e nous data on ex ist ing and planned ca pac ity

ad di tions and new ad di tions de ter mined from an en gi -

neer ing and eco nomic eval u a tion. Existing CHP ca pac -

ity and planned ad di tions are derived from Form

EIA-860, “An nual Elec tric Generator Re port,” for merly 

Form EIA-867, “An nual Nonutility Power Pro ducer Re -

port.” Existing CHP ca pac ity is as sumed to re main in

ser vice through out the pro jec tion or, equiv a lently, to be

re fur bished or re placed with sim i lar units of equal

capacity.

Cal cu la tion of un planned CHP ca pac ity ad di tions be -

gins in 2009. Mod el ing of un planned ca pac ity ad di tions

is done in two parts: bio mass-fu eled and fossil-fu eled.

Bio mass CHP ca pac ity is as sumed to be added to the

ex tent pos si ble as ad di tional bio mass waste prod ucts

are pro duced, pri mar ily in the pulp and pa per in dus try. 

The amount of bio mass CHP ca pac ity added is equal to 

the quan tity of new bio mass avail able (in Btu), divided

by the to tal heat rate from bio mass steam tur bine CHP.

Energy Information Administration / The National Energy Modeling System: An Overview 2009 33

Industrial Demand Module

Industrial Demand Module

Unit Energy Consumption

 by Industry,

Production Stage Information

 for Energy-Intensive Industries,

Technology Possibility Curves,

Stock Retirement Rates

Exogenous

Boilers/Steam/Cogeneration

Component

Buildings

Component

Process/Assembly

Component

Steam and Cogenerated Electricity

N

E

M

S

Macroeconomic

Activity Module

Electricity 

Market

Module

Coal Market

Module

Oil and Gas

Supply Module

Natural Gas 

Transmission 

and Distribution

Module

Petroleum Market 

Module

Economic Activity by 

Sector

Electricity Prices

Electricity Demand

Electricity Sales to Grid

Cogeneration Output 

and Fuel Consumption 

Coal Prices

Coal Demand

Cogeneration

Natural Gas Prices

Natural Gas Demand

Lease and Plant Fuel 

Consumption

Petroleum Product 

Prices

Refinery Fuel Consumption

Cogeneration

Petroleum Demand 

Energy Byproducts

Industrial Demand Module

Boilers/Steam/Cogeneration

Component

Buildings

Component

Process/Assembly

Component

Steam and Cogenerated Electricity

N

E

M

S

Macroeconomic

Activity Module

Electricity 

Market

Module

Coal Market

Module

Oil and Gas

Supply Module

Natural Gas 

Transmission 

and Distribution

Module

Petroleum Market 

Module

Economic Activity by 

Sector

Electricity Prices

Electricity Demand

Electricity Sales to Grid

CHP Output 

and Fuel Consumption 

Coal Prices

Coal Demand

Natural Gas Prices

Natural Gas Demand

Lease and Plant Fuel 

Consumption

Petroleum Product 

Prices

Refinery Fuel Consumption

CHP

Petroleum Demand 

Energy Byproducts

CHP 

Fig ure 7. In dus trial De mand Mod ule Structure
It is as sumed that the tech ni cal po ten tial for fos sil-fuel

source CHP is based pri mar ily on sup ply ing ther mal re -

quire ments. First, the model as sesses the amount of ca -

pac ity that could be added to gen er ate the in dus trial

steam re quire ments not met by ex isting CHP. The sec -

ond step is an eco nomic eval u a tion of gas tur bine pro to -

types for each steam load segment. Fi nally, CHP

ad di tions are pro jected based on a range of acceptable

payback periods.

The PA com po nent ac counts for the larg est share of di -

rect en ergy con sump tion for heat and power, 55 per -

cent. For the seven most en ergy-in ten sive industries,

pro cess steps or end uses are mod eled us ing engineering

con cepts. The pro duc tion pro cess is decomposed into the

ma jor steps, and the en ergy re la tion ships among the

steps are spec i fied.

The en ergy in ten si ties of the pro cess steps or end uses

vary over time, both for ex ist ing tech nol ogy and for tech nol -

o gies ex pected to be adopted in the fu ture. In IDM, this

vari a tion is based on en gi neer ing judgement and is re -

flected in the pa ram e ters of tech nol ogy pos si bil ity curves,

which show the de clin ing en ergy in ten sity of ex ist ing and 

new cap i tal rel a tive to the 2002 stock.

IDM uses “tech nol ogy bun dles” to char ac ter ize

technological change in the en ergy-in ten sive in dus tries.

These bun dles are de fined for each pro duc tion process 

step for five of the in dus tries and for end uses in the

remaining two en ergy-in ten sive in dus tries. The pro cess

step in dus tries are pulp and pa per, glass, ce ment, steel,

and alu mi num. The end-use in dus tries are food and bulk

chem i cals (see Ta ble 7).

Ma chine drive elec tric ity con sump tion in the food, bulk

chem i cals, metal-based durables, and bal ance of man u -

fac tur ing sec tors is cal cu lated by a mo tor stock model.

The be gin ning stock of mo tors is modified over the pro jec -

tion ho ri zon as mo tors are added to ac com mo date

growth in ship ments for each sec tor, as mo tors are re -

tired and re placed, and as failed motors are re wound. 

When a new mo tor is added, either to ac com mo date

growth or as a re place ment, an eco nomic choice is made

be tween pur chas ing a mo tor that meets the EPACT min i -

mum for ef fi ciency or a pre mium ef fi ciency mo tor. There

are seven mo tor size groups in each of the four in dus -

tries. The EPACT ef fi ciency stan dards only ap ply to

the five small est groups (up to 200 horse power). As the

motor stock changes over the pro jec tion ho ri zon, the

overall ef fi ciency of the mo tor pop u la tion changes as

well.

The Unit En ergy Con sump tion (UEC) is de fined as the

energy use per ton of through put at a pro cess step or as

en ergy use per dol lar of ship ments for the end-use

industries. The “Ex ist ing UEC” is the cur rent av er age in -

stalled in ten sity as of 2002. The “New 2002 UEC” is the

in ten sity as sumed to pre vail for a new installation in 2002.

Sim i larly, the “New 2030 UEC” is the in ten sity ex pected to 

pre vail for a new in stal la tion in 2030. For in ter ven ing

years, the in ten sity is interpolated.

The rate at which the av er age in ten sity de clines is de -

ter mined by the rate and tim ing of new ad di tions to ca pac -

ity. In IDM, the rate and tim ing of new additions are

func tions of re tire ment rates and in dus try growth rates.

IDM uses a vintaged cap i tal stock ac count ing frame work

that mod els en ergy use in new ad di tions to the stock and

in the ex ist ing stock. This cap i tal stock is rep re sented as

the ag gre gate vin tage of all plants built within an in dus try

and does not im ply the inclusion of spe cific tech nol o gies

or cap i tal equip ment.

The cap i tal stock is grouped into three vin tages: old, mid -

dle, and new. The old vin tage con sists of cap i tal in pro -

duc tion prior to 2002, which is as sumed to retire at a fixed

rate each year. Mid dle-vin tage cap i tal is that added af ter

2002. New pro duc tion ca pac ity is built in the pro jec tion

years when the capacity of the existing stock of capital in

34 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Industrial Demand Module

End Use Characterization

Food: direct fuel, hot water/steam, refrigeration, and other

energy uses.

Bulk Chemicals: direct fuel, hot water/steam, electrotytic, and

other energy uses.

Process Step characterization

Pulp and Paper: wood preparation, waste pulping, mechanical

pulping, semi-chemical pulping, kraft pulping, bleaching, and

paper making.

Glass: batch preparation, melting/refining, and forming.

Cement: dry process clinker, wet process clinker, and finish

grinding.

Steel: coke oven, open hearth steel making, basic oxygen

furnace steel making, electric arc furnace steel making, ingot

casting, continuous casting, hot rolling, and cold rolling.

Aluminum: primary and secondary (scrap) aluminum smelting,

semi-fabrication (e.g. sheet, wire, etc.).

Table 7. Fuel-Con sum ing Ac tiv i ties for the En ergy-In ten sive 

Man u fac tur ing Subsectors
IDM cannot produce the output pro jected by the NEMS

regional submodule of the macroeconomic activity

module. Capital additions during the pro jec tion ho ri zon

are retired in subsequent years at the same rate as the

pre-2002 capital stock.

The en ergy-in ten sive and/or large en ergy-consuming

in dus tries are mod eled with a struc ture that explicitly de -

scribes the ma jor pro cess flows or “stages of pro duc tion” 

in the in dus try (some in dus tries have ma jor con sum ing

uses).

Tech nol ogy pen e tra tion at the level of ma jor proces ses

in each in dus try is based on a tech nol ogy penetration

curve re la tion ship. A sec ond re la tion ship can pro vide

ad di tional en ergy con ser va tion re sult ing from in creases in

rel a tive en ergy prices. Ma jor process choices (where

ap pli ca ble) are de ter mined by industry pro duc tion, spe cific 

pro cess flows, and ex og e nous as sump tions. 

Re cy cling, waste prod ucts, and by prod uct con sump tion

are mod eled us ing pa ram e ters based on off-line anal y sis

and as sump tions about the man u fac tur ing pro cesses or

tech nol o gies ap plied within in dus try. These anal y ses

and as sump tions are mainly based upon en vi ron men -

tal reg u la tions such as gov ern ment re quire ments about

the share of re cy cled pa per used in of fices. IDM also ac -

counts for trends within industry to ward the pro duc tion of

more spe cial ized products such as spe cial ized steel

which can be pro duced us ing scrap ma te rial ver sus raw

iron ore. 

Energy Information Administration / The National Energy Modeling System: An Overview 2009 35

Industrial Demand Module
Transportation Demand

Mod ule
The trans por ta tion de mand mod ule (TRAN) pro jects

the consumption of transportation sector fuels by

transportation mode, including the use of renewables 

and alternative fuels, subject to delivered prices of en -

ergy and macroeconomic variables, in clud ing dis pos -

able personal income, gross domestic product, level of

imports and exports, industrial output, new car and light 

truck sales, and population. The structure of the

module is shown in Figure 8.

Pro jec tions of fu ture fuel prices in flu ence fuel ef fi ciency, 

ve hi cle-miles trav eled, and alternative-fuel ve hi cle

(AFV) mar ket pen e tra tion for the cur rent fleet of ve hi -

cles. Al ter na tive-fuel ve hi cle shares are pro jected on the

ba sis of a multinomial logit model, sub ject to State and

Fed eral gov ernment man dates for minimum AFV sales

volumes.

Fuel Economy Submodule

This submodule pro jects new light-duty ve hi cle fuel econ -

omy by 12 U.S. En vi ron men tal Pro tec tion Agency

(EPA) ve hi cle size classes and 16 pro pul sion tech nol o -

gies (gas o line, die sel, and 14 AFV technologies) as a 

func tion of en ergy prices and income-re lated vari -

ables. There are 61 fuel-sav ing tech nol o gies which

vary in cost and mar ginal fuel sav ings by size class.

Char ac ter is tics of a sam ple of these tech nol o gies are

shown in Ta ble 8, a com plete list is pub lished in As -

sump tions to the An nual Energy Out look 2009.14 Tech -

nol o gies pen e trate the mar ket based on a costef fec tive ness al go rithm that com pares the tech nol ogy 

cost to the discounted stream of fuel sav ings and the

value of performance to the con sumer. In gen eral,

higher fuel prices lead to higher fuel ef fi ciency es ti mates 

within each size class, a shift to a more fuel-ef fi cient

size class mix, and an in crease in the rate at which al terna -

tive-fuel ve hi cles en ter the mar ket place.

Regional Sales Submodule

Ve hi cle sales from the MAM are di vided into car and 

light truck sales. The re main der of the submodule is a

sim ple ac count ing mech a nism that uses en dog e nous

es ti mates of new car and light truck sales and the his -

tor i cal re gional ve hi cle sales ad justed for re gional pop u -

la tion trends to produce es ti mates of re gional sales,

which are subsequently passed to the al ter na tive-fuel

ve hi cle and the light-duty vehicle stock submodules.

Alternative-Fuel Vehicle Submodule

This submodule pro jects the sales shares of al ter na -

tive-fuel tech nol o gies as a func tion of technology at trib -

utes, costs, and fuel prices. The alternative-fuel ve hi cles

at trib utes are shown in Ta ble 9, de rived from As sump tions 

to the An nual En ergy Out look 2009. Both con ven tional

and new tech nol ogy ve hi cles are con sid ered. The al ter -

na tive-fuel ve hi cle submodule re ceives re gional new

car and light truck sales by size class from the re gional

sales submodule.

The pro jec tion of ve hi cle sales by tech nol ogy uti lizes a

nested multinomial logit (NMNL) model that predicts

sales shares based on rel e vant ve hi cle and fuel at trib -

utes. The nest ing struc ture first pre dicts the prob a bil ity

of fuel choice for multi-fuel vechicles within a tech nol -

ogy set. The sec ond level nest ing pre dicts pen e tra tion 

among sim i lar tech nol o gies within a tech nol ogy set

(i.e. gas o line ver sus die sel hy brids). The third level

choice de ter mines mar ket share among the the dif fer -

ent tech nol ogy sets.15

Energy Information Administration / The National Energy Modeling System: An Overview 2009 37

Transportation Demand Module

TRAN Outputs Inputs from NEMS Exogenous Inputs

Fuel de mand by mode

Sales, stocks, and char ac ter is tics of ve hi cle

 types by size class

Ve hi cle-miles trav eled

Fuel econ omy by tech nol ogy type

Al ter na tive-fuel ve hi cle sales by tech nol ogy type

Light-duty com mer cial fleet ve hi cle char ac ter is tics

En ergy prod uct prices

Gross do mes tic prod uct

Dis pos able per sonal in come

In dus trial out put

Ve hi cle sales

In ter na tional trade

Nat u ral gas pipe line

Pop u la tion

Ex ist ing ve hi cle stocks by vin tage and fuel econ omy

Ve hi cle sur vival rates

New ve hi cle technology char ac ter is tics

Fuel avail abil ity

Com mer cial avail abil ity

Ve hi cle safety and emis sions reglations

Ve hi cle miles-per-gal lon deg ra da tion rates

14 Energy Information Administration, Assumptions to the Annual Energy Outlook 2009

http://www.eia.doe.gov/oiaf/aeo/assumption/pdf/0554(2009) (Washington, DC, January 2009).

15 Greene, David L. and S.M. Chin, "Alternative Fuels and Vehicles (AFV) Model Changes," Center for Transportation

Analysis, Oak Ridge National Laboratory, page 1, (Oak Ridge, TN, November 14, 2000).
Energy Information Administration / The National Energy Modeling System: An Overview 2009 38

 Transportation Demand Module

 

Fractional Fuel

Efficiency Change First Year Introduced

 

Fractional

Horsepower Change

 Ma te rial Sub sti tu tion IV 0.099 2006 0

 Drag Re duc tion IV 0.042 2000 0

 5-Speed Au to matic 0.025 1995 0

 CVT 0.052 1998 0

 Au to mated Man ual Trans 0.073 2004 0

 VVL-6 Clinder 0.033 2000 0.10

 Camless Valve Ac tu a tion 6 Cylinder 0.058 2020 0.13

 Elec tric Power Steering 0.015 2004 0

 42V-Launch As sist and Regen 0.075 2005 -0.05

Table 8. Selected Technology Characteristics for Automobiles

Year Gasoline TDI Diesel Ethanol Flex LPG Bi-Fuel

Electric

Gasoline

 Hybrid

Fuel Cell

Hydrogen

Ve hi cle Price (thousand 2007 dol lars) 2006 28.0 29.8 28.7 33.3 31.1 78.6*

2030 29.8 30.7 30.2 35.0 31.0 54.2

Ve hi cle Miles per Gal lon 2006 29.5 39.8 29.9 29.6 42.7 53.3*

2030 37.8 48.2 38.1 37.7 51.0 54.9

Ve hi cle Range (miles) 2006 521 704 381 417 652 594*

2030 674 910 492 539 843 674

*First year of avail abil ity

Ta ble 9. Ex am ples of Midsize Au to mo bile At trib utes
The technology sets include:

• Con ven tional fuel ca pa ble (gas o line, die sel, bi-fuel

and flex-fuel),

• Hy brid (gas o line and die sel) and plug-in hy brid

• Ded i cated al ter na tive fuel (com pressed nat u ral

gas (CNG), liquified pe tro leum gas (LPG), and

ethanol),

• Fuel cell (gas o line, meth a nol, and hy dro gen),

• Elec tric bat tery pow ered (nickel-metal hy dride,

lith ium)

The ve hi cles at trib utes con sid ered in the choice algorithm

in clude: price, main te nance cost, bat tery replace ment

cost, range, multi-fuel ca pa bil ity, home re fu el ing ca pa -

bil ity, fuel econ omy, ac cel er a tion and lug gage space.

Transportation Demand Module

39 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Transportation Demand Module

Freight

Transport

Submodule

Miscellaneous

Energy Use

Submodule

Aircraft

Fleet Efficiency

Submodule

Commercial 

Light Truck 

Submodule

Vehicle Miles

Traveled

Submodule

Fuel

Demand

Submodule

Light-Duty 

Vehicle 

Commercial

Fleet

Submodule

AlternativeFuel Vehicle

Submodule

Regional

Sales

Submodule

Fuel

Economy 

Submodule

Air Travel

Demand

Submodule

N

E

M

S

Gross Domestic 

Product

Disposable Personal

Income

Industrial Output

International Trade

New Car and

Light Truck Sales

Petroleum Demand

Natural Gas Prices

Pipeline Fuel

Consumption

Natural Gas Demand

Electricity Prices

Electricity Demand

Electricity

Market

Module

Natural Gas

Transportation

and 

Distribution

Module

Petroleum

Market

Module

Macroeconomic

Activity Module

Exogenous

 Current and Projected

 Demographics, Existing Vehicle 

 Stocks by Vintage and Fuel Efficiency, 

 Vehicle Survival Rates, New Vehicle

Technology Characteristics, Fuel 

 Availability, Commercial

 Availability, Vehicle Safety and

 Emissions Regulations, Vehicle

 Miles-per-Gallon Degradation Rates

 

Petroleum Product 

Prices

Population

Fig ure 8. Trans por ta tion De mand Mod ule Structure

Alternative Fuel Vehicles

Ethanol flex-fueled

Ethanol neat (85 percent ethanol)

Compressed natural gas (CNG)

CNG bi-fuel

Liquefied petroleum gas (LPG)

LPG bi-fuel

Battery electric vehicle

Plug-in hybrid with 10 mile all electric range

Plug-in hybrid with 40 mile all electric range

Gasoline hybrid

Diesel Hybrid

Fuel cell gasoline

Fuel cell hydrogen

Fuel cell methanol
With the ex cep tion of main te nance cost, bat tery re -

place ment cost, and lug gage space, ve hi cle at trib utes

are de ter mined en dog e nously.16 The fuel at trib utes

used in mar ket share es ti ma tion in clude avail abil ity and

price. Ve hi cle at trib utes vary by six EPA size classes for

cars and light trucks and fuel avail abil ity var ies by Cen sus 

di vi sion. The NMNL model co ef fi cients were de vel oped

to re flect pur chase pref er ences for cars and light trucks

separately.

Light-Duty Ve hi cle (LDV) Stock Submodule

This submodule spec i fies the in ven tory of LDVs from year 

to year. Sur vival rates are ap plied to each vin tage, and

new ve hi cle sales are in tro duced into the ve hi cle stock

through an ac count ing framework. The fleet of ve hi cles

and their fuel ef fi ciency char ac ter is tics are im por tant to

the trans la tion of trans por ta tion ser vices de mand into

fuel de mand. 

TRAN main tains a level of de tail that in cludes twenty

vin tage clas si fi ca tions and six pas sen ger car and six light

truck size classes cor re spond ing to EPA in te rior vol ume

clas si fi ca tions for all ve hi cles less than 8,500 pounds,

as follows:

Vehicle-Miles Traveled (VMT) Submodule

This submodule pro jects travel de mand for automobiles

and light trucks. VMT per ca pita es ti mates are based on

the fuel cost of driv ing per mile and per ca pita dis pos able

per sonal in come. To tal VMT is calculated by mul ti ply ing 

VMT by the number of li censed drivers.

LDV Commercial Fleet Submodule

This submodule gen er ates es ti mates of the stock of cars 

and trucks used in busi ness, gov ern ment, and util ity

fleets. It also es ti mates travel de mand, fuel efficiency, and

en ergy con sump tion for the fleet vehicles prior to their

tran si tion to the pri vate sec tor at pre de ter mined vin tages.

Commercial Light Truck Submodule

The com mer cial light truck submodule es ti mates sales,

stocks, fuel ef fi cien cies, travel, and fuel demand for all

trucks greater than 8,500 pounds and less than 10,000

pounds gross ve hi cle weight rat ing.

Air Travel Demand Submodule

This submodule es ti mates the de mand for both

passenger and freight air travel. Pas sen ger travel is

pro jected by do mes tic travel (within the U.S.), in ter na -

tional travel (be tween U.S. and Non U.S.), and Non

U.S. travel. Ded i cated air freight travel is es ti mated for

U.S. and Non U.S. de mand. In each of the mar ket

segments, the de mand for air travel is es ti mated as a

func tion of the cost of air travel (in clud ing fuel costs) and

eco nomic growth (GDP, dis pos able in come, and

merchandise exports).

Air craft Fleet Ef fi ciency Submodule

This submodule pro jects the to tal world-wide stock and

the average fleet ef fi ciency of nar row body, wide body,

and re gional jets re quired to meet the pro jected travel

demand. The stock es ti ma tion is based on the growth

of travel de mand and the flow of air craft into and out of

the United States The over all fleet efficiency is de ter -

mined by the weighted av er age of the sur viv ing air craft

ef fi ciency (in clud ing retro fits) and the ef fi cien cies of the

newly ac quired air craft. Efficiency im prove ments of

new air craft are determined by pro ject ing the mar ket

pen e tra tion of ad vanced air craft tech nol o gies.

 Transportation Demand Module

40 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Light Duty Vehicle Size Classes

Cars:

 Mini-compact - less than 85 cubic feet

 Subcompact - between 85 and 99 cubic feet

 Compact - between 100 and 109 cubic feet

 Mid-size - between 110 and 119 cubic feet

 Large - 120 or more cubic feet

 Two-seater - designed to seat two adults

Trucks:

 Small vans - gross vehicle weight rating (GVWR) less than

 4,750 pounds

 Large vans - GVWR 4,750 to 8,500 pounds

 Small pickups - GVWR less than 4,750 pounds

 Large pickups - GVWR 4,750 to 8,500 pounds 

 Small utility - GVWR less than 4,750 pounds

 Large utility - GVWR 4,750 to 8,500 pounds

16 Energy and Environmental Analysis, Inc., Updates to the Fuel Economy Model (FEM) and Advanced Technology

Vehicle (ATV:) Module of the National Energy Modeling System (NEMS) Transportation Model, prepared for the

Energy Information Administration (EIA), 
Freight Trans port Submodule

This submodule trans lates NEMS es ti mates of

industrial pro duc tion into ton-miles trav eled for rail and

ships and into vehicle ve hi cle-miles trav eled for trucks,

then into fuel de mand by mode of freight travel. The

freight truck stock is sub di vided into me dium and

heavy-duty trucks. VMT freight estimates by truck size

class and tech nol ogy are based on match ing freight

needs, as mea sured by the growth in in dus trial out put

by NAICS code, to VMT lev els as so ci ated with truck

stocks and new ve hi cles. Rail and shipping ton-miles

trav eled are also es ti mated as a function of growth in in -

dus trial out put.

Freight truck fuel ef fi ciency growth rates are tied to his tor i -

cal growth rates by size class and are also depen dent on

the max i mum pen e tra tion, in tro duc tion year, fuel trig ger

price (based on cost-ef fec tive ness), and fuel econ omy

im prove ment of ad vanced technologies, which in clude

al ter na tive-fuel tech nol o gies. A sub set of the tech nol ogy

char ac ter is tics are shown in Ta ble 10. In the rail and ship -

ping modes, en ergy efficiency es ti mates are struc tured

to eval u ate the potential of both tech nol ogy trends and

ef fi ciency improvements re lated to en ergy prices.

Miscellaneous Energy Use Submodule

This submodule pro jects the use of en ergy in mil i tary op er -

a tions, mass tran sit ve hi cles, rec re ational boats, and lu bri -

cants, based on en dog e nous vari ables within NEMS

(e.g., ve hi cle fuel ef fi cien cies) and exogenous vari ables

(e.g., the mil i tary bud get).

Transportation Demand Module

Energy Information Administration / The National Energy Modeling System: An Overview 2009 41

Fuel Economy

Improvement

(percent)

Maximum Penetration

 (percent) Introduction Year

Capital Cost 

(2001 dollars)

Medium Heavy Medium Heavy Medium Heavy Medium Heavy

Aero Dy nam ics: bumper, un der side air

bat tles, wheel well covers 3.6 2.3 50 40 2002 N/A N/A $1,500

Low roll ing resistence tires 2.3 2.7 50 66 2004 2005 $180 $550

Trans mis sion: lock-up, elec tronic con trols,

re duced friction 1.8 1.8 100 100 2005 2005 $750 $1,000

Die sel En gine: hy brid elec tric powertrain 36.0 N/A 15 N/A 2010 N/A $6,000 N/A

Re duce waste heat, ther mal mgmt N/A 9.0 N/A 35 N/A 2010 N/A $2,000

Weight re duc tion 4.5 9.0 20 30 2010 2005 $1,300 $2,000

Die sel Emis sion Nox non-ther mal plasma

cat a lyst -1.5 -1.5 25 25 2007 2007 $1,000 $1,250

PM cat a lytic filter -2.5 -1.5 95 95 2008 2006 $1,000 $1,500

HC/CO: ox i da tion cat a lyst -0.5 -0.5 95 95 2002 2002 $150 $250

NOx adsorbers -3.0 -3.0 90 90 2007 2007 $1,500 $2,500

Table 10. Ex am ple of Truck Tech nol ogy Char ac ter is tics (Die sel)
Elec tric ity Mar ket Mod ule
The elec tric ity mar ket mod ule (EMM) rep re sents the

generation, transmission, and pricing of electricity,

subject to: delivered prices for coal, petroleum

products, and natural gas; the cost of centralized gen -

eration from renewable fuels; macroeconomic

variables for costs of capital and domestic investment;

and electricity load shapes and demand. The

submodules consist of capacity planning, fuel

dispatching, finance and pricing, and load and de mand

(Figure 9). In addition, nonutility supply and electricity

trade are represented in the fuel dispatching and ca -

pac ity planning submodules. Nonutility generation 

from CHP and other facilities whose primary business

is not electricity generation is represented in the

demand and fuel supply modules. All other nonutility

generation is represented in the EMM. The generation

of electricity is accounted for in 15 supply regions

(Figure 10), and fuel consumption is allocated to the 9

Census divisions.

The EMM de ter mines air borne emis sions pro duced by

the gen er a tion of elec tric ity. It rep re sents lim its for sul -

fur di ox ide and ni tro gen ox ides spec i fied in the Clean

Air Act Amend ments of 1990 (CAAA90) and the Clean

Air In ter state Rule. The AEO2009 also mod els

State-level reg u la tions im ple ment ing mer cury stan -

dards. The EMM also has the abil ity to track and limit

emis sions of car bon di ox ide, and the AEO2009 in -

cludes the re gional car bon re stric tions of the Re gional

Green house Gas Ini tia tive (RGGI). 

Op er at ing (dis patch) de ci sions are pro vided by the

cost-min i miz ing mix of fuel and vari able op er at ing and

main te nance (O&M) costs, sub ject to en vi ronmen tal

costs. Ca pac ity ex pan sion is de ter mined by the

least-cost mix of all costs, in clud ing cap i tal, O&M, and

fuel. Elec tric ity de mand is rep re sented by load curves,

which vary by re gion and sea son. The so lu tion to the

submodules of EMM is simultaneous in that, di rectly or

in di rectly, the so lu tion for each submodule de pends on

the so lu tion to ev ery other submodule. A so lu tion se -

quence through the submodules can be viewed as

fol lows:

• The elec tric ity load and de mand submodule pro -

cesses elec tric ity de mand to con struct load curves

• The elec tric ity ca pac ity plan ning submodule pro -

jects the con struc tion of new util ity and nonutility 

plants, the level of firm power trades, and the 

ad di tion of equip ment for en vi ron men tal com pli -

ance

• The elec tric ity fuel dis patch submodule dis -

patches the avail able gen er at ing units, both util ity

and nonutility, al low ing sur plus ca pac ity in se lect re -

gions to be dis patched to meet an other re gions needs 

(econ omy trade)

• The elec tric ity fi nance and pric ing submodule cal cu -

lates to tal rev e nue re quire ments for each op er a tion

and com putes av er age and mar ginal-cost based

elec tric ity prices.

Electricity Capacity Planning Submodule

The elec tric ity ca pac ity plan ning (ECP) submodule de -

ter mines how best to meet ex pected growth in elec tric -

ity de mand, given avail able re sources, expected load 

shapes, ex pected de mands and fuel prices, en vi ron -

men tal con straints, and costs for utility and nonutility

tech nol o gies. When new ca pac ity is re quired to meet

growth in elec tric ity de mand, the tech nol ogy cho sen is

de ter mined by the tim ing of the de mand in crease, the

ex pected uti li za tion of the new ca pac ity, the op er at ing ef fi -

cien cies, and the construction and op er at ing costs of

avail able technologies.

The ex pected uti li za tion of the ca pac ity is im por tant in the

de ci sion-mak ing pro cess. A tech nol ogy with rel a tively

high cap i tal costs but com par a tively low op er at ing

costs (pri mar ily fuel costs) may be the ap pro pri ate

choice if the ca pac ity is ex pected to op er ate con tin u -

ously (base load). How ever, a plant type with high op er -

at ing costs but low cap i tal costs may be the most

eco nom i cal se lec tion to serve the peak load (i.e., the

high est de mands on the sys tem), which oc curs in fre -

quently. In ter me di ate or cy cling load oc cu pies a mid dle 

ground be tween base and peak load and is best served

Energy Information Administration / The National Energy Modeling System: An Overview 2009 43

Electricity Market Module

EMM Outputs Inputs from NEMS Exogenous Inputs

Elec tric ity prices and price com po nents

Fuel de mands

Ca pac ity ad di tions

Cap i tal re quire ments

Emis sions

Re new able ca pac ity

Avoided costs

Elec tric ity sales

Fuel prices

Cogeneration sup ply and fuel con sump tion

Elec tric ity sales to the grid

Re new able tech nol ogy char ac ter is tics, al low able

 ca pac ity, and costs

Re new able ca pac ity fac tors

Gross do mes tic prod uct

In ter est rates

Fi nan cial data

Tax assumptions

Cap i tal costs

Op er a tion and main te nance costs

Op er at ing pa ram e ters

Emmissions rates

New tech nol o gies

Ex ist ing fa cil i ties

Trans mis sion constraints
by plants that are cheaper to build than baseload plants 

and cheaper to op er ate than peak load plants.

Tech nol o gies are com pared on the ba sis of to tal capital

and op er at ing costs in curred over a 20-year period. As

new tech nol o gies be come avail able, they are com peted

against con ven tional plant types. Fossil-fuel, nu clear,

and re new able cen tral-sta tion generating tech nol o gies

are rep re sented, as listed in Ta ble 11. The EMM also

con sid ers two dis trib uted gen er a tion tech nol o gies

-baseload and peak. The EMM also has the abil ity to

model a de mand stor age tech nol ogy to rep re sent load

shift ing.

Un cer tainty about in vest ment costs for new technologies

is cap tured in ECP us ing tech no log i cal optimism and

learn ing fac tors. The tech no log i cal optimism fac tor re -

flects the in her ent ten dency to un deres ti mate costs for

new tech nol o gies. The de gree of tech no log i cal op ti mism 

de pends on the com plex ity of the en gi neer ing de sign

and the stage of de velopment. As de vel op ment pro -

ceeds and more data become avail able, cost es ti mates

be come more ac cu rate and the tech no log i cal op ti mism

fac tor de clines.

Learn ing fac tors rep re sent re duc tions in cap i tal costs

due to learn ing-by-do ing. For new technologies, cost re -

duc tions due to learn ing also ac count for in ter na tional ex -

pe ri ence in build ing gen er at ing capacity. These fac tors

44 Energy Information Administration / The National Energy Modeling System: An Overview 2009

 Electricity Market Module

Electricity Market Module

Electricity Capacity 

Planning

Submodule

Electricity Fuel 

Dispatch 

Submodule

Electricity Finance 

and Pricing 

Submodule

Load and

Demand-Side 

Management

Submodule

Available Capacity

Fuel Demands

Average Electricity Prices

Load Curves

Capacity

Additions

Electricity Sales

Cogeneration Supply and 

Fuel Consumption 

Sales to Grid

Electricity Prices

Natural Gas Peak and 

Offpeak, Core and Noncore 

Demand

Petroleum Product Prices

Petroleum Demand

Electricity Price

Electricity Price

Coal Prices

Coal Demand

Renewable Capacity, Avoided 

Costs, Discount Rate, Biomass 

Consumption

Electricity Prices

Electric Industry Output

Macroeconomic 

Activity Module

Renewable 

Fuels

Module

Coal Market

Module

Oil and Gas

Supply Module

Petroleum

Market Module

Natural Gas

Transmission and 

Distribution 

Module

Demand 

Modules

Exogenous

 Financial Data, Tax Assumptions, Capital

 Costs, Operating and Maintenance Costs,

Operating Parameters, Emission Rates,

 Existing Facilities, New Technologies, 

 Transmission Constraints, Hydropower

 Capacity and Capacity Factors

Renewable Technology

Characteristics, Allowable Capacity, 

Capacity Factors, Costs

Expectations

Interest Rates, Gross 

Domestic Product

Natural Gas Prices

Fig ure 9. Elec tric ity Mar ket Mod ule Strucuture
are cal cu lated for each of the ma jor de sign com po nents 

of a plant type de sign. For mod el ing pur poses, com po -

nents are iden ti fied only if the com po nent is shared be -

tween mul ti ple plant types, so that the ECP can re flect

the learn ing that oc curs across tech nol o gies. The cost

ad just ment fac tors are based on the cu mu la tive ca pac -

ity of a given com po nent. A 3-step learn ing curve is uti -

lized for all de sign com po nents. 

Typ i cally, the great est amount of learn ing oc curs dur ing 

the ini tial stages of de vel op ment and the rate of cost re -

duc tions de clines as com mer cial iza tion pro gresses.

Each step of the curve is char ac ter ized by the learn ing

rate and the num ber of doublings of ca pac ity in which

this rate is ap plied. De pend ing on the stage of de vel op -

ment for a par tic u lar com po nent, some of the learn ing

may al ready be in cor po rated in the ini tial cost es ti mate.

Cap i tal costs for all new elec tric ity gen er at ing tech nol o -

gies (fos sil, nu clear, and re new able) de crease in re -

sponse to for eign and do mes tic ex pe ri ence. Foreign

units of new tech nol o gies are as sumed to contrib ute to

re duc tions in cap i tal costs for units that are in stalled in

the United States to the ex tent that (1) the tech nol ogy

char ac ter is tics are sim i lar to those used in U.S. mar kets,

(2) the de sign and con struc tion firms and key per son nel

com pete in the U.S. mar ket, (3) the own ing and op er at ing

firm com petes ac tively in the United States, and (4) there

ex ists rel a tively com plete in for ma tion about the sta tus of

the associated fa cil ity. If the new for eign units do not

sat isfy one or more of these re quire ments, they are given

a re duced weight or not in cluded in the learn ing effects

cal cu la tion. Cap i tal costs, heat rates, and first year of

availablilty from the AEO2009 ref er ence case are shown 

in Ta ble 12; cap i tal costs rep re sent the costs of building

Energy Information Administration / The National Energy Modeling System: An Overview 2009 45

Electricity Market Module

13

11

12

5

2

10

8

9

4

1

3

6

7

11

14

15

1 ECAR

2 ERCOT

3 MAAC

4 MAIN

5 MAPP

6 NY

7 NE

8 FL

9 STV

12 RA

10 SPP

11 NWP

13 CNV

14

15

AK

HI

Electricity

Supply

Regions

Fig ure 10. Elec tric ity Mar ket Mod ule Sup ply Regions
new plants or dered in 2008. Ad di tional in for ma tion

about costs and per for mance char ac ter is tics can be

found on page 89 of the "As sump tions to the An nual En -

ergy Out look 2009."17

Ini tially, in vest ment de ci sions are de ter mined in ECP

us ing cost and per for mance char ac ter is tics that are

rep re sented as sin gle point es ti mates corresponding to 

the av er age (ex pected) cost. How ever, these pa ram e -

ters are also sub ject to un cer tainty and are better rep re -

sented by dis tri bu tions. If the distributions of two or

more op tions over lap, the op tion with the low est av er -

age cost is not likely to cap ture the en tire mar ket. 

There fore, ECP uses a mar ket-shar ing al go rithm to

ad just the ini tial solution and re al lo cate some of the ca -

pac ity ex pan sion decisions to technologies that are

competitive but do not have the lowest average cost.

Fos sil-fired steam and nu clear plant re tire ments are

cal cu lated en dog e nously within the model. Plants are

re tired if the mar ket price of elec tric ity is not suf fi cient to

sup port con tin ued op er a tion. The ex pected rev e nues

from these plants are com pared to the an nual go -

ing-for ward costs, which are mainly fuel and O&M

costs. A plant is re tired if these costs ex ceed the rev e nues 

and the over all cost of elec tric ity can be re duced by

building replacement capacity.

The ECP submodule also de ter mines whether to con -

tract for un planned firm power im ports from Can ada

and from neigh bor ing elec tric ity sup ply regions. Im ports

from Can ada are com peted us ing sup ply curves de vel -

oped from cost es ti mates for potential hy dro elec tric pro -

jects in Can ada. Im ports from neigh bor ing elec tric ity

sup ply re gions are competed in the ECP based on the cost 

of the unit in the export ing re gion plus the ad di tional cost of 

trans mitting the power. Trans mis sion costs are com puted 

as a fraction of revenue.

Af ter build ing new ca pac ity, the submodule passes to tal

avail able ca pac ity to the elec tric ity fuel dispatch

submodule and new ca pac ity ex penses to the elec tric ity

fi nance and pric ing submodule. 

Elec tric ity Fuel Dis patch Submodule 

Given avail able ca pac ity, firm pur chased-power 

agree ments, fuel prices, and load curves, the elec tricity 

fuel dis patch (EFD) submodule min i mizes variable

costs as it solves for gen er a tion fa cil ity utilization and

econ omy power ex changes to sat isfy demand in each

time pe riod and re gion. Lim its on emis sions of sul fur di -

ox ide from gen er at ing units and the en gi neer ing char ac -

ter is tics of units serve as con straints. Coal-fired ca pac ity 

can co-fire with biomass in or der to lower op er at ing

costs and/or emissions.

The EFD uses a lin ear pro gram ming (LP) ap proach to

pro vide a min i mum cost so lu tion to al lo cat ing (dis patch -

ing) ca pac ity to meet de mand. It sim u lates the elec tric

trans mis sion net work on the NERC re gion level and si -

mul ta neously dis patches ca pac ity re gion ally by time

slice un til de mand for the year is met. Tra di tional

cogeneration and firm trade ca pac ity is re moved from

the load du ra tion curve prior to the dis patch de ci sion.

Ca pac ity costs for each time slice are based on fuel and 

vari able O&M costs, mak ing ad just ments for RPS

46 Energy Information Administration / The National Energy Modeling System: An Overview 2009

 Electricity Market Module

Fossil

Existing coal steam plants (with or without environmental

controls)

New pulverized coal with environmental controls

Advanced clean coal technology

Advanced clean coal technology with sequestration

Oil/Gas steam

Conventional combined cycle

Advanced combined cycle 

Advanced combined cycle with sequestration

Conventional combusion turbine

Fuel cells

Nuclear

Conventional nuclear

Advanced nuclear

Renewables

Conventional hydropower

Pumped storage

Geothermal

Solar-thermal

Solar-photovoltaic

Wind - onshore and offshore

Wood

Municipal solid waste

En vi ron men tal con trols in clude flue gas desulfurization (FGD), se lec tive cat -

a lytic re duc tion (SCR), se lec tive non-cat a lytic re duc tion (SNCR), fab ric fil -

ters, spray cool ing, activated car bon in jec tion (ACI), and par tic u late re moval

equipiment.

Ta ble 11. Gen er at ing Technologies

17 Energy Information Administration, Assumptions to the Annual Energy Outlook 2009,

http://www.eia.doe.gov/oiaf/aeo/assumption/pdf/0554(2009).pdf (March 2009)
cred its, if ap pli ca ble, and pro duc tion tax cred its. Gen er -

a tors are re quired to meet planned main te nance re -

quire ments, as defined by plant type.

In ter re gional econ omy trade is also rep re sented in the

EFD submodule by al low ing sur plus gen er a tion in one re -

gion to sat isfy elec tric ity de mand in an import ing re gion,

re sult ing in a cost sav ings. Econ omy trade with Can ada

is de ter mined in a sim i lar manner as in ter re gional econ -

omy trade. Sur plus Canadian en ergy is al lowed to dis -

place en ergy in an import ing re gion if it re sults in a cost

sav ings. Af ter dispatch ing, fuel use is re ported back to the

fuel sup ply mod ules and op er at ing ex penses and rev e -

nues from trade are re ported to the elec tric ity fi nance and

pricing submodule.

Electricity Finance and Pricing Submodule

The costs of build ing ca pac ity, buy ing power, and gen -

er at ing elec tric ity are tal lied in the elec tric ity finance and

pric ing (EFP) submodule, which simulates both com -

pet i tive elec tric ity pric ing and the cost-of-ser vice

method of ten used by State regulators to de ter mine the

price of elec tric ity. The AEO2009 ref er ence case as -

sumes a tran si tion to full com pet i tive pric ing in New

York, Mid-At lan tic Area Coun cil, and Texas, and a 95

per cent tran si tion to com pet i tive pric ing in New Eng -

land (Ver mont be ing the only fully-reg u lated State in

that re gion). Cal i for nia re turned to al most fully reg u -

lated pric ing in 2002, af ter be gin ning a tran si tion to

com pe ti tion in 1998. In ad di tion elec tric ity prices in the

Energy Information Administration / The National Energy Modeling System: An Overview 2009 47

Electricity Market Module

Technology

Capital Costs1

(2007$/KW)

Heatrate in 2008 

(Btu/kWhr) Online Year2

Scrubbed Coal New 2058 9200 2012

Integrated Coal-gasification Comb Cycle (IGCC) 2378 8765 2012

IGCC with carbon sequestration 3496 10781 2016

Coventional Gas/Oil Comb Cycle 962 7196 2011

Advanced Gas/Oil Comb Cycle (CC) 948 6752 2011

Advanced CC with carbon sequestration 1890 8613 2016

Conventional Combusion Turbine 670 10810 2010

Advanced Combusition Turbine 634 9289 2010

Fuel Cells 5360 7930 2011

Adv nuclear 3318 10434 2016

Distributed Generation - Base 1370 9050 2011

Distributed Generation - Peak 1645 10069 2010

Biomass 3766 9646 2012

MSW - Landfill Gas 2543 13648 2010

Geothermal3 1711 34633 2010

Conventional Hydropower 3,4 2242 9919 2012

Wind4 1923 9919 2009

Wind Offshore4 3851 9919 2012

Solar Thermal 5021 9919 2012

Photovoltaic 6038 9919 2011

Table 12. 2008 Over night Cap i tal Costs (in clud ing Con tin gen cies), 2008 Heat Rates, and On line Year by Tech nol ogy for the

AEO2009 Ref er ence Case

1Over night cap i tal cost in clud ing con tin gency fac tors, ex clud ing reigonal mul ti pli ers and learn ing ef fects. In ter est charges are also ex cluded. These rep re sent costs

of new pro jects ini ti ated in 2008. Cap i tal costs are shown be fore in vest ment tax cred its are ap plied, where ap pli ca ble.

2On line year rep re sents the first year that a new unit could be com pleted, given an or der date of 2008. For wind, geo ther mal and land fill gas, the on line year was

moved ear lier to ac knowl edge the sig nif i cant mar ket ac tiv ity al ready occuring in anticipation of the ex pi ra tion of the Pro duc tion Tax Credit in 2009 for wind and 2010 for

the oth ers.

3Be cause geo ther mal and hy dro cost and per for mance char ac ter is tics are spe cific for each site, the ta ble en tries rep re sent the cost of the least ex pen sive plant that

could be built in the North west Power Pool re gion, where most of the pro posed sites are lo cated.

4For hy dro, wind, and so lar tech nol o gies, the heatrate shown rep re sents the av er age heatrate for con ven tional ther mal gen er a tion as of 2007. This isused for pur -

poses of cal cu lat ing pri mary en ergy con sump tion dis placed for these re sources, and does not im ply an es ti mate of their ac tual en ergy con ver sion ef fi ciency.
East Cen tral Area Re li abil ity Coun cil, the Mid-Amer i can 

In ter con nected Net work, the South east ern Elec tric Re -

li abil ity Coun cil, the South west Power Pool, the North -

west Power Pool, and the Rocky Moun tain Power

Area/Ar i zona are a mix of both com pet i tive and reg u -

lated prices. Since some States in each of these re -

gions have not taken ac tion to de reg u late their pric ing

of elec tric ity, prices in those States are as sumed to

con tinue to be based on tra di tional cost-of-ser vice pric -

ing. The price for mixed re gions is a load-weighted av -

er age of the com pet i tive price and the reg u lated price,

with the weight based on the per cent of elec tric ity load

in the re gion that has taken ac tion to de reg u late. In re -

gions where none of the states in the re gion have in tro -

duced com pe ti tion—Florida Re li abil ity Co or di nat ing

Coun cil and Mid-Con ti nent Area Power Pool—elec tric -

ity prices are as sumed to re main reg u lated and the

cost-of-ser vice calculation is used to determine

electricity prices.

Us ing his tor i cal costs for ex ist ing plants (de rived from

var i ous sources such as Fed eral En ergy Regulatory Com -

mis sion Form 1, An nual Re port of Ma jor Elec tric Util i -

ties, Li cens ees and Oth ers, and Form EIA-412, An nual

Re port of Pub lic Elec tric Util i ties), cost es ti mates for

new plants, fuel prices from the NEMS fuel sup ply mod -

ules, unit op er at ing lev els, plant de com mis sion ing costs,

plant phase-in costs, and pur chased power costs, the 

EFP submodule cal cu lates to tal rev e nue re quire ments for

each area of op er a tion—gen er a tion, trans mis sion, and

dis tri bu tion—for pric ing of elec tric ity in the fully reg u -

lated States. Rev e nue re quire ments shared over sales 

by cus tomer class yield the price of elec tric ity for each

class. Elec tric ity prices are returned to the de mand

mod ules. In ad di tion, the submodule gen er ates de tailed

fi nan cial state ments.

For those States for which it is ap pli ca ble, the EFP also

de ter mines com pet i tive prices for elec tric ity gen eration. 

Un like cost-of-ser vice prices, which are based on av er -

age costs, com pet i tive prices are based on mar ginal

costs. Mar ginal costs are pri mar ily the operating costs of

the most ex pen sive plant re quired to meet de mand. The

com pet i tive price also in cludes a re li abil ity price ad just -

ment, which rep re sents the value con sum ers place on

re li abil ity of ser vice when de mands are high and avail able 

ca pac ity is lim ited. Prices for trans mis sion and dis tri bu -

tion are assumed to re main reg u lated, so the de liv ered

elec tricity price un der com pe ti tion is the sum of the

marginal price of gen er a tion and the av er age price of

transmission and distribution.

Electricity Load and Demand Submodule

The elec tric ity load and de mand (ELD) submodule gen -

er ates load curves rep re sent ing the de mand for elec -

tric ity. The de mand for elec tric ity var ies over the course 

of a day. Many dif fer ent tech nol o gies and end uses, each

re quir ing a dif fer ent level of ca pac ity for dif fer ent lengths

of time, are pow ered by elec tric ity. For op er a tional and

plan ning anal y sis, an an nual load du ra tion curve, which

repre sents the ag gre gated hourly de mands, is 

constructed. Be cause de mand var ies by geo graphic area 

and time of year, the ELD submodule gen er ates load

curves for each re gion and sea son.

Emissions

EMM tracks emis sion lev els for sul fur di ox ide (SO2)

and ni tro gen ox ides (NOx). Fa cil ity development, retro -

fit ting, and dis patch are con strained to com ply with the

pol lu tion con straints of the CAAA90 and other pol lu tion

con straints in clud ing the Clean Air In ter state Rule. An

in no va tive fea ture of this leg is la tion is a sys tem of trad -

ing emis sions al low ances. The trad ing sys tem al lows a 

util ity with a rel a tively low cost of com pli ance to sell its

ex cess com pli ance (i.e., the de gree to which its emis -

sions per unit of power gen er ated are be low max i mum

al low able lev els) to util i ties with a rel a tively high cost of

com pli ance. The trad ing of emis sions al low ances does 

not change the na tional ag gre gate emis sions level set

by CAAA90, but it does tend to min i mize the over all

cost of com pli ance.

In ad di tion to SO2, and NOx, the EMM also de ter mines

mer cury and car bon di ox ide emis sions. It rep re sents

con trol op tions to re duce emis sions of these four

gases, ei ther in di vid u ally or in any com bi na tion. Fuel

switch ing from coal to nat u ral gas, renewables, or nu -

clear can re duce all of these emis sions. Flue gas

desulfurization equip ment can de crease SO2 and mer -

cury emis sions. Se lec tive cat a lytic re duc tion can re -

duce NOx and mer cury emis sions. Se lec tive

non-cat a lytic re duc tion and low-NOx burn ers can lower

NOx emis sions. Fab ric fil ters and ac ti vated car bon in -

jec tion can re duce mer cury emis sions. Lower emis -

sions re sult ing from de mand re duc tions are de ter mined 

in the end-use de mand mod ules.

The AEO2009 in cludes a gen er al ized struc ture to

model cur rent state-level reg u la tions call ing for the best 

avail able con trol tech nol ogy to con trol mer cury. The

AEO2009 also in cludes the car bon caps for States that

are part of the RGGI. 

48 Energy Information Administration / The National Energy Modeling System: An Overview 2009

 Electricity Market Module
Renew able Fuels Mod ule
The re new able fu els mod ule (RFM) rep re sents re new -

able en ergy resoures and large–scale tech nol o gies

used for grid-con nected U.S. elec tric ity sup ply (Fig ure

11). Since most renewables (bio mass, con ven tional

hy dro elec tric ity, geo ther mal, land fill gas, so lar

photovoltaics, so lar ther mal, and wind) are used to gen -

er ate elec tric ity, the RFM pri mar ily in ter acts with the

electricity market module (EMM). 

New re new able en ergy gen er at ing ca pac ity is ei ther

model–de ter mined or based on sur veys or other pub -

lished in for ma tion. A new unit is only in cluded in sur -

veys or acccepted from pub lished in for ma tion if it is

re ported to or iden ti fied by the EIA and the unit meets

EIA cri te ria for in clu sion (the unit ex ists, is un der con -

struc tion, un der con tract, is pub licly de clared by the

ven dor, or is man dated by state law, such as un der a

state re new able port fo lio stan dard). EIA may also as -

sume min i mal builds for rea sons based on his tor i cal ex -

pe ri ence (floors). The pen e tra tion of grid-con nected

re new able en ergy gen er at ing tech nol o gies, with the

exception of landfill gas, is determined by the EMM. 

Each re new able en ergy submodule of the RFM is

treated in de pend ently of the oth ers, ex cept for their

least-cost com pe ti tion in the EMM. Be cause vari able

op er a tion and main te nance costs for re new able tech -

nol o gies are lower than for any other ma jor gen er at ing

tech nol ogy, and be cause they gen er ally pro duce lit tle

or no air pol lu tion, all avail able re new able ca pac ity, ex -

cept bio mass, is as sumed to be dis patched first by the

EMM. Be cause of its po ten tially sig nif i cant fuel cost,

bio mass is dis patched according to its variable cost by

the EMM. 

With sig nif i cant growth over time, in stal la tion costs are

as sumed to be higher be cause of grow ing con straints

on the avail abil ity of sites, nat u ral re source deg ra da -

tion, the need to up grade ex ist ing trans mis sion or dis tri -

bu tion net works, and other re source-spe cific fac tors.

Geothermal-Electric Submodule

The geo ther mal-elec tric submodule pro vides the EMM

the amounts of new geo ther mal ca pac ity that can be

built at known and well char ac ter ized geo ther mal re -

source sites, along with re lated cost and per for mance

data. The in for ma tion is ex pressed in the form of a

three–step sup ply func tion that rep re sents the ag gre -

gate amount of new ca pac ity and as so ci ated costs that

can be of fered in each year in each region. 

Only hy dro ther mal (hot wa ter and steam) re sources

are con sid ered. Hot dry rock re sources are not in -

cluded, be cause they are not ex pected to be eco nom i -

cally ac ces si ble dur ing the NEMS pro jec tion horizon. 

Cap i tal and op er at ing costs are es ti mated sep a rately,

and life-cy cle costs are cal cu lated by the RFM. The

cost ing meth od ol ogy in cor po rates any ap pli ca ble ef -

fects of Fed eral and State en ergy tax con struc tion and

pro duc tion in cen tives

Wind-Electric Submodule 

The wind-elec tric submodule pro jects the avail abil ity of

wind re sources as well as the cost and per for mance of

wind tur bine gen er a tors. This in for ma tion is passed to

EMM so that wind tur bines can be built and dis patched

in com pe ti tion with other elec tric ity gen er at ing tech nol -

o gies. The wind tur bine data are ex pressed in the form

of en ergy sup ply curves that pro vide the max i mum

amount, cap i tal cost, and ca pac ity fac tor of tur bine gen -

er at ing ca pac ity that could be in stalled in a re gion in a

year, given the avail able land area and wind speed.

The model also eval u ates the con tri bu tion of the wind

ca pac ity to meet ing sys tem re li abil ity re quire ments so

that the EMM can ap pro pri ately in cor po rate wind ca -

pac ity into cal cu la tions for re gional reliability reserve

margins.

So lar-Elec tric Submodule

The so lar-elec tric submodule rep re sents both pho to -

vol taic and high-tem per a ture ther mal elec tric (concen50 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Renewable Fuels Module

RFM Outputs Inputs from NEMS Exogenous Inputs

En ergy pro duc tion ca pac i ties

Cap i tal costs

Op er at ing costs (in clud ing wood sup ply prices for

 the wood submodule)

Ca pac ity factors

Avail able ca pac ity

Bio mass fuel costs

Bio mass sup ply curves

In stalled en ergy pro duc tion capacity

Gross do mes tic prod uct

Pop u la tion

In ter est Rates

Avoided cost of elec tric ity

Dis count rate

Ca pac ity additions

Bio mass con sump tion

Site-spe cific geo ther mal re source quan tity data

Site-spe cific and re source qual ity data

Plant uti li za tion (capacity fac tor)Trans mis sion and dis tri bu tion costs

Tech nol ogy cost and per for mance pa ram e ters

Land fill gas ca pac ity
Energy Information Administration / The National Energy Modeling System: An Overview 2009 51

Renewable Fuels Module

Cost Characteristics,

Capacity Factor,

Available Capacity

Avoided Cost,

Installed Capacity,

Discount Rate

Cost Characteristics,

Time Segment Capacity 

Factors, Available Capacity 

by Wind Class

Capacity Additions

Cost Characteristics, 

Capacity Factor,

Installed Capacity

Cost Characteristics,

Capacity Factor,

Fuel Cost,

Available Capacity

Biomass Consumption

Macroeconomic 

Activity 

Module

Demand

Modules

Biomass

Consumption

Gross Domestic

Product, Population,

Interest Rates

 Electricity

Market

Module

Geothermal

Electric

Submodule

Wind-Electric

Submodule

Solar-Electric

Submodule

Cost Characteristics,

Time Segment Capacity 

Factors, Available Capacity 

Renewable

Fuels

Module Exogenous

Technological Characteristics,

Capital Costs, Operating 

Costs, Resource Data,

Landfill Gas Capacity,

Municipal Solid Waste Trends

Municipal

Solid Waste

Submodule

Biomass

Fuels

Submodule Petroleum

Market

Module

Biomass

Supply Curves

-

Biomass

Consumption

Hydroelectricity

Submodule

Cost Characteristics

Capacity Factor

Available Capacity

Avoided

Cost

Installed Capacity

Discount Rate

Fig ure 11. Re new able Fu els Mod ule Structure
trating so lar power) in stal la tions. Only cen tral-sta tion,

grid-con nected ap pli ca tions con structed by a util ity or

in de pend ent power pro ducer are con sid ered in this

por tion of the model.

The so lar-elec tric submodule pro vides the EMM with

time-of-day and sea sonal so lar avail abil ity data for

each re gion, as well as cur rent costs. The EMM uses

this data to eval u ate the cost and per for mance of so -

lar-elec tric tech nol o gies in re gional grid ap pli ca tions.

The com mer cial and res i den tial de mand mod ules of

NEMS also model pho to vol taic sys tems in stalled by

con sum ers, as dis cussed in the de mand mod ule de -

scrip tions un der “Dis trib uted Gen er a tion.” 

Land fill Gas Submodule 

The land fill gas submodule pro vides an nual pro jec tions 

of elec tric ity gen er a tion from meth ane from land fills

(land fill gas). The submodule uses the quan tity of mu -

nic i pal solid waste (MSW) that is pro duced, the pro por -

tion of MSW that will be re cy cled, and the meth ane

emis sion char ac ter is tics of three types of land fills to

pro duce pro jec tions of the fu ture elec tric power gen er -

at ing ca pac ity from land fill gas. The amount of meth -

ane avail able is cal cu lated by first de ter min ing the

amount of to tal waste gen er ated in the United States.

The amount of to tal waste gen er ated is de rived from an

ec ono met ric equa tion that uses gross do mes tic prod -

uct and pop u la tion as the pro jec tion driv ers. It is as -

sumed that no new mass burn waste–to–en ergy

(MSW) fa cil i ties will be built and op er ated dur ing the

pro jec tion pe riod in the United States. It is also as -

sumed that op er a tional mass-burn fa cil i ties will con -

tinue to op er ate and re tire as planned through out the

pro jec tion pe riod. The land fill gas submodule passes

cost and per for mance char ac ter is tics of the land fill

gas–to–elec tric ity tech nol ogy to the EMM for ca pac ity

plan ning de ci sions. The amount of new land-fill-gas-toelec tric ity ca pac ity com petes with other tech nol o gies

us ing sup ply curves that are based on the amount of

high, me dium, and low meth ane pro duc ing land fills lo -

cated in each EMM re gion.

Bio mass Fu els Submodule 

The bio mass fu els submodule pro vides bio mass-fired

plant tech nol ogy char ac ter iza tions (cap i tal costs, op er -

at ing costs, ca pac ity fac tors, etc.) and fuel in for ma tion

for EMM, thereby al low ing bio mass-fu eled power

plants to com pete with other elec tric ity gen er at ing

tech nol o gies. 

Bio mass fuel prices are rep re sented by a sup ply curve

con structed ac cord ing to the ac ces si bil ity of re sources

to the elec tric ity gen er a tion sec tor. The sup ply curve

em ploys re source in ven tory and cost data for four cat e -

go ries of bio mass fuel - ur ban wood waste and mill res i -

dues, for est res i dues, en ergy crops, and ag ri cul tural

res i dues. Fuel dis tri bu tion and prep a ra tion cost data

are built into these curves. The sup ply sched ule of bio -

mass fuel prices is com bined with other vari able op er -

at ing costs as so ci ated with burn ing bio mass. The

ag gre gate vari able cost is then passed to EMM.

Hydroelectricity Submodule

The hy dro elec tric ity submodule pro vides the EMM the

amounts of new hy dro elec tric ca pac ity that can be built

at known and well char ac ter ized sites, along with re -

lated cost and per for mance data. The in for ma tion is ex -

pressed in the form of a three–step sup ply func tion that

rep re sents the ag gre gate amount of new ca pac ity and

as so ci ated costs that can be of fered in each year in

each re gion. Sites in clude un de vel oped stretches of

rivers, ex ist ing dams or di ver sions that do not cur rently

pro duce power, and ex ist ing hy dro elec tric plants that

have known ca pa bil ity to ex pand op er a tions through

the ad di tion of new gen er at ing units. Ca pac ity or ef fi -

ciency im prove ments through the re place ment of ex ist -

ing equip ment or changes to op er at ing pro ce dures at a

fa cil ity are not in cluded in the hy dro elec tric ity sup ply.

52 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Renewable Fuels Module
Oil And Gas Supply Mod ule
The OGSM con sists of a se ries of pro cess submodules

that pro ject the avail abil ity of do mes tic crude oil

production and dry natural gas production from

onshore, offshore, and Alaskan res er voirs, as well as

con ven tional gas pro duc tion from Can ada. The OGSM

re gions are shown in Fig ure 12. 

The driv ing as sump tion of OGSM is that do mes tic oil

and gas ex plo ra tion and de vel op ment are un der taken if 

the dis counted pres ent value of the re cov ered re -

sources at least cov ers the pres ent value of taxes and

the cost of cap i tal, ex plo ra tion, de vel op ment, and pro -

duc tion. Crude oil is trans ported to re fin er ies, which are

sim u lated in the PMM, for con ver sion and blend ing into

re fined pe tro leum prod ucts. The in di vid ual submodules 

of the OGSM are solved in de pend ently, with feed backs 

achieved through NEMS solu tion iterations (Figure 13).

Tech no log i cal prog ress is rep re sented in OGSM

through an nual in creases in the find ing rates and suc -

cess rates, as well as an nual de creases in costs. For

con ven tional on shore, a time trend was used in

econometrically es ti mated equa tions as a proxy for

tech nol ogy. Re serve ad di tions per well (or find ing

rates) are pro jected through a set of equa tions that

distinquish be tween new field dis cov er ies and dis cov -

er ies (ex ten sions) and re vi sions in known fields. The

find ing rate equa tions cap ture the im pacts of tech nol -

ogy, prices, and de clin ing re sources. An other rep re -

sen ta tion of tech nol ogy is in the suc cess rate

equa tions. Suc cess rates cap ture the im pact of tech -

nol ogy and sat u ra tion of the area through cu mu la tive

drill ing. Tech nol ogy is fur ther rep re sented in the de ter -

mi na tion of drill ing, lease equip ment, and op er at ing

costs. Tech no log i cal prog ress puts down ward pres -

sure on the drill ing, lease equip ment, and op er at ing

cost pro jec tions. For un con ven tional gas, a se ries of

eleven dif fer ent tech nol ogy groups are rep re sented by

time–de pend ent ad just ments to fac tors which in flu ence 

find ing rates, success rates, and costs. 

Con ven tional nat u ral gas pro duc tion in West ern Can -

ada is mod eled in OGSM with three econometrically

es ti mated equa tions: to tal wells drilled, re serves added 

per well, and ex pected pro duc tion-to-re serves ra tio. 

The model per forms a sim ple re serves ac count ing and

ap plies the ex pected pro duc tion-to-re serve ra tio to es ti -

mate an ex pected pro duc tion level, which in turn is

used to es tab lish a sup ply curve for con ven tional West -

ern Can ada nat u ral gas. The rest of the gas pro duc tion

sources in Can ada are rep re sented in the Nat u ral Gas

Trans mis sion and Dis tri bu tion Mod ule (NGTDM).

Lower 48 Onshore and Shallow Offshore Supply

Submodule 

The lower 48 on shore sup ply submodule pro jects

crude oil and nat u ral gas pro duc tion from con ven tional

re cov ery tech niques. This submodule ac counts for drill -

ing, re serve ad di tions, to tal re serves, and pro duc tion

-to-re serves ra tios for each lower 48 on shore sup ply

region. 

The ba sic pro ce dure is as fol lows: 

• First, the pro spec tive costs of a rep re sen ta tive drill -

ing pro ject for a given fuel cat e gory and well class

within a given re gion are com puted. Costs are a

func tion of the level of drill ing ac tiv ity, av er age well

depth, rig avail abil ity, and the ef fects of tech no log i -

cal progress. 

• Sec ond, the pres ent value of the dis counted cash

flows (DCF) as so ci ated with the rep re sen ta tive pro -

ject is com puted. These cash flows in clude both the

cap i tal and op er at ing costs of the pro ject, in clud ing

roy al ties and taxes, and the rev e nues de rived from

a de clin ing well pro duc tion pro file, com puted af ter

tak ing into ac count the pro gres sive ef fects of re -

source de ple tion and val ued at con stant real prices

as of the year of initial valuation. 

• Third, drill ing lev els are cal cu lated as a func tion of

pro jected prof it abil ity as mea sured by the pro jected

DCF lev els for each pro ject and na tional level cash -

flow.

54 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Oil and Gas Supply Module

OGSM Outputs Inputs from NEMS Exogenous Inputs

Crude oil pro duc tion

Do mes tic nonassociated and Ca na dian 

 con ven tional nat u ral gas suply curves

Cogeneration from oil and gas producton

Re serves and re serve ad di tions

Drill ing lev els

Do mes tic as so ci ated-dis solved gas pro duc tion

Do mes tic and Ca na dian nat u ral

 gas pro duc tion and well head prices

Crude oil de mand

World oil price

Elec tric ity price

Gross do mes tic prod uct

In fla tion rate

Re source lev els

Ini tial find ing rate parameters and costs

Pro duc tion pro files

Tax parameters
• Fourth, re gional find ing rate equa tions are used to

pro ject new field dis cov er ies from new field wild -

cats, new pools, and ex ten sions from other ex plor -

atory drill ing, and re serve re vi sions from

de vel op ment drill ing. 

• Fifth, pro duc tion is de ter mined on the ba sis of re -

serves, in clud ing new re serve ad di tions, pre vi ous

pro duc tive ca pac ity, flow from new wells, and, in the 

case of nat u ral gas, fuel de mands. This oc curs

within the mar ket equil i bra tion of the NGTDM for

nat u ral gas and within OGSM for oil.

Un con ven tional Gas Re cov ery Sup ply Submodule 

Un con ven tional gas is de fined as gas pro duced from

nonconventional geo logic for ma tions, as op posed to

con ven tional (sand stones) and car bon ate rock for ma -

tions. The three un con ven tional geo logic for ma tions

con sid ered are low–per me abil ity or tight sand stones,

gas shales and coalbed methane.

For un con ven tional gas, a play–level model cal cu lates

the eco nomic fea si bil ity of in di vid ual plays based on lo -

cally spe cific well head prices and costs, re source

quan tity and qual ity, and the var i ous ef fects of tech nol -

ogy on both re sources and costs. In each year, an ini tial 

re source char ac ter iza tion de ter mines the ex pected ul ti -

mate re cov ery (EUR) for the wells drilled in a par tic u lar

play. Re source pro files are ad justed to re flect as sumed 

tech no log i cal im pacts on the size, avail abil ity, and in -

dus try knowl edge of the re sources in the play. 

 

Energy Information Administration / The National Energy Modeling System: An Overview 2009 55

Oil and Gas Supply Module

Rocky Mountain

West Coast

Pacific

Midcontinent

South West 

Northwest

Gulf Coast

Gulf of Mexico

Offshore

North Slope

Other

Alaska

North Slope

Onshore

Ocean

Atlantic 

Ocean

Fig ure 12. Oil and Gas Supply Module Regions
Sub se quently, prices re ceived from NGTDM and en -

dog e nously de ter mined costs ad justed to re flect tech -

no log i cal prog ress are uti lized to cal cu late the

eco nomic prof it abil ity (or lack thereof) for the play. If the 

play is prof it able, drill ing oc curs ac cord ing to an as -

sumed sched ule, which is ad justed an nu ally to ac count

for tech no log i cal im prove ments, as well as vary ing eco -

nomic con di tions. This drill ing re sults in re serve ad di -

tions, the quan ti ties of which are di rectly re lated to the

EURs for the wells in that play. Given these re serve ad -

di tions, re serve lev els and ex pected pro duc tion–to–re -

serves (P/R) ra tios are cal cu lated at both the OGSM

and the NGTDM re gion level. The re sul tant val ues are

ag gre gated with sim i lar val ues from the con ven tional

on shore and off shore submodules. The ag gre gate P/R 

ra tios and re serve lev els are then passed to NGTDM,

which de ter mines the prices and pro duc tion for the fol -

low ing year through mar ket equil i bra tion.

Off shore Sup ply Submodule

This submodule uses a field-based en gi neer ing ap -

proach to rep re sent the ex plo ra tion and de vel op ment of 

U.S. off shore oil and nat u ral gas re sources. The

submodule sim u lates the eco nomic de ci sion-mak ing at 

each stage of de vel op ment from fron tier ar eas to

post-ma ture ar eas. Off shore re sources are di vided into 

3 cat e go ries:

• Un dis cov ered Fields. The num ber, lo ca tion, and

size of the un dis cov ered fields are based on the

MMS's 2006 hy dro car bon re source as sess ment.

• Dis cov ered, Un de vel oped Fields. Any dis cov ery

that has been an nounced but is not cur rently pro -

duc ing is eval u ated in this com po nent of the model. 

The first pro duc tion year is an in put and is based on

an nounced plans and ex pec ta tions.

56 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Oil and Gas Supply Module

NEMS

Oil and Gas 

Supply Module

Resource Levels,

Finding Rates,

Costs, Production

Profiles, Taxes

Exogenous

Natural Gas

Transmission

and Distribution

Module

Industrial

Demand Module

Electricity Market

Module

Electricity Price

Oil and Gas

Supply

Submodules

Petroleum

Market Module

Associated-Dissolved 

Gas Production

Supply Curves for

Domestic and

Canadian Natural Gas

Crude Oil

Production

Crude Oil Demand

Combined Heat

and Power

Natural Gas Production and 

Expectations

International Energy 

Module World Oil Price

Wellhead Prices

Domestic and Canadian

Fig ure 13. Oil and Gas Sup ply Mod ule Structure
• Pro duc ing Fields. The fields in this cat e gory have

wells that have pro duced oil and/or gas through the

year prior to the AEO pro jec tion. The pro duc tion

vol umes are from the Min er als Man age ment Ser -

vice (MMS) da ta base.

Re source and eco nomic cal cu la tions are per formed at

an eval u a tion unit ba sis. An eval u a tion unit is de fined

as the area within a plan ning area that falls into a spe -

cific wa ter depth cat e gory. Plan ning ar eas are the

West ern Gulf of Mex ico (GOM), Cen tral GOM, East ern

GOM, Pa cific, and At lan tic. There are six wa ter depth

cat e go ries: 0-200 me ters, 200-400 me ters, 400-800

me ters, 800-1600 me ters, 1600-2400 me ters, and

greater than 2400 me ters. 

Sup ply curves for crude oil and nat u ral gas are gen er -

ated for three off shore re gions: Pa cific, At lan tic, and

GOM. Crude oil pro duc tion in cludes lease con den sate.

Nat u ral gas pro duc tion ac counts for both

nonassociated gas and as so ci ated-dis solved gas. The 

model is re spon sive to changes in oil and nat u ral gas

prices, roy alty re lief as sump tions, oil and nat u ral gas

re source base, and tech no log i cal im prove ments af fect -

ing ex plo ra tion and de vel op ment. 

Alaska Oil and Gas Submodule 

This submodule pro jects the crude oil and nat u ral gas

pro duced in Alaska. The Alas kan oil submodule is di -

vided into three sec tions: new field dis cov er ies, de vel -

op ment pro jects, and pro duc ing fields. Oil

trans por ta tion costs to lower 48 fa cil i ties are used in 

con junc tion with the rel e vant mar ket price of oil to cal -

cu late the es ti mated net price re ceived at the well head,

some times called the netback price. A dis counted cash

flow method is used to de ter mine the eco nomic vi a bil ity

of each pro ject at the netback price.

Alas kan oil sup plies are mod eled on the ba sis of dis -

crete pro jects, in con trast to the on shore lower 48 con -

ven tional oil and gas sup plies, which are mod eled on

an ag gre gate level. The con tin u a tion of the ex plo ra tion

and de vel op ment of multiyear pro jects, as well as the

dis cov ery of new fields, is de pend ent on prof it abil ity.

Pro duc tion is de ter mined on the ba sis of as sumed drill -

ing sched ules and pro duc tion pro files for new fields and 

de vel op men tal pro jects, his tor i cal pro duc tion pat terns,

and an nounced plans for cur rently pro duc ing fields. 

• Alas kan gas pro duc tion is set sep a rately for any

gas tar geted to flow through a pipe line to the lower

48 States and gas pro duced for con sump tion in the

State and for ex port to Ja pan. The lat ter is set

based on a pro jec tion of Alas kan con sump tion in

the NGTDM and an ex og e nous spec i fi ca tion of ex -

ports. North Slope pro duc tion for the pipe line is de -

pend ent on con struc tion of the pipe line, set to

com mence if the lower 48 av er age well head price is 

main tained at a level ex ceed ing the es tab lished

com pa ra ble cost of de liv ery to the lower 48 States.

Energy Information Administration / The National Energy Modeling System: An Overview 2009 57

Oil and Gas Supply Module
Nat u ral Gas Trans mis sion

and Dis tri bu tion Mod ule
The NGTDM of NEMS rep re sents the nat u ral gas mar -

ket and de ter mines re gional mar ket–clear ing prices for

nat u ral gas sup plies and for end–use con sump tion,

given the in for ma tion passed from other NEMS mod -

ules (Fig ure 14). A trans mis sion and dis tri bu tion net -

work (Fig ure 15), com posed of nodes and arcs, is used

to sim u late the in ter re gional flow and pric ing of gas in

the con tig u ous United States and Can ada in both the

peak (De cem ber through March) and offpeak (April

through No vem ber) pe riod. This net work is a sim pli fied

rep re sen ta tion of the phys i cal nat u ral gas pipe line sys -

tem and es tab lishes the pos si ble in ter re gional flows

and as so ci ated prices as gas moves from supply

sources to end users. 

Flows are fur ther rep re sented by es tab lish ing arcs from 

trans ship ment nodes to each de mand sec tor rep re -

sented in an NGTDM re gion (res i den tial, com mer cial,

in dus trial, elec tric gen er a tors, and trans por ta tion).

Mex i can ex ports and net stor age in jec tions in the

offpeak pe riod are also rep re sented as flow ex it ing a

trans ship ment node. Sim i larly, arcs are also es tab -

lished from sup ply points into a trans ship ment node.

Each trans ship ment node can have one or more en ter -

ing arcs from each sup ply source rep re sented: U.S. or

Ca na dian on shore or U.S. off shore pro duc tion, liq ue -

fied nat u ral gas im ports, sup ple men tal gas pro duc tion,

gas pro duced in Alaska and trans ported via pipe line,

Mex i can im ports, or net stor age with draw als in the re -

gion in the peak pe riod. Most of the types of sup ply

listed above are set in de pend ently of cur rent year

prices and be fore NGTDM de ter mines a mar ket equi -

lib rium so lu tion.

Only the on shore and off shore lower 48 U.S. and West -

ern Ca na dian Sed i men tary Ba sin pro duc tion, along

with net stor age with draw als, are rep re sented by

short–term sup ply curves and set dy nam i cally dur ing

the NGTDM so lu tion pro cess. The con struc tion of nat u -

ral gas pipe lines from Alaska and Can ada’s Mac Ken zie 

Delta are trig gered when mar ket prices ex ceed es ti -

mated pro ject costs. The flow of gas dur ing the peak

pe riod is used to es tab lish in ter re gional pipe line and

stor age ca pac ity re quire ments and the as so ci ated ex -

pan sion. These ca pac ity lev els pro vide an upper limit

for the flow during the offpeak period. 

Arcs be tween trans ship ment nodes, from the trans -

ship ment nodes to end–use sec tors, and from sup ply

sources to trans ship ment nodes are as signed tar iffs.

The tar iffs along in ter re gional arcs re flect res er va tion

(rep re sented with vol ume de pend ent curves) and us -

age fees and are es tab lished in the pipe line tar iff

submodule. The tar iffs on arcs to end–use sec tors rep -

re sent the in ter state pipe line tar iffs in the re gion, in tra -

state pipe line tar iffs, and dis trib u tor mark ups set in the

dis trib u tor tar iff submodule. Tar iffs on arcs from sup ply

sources rep re sent gath er ing charges or other dif fer en -

tials be tween the price at the sup ply source and the re -

gional mar ket hub. The tar iff as so ci ated with in ject ing,

stor ing, and with draw ing from stor age is as signed to

the arc rep re sent ing net stor age with draw als in the

peak pe riod. Dur ing the pri mary so lu tion pro cess in the

in ter state trans mis sion submodule, the tar iffs along an

in ter re gional arc are added to the price at the source

node to ar rive at a price for the gas along the arc right

be fore it reaches its des ti na tion node.

Interstate Transmission Submodule 

The in ter state trans mis sion submodule (ITS) is the

main in te grat ing mod ule of NGTDM. One of its ma jor

func tions is to sim u late the nat u ral gas price de ter mi na -

tion pro cess. ITS brings to gether the ma jor eco nomic

fac tors that in flu ence re gional nat u ral gas trade on a

sea sonal ba sis in the United States, the bal anc ing of

the de mand for and the do mes tic sup ply of nat u ral gas,

in clud ing com pe ti tion from im ported nat u ral gas. These 

are ex am ined in com bi na tion with the rel a tive prices as -

so ci ated with mov ing the gas from the pro ducer to the

end user where and when (peak ver sus offpeak) it is 

Energy Information Administration / The National Energy Modeling System: An Overview 2009 59

Natural Gas Transmission And Distribution Module

NGTDM Outputs Inputs from NEMS Exogenous Inputs

Nat u ral gas de liv ered prices

Do mes tic and Ca na dian nat u ral gas wellhead 

 prices

Do mes tic nat u ral gas pro duc tion

Mex i can and liq ue fied nat u ral gas im ports and ex ports

Ca na dian nat u ral gas im ports and pro duc tion

Lease and plant fuel con sump tion

Pipe line and dis tri bu tion tar iffs

In ter re gional nat u ral gas flows

Stor age and pipe line ca pac ity ex pan sion

Sup ple men tal gas production

Nat u ral gas de mands

Do mes tic and ca na dian nat u ral gas 

 sup ply curves

Mac ro eco nomic vari ables

As so ci ated-dis solved nat u ral gas

 production

His tor i cal con sump tion and flow pat terns

His tor i cal sup plies

Pipe line com pany-level fi nan cial data

Pipe line and stor age ca pac ity and uti li za tion

 data

His tor i cal end-use citgate, and well head

 prices

State and Fed eral tax pa ram e ters

Pipe line and stor age ex pan sion cost data

Liq ue fied nat u ral gas sup ply curves

Can ada and Mex ico con sump tion projections
60 Energy Information Administration / The National Energy Modeling System 2009

Natural Gas Transmission And Distribution Module

NEMS

Macroeconomic

Activity Module

Oil and Gas 

Supply 

Module

Petroleum

Market Module

Demand

Modules

Electricity

Market

Module

Exogenous

Historical values 

Company-Level Financial Data,

Pipeline and Storage Capacity,

Utilization and Cost Data,

State and Federal Tax Parameters,

Liquefied Natural Gas Supply Curves

Canada and Mexico Consumption Projections 

Interstate

Transmission

Submodule

Distributor

Tariff

Submodule

Natural Gas Transmission and 

Distribution Module

Expansion 

Cost Curves

(Exogenous)

Pipeline

Tariff

Submodule

Distributor

Markups

Lease and Plant Fuel 

Consumption, 

Pipeline Fuel Consumption

Macroeconomic

Variables

Natural Gas Prices, 

Production, and 

Industry Activity

Domestic and Canadian 

Natural Gas

Supply Curves

Associated-Dissolved

Gas Production

Domestic and 

Canadian Natural Gas

Wellhead Prices

Domestic

and Canadian Natural Gas

Production

Natural Gas Prices

and Production

Natural Gas Core and

Noncore Consumption 

Natural Gas EndUse Prices

Peak and Offpeak,

Core and Noncore

Consumption

Natural Gas Prices

Interstate

Tariffs

Curves

Interstate Flows 

and Capacity

Expansion

Natural Gas

Imports and

Exports

Fig ure 14. Nat u ral Gas Trans mis sion and Dis tri bu tion Mod ule Structure
needed. In the pro cess, ITS sim u lates the de ci -

sion–mak ing pro cess for ex pand ing pipe line and/or

sea sonal stor age ca pac ity in the U.S. gas mar ket, de -

ter min ing the amount of pipe line and stor age ca pac ity

to be added be tween or within re gions in NGTDM. Stor -

age serves as the pri mary link be tween the two sea -

sonal pe ri ods rep re sented. 

ITS em ploys an it er a tive heu ris tic al go rithm, along with

an acy clic hi er ar chi cal rep re sen ta tion of the pri mary

arcs in the net work, to es tab lish a mar ket equi lib rium

so lu tion. Given the con sump tion lev els from other

NEMS mod ules, the ba sic pro cess fol lowed by ITS in -

volves first es tab lish ing the back ward flow of nat u ral

gas in each pe riod from the con sum ers, through the

net work, to the pro duc ers, based pri mar ily on the rel a -

tive prices of fered for the gas from the pre vi ous ITS it er -

a tion. This pro cess is per formed for the peak pe riod first 

since the net with draw als from stor age dur ing the peak

pe riod will es tab lish the net in jec tions dur ing the

offpeak pe riod. Sec ond, us ing the model’s sup ply

curves, well head and im port prices are set cor re spond -

ing to the de sired pro duc tion vol umes. Also, us ing the

pipe line and stor age tar iffs from the pipe line tar iff

submodule, pipe line and stor age tar iffs are set cor re -

spond ing to the as so ci ated flow of gas, as de ter mined

in the first step. These prices are then trans lated from

the pro duc ers, back through the net work, to the city

gate and the end us ers, by add ing the ap pro pri ate tar -

iffs along the way. A re gional stor age tar iff is added to

the price of gas in jected into stor age in the offpeak to

ar rive at the price of the gas when with drawn in the

peak pe riod. This pro cess is then re peated un til the so -

lu tion has con verged. Fi nally, de liv ered prices are de -

rived for res i den tial, com mer cial, and trans por ta tion

cus tom ers, as well as for both core and noncore in dus -

trial and elec tric gen er a tion sec tors us ing the dis trib u tor 

tar iffs pro vided by the dis trib u tor tar iff submodule.

Pipeline Tariff Submodule 

The pipe line tar iff submodule (PTS) pro vides us age

fees and vol ume de pend ent curves for com put ing unit -

ized res er va tion fees (or tar iffs) for in ter state trans por -

ta tion and stor age ser vices within the ITS. These

curves ex tend be yond cur rent ca pac ity lev els and re -

late in cre men tal pipe line or stor age ca pac ity ex pan sion 

to cor re spond ing es ti mated rates. The un der ly ing ba sis 

for each tar iff curve in the model is a pro jec tion of the

as so ci ated reg u lated rev e nue re quire ment. Economet ri cally es ti mated equa tions within a gen eral ac -

count ing frame work are used to track costs and com -

pute rev e nue re quire ments as so ci ated with both

res er va tion and us age fees un der cur rent rate de sign

and reg u la tory sce nar ios. Other than an as sort ment of

mac ro eco nomic in di ca tors, the pri mary in put to PTS

from other mod ules in NEMS is pipe line and stor age

ca pac ity uti li za tion and ex pan sion in the pre vi ous pro -

jec tion year. 

Once an ex pan sion is pro jected to oc cur, PTS cal cu -

lates the re sult ing im pact on the rev e nue re quire ment.

PTS as sumes rolled–in (or av er age), not in cre men tal,

rates for new ca pac ity. The pipe line tar iff curves gen er -

ated by PTS are used within the ITS when de ter min ing

the rel a tive cost of pur chas ing and mov ing gas from

one source ver sus an other in the peak and offpeak

sea sons. 

Distributor Tariff Submodule 

The dis trib u tor tar iff submodule (DTS) sets dis trib u tor

mark ups charged by lo cal dis tri bu tion com pa nies for

the dis tri bu tion of nat u ral gas from the city gate to the

end user. For those that do not typ i cally pur chase gas

through a lo cal dis tri bu tion com pany, this markup rep -

re sents the dif fer en tial be tween the citygate and de liv -

ered price. End–use dis tri bu tion ser vice is

dis tin guished within the DTS by sec tor (res i den tial,

com mer cial, in dus trial, elec tric gen er a tors, and trans -

por ta tion), sea son (peak and offpeak), and ser vice type 

(core and noncore). 

Dis trib u tor tar iffs for all but the trans por ta tion sec tor are

set us ing econometrically es ti mated equa tions. The

nat u ral gas ve hi cle sec tor mark ups are cal cu lated sep -

a rately for fleet and per sonal ve hi cles and ac count for

dis tri bu tion to de liv ery sta tions, re tail mark ups, and fed -

eral and state mo tor fu els taxes.

Natural Gas Imports and Exports

Liq ue fied nat u ral gas im ports for the U.S., Can ada, and

Baja, Mex ico are set at the be gin ning of each NEMS it -

er a tion within the NGTDM by eval u at ing sea sonal east

and west sup ply curves, based on out puts from EIA’s

In ter na tional Nat u ral Gas Model, at as so ci ated

regasification tail gate prices set in the pre vi ous NEMS

it er a tion. A shar ing al go rithm is used to al lo cate the re -

sult ing im port vol umes to par tic u lar re gions. LNG ex -

ports to Ja pan from Alaska are set ex og e nously by the

OGSM.

The Mex ico model is largely based on ex og e nously

spec i fied as sump tions about con sump tion and pro duc -

tion growth rates and LNG im port lev els. For the most

part, nat u ral gas im ports from Mex ico are set ex og e -

nously for each of the three bor der cross ing points with

Natural Gas Transmission And Distribution Module

Energy Information Administration / The National Energy Modeling System 2009 61
the United States, with the ex cep tion of any gas that is

im ported into Baja, Mex ico in liq uid form only to be ex -

ported to the United States. Ex ports to Mex ico from the

United States are es tab lished be fore the NGTDM

equilibrates and rep re sent the re quired level to bal ance 

the as sumed con sump tion in (and ex ports from) Mex -

ico against do mes tic pro duc tion and LNG im ports. The

pro duc tion lev els are also largely as sump tion based,

but are set to vary with changes in the ex pected well -

head price in the United States. 

A node for east and west Can ada is in cluded in the

NGTDM equil i bra tion net work, as well as seven bor der

cross ings into the United States. The model in cludes a 

rep re sen ta tion/ac count ing of the U.S. bor der cross ing

pipe line ca pac ity, east and west sea sonal stor age

trans fers, east and west con sump tion, east and west

LNG im ports, east ern pro duc tion, con ven tional/tight

sands pro duc tion in the west, and coalbed/shale pro -

duc tion. Im ports from the United States, con ven tional

pro duc tion in east ern Can ada, and base level nat u ral 

gas con sump tion (which var ies with the world oil price)

are set ex og e nously. Con ven tional/tight sands pro duc -

tion in the west is set us ing a sup ply curve from the

OGSM. Coalbed and shale gas pro duc tion are ef fec -

tively based on an as sumed pro duc tion growth rate

which is ad justed with re al ized prices.

Natural Gas Transmission And Distribution Module

62 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Fig ure 15. Natural Gas Transmission and Distribution Module Network
Petro leum Market Mod ule
The PMM rep re sents domestic refinery operations and

the marketing of liq uid fu els to consumption regions. 

PMM solves for liq uid fuel prices, crude oil and product

import activity (in conjunction with the IEM and the

OGSM), and domestic refinery capacity expansion and

fuel consumption. The so lu tion sat is fies the demand for

liq uid fu els, incorporating the prices for raw material in -

puts, im ported liq uid fu els, cap i tal investment, as well

as the domestic production of crude oil, natural gas liq -

uids, and other un con ven tional re fin ery inputs. The

relationship of PMM to other NEMS modules is

illustrated in Figure 16.

The PMM is a re gional, lin ear pro gram ming for mu la tion

of the five Pe tro leum Administration for De fense Dis tricts

(PADDs) (Fig ure 17). For each re gion two dis tinct re -

finery are mod eled. One is highly com plex us ing over

40 dif fer ent refinrry pro cesses, while the sec ond is de -

fined as a sim ple re fin ery that pro vides mar ginal cost

eco nom ics. Re fin ing ca pac ity is al lowed to ex pand in

each re gion, but the model does not dis tin guish between 

ad di tions to ex ist ing re fin er ies or the build ing of new fa cil i -

ties. In vest ment cri te ria are de vel oped ex og e nously, al -

though the de ci sion to in vest is endogenous.

PMM as sumes that the pe tro leum re fin ing and marketing

in dus try is com pet i tive. The mar ket will move to ward

lower-cost re fin ers who have ac cess to crude oil and mar -

kets. The se lec tion of crude oils, re fin ery pro cess uti li za -

tion, and lo gis tics (trans por ta tion) will ad just to min i mize

the over all cost of sup ply ing the mar ket with liq uid fu els.

PMM's model for mu la tion re flects the op er a tion of do -

mes tic liquuid fu els. If demand is un usu ally high in one

re gion, the price will in crease, driv ing down de mand and 

pro vid ing economic in cen tives for bring ing sup plies in

from other re gions, thus re stor ing the sup ply and de mand

bal ance.

Ex ist ing reg u la tions con cern ing prod uct types and

spec i fi ca tions, the cost of en vi ron men tal com pli ance,

and Fed eral and State taxes are also mod eled. PMM

in cor po rates pro vi sions from the En ergy In de pend ence 

and Se cu rity Act of 2007 (EISA2007) and the En ergy

Pol icy Act of 2005 (EPACT05). The costs of pro duc ing

new for mu la tions of gas o line and die sel fuel as a re sult

of the CAAA90 are de ter mined within the lin ear-pro -

gram ming rep re sen ta tion by in cor po rat ing spec i fi ca -

tions and de mands for these fuels.

PMM also in cludes the in ter ac tion be tween the do mes -

tic and in ter na tional mar kets. Prior to AEO2009, PMM

pos tu lated en tirely ex og e nous prices for oil on the in ter -

na tional mar ket (the world oil price). Sub se quent AEOs 

in clude an In ter na tional En ergy Mod ule (IEM) that es ti -

mates sup ply curves for im ported crude oils and prod -

ucts based on, among other fac tors, U.S. par tic i pa tion

in global trade of crude oil and liq uid fu els.

Re gions

PMM mod els U.S. crude oil re fin ing ca pa bil i ties based

on the five PADDs which were es tab lished dur ing

World War II and are still used by EIA for data col lec tion

and anal y sis. The use of PADD data per mits PMM to take 

full ad van tage of EIA’s historical da ta base and al lows

anal y sis within the same frame work used by the pe tro -

leum in dus try.

64 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Petroleum Market Module

PMM Outputs Inputs from NEMS Exogenous Inputs

Pe tro leum prod uct prices

Crude oil im ports and ex ports

Crude oil de mand

Pe tro leum prod uct im ports and ex ports

Re fin ery ac tiv ity and fuel use

Eth a nol de mand and price

Com bined heat and power (CHP)

Nat u ral gas plant liq uids pro duc tion

Pro cess ing gain

Ca pac ity ad di tions

Cap i tal ex pen di tures

Revenues

Pe tro leum prod uct de mand by sec tor

Do mes tic crude oil pro duc tion

World oil price

In ter na tional crude oil sup ply curves

In ter na tional prod uct sup ply curves

In ter na tional ox y gen ates sup ply curves

Nat u ral gas prices

Elec tric ity prices

Nat u ral glas pro duc tion

Mac ro eco nomic vari ables

Bio mass sup ply curves

Coal prices

Pro cess ing unit op er at ing pa ram e ters

Pro cess ing unit ca pac i ties

Prod uct spec i fi ca tions

Op er at ing costs

Cap i tal costs

Trans mis sion and dis tri bu tion costs

Fed eral and State taxes

Ag ri cul tural feedstock quan ti ties and costs

CHP unit op er at ing pa ram e ters

CHP unit capacities
Energy Information Administration / The National Energy Modeling System: An Overview 2009 65

Petroleum Market Module

N

E

M

S

Petroleum

Market

Module

Oil and Gas 

Supply

 Module

Natural Gas

Transmission

and

Distribution

Module

N

E

M

S

Exogenous

Expectations

Crude Oil Production

Crude Oil Demand

Natural Gas Prices 

and Production

Demand

Modules

Electricity 

Market 

Module

International

Energy

Module

Macroeconomic

Activity 

Module

Liquid Fuels 

Demand

Liquid Fuel Prices

Electricity Prices

Liquid Fuel 

Demands

World Oil Price

Domestic Oil 

Production and Demand

Product Imports,

Exports

Crude Oil and 

Product Supply Curves

Oxygenate Supply 

Curves and Prices

Refinery Activity

Interest Rates, Gross 

Domestic 

Product, Deflators

Refinery Fuel

Use, Cogeneration

Liquid Fuels Prices

 Processing Unit Parameters 

 and Capacities, Product Specifications,

Operating and Capital Costs,

 Transmission and Distribution Costs,

 Federal and State Taxes,

 Agricultural Feedstock Quantities and

 Parameters and Capacities

Oil Production

Liquid Fuel Prices

Coal Market

Module Diesel Fuel 

Prices

Renewable

Fuels Module Biomass Supply 

Curves

Costs, Cogeneration Unit 

Fig ure 16. Pe tro leum Mar ket Mod ule Structure
66 Energy Information Administration / The National Energy Modeling System: An Overview 2009

Petroleum Market Module

Fig ure 17. Pe tro leum Ad min is tra tion for De fense Districts
Prod uct Cat e go ries

Prod uct cat e go ries, spec i fi ca tions and rec ipe blends

mod eled in PMM in clude the fol low ing:

Liquid Fuels Mod eled in PMM

Fuel Use

PMM de ter mines re fin ery fuel use by re fin ing re gion for

pur chased elec tric ity, nat u ral gas, dis til late fuel, re sid -

ual fuel, liq ue fied pe tro leum gas, and other pe tro leum.

The fu els (nat u ral gas, pe tro leum, other gas eous fu els,

and other) con sumed within the re fin ery to gen er ate

elec tric ity from CHP fa cil i ties are also de ter mined.

Crude Oil Cat e go ries

Both do mes tic and im ported crude oils are ag gre gated

into five cat e go ries as de fined by API grav ity and sul fur

con tent ranges. This ag gre ga tion of crude oil types al -

lows PMM to ac count for changes in crude oil com po si -

tion over time. A com pos ite crude oil with the

ap pro pri ate yields and qual i ties is de vel oped for each

cat e gory by av er ag ing char ac ter is tics of for eign and

do mes tic crude oil streams.

Re fin ery Pro cesses

The fol low ing dis tinct pro cesses are rep re sented in the

PMM:

Natural Gas Plants

Nat u ral gas plant liq uids (eth ane, pro pane, nor mal bu -

tane, iso bu tane, and nat u ral gas o line) pro duced from

nat u ral gas pro cess ing plants are mod eled in PMM.

Their pro duc tion lev els are based on the pro jected nat -

u ral gas sup ply and his tor i cal liq uids yields from var i ous 

nat u ral gas sources. These prod ucts move di rectly into

the mar ket to meet de mand (e.g., for fuel or pet ro chem -

i cal feedstocks) or are in puts to the re fin ery.

Petroleum Market Module

Energy Information Administration / The National Energy Modeling System: An Overview 2009 67

Mo tor gas o line: conventional (oxygenated and

non-oxygentated), reformulated, and California

reformulated

Jet fu els: ker o sene-based

Dis til lates: kerosene, heating oil, low sulfur (LSD)

and ultra-low-sulfur (ULSD) highway diesel,

distillate fuel oil, and distillate fuel from various

non-crude feedstocks (coal, biomass, natural gas)

via the Fischer-Tropsch process (BTL, CTL, GTL)

Alternative Fuel: Biofuels [including ethanol, 

biodiesel (methyl-ester), renewable diesel, 

biomass-to-liquids (BTL)], coal-to-liquids (CTL), 

gas-to-liquids (GTL).

Re sid ual fu els: low sulfur and high sulfur residual

fuel oil

Liq ue fied pe tro leum gas (LPG): a light-end 

mixture used for fuel in a wide range of sectors

comprised primarily of propane

Natural gas plant:ethane, propane, iso and normal

butane, and pentanes plus (natural gasoline)

Pet ro chem i cal feedstocks

Other: as phalt and road oil, still gas, (refinery fuel)

pe tro leum coke, lu bes and waxes, special naphthas

1) Crude Oil Dis til la tion 

 a. At mo spheric Crude Unit

 b. Vac uum Crude Unit

2) Re sid ual Oil Up grad ing

 a. Coker - De layed, fluid

 b. Ther mal Cracker/Visbreaker

 c. Re sid uum Hydrocradker

 d. Sol vent Deasphalting

3) Crack ing

 a. Fluidized Cat a lytic Cracker

 b. Hydrocracker

4) Fi nal Prod uct Treat ing/Up grad ing

 a. Tra di tional Hydrotreating

 b. Mod ern Hydrotreating

 c. Alkylation

 d. Jet Fuel Pro duc tion

 e. Ben zene Sat u ra tion

 f. Cat a lytic Re form ing

5) Light End Treat ing

 a. Sat u rated Gas Plant

 b. Isomerization

 c. Dimerization/Poly mer iza tion

 d. C2-C5 Dehydrogenation

6) Non-Fuel Pro duc tion

 a. Sul fur Plant

 b. Meth a nol Pro duc tion

 c. Oxgenate Pro duc tion

 d. Lube and Wax Pro duc tion

 e. Steam/Power Gen er a tion

 f. Hy dro gen Pro duc tion

 g. Aromatics Pro duc tion

7) Spe cialty Unit Op er a tions

 a. Olefins to Gas o line/Die sel

 b. Meth a nol to Olefins

8) Mer chant Fa cil i ties

 a. Coal/Gas/Bio mass to Liq uids

 b. Nat u ral Gas Plant

 c. Eth a nol Pro duc tion

 d. Biodiesel Plant
Biofuels

PMM con tains submodules which pro vide re gional sup -

plies and prices for biofuels: eth a nol (con ven -

tional/corn, ad vanced, cel lu losic) and var i ous forms of

bio mass-based die sel: FAME (methyl es ter), bio -

mass-to-liq uid (Fisher-Tropsch), and re new able

(“green”) die sel (hy dro ge na tion of veg e ta ble oils or

fats). Eth a nol is as sumed to be blended ei ther at 10

per cent into gas o line (con ven tional or re for mu lated) or

as E85. Food feedstock sup ply curves (corn, soy bean

oil, etc.) are up dated to USDA base line pro jec tions; bio -

mass feedstocks are drawn from the same sup ply

curves that also sup ply bio mass fuel to re new able

power gen er a tion within the Re new able Fu els Mod ule

of NEMS. The mer chant pro cess ing units which gen er -

ate the biofuels sup plies sum these feedstock costs

with other cost in puts (e.g., cap i tal, op er at ing). A ma jor

driv ing force be hind the pro duc tion of these biofuels is

the Re new able Fu els Stan dard un der EISA2007. De -

tails on the mar ket pen e tra tion of the ad vanced biofuels 

pro duc tion ca pac ity (such as cel lu losic eth a nol and

BTL) which are not yet com mer cial ized can be found in

the PMM doc u men ta tion.

End-Use Mark ups

The lin ear pro gram ming por tion of the model pro vides unit

prices of prod ucts sold in the re fin ery re gions (re fin ery

gate) and in the de mand re gions (whole sale). End use

mark ups are added to pro duce a re tail price for each of

the Cen sus Di vi sions. The mark ups are based on an av -

er age of his tor i cal mark ups, de fined as the dif fer ence be -

tween the end-use prices by sec tor and the

cor re spond ing whole sale price for that prod uct. The av er -

age is cal cu lated us ing data from 2000 to the pres ent. Be -

cause of the lack of any con sis tent trend in the his tor i cal

end-use mark ups, the mark ups re main at the his tor i cal av -

er age level over the projection period.

State and Fed eral taxes are also added to transportation

fuel prices to de ter mine fi nal end-use prices. Pre vi ous

tax trend anal y sis in di cates that state taxes in crease at

the rate of in fla tion, while Fed eral taxes do not. In

PMM, there fore state taxes are held con stant in real

terms through out the pro jec tion while Fed eral taxes are 

felated at the rate of in fla tion.18

Gas o line Types

Mo tor ve hi cle fuel in PMM is cat e go rized into four gas o -

line blends (con ven tional, ox y gen ated con ven tional, re -

for mu lated, and Cal i for nia re for mu lated) and also E85.

While fed eral law does not man date gas o line to be ox y -

gen ated, all gas o line com ply ing with the Fed eral re for -

mu lated gas o line pro gram is as sumed to con tain 10

per cent eth a nol, while con ven tional gas o line may be

“clear” (no eth a nol) or used as E10. As the man date for

biofuels grows un der the Re new able Fu els Stan dard,

the pro por tion of con ven tional gas o line that is E10 also

gen er ally grows. Cal i for nia re for mu lated mo tor gas o -

line is as sumed to con tain 5.7% eth a nol in 2009 and 10

per cent there af ter in line with its ap proval of the use of

California’s Phase 3 reformulated gasoline.

EIA de fines E85 as a gas o line type but is treated as a

sep a rate fuel in PMM. The trans por ta tion mod ule in

NEMS pro vides PMM with a flex fuel ve hi cle (FFV) de -

mand, and PMM com putes a sup ply curve for E85. This 

curve in cor po rates E85 in fra struc ture and sta tion costs, 

as well as a logit re la tion ship be tween the E85 sta tion

avail abil ity and de mand of E85. In fra struc ture costs dic -

tate that the E85 sup plies emerge in the Mid west first,

fol lowed by an ex pan sion to the coasts. 

Ul tra–Low–Sul fur Die sel 

By def i ni tion, Ul tra Low Sul fur Die sel (ULSD) is high -

way die sel fuel that con tains no more than 15 ppm sul -

fur at the pump. As of June 2006, 80 per cent of all

high way die sel pro duced or im ported into the United

States was re quired to be ULSD, while the re main ing

20 per cent con tained a max i mum of 500 parts per mil -

lion. By De cem ber 1, 2010 all high way fuel sold at the

pump will be re quired to be ULSD. Ma jor as sump tions

re lated to the ULSD rule are as fol lows:

• Highway die sel at the re fin ery gate will con tain a max i -

mum of 7-ppm sul fur. Al though sul fur con tent is lim ited

to 15 ppm at the pump, there is a gen eral consensus that 

re fin er ies will need to pro duce diesel be low 10 ppm sul -

fur in or der to al low for contamination dur ing the dis tri -

bu tion pro cess.

• De mand for high way grade die sel, both 500 and 15 ppm

com bined, is as sumed to be equiv a lent to the total

trans por ta tion dis til late de mand. His tor i cally, highway 

grade die sel sup plied has nearly matched to tal trans por -

ta tion dis til late sales, al though some high way grade

Petroleum Market Module

68 Energy Information Administration / The National Energy Modeling System: An Overview 2009

18 http://www.eia.doe.gov/oiaf/archive/aeo07/leg_reg.html.
die sel has gone to non-transportation uses such as

con struc tion and ag ri cul ture.

Gas, Coal and Bio mass to Liq uids

Nat u ral gas, coal, and bio mass con ver sion to liq uid fu -

els is mod eled in the PMM based on a three step pro -

cess known as in di rect liq ue fac tion. This pro cess is

some times called Fischer-Tropsch (FT) liq ue fac tion af -

ter the in ven tors of the sec ond step. 

The liq uid fu els pro duced in clude four sep a rate prod -

ucts: FT light naph tha, FT heavy naph tha, FT ker o -

sene, and FT die sel. The FT des ig na tion is used to

dis tin guish these liq uid fu els from their pe tro leum coun -

ter parts. This is nec es sary due to the dif fer ent phys i cal

and chem i cal prop er ties of the FT fu els. For ex am ple,

FT die sel has a typ i cal cetane rat ing of ap prox i mately

70-75 while that of pe tro leum die sel is typ i cally much

lower (about 40). In ad di tion, the above pro duc tion

meth ods have dif fer ing im pacts with re gard to cur rent

and po ten tial leg is la tion, par tic u larly RFS and CO2.

Energy Information Administration / The National Energy Modeling System: An Overview 2009 69

Petroleum Market Module
Coal Mar ket Module
The coal mar ket mod ule (CMM) rep re sents the mining,

transportation, and pricing of coal, subject to end-use

demand. Coal supplies are differentiated by ther mal

grade, sul fur con tent, and min ing method (un der ground

and sur face). CMM also determines the minimum cost

pattern of coal supply to meet exogenously defined U.S.

coal export demands as a part of the world coal market.

Coal dis tri bu tion, from sup ply re gion to de mand re gion, is

projected on a cost-minimizing ba sis. The domestic

production and distribution of coal is pro jected for 14

demand regions and 14 supply regions (Figures 18 and

19).

The CMM com po nents are solved si mul ta neously. The

se quence of so lu tion among com po nents can be sum -

ma rized as fol lows. Coal sup ply curves are produced by

the coal pro duc tion submodule and in put to the coal dis tri -

bu tion submodule. Given the coal supply curves, dis tri bu -

tion costs, and coal de mands, the coal dis tri bu tion

submodule pro jects de liv ered coal prices. The mod ule

is it er ated to con ver gence with re spect to equi lib rium

prices to all de mand sec tors. The struc ture of the CMM is 

shown in Figure 20.

Coal Production Submodule

This submodule pro duces an nual coal sup ply curves, re -

lat ing an nual pro duc tion to minemouth prices. The sup -

ply curves are con structed from an economet ric

anal y sis of prices as a func tion of pro duc tive ca pac ity,

ca pac ity uti li za tion, pro duc tiv ity, and var i ous fac tor in put

costs. A sep a rate sup ply curve is pro vided for sur face

and un derground min ing for all sig nif i cant pro duc tion by

coal ther mal grade (met al lur gi cal, bi tu mi nous,

subbituminous and lig nite), and sul fur level in each sup -

ply re gion. Each supply curve is as signed a unique heat,

sul fur, and mer cury con tent, and car bon di ox ide emis -

sions fac tor. Con struct ing curves for the coal types avail -

able in each re gion yields a to tal of 40 curves that are

used as inputs to the coal distribution submodule.

Supply curves are updated for each year in the pro jec tion

pe riod. Coal sup ply curves are shared with both the EMM 

and the PMM. For de tailed as sump tions, please see the

As sump tions to the An nual En ergy Out look up dated each 

year with the re lease of the AEO. 

Coal Distribution Submodule: Domestic Component

The coal dis tri bu tion submodule is a lin ear pro gram that

de ter mines the least-cost sup plies of coal for a given set

of coal de mands by de mand re gion and sector, ac count -

ing for trans por ta tion costs from the different sup ply

curves, heat and sul fur con tent, and ex isting coal sup ply

con tracts. Ex ist ing sup ply con tracts be tween coal pro -

duc ers and elec tric ity gen er a tors are in cor po rated in

the model as min i mum flows for sup ply curves to coal

de mand re gions. De pend ing on the spe cific sce nario,

coal dis tri bu tion may also be af fected by any re stric -

tions on sul fur di ox ide, mer cury, or car bon di ox ide

emis sions.

Coal trans por ta tion costs are sim u lated us ing interre -

gional coal trans por ta tion costs de rived by subtracting

re ported minemouth costs for each sup ply curve from

re ported de liv ered costs for each de mand type in each

de mand re gion. For the elec tric ity sec tor, higher trans -

por ta tion costs are as sumed for mar ket ex pan sion in

cer tain sup ply and de mand re gion com bi na tions.

Trans por ta tion rates are modified over time us ing

econometrically based mul ti pli ers which con sid ers the

im pact of chang ing pro duc tiv ity and equip ment costs.

When die sel fuel prices are suf fi ciently high, a fuel sur -

charge is also added to the trans por ta tion costs.

Coal Distribution Submodule: International

Component

The in ter na tional com po nent of the coal dis tri bu tion

submodule pro jects quan ti ties of coal im ported and ex -

ported from the United States. The quan ti ties are de ter -

mined within a world trade con text, based on as sumed

char ac ter is tics of for eign coal sup ply and de mand. The

com po nent disaggregates coal into 17 ex port re gions

and 20 im port re gions, as shown inTable 13. The sup -

ply and de mand com po nents of world coal trade are

Energy Information Administration / The National Energy Modeling System: An Overview 2009 71

Coal Market Module

CMM Outputs Inputs from NEMS Exogenous Inputs

Coal pro duc tion and dis tri bu tion

Minemouth coal prices

End-use coal prices

U.S. coal ex ports and im ports

Trans por ta tion rates

Coal qual ity by source, des ti na tion, and end-use sec tor

World coal flows

Coal de mand

In ter est rates

Price in di ces and de fla tors

Die sel fuel prices

Elec tric ity prices

Base year pro duc tion, pro duc tive ca pac ity, ca pac ity 

 uti li za tion, prices, and coal qual ity pa ram e ters

Con tract quan ti ties

La bor pro duc tiv ity

La bor costs

Do mes tic trans por ta tion costs

In ter na tional trans por ta tion costs

In ter na tional sup ply curves

In ter na tional coal im port demands
seg mented into two sep a rate mar kets: 1) cok ing coal,

which is used for the pro duc tion of coke for the

steelmaking pro cess; and 2) steam coal, which is pri -

mar ily con sumed in the elec tric ity and in dus trial

sec tors.

The in ter na tional com po nent is solved as part of the lin ear

pro gram that optimizes U.S. coal sup ply. It de ter mines

world coal trade dis tri bu tion by min i miz ing over all costs

for coal, sub ject to coal sup ply prices in the United 

States and other coal ex port ing re gions plus trans por -

ta tion costs. The com po nent also in cor po rates sup ply

di ver sity con straints that re flect the ob served tendency

of coal-im port ing coun tries to avoid ex ces sive de pend -

ence upon one source of sup ply, even at a some what

higher cost.

72 Energy Information Administration / The National Energy Modeling System: An Overview 20099

Coal Market Module

Fig ure 18. Coal Mar ket Mod ule De mand Regions
Coal Market Module

Energy Information Administration / The National Energy Modeling System: An Overview 2009 73

Fig ure 19. Coal Mar ket Mod ule Sup ply Regions
74 Energy Information Administration / The National Energy Modeling System: An Overview 20099

Coal Market Module

Coal Export Regions Coal Import Regions

U.S. East Coast U.S. East Coast

U.S. Gulf Coast U.S. Gulf Coast

U.S. Southwest and West U.S. Northern Interior

U.S. Northern Interior U.S. Noncontiguous

U.S. Noncontiguous Eastern Canada

Australia Interior Canada

Western Canada Scandinavia

Interior Canada United Kingdom and Ireland

Southern Africa Germany and Austria

Poland Other Northwestern Europe

Eurasia-exports to Europe Iberia

Eurasia-exports to Asia Italy

China Mediterranean and Eastern Europe

Colombia Mexico

Indonesia South America

Venezuela Japan

Vietnam East Asia

China and Hong Kong

ASEAN (Association of Southeast Asian Nations)

India and South Asia

Ta ble 13. Coal Ex port Component
Coal Market Module

Energy Information Administration / The National Energy Modeling System: An Overview 2009 75

Petroleum

Market Module

Macroeconomic

Activity

 Module

Demand

Modules

Electricity

Market

Module

Coal

Distribution

Submodule

Coal 

Production

Submodule

Coal Market Module

International

Component

Coal 

Supply 

Curves

Coal

Production and

Distribution

Coal Prices

Coal Demand

Coal Prices

Coal Demand

Diesel Fuel

Prices

Export 

Demand 

Minemouth

Coal Prices

N

E

M

S

Exogenous

Coal Export Demands

International Transportation Costs

International Supply Curves

Exogenous

 Domestic Transportation Costs 

 Contract Quantities

 Emissions Limits

Exogenous

 Base Year Production Capacity

 Base Year Capacity Utilization

 Labor Productivity

 Labor Costs

 Labor Cost Escalators

Electricity Prices

Coal Prices

Coal for Liquids

Derived

Interest Rates,

Price Indices, and

Deflators

Coal Production

and Prices

Fig ure 20. Coal Mar ket Mod ule Structure
Appen dix
The Na tional En ergy Mod el ing Sys tem is documented

in a series of model documentation reports, available on

the EIA Web site at http://tonto.eia.doe.gov/re ports/

reports_kindD.asp?type=model doc u men ta tion or by

contacting the National Energy Information Center

(202/586-8800).

En ergy In for ma tion Ad min is tra tion, In te grat ing Mod -

ule of the Na tional En ergy Mod el ing Sys tem: Model

Doc u men ta tion DOE/EIA-M057(2009) (Washington, DC,

May 2009).

En ergy In for ma tion Ad min is tra tion, Model Documentation Re port: Mac ro eco nomic Ac tiv ity Mod ule (MAM) of

the Na tional En ergy Mod el ing Sys tem,

DOE/EIA-M065(2009) (Wash ing ton, DC, Jan u ary

2009).

En ergy In for ma tion Ad min is tra tion, NEMS In ter na tional

En ergy Mod ule: Model Doc u men ta tion Re port,

DOE/EIA-M071(2007) (Wash ing ton, DC, May 2007).

En ergy In for ma tion Ad min is tra tion, Model Documentation Re port: Res i den tial Sec tor De mand Mod ule of the 

Na tional En ergy Mod el ing Sys tem, DOE/EIA-M067

(2009) (Wash ing ton, DC, May 2009).

En ergy In for ma tion Ad min is tra tion, Model

Documentation Re port: Com mer cial Sec tor De mand

Module of the Na tional En ergy Mod el ing Sys tem,

DOE/EIA-M066(2009) (Wash ing ton, DC, May 2009).

En ergy In for ma tion Ad min is tra tion, Model

Documentation Re port: In dus trial Sec tor De mand Mod ule 

of the Na tional En ergy Mod el ing Sys tem,

DOE/EIA-M064(2009) (Wash ing ton, DC, May 2009).

En ergy In for ma tion Ad min is tra tion, Trans por ta tion Sec tor 

Mod ule of the Na tional En ergy Mod el ing System: Model 

Doc u men ta tion Re port, DOE/EIA-M070(2009) (Wash -

ing ton, DC, June 2009).

En ergy In for ma tion Ad min is tra tion, The Elec tric ity Mar ket

Mod ule of the Na tional En ergy Mod el ing System: Model 

Doc u men ta tion Re port, DOE/EIA-M068(2009) (Wash -

ing ton, DC, May 2009).

En ergy In for ma tion Ad min is tra tion, Doc u men ta tion of the

Oil and Gas Sup ply Mod ule (OGSM), DOE/

EIA-M063(2009) (Wash ing ton, DC, July 2009).

En ergy In for ma tion Ad min is tra tion, Model

Documentation: Nat u ral Gas Trans mis sion and Distribution Model of the Na tional En ergy Mod el ing Sys tem,

DOE/EIA-M062(2009) (Wash ing ton, DC, June 2009).

En ergy In for ma tion Ad min is tra tion, Model

Documentation: Coal Mar ket Mod ule of the Na tional Energy Mod el ing Sys tem, DOE/EIA-M060(2009)

(Wash ing ton, DC, June 2009).

En ergy In for ma tion Ad min is tra tion, Model

Documentation: Re new able Fu els Mod ule of the Na tional 

En ergy Mod el ing Sys tem, DOE/EIA-M069(2009)

(Wash ing ton, DC, July 2009).

En ergy In for ma tion Ad min is tra tion, An nual En ergy Re -

view 2008, DOE/EIA-0384(2008) (Wash ing ton, DC,

June 2009).

En ergy In for ma tion Ad min is tra tion, Model Doc u men ta -

tion: Pe tro leum Mar ket Model of the Na tional En ergy

Mod el ing Sys tem, DOE/EIA-M059(20009) (Wash ing -

ton, DC, Au gust 2009).

Energy Information Administration / The National Energiy Modeling System: An Overview 2009 77

Appendix Bibliography