U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2013–5188

USGS National Water Census and National Streamflow Information Program

Methods for Estimating Water Consumption for 

Thermoelectric Power Plants in the United States
Cover. Aerial photographs of thermoelectric power plants in the United States from the U.S. Department of Agriculture (USDA) 

National Agriculture Imagery Program (NAIP), 2007. Refer to figures 2, 3, 5, and 6.
Methods for Estimating Water Consumption 

for Thermoelectric Power Plants in the 

United States

By Timothy H. Diehl, Melissa A. Harris, Jennifer C. Murphy, Susan S. Hutson, and 

David E. Ladd

Scientific Investigations Report 2013–5188

U.S. Department of the Interior

U.S. Geological Survey

USGS National Water Census and National Streamflow Information Program 
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

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Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Diehl, T.H., Harris, M.A., Murphy, J.C., Hutson, S.S., and Ladd, D.E., 2013, Methods for estimating water consumption 

for thermoelectric power plants in the United States: U.S. Geological Survey Scientific Investigations Report 

2013–5188, 78 p., http://dx.doi.org/10.3133/sir20135188.

ISSN 2328-031X (print)

ISSN 2328-0328 (online)

ISBN 978-1-4113-3714-5
iii

Contents

Abstract .............................................................................................................................................................................1

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

Purpose and Scope ................................................................................................................................................2

A Heat Budget Approach to Thermoelectric Water Consumption...........................................................................2

Data Compilation and Quality Assurance ....................................................................................................................4

Plant Master List.....................................................................................................................................................4

Generation Data......................................................................................................................................................4

Cooling System Data ..............................................................................................................................................5

Power Plant Locations and Elevations......................................................................................................5

Cooling-System Type Validation.................................................................................................................5

Environmental Data ......................................................................................................................................8

Data Sources.......................................................................................................................................9

Data Quality .......................................................................................................................................10

Methods of Determination for Environmental Input Variables..................................................11

Uncertainty Analysis ........................................................................................................................13

Supplemental Methods....................................................................................................................14

Computing Heat and Water Budgets...........................................................................................................................14

Estimation of Condenser Duty by Generation Type.........................................................................................18

Combustion-Steam Generation ................................................................................................................18

Combined-Cycle Generation.....................................................................................................................21

Nuclear Generation....................................................................................................................................21

Geothermal Generation .............................................................................................................................22

Estimating Thermoelectric Evaporation by Cooling-System Type................................................................23

Estimating Evaporation from Cooling Towers ........................................................................................24

Estimating Forced Evaporation from Surface Water ...........................................................................25

Error Analysis and the Prediction of Maximum and Minimum Likely Consumption..................................28

Other Types of Water Consumption.............................................................................................................................29

Solar-Thermal Generation...................................................................................................................................29

Flue-Gas Desulfurization......................................................................................................................................29

Minor Water Consumption at Combined-Cycle Plants...................................................................................29

Conclusions......................................................................................................................................................................29

Acknowledgments..........................................................................................................................................................30

Selected References......................................................................................................................................................30

Glossary............................................................................................................................................................................35

Appendix 1: The 1,284 thermoelectric plants one megawatt nameplate capacity or 

greater with water-cooling systems in the United States, 2010 ..............................................................38

Appendix 2: Guide to data contained in the U.S. Department of Energy, Energy Information 

Administration (EIA) 2010 Annual Electric Generator Data, Form-860, and the 2010 

Power Plant Operations Report, Form EIA-923, used for the classification of 

thermoelectric plants and consumption estimation model input ............................................................72

Appendix 2. Table 1: Data used in the two-tiered classification system for 

thermoelectric plants and heat and water budget models to estimate water 

consumption from the 2010 Annual Electric Generator Data, EIA Form-860 and 

2010 Power Plant Operations Report, Form EIA-923..................................................................................73

Appendix 2. Table 2: Prime mover types used to classify plants according to 

generation type ................................................................................................................................................75

Appendix 2. Table 3: Energy sources used to classify plants by generation type ...............................................76

Appendix 2. Table 4: Cooling-sytem types used to classify plants by cooling 

system technology...........................................................................................................................................77

Appendix 3: Databases accessed for thermoelectric plant classification and 

modeling data...................................................................................................................................................78

Appendix 4: Forced Evaporation from Water Surface (FEWS) spreadsheet............................... available online 

in an Excel file at http://pubs.usgs.gov/sir/2013/5188/appendix/sir2013-5188_appendix4_fews_

version_3.104.xlsx
iv

Figures

1. Map showing geographic distribution of the 1,284 thermoelectric plants 

one megawatt nameplate capacity or greater with water-cooling systems 

in the United States, 2010 ....................................................................................................................6

2. Aerial photograph showing natural-draft cooling towers and one of two 

intake screens and associated pumps for the Tennessee Valley Authority, 

Sequoyah Nuclear Power Plant, Tennessee....................................................................................7

3. Aerial photograph showing mechanical induced-draft cooling towers 

(wet cooling towers) and the discharge outlet for Entergy Vermont, 

Vermont Yankee Nuclear Power Plant, Vermont.............................................................................8

4. Aerial photograph showing a mechanical forced-draft tower for the 

Arvah B. Hopkins Power Generating Station, Florida.....................................................................9

5. Aerial photograph showing dry cooling towers for the Midlothian Power 

Plant, Texas..........................................................................................................................................10

6. Aerial photograph showing intake screen and pumps used for once-through 

cooling at the Browns Ferry Nuclear Plant, Alabama..................................................................11

7. Aerial photograph showing the approximately 2.5-mile-long cooling pond 

for the Lewis Creek Generating Plant, Texas .................................................................................12

8. Aerial photograph showing the approximately 8-mile-long cooling-water 

reservoir for the Lieberman Power Plant, Louisiana ....................................................................13

9. Graph showing the distribution of cooling-water types for modeling 

evaporation for 1,284 thermoelectric plants ..................................................................................14

10. Map showing geographic distribution of the 554 weather stations containing 

12 months of monthly mean dry bulb and wet bulb temperatures in 2010 ................................15

11. Map showing geographic distribution of the 727 weather stations containing 

12 months of monthly mean wind speed in 2010 ...........................................................................16

12. Map showing geographic distribution of the 755 water temperature stations 

(U.S. Geological Survey stations and greater than 100 megawatt power 

plants reporting intake temperature) with at least one month of monthly 

mean water temperature in 2010 .....................................................................................................17

13. Sankey diagram for an example combustion steam power plant with 

combined heat and power ................................................................................................................19

14. Sankey diagram for combustion steam plants...............................................................................19

15. Sankey diagram for combined cycle plants ...................................................................................22

16. Sankey diagram for nuclear plants..................................................................................................22

17. Sankey diagram for geothermal plants ...........................................................................................23

18. Graph showing evaporation ratio in relation to wind speed, estimated 

using Brady and others (1969) wind function.................................................................................27

19. Graph showing evaporation ratio in relation to wind speed at 20 degrees 

Celsius for four selected wind functions ........................................................................................27

20. Graph showing effect of pond heat loading on evaporation ratio..............................................28

Tables

1. Generation-type classification categories used to estimate condenser duty ...........................3

2. Cooling-system type classification categories used to estimate evaporation...........................4

3. Boiler efficiency, reported fuel heat, and estimated fuel heat by fuel type for 

combustion steam plants ..................................................................................................................20
v

Multiply By To obtain

Length

kilometer (km) 0.6214 mile (mi)

mile (mi) 1.609 kilometer (km)

Area

acre 4,047.0 square meter (m2)

acre 0.4047 hectare (ha)

acre 0.4047 square hectometer (hm2) 

acre 0.004047 square kilometer (km2)

square centimeter (cm2) 0.001076 square foot (ft2)

square centimeter (cm2) 0.1550 square inch (ft2) 

square meter (m2) 0.0002471 acre

square meter (m2) 10.76 square foot (ft2)

Volume

gallon (gal) 3.785 liter (L) 

gallon (gal) 0.003785 cubic meter (m3) 

gallon (gal) 0.1337 cubic foot (ft3) 

liter (L) 33.82 ounce, fluid (fl. oz)

liter (L) 2.113 pint (pt)

liter (L) 1.057 quart (qt)

liter (L) 0.2642 gallon (gal)

liter (L) 0.0353 cubic foot (ft3)

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

gram (g) 0.0022 pound, avoirdupois (lb)

kilogram (kg) 35.27 ounce, avoirdupois (oz)

kilogram (kg) 2.205 pound, avoirdupois (lb)

Density

gram per cubic centimeter (g/cm3) 62.4220 pound per cubic foot (lb/ft3) 

Pressure

Bar 100 kilopascal (kPa) 

Millibar 0.1 kilopascal (kPa) 

Energy

British thermal unit (Btu) 1,055.06 joule (J)

British thermal unit (Btu) 252 calorie (cal)

British thermal unit (Btu) 0.0002931 kilowatthour (kWh)

calorie 4.184 joule (J)

joule (J) 0.2390057 calorie (cal)

joule (J) 0.0000002 kilowatthour (kWh)

kilowatt hour (kWh) 3.6 x 106 joule (J)

kilowatt hour (kWh) 3,412.14 British thermal unit (Btu)

megawatt (MW) 1.0 x 106 joule per second (J/s)

megawatt (MW) 8.6 x 108 calories per hour (cal/h)

megawatt hour (MWh) 3.6 x 109 joule (J)

megawatt hour (MWh) 3.4 x 106 British thermal unit (Btu)

million British thermal unit 

(MMBtu)

1.055 x 109 joule (J)

Conversion Factors and Datum
vi

Multiply By To obtain

Rate

calorie per centimeter squared 

degrees Kelvin per day 

(cal/cm2°K/d)

0.484259 kilogram degrees kelvin per cubic 

second (kg°K/s3)

calorie per centimeter squared per 

second [cal/(cm2/s)]

41,840 watts per meter squared (W/m2)

calorie per gram (cal/g) 4,184 joule per kilogram (J/kg)

calorie per gram degrees Kelvin 

(cal/g°K)

4,184 joule per kilogram degrees Kelvin 

[J/(kg°K)]

gallon per kilowatt hour (gal/kWh) 1.0515 x 10-6 liter per joule (L/J)

gallon per kilowatt hour (gal/kWh) 4.3995 x 10-6 liter per calorie (L/cal)

gallon per megawatt hour 

(gal/MWh)

1.0515 x 10-9 liter per joule (L/J)

gallon per megawatt hour 

(gal/MWh)

4.3995 x 10-9 liter per calorie (L/cal)

megawatt per acre (MW/acre) 0.0059 calorie per square centimeter per 

second [cal/(cm2/s)]

mile per hour (mi/h) 1.60934 kilometer per hour (km/h)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

 °F = (1.8 × °C) + 32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

 °C = (°F - 32) / 1.8

Temperature in degrees Kelvin (°K) may be converted to degrees Celsius (°C) as follows:

 °C = (°K - 273.15)

Temperature in degrees Kelvin (°K) may be converted to degrees Fahrenheit (°F) as follows:

 °F = ((°K - 273.15) x 1.8) + 32

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Elevation, as used in this report, refers to distance above the vertical datum.

Btu British thermal units

CPPDB Coal Power Plant Database

DEM Digital Elevation Model

ECHO USEPA database, Enforcement and Compliance History Online 

eGRID Emission and Generation Resource Integrated Database

FERC Federal Energy Regulatory Commission

FGD Flue-gas desulfurization 

GIS Geographic Information System

GLERL Great Lakes Environmental Research Laboratory

Abbreviations
vii

GLSEA2 Great Lakes Surface Environmental Analysis 

HRSGs Heat-Recovery Steam Generators

HSIP Homeland Security Infrastructure Program

ICIS Integrated Compliance Information System

kWh Kilowatt hour

L/G Mass ratio of water flow to dry air flow in a cooling tower 

MMBtu Million British thermal units

MW Megawatt

MWh Megawatt hour 

MWhe Megawatt hour electric

NAICS North American Industry Classification System

NEEDS National Electric Energy Data System

NETL National Energy Technology Laboratory

NHDPlus National Elevation Dataset, Horizon Systems Corporation

NOAA National Oceanic and Atmospheric Administration

NOx Nitrogen Oxides

NPDES National Pollutant Discharge Elimination System

PCS Permit Compliance System

QCLCD Quality Controlled Local Climatological Data; also LCD

USEIA U.S. Energy Information Administration; also EIA

USEPA U.S. Environmental Protection Agency; also EPA

USGS U.S. Geological Survey

Methods for Estimating Water Consumption for 

Thermoelectric Power Plants in the United States 

By Timothy H. Diehl, Melissa A. Harris, Jennifer C. Murphy, Susan S. Hutson, and David E. Ladd

Abstract 

Water consumption at thermoelectric power plants represents a small but substantial share of total water consumption 

in the U.S. However, currently available thermoelectric water 

consumption data are inconsistent and incomplete, and coefficients used to estimate consumption are contradictory. The 

U.S. Geological Survey (USGS) has resumed the estimation 

of thermoelectric water consumption, last done in 1995, based 

on the use of linked heat and water budgets to complement 

reported water consumption. This report presents the methods 

used to estimate freshwater consumption at a study set of 

1,284 power plants based on 2010 plant characteristics and 

operations data. 

Power plants were categorized for estimation of water 

consumption in two tiers. First, generating units were assigned 

to categories based on the technology used to generate 

electricity. These generation-type categories are combustion 

steam, combined-cycle, nuclear, geothermal, and solar 

thermal. Second, cooling systems were separately categorized 

as either wet cooling towers or surface-water cooling systems, 

and the surface-water cooling systems were subcategorized as 

cooling ponds, lakes, and rivers. 

Heat budgets were constructed for the first four generation-type categories; data at solar thermal plants were insufficient for heat budgets. These heat budgets yielded estimates 

of the amount of heat transferred to the condenser. The ratio 

of evaporation to the heat discharged through the condenser 

was estimated using existing heat balance models that are 

sensitive to environmental data; this feature allows estimation 

of consumption under different climatic conditions. These two 

estimates were multiplied to yield an estimate of consumption 

at each power plant.

Introduction

Thermoelectric water consumption1 is the water evaporated or incorporated into by-products as a result of the production of electricity from heat. Evaporation from the cooling 

system1accounts for most of the water consumption at most 

1

 Words and phrases introduced in bold are listed in the Glossary.

thermoelectric plants (U.S. Department of Energy, National 

Energy Technology Laboratory, 2010). In contrast to thermoelectric water withdrawals, which are the largest single water 

withdrawal category (Kenny and others, 2009), thermoelectric consumption has been estimated to be about 2 percent of 

total U.S. water consumption, though locally it can be greater 

(Solley and others, 1998). Thermoelectric water consumption 

is a critical water use in that thermoelectric plants cannot 

operate without consuming water, and shutting down power 

plants imposes costs on society (Eaton, 2012). At the same 

time, water consumed in thermoelectric power generation is 

unavailable for other uses.

Thermoelectric water consumption is projected to 

increase with increasing energy demand (U.S. Department 

of Energy, National Energy Technology Laboratory, 2010). 

Moreover, because it is dominated by evaporation, thermoelectric water consumption is inherently sensitive to ambient 

temperatures and likely to respond to heat waves and changing 

climate. However, existing reported values and estimates of 

thermoelectric water consumption do not provide the accuracy, 

transparency, and temperature sensitivity needed to monitor 

water consumption and predict its growth. 

Available thermoelectric water use data are inconsistent 

and incomplete (Diehl, 2011). The available thermoelectric 

water use data are self-reported by plant operators, and techniques for measuring or estimating the main water flows are 

not standardized. The U.S. Energy Information Administration 

(EIA) maintains the most complete and consistent database of 

thermoelectric water use, but plants with water-using generation capacities of less than 100 megawatts (MW) are not 

required to report water consumption to the EIA, and in 2010 

nearly half of the plants that were required to report consumption either did not report consumption or reported zero 

consumption (U.S. Department of Energy, Energy Information 

Administration, 2011a, 2011b). 

Because reported values are incomplete and inconsistent, 

water consumption is frequently estimated by multiplying 

total net generation for various combinations of generating 

technology and cooling-system types by coefficients that 

are supposed to give average consumption in gallons per 

kilowatt hour for each plant category. However, published 

coefficients disagree widely with one another (Macknick and 

others, 2011). All are based on data subject to the limitations 
2 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

discussed above; some are derived using poorly documented 

methods, and some are thermodynamically unrealistic (Diehl, 

2011). Moreover, published coefficients relating typical evaporation to electric generation do not vary with environmental 

conditions; the coefficients cannot be used to estimate regional 

or seasonal variability or responses of water consumption to 

unusual weather or climate change.

In 2010, the U.S. Geological Survey (USGS) inititated a 

study to estimate water consumption by thermoelectric power 

plants as part of the USGS National Water Use Information 

Program and the agency’s broader mission to provide scientific information to manage U.S. water resources. This study 

was motivated in part by recommendations of the Government 

Accountability Office (U.S. Government Accountability 

Office, 2009) that the USGS resume reporting thermoelectric 

water consumption in the U.S., last reported for 1995 (Solley 

and others, 1998). This study was undertaken with the support 

of the National Water Census and National Streamflow 

Information Program. The methods developed to estimate 

water consumption for this study based on linked heat and 

water budgets are part of the ongoing nationwide assessment 

of water supply and demand and complement existing recommended methods for quantifying water use (Hutson, 2007; 

Templin and others, 1999).

The methods in this report describe how electric-power 

generation technology and cooling system technology interact 

to determine the amount of water consumed. They can be used 

to estimate water consumption for individual plants, and to 

define ranges within which reported values of consumption 

are thermodynamically realistic. They reflect the dependence 

of water consumption on air temperatures, wind speed, water 

temperatures, flow, and plant characteristics and operations. 

They include forced evaporation—the additional evaporation 

downstream from power plants caused by heat added to water 

bodies by power plants, over and above the evaporation that 

would occur in the absence of artificially added heat.

Purpose and Scope

This report describes (1) methods for estimating consumptive freshwater use at thermoelectric plants, and (2) 

a two-tiered classification system for thermoelectric plants, 

based on generation technology and cooling technology. 

This report develops heat and water budget models to 

estimate water consumption at U.S. power plants with waterusing cooling systems and generating capacities greater than 

1 megawatt (MW). Model development included three major 

tasks: 

• Compiling data on selected plant characteristics, plant 

operations, and environmental conditions, 

• Combining these data into budgets of the major flows 

of heat and water at the level of the plant or subunits of 

the plant, and

• Modeling water consumption based on these heat and 

water budgets. 

Geographic areas include the 50 States and the District 

of Columbia. Power plants in Puerto Rico and the U.S. 

Virgin Islands are not surveyed by the Energy Information 

Administration.

A Heat Budget Approach to 

Thermoelectric Water Consumption

Heat budgets were used to model thermoelectric water 

consumption at the plant scale for 1,284 water-using power 

generation facilities in the U.S. A linked heat and water budget 

was constructed for all thermoelectric plants that provided 

enough information on plant characteristics and operations. In 

addition to the estimated most-likely consumption, high and 

low limit values were estimated based on estimated errors in 

the budget model. These limit values define a range of consumption values that are likely to be consistent with thermodynamic constraints. Reported values of consumption outside 

this range are unlikely to be correct based on apparent violation of the thermodynamic constraints. Where the reported 

amount of water consumption is consistent with the estimated 

consumption, the reported number can be considered validated. At plants without reported water use, the budget results 

provide a useful, if imprecise, estimate of thermoelectric water 

consumption.

For a given period of time at a given plant, the energy 

made available to drive water consumption through evaporation cannot exceed the difference between the energy contained in consumed fuel and that contained in the plant’s electrical output. This difference represents total energy (or heat) 

dissipated to the environment—a large portion of which drives 

evaporation of water unless dry cooling is used. Heat-budget 

computations based on the conservation of mass and energy 

and records of fuel use and power generation can provide 

an important, independent means to constrain estimates of 

thermoelectric water consumption (Rutberg and others, 2011) 

within bounds determined by the availability of waste heat to 

evaporate water and the design of the cooling system. 

Evaporation at thermoelectric plants depends on both the 

method by which electricity is generated (generation type) 

and the method by which waste heat is removed from the 

system (cooling-system type). Because generation type and 

cooling-system type represent two distinct and independent 

stages in the overall process of thermoelectric water consumption, it is useful to consider them separately. This leads to a 

two-tiered classification of thermoelectric plants that provides 

an analytical framework for estimation based on two key 

processes: the production of waste heat and the conversion of 

waste heat to evaporation. 

The share of fuel energy converted to waste heat depends 

for the most part on the generation type and design but is little 
A Heat Budget Approach to Thermoelectric Water Consumption 3

affected by cooling-system type, plant elevation, and environmental conditions. Generation type represents a complex of 

various systems that convert fuel to heat or otherwise capture 

heat, and convert heat to electricity and waste heat. In typical 

steam plants, these systems include a boiler, steam turbine, 

condenser, and auxiliary equipment. Combined-cycle plants 

also include combustion turbines. In either case, distilled water 

is converted to high-pressure steam in a boiler and drives a 

steam turbine before being re-condensed and returned to the 

boiler in a closed loop. The heat removed from the steam and 

transferred to cooling water by a condenser is the majority of 

waste heat and is defined as the condenser duty of the plant. 

Five generation types classified by electrical generation technology and energy source (table 1) produce distinct estimates 

of condenser duty. Plants in each of these types report different 

kinds of information at different levels of aggregation to the 

EIA. These differences, combined with known technological 

characteristics of each type, make it necessary to estimate condenser duty with different techniques for each generation type. 

The share of condenser duty that goes to produce evaporation depends on cooling-system type as well as a number of 

environmental conditions, such as plant elevation, air temperature, humidity, wind speed, and ambient water temperature. 

Generally, cooling systems transfer the bulk of condenser 

duty to the atmosphere through evaporation and the rest is lost 

through conduction and radiation (Huston, 1975). Six cooling-system types are common (table 2), of which only three 

consume freshwater. Of these, most thermoelectric plants built 

recently have one or more wet cooling towers. Inside these 

towers, hot water is exposed to flowing air over a large surface 

area. Heat in the cooling water is transferred to air mostly 

as latent heat through vaporization and to a lesser extent as 

sensible heat through conduction. These two processes warm 

the air as it passes through the tower and leave it approximately saturated with moisture. The main alternative to the 

use of wet cooling towers is surface-water cooling, whereby 

heat is transferred to the atmosphere from the free surface of 

an extensive body of open water. Though a greater portion 

of waste heat may leave as convection and long-wave radiation from surface-water cooling systems, a substantial portion 

remains to be accounted for in evaporation. 

For any given cooling system, the fraction of condenser 

duty that goes to produce evaporation is expressed as the 

evaporation ratio of that system. Because all systems lose 

at least some waste heat through other processes, evaporation 

ratios have values less than one. Evaporation ratios for towers 

are primarily sensitive to ambient vapor pressure and air 

temperature (wet bulb and dry bulb); ratios for surface-water 

cooling systems are primarily sensitive to ambient water temperature and wind speed. 

Based on the characterizations and analyses described 

above, a heat-budget approach can be applied in four steps: 

(1) model the condenser duty for each generating unit independent of the type of cooling system; (2) model the evaporation ratio for the various types of cooling systems used by 

the generating unit; (3) compute the heat transferred to the 

atmosphere as the product of condenser duty and evaporation 

ratio; and (4) compute the weight of evaporated water and its 

liquid volume from the water temperature and the specific heat 

of evaporation. Each of these steps requires specific information, which may be available in varying degrees of quality and 

completeness. The available heat in the fuel entering the plant 

and the energy in the electricity that leaves the plant are well 

measured at most plants, and the difference between them is 

the heat rejected by the plant. Condenser duty is the largest 

component of heat rejection, and the other components, losses 

from auxiliary equipment and heat in the exhaust gas, are estimated based on generation type. Environmental variables and 

the type of cooling system control the evaporation ratio. The 

cooling system models use monthly averages of dry bulb air 

temperature, wet bulb air temperature, ambient surface-water 

temperature, and wind speed, as well as plant elevation data. 

Verified plant locations were used to validate cooling-system 

types and to link environmental variables and elevations to 

plants. 

Table 1. Generation-type classification categories used to estimate condenser duty.

[The prime mover is the turbine that converts the energy in heated gases to mechanical energy.]

Generation type Prime mover Energy source Condenser duty estimation method

Combustion steam Steam All combustible fuels used in steam 

prime movers; dominated by coal

Linked heat and water budgets, 

with evaporation modeling 

approach determined by coolingsystem type

Combined cycle

Combined cycle combustion 

turbine part All fuels used in combined-cycle 

prime movers; dominated by 

natural gas Combined cycle steam part

Combined cycle single shaft

Nuclear Steam Nuclear

Geothermal Steam Geothermal

Solar thermal Steam Solar Water-consumption coefficient
4 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Data Compilation and Quality 

Assurance 

Classifying power plant technologies and developing 

methods for estimating water consumption required specific 

thermoelectric plant-characteristic, operational, locational, 

and environmental data. Data were compiled from EIA, the 

U.S. Environmental Protection Agency (USEPA), the National 

Energy Technology Laboratory (NETL), the National Oceanic 

and Atmospheric Administration (NOAA), and USGS national 

databases. The data were incorporated into the classifications 

and methods either as initially reported, or as variables to 

calculate numerical values needed to estimate condenser duty 

and evaporation. 

Plant Master List

The final list of plants for this study (herein referred to as 

the plant Master List) has 1,284 thermoelectric, water-using 

power plants. These plants were identified using plant-characteristic data from the 2010 EIA-860 Annual Electric Generator 

Report database (Appendix 1). For this study, power plants 

were limited to electricity-generating facilities that have been 

assigned unique plant codes by the EIA. At the broadest level, 

power plants with existing generators (as opposed to plants 

with retired or proposed generators) had 5,824 unique records 

in the EIA-860 database. Eliminating plants with existing 

generators that were not active in 2010 narrowed the group 

to 5,344 plants. Of these, 1,713 were identified as having 

water-using thermoelectric generators based on prime mover, 

which is the turbine that converts the energy in heated gases 

to mechanical energy. Based on the North American Industry 

Classification System (NAICS) codes (U.S. Census Bureau, 

2007), water-using thermoelectric generators can be further 

subdivided into industrial and commercial facilities and 

electric utilities. After eliminating industrial and commercial 

facilities, which are grouped by the USGS in separate water 

use categories, 1,329 electric utility power plants remained. 

Some of these plants use dry cooling systems, which do not 

evaporate water, and therefore were removed leaving a revised 

total of 1,284 plants that fit all study criteria. Within the final 

Master List, 217 plants were described as combined heat 

and power plants and required a modified water consumption 

estimation model. 

Generation Data

Generation data, compiled from the 2010 EIA-860 

Annual Electric Generator Report and the EIA-923 Power 

Plant Operations Report databases, include both the plantcharacteristic data used to classify generation types and 

operational data used to model condenser duty (Appendix 

2, table 1). Plant-characteristic data, including prime mover, 

energy source, boiler efficiency, interconnections (associations) between boilers and generators, and flue-gas desulfurization (FGD) types, are generally the same from year to 

year and are not dependent on annual operations unless entire 

boilers, generators, or cooling systems are brought on or off 

line. Operational data including the amount of fuel consumed 

(electric and total fuel heat produced) and the net amount 

of electricity generated by each plant will typically vary by 

month and year. 

The portion of fuel heat converted to condenser duty 

depends on the design of each plant. Five generation types 

have been identified based on prime mover and energy source: 

combustion steam, combined-cycle, nuclear, geothermal, and 

solar thermal (table 1). Steam turbines, which are associated 

with water-using cooling systems, are used with all of these 

generation types but are fueled by different energy sources. 

Whereas coal provides most of the heat to combustion steam 

turbines, a wide variety of other fuels also is used. Steam 

turbines at nuclear, geothermal, and solar thermal plants are 

powered by their respective noncombustive energy sources. 

Combined-cycle plants use a combination of combustion 

turbines and steam turbines to generate electricity in which 

exhaust from a combustion turbine (commonly powered by 

natural gas) boils water to power a steam turbine. Each generation type has a condenser duty range that is contingent upon 

the amount of fuel heat used to generate electricity. Detailed 

lists of prime movers, energy sources, and associated EIA 

codes are given in Appendix 2, tables 2 and 3. 

Data on fuel consumption, electric generation, and plant 

characteristics are fundamental to the heat-budget approach, 

but the availablility and quality of these data vary widely 

among different generation types. In general, reported net 

electric generation data for all generation types were of good 

quality, principally because the amount of electricity transmitted to the electrical distribution grid is well measured. Fuelconsumption data, on the other hand, were of consistently 

good quality only for combustion-steam and combined-cycle 

plants. Because total heat values from fuel consumption in 

nuclear, geothermal, and solar thermal plants are difficult to 

define and measure, reported values for these plants were 

Table 2. Cooling-system type classification categories used to 

estimate evaporation.

Cooling-system type Evaporation modeling approach

Wet freshwater recirculating 

cooling tower Wet tower evaporation model

Recirculating pond or canal Water surface evaporation model Once-through freshwater 

Wet saline recirculating cooling 

tower

Not modeled Once-through saline water 

Dry cooling systems
Data Compilation and Quality Assurance 5

nominal at best (producing unrealistic thermal efficiencies) 

and had to be estimated. Boiler efficiencies reported for most 

combustion-steam plants were thermodynamically realistic, 

but at some plants boiler efficiency had to be estimated based 

on the types of fuels burned. The boiler efficiencies reported 

for combined-cycle plants were thermodynamically unrealistic 

and were not used in heat budgets. FGD-type data reported 

for combustion-steam plants were assumed to be accurate. 

Electric-fuel consumption data reported for combined heat and 

power plants were generally of adequate quality for computing the amount of heat exported to associated heat-using 

processes. 

Cooling System Data

Cooling system data are the plant characteristics used to 

classify cooling-system types, interconnections between cooling systems and boilers, and the locational and environmental 

data used to estimate water consumption. The amount of waste 

heat transferred from the condenser to the atmosphere through 

evaporation depends on the cooling-system technology and the 

environmental variables associated with the plant locations. 

Power Plant Locations and Elevations

Locations for each thermoelectric plant link aerial imagery, plant elevation, environmental data, and catchment areas 

necessary to estimate power plant-specific water consumption. 

To ensure correct links among these datasets, multiple, often 

conflicting, power plant locations from EIA, NETL, and 

USEPA databases were evaluated using standard geographic 

information system (GIS) procedures (U.S. Department 

of Energy, Energy Information Administration, 2001, 

2009, 2011a; U.S. Department of Energy, National Energy 

Technology Laboratory, 2007b; U.S. Environmental Protection 

Agency, 2007a, [n.d.]). Power plant locations were verified 

and then matched to USEPA’s National Hydrography Dataset 

Plus (NHDPlus) to determine elevation and assign river catchments (U.S. Environmental Agency, 2007b). Those locations 

and associated metadata were stored in an internal USGS GIS 

database.

Locations of record for power plants were verified by 

inspecting digitally mapped site locations using GIS and 

other computer-aided techniques. A point-data layer was 

created from seven sets of coordinates composed of 19,496 

power plant locations. The sets of coordinates were from 

four U.S. Department of Energy (DOE) databases: the 2000, 

2008, and 2010 Form EIA-860 databases (U.S. Department 

of Energy, Energy Information Administration 2001, 2009, 

2011a), and the NETL Coal Power Plant Database [CPPDB] 

(U.S. Department of Energy, National Energy Technology 

Laboratory, 2007b), and two USEPA databases: the 

Enforcement and Compliance History Online [ECHO], which 

is composed of two datasets (U.S. Environmental Protection 

Agency, [n.d.]), and the Emissions & Generation Resource 

Integrated Database [eGRID] (U.S. Environmental Protection 

Agency, 2007a). In many instances, these different sources 

produced more than one location for each power plant. To 

reconcile those locations, a second data layer containing the 

mean center location for sites with multiple locations was created. Mean center locations for each plant were then adjusted 

in a final point layer based on GIS-facilitated analysis of aerial 

imagery and other available information. A reference layer of 

power plants was extracted from the 2011 Homeland Security 

Infrastructure Program (HSIP) Gold database (Appendix 3) 

and used for visual site verification. This information, however, could not be directly linked to EIA, NETL, or USEPA 

location datasets by plant name or EIA plant code. Linkages 

were made using other location information provided in 

several base layers obtained from USGS topographic maps 

at various scales, road maps, aerial photography, and satellite 

imagery as well as GIS-facilitated internet searches of publically available mapping services. 

The location verification procedure involved a series of 

power plant-specific queries within the GIS display points 

by plant code number. This reduced the number of points by 

eliminating all other power plants from the screen so that only 

the multiple locations of the queried power plant were displayed. The reduced area provided a general location of a plant 

from which a search could be conducted. The search began 

by finding the HSIP power plant location, and determining 

its proximity to the mean center location. If a plant could not 

be located and verified by aerial imagery, the plant’s address 

was used to conduct a visual search in the area defined by the 

extent of all points with the same EIA power plant code, and 

to execute an internet search for any other information about 

the plant that could be used to find its actual location (fig. 1). 

Power plant locations were then overlain with NHDPlus version 1.1 data to determine elevation and river catchment area 

(U.S. Environmental Protection Agency, 2007b).

Cooling-System Type Validation

Cooling-system types were classified into two basic 

categories: wet cooling towers and surface-water cooling 

systems (table 2). Power plants that have water-using generation capacities of 100 MW or greater are required to report 

cooling-system types to the EIA; 802 Master List plants fit this 

criterion. However, this information is sometimes inaccurate 

or is incomplete, as is the case with the 482 Master List plants 

not required to report. Therefore, verified power plant locations and internet searches were used to validate the coolingsystem types for the Master List power plants. 

Analysts were trained to identify the various types of 

cooling systems using aerial imagery in Google Earth and 

Bing Maps. Wet cooling towers include natural-draft towers 

and two types of mechanical-draft towers, induced-draft and 

forced-draft towers, which are all readily identifiable from 

aerial imagery. Natural-draft towers are identifiable as large, 

hyperboloid structures with pronounced top openings 

(fig. 2); air flow through natural-draft towers is driven by 
6 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Figure 1. Geographic distribution of the 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United States, 2010.[Power plants in Puerto Rico and the U.S. Virgin Islands are not surveyed by the Department of Energy, Energy Information Administration.]
Data Compilation and Quality Assurance 7

convection. Induced-draft towers have large fans on top of 

the tower structure that pull air up through the tower (fig. 3). 

Forced-draft towers are generally rectangular and have a 

radiator-like appearance across the top; the fans are located in 

the bottom of these towers and push air up through the tower 

structure (fig. 4). Many dry cooling systems, known as aircooled condensers, also have a radiator-like appearance, but 

most have large pipes running the length of the structure that 

aid in identification (fig. 5). Surface-water cooling systems 

include once-through cooling systems and recirculating 

cooling pond or canal systems. Once-through cooling systems 

take water from a surface-water body, transfer heat into it, and 

discharge the water to a surface-water body; little or none of 

this discharged water is recirculated through the condenser. 

Recirculating ponds and canals are similar but have much 

smaller surface-water inflows and outflows, so that the water 

in them is recirculated many times through the condenser. 

Once-through cooling systems were identified by the coolingwater intake structures that feature intake pumps and screens 

located in and beside water bodies (fig. 6) and discharge 

outlets (fig. 3) characterized by concentrated, fast-flowing 

water with visible foam. Once-through cooling systems can 

be located on coastal waters, rivers, lakes, or ponds. Because 

the type of water body determined the surface-water evaporation model that was used, the areas of lakes and ponds were 

measured to assist in establishing a surface-water cooling 

subcategory (coastal waters and rivers are evident). Ponds 

were distinguished from lakes at this stage as being smaller 

than lakes and lacking irregular natural shorelines (fig. 7). 

Both natural lakes and reservoirs were assigned to the lakes 

Figure 2. Natural-draft cooling towers and one of two intake screens and associated pumps for the Tennessee Valley 

Authority, Sequoyah Nuclear Power Plant, Tennessee. Natural-draft tower airflow is drawn through the packing or fill 

(distributed at the base) by means of the small density difference between the warm moist air inside the tower and the 

cooler ambient air outside the tower (Stultz and Kitto, 1992). Because of the small temperature and density difference, these 

cooling towers tend to be tall. These towers are relatively open cylindrical structures. Modern units are sometimes referred 

to as hyperbolic towers because of their shape. The heat rejected from the steam to the cooling water is circulated through 

the cooling tower where the heat is rejected to the atmosphere as latent and sensible heat.
8 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

subcategory (fig. 8). Later, several lakes were redefined as 

ponds on the basis of high heat loading. A list of the coolingsystem types and their associated EIA codes is presented in 

Appendix 2, table 4. 

Determining cooling-system types for some plants 

proved more difficult and professional judgement was needed 

for identification. Some once-through plants, for example, 

have intakes or outlets that are submerged and cannot be 

located using aerial imagery. In such cases, if no other cooling 

system could be identified (for example, towers) and the plant 

was located next to a river, lake, or coastal water body, it was 

determined to have a once-through system. Some dry cooling 

systems known as heat exchangers have a similar appearance 

to wet cooling towers and are difficult to identify. Additionally, 

locating and linking cooling systems to plants within industrial 

complexes or within close proximity of another plant relied on 

subjective decisions.

After validating cooling-system types, most of the 

reported forced-draft cooling towers were reclassified as 

induced-draft cooling towers. For the non-reporting plants, 

the majority of the cooling-system types were identified as 

induced-draft cooling towers. Overall, of the 1,284 power 

plants in the Master List, most were classified as wet tower 

systems (fig. 9). Some, with multiple cooling-system types, 

were classified as complicated and consumption by each 

cooling-system type was modeled separately. 

Environmental Data

The evaporation models require several environmental 

input variables that are specific to each power plant. The 

environmental inputs needed for the surface-water evaporation 

model include: site elevation, monthly mean wet bulb and dry 

Figure 3. Mechanical induced-draft cooling towers (wet cooling towers) and the discharge outlet for Entergy Vermont, 

Vermont Yankee Nuclear Power Plant, Vermont. With the mechanical induced-draft cooling towers, the fan is located 

downstream of the air and water interface and the air is drawn through the cooling tower (Stultz and Kitto, 1992). Airflow 

distribution is typically more uniform and less prone to ground interference or recirculation. The heat rejected from the 

steam to the cooling water is then circulated through the cooling tower where the heat is rejected to the atmosphere as 

latent and sensible heat.
Data Compilation and Quality Assurance 9

bulb air temperatures, monthly mean water temperature, and 

monthly mean wind speed. Environmental inputs needed for 

the tower evaporation model include: elevation, monthly mean 

wet bulb and dry bulb temperatures. Acquisition of elevation 

data has been described previously; the following discussion 

focuses on the data sources and determination methods of 

climate inputs (wet bulb and dry bulb temperatures, and wind 

speed) and water temperature inputs.

Data Sources

Land-surface observations from Quality Controlled 

Local Climatological Data (QCLCD or LCD) collected by the 

NOAA, National Climatic Data Center (Appendix 3) serve as 

the foundation for dry bulb, wet bulb, and wind speed determinations. LCD consists of hourly, daily, and monthly summaries of meteorological observations collected at major airports 

nationwide. In 2010, the LCD network consisted of 1,152 

weather stations reporting at least 1 month of meteorological 

observations.

USGS water temperature measurements, EIA plantreported values, and satellite imagery were the basis for watertemperature determinations, depending on plant location. For 

plants located on one of the Great Lakes, satellite imagery 

provided water temperature information. The NOAA Great 

Lakes Environmental Research Laboratory (GLERL) produces 

the Great Lakes Surface Environmental Analysis (GLSEA2), 

which is a daily digital map of lake-surface temperatures 

and ice cover derived from imagery collected by the NOAA 

polar-orbiting satellite (Appendix 3). For all other plants, temperature measurements of lakes and rivers from USGS water 

temperature stations and plant-reported intake temperatures 

provided by the EIA were used for water-temperature determinations. In 2010, approximately 700 USGS streamgaging 

Figure 4. A mechanical forced-draft tower for the City of Tallahassee, Arvah B. Hopkins Power Generating Station, Florida. 

In mechanical forced-draft cooling towers, fans are typically located upstream of the air/water interface (Stultz and Kitto, 

1992). In this case the air is drawn through the cooling tower. The heat rejected from the steam to the cooling water is then 

circulated through the cooling tower where the heat is rejected to the atmosphere as latent and sensible heat.
10 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

stations recorded daily mean water temperature in the U.S. 

Monthly mean water temperatures were calculated from this 

record for months in which fewer than seven daily values were 

missing. The EIA also collects plant-reported average monthly 

intake water temperatures (using Form EIA-923) for facilities 

generating greater than 100 MW, which accounted for 381 

power plants nationwide in 2010.

Data Quality

The quality of the LCD data (dry bulb temperature, wet 

bulb temperature, and wind speed) is controlled by NOAA, 

and no additional filtering based on data quality was required. 

Only stations with complete monthly observations for 2010 

were used, and only stations with both dry bulb and wet bulb 

temperature observations were retained. Of the 1,152 weather 

stations reporting meteorological observations, 554 stations 

reported 12 months of monthly mean dry bulb and wet bulb 

temperatures, and 727 stations reported 12 months of monthly 

mean wind speed. Stations missing 1 or more months of observations were compiled in separate validations for dry bulb and 

wet bulb temperature or wind speed and used in an analysis of 

uncertainty. The geographic distribution of dry bulb and wet 

bulb stations and wind speed stations used for input determination was fairly uniform across the U.S. (figs. 10 and 11). 

No additional quality control was performed on the Great 

Lakes water temperature map (GLSEA2), however several 

filtering processes were used to check the quality of USGS 

and EIA measurements of water temperature. There were 

622 USGS water temperature stations after filtering. USGS 

monthly mean water temperatures were filtered through an 

iterative process. First, monthly temperatures were screened 

for extreme values and irregular seasonal patterns such as 

deviations from a generally sinusoidal curve. Sites that were 

flagged during this process were visually evaluated and 

removed from the dataset if they were located in a geothermal 

Figure 5. Dry cooling towers for the ONCOR, Midlothian Power Plant, Texas. An air-cooled system may be mechanical or 

natural draft (Stultz and Kitto, 1992). All the heat rejected from the steam is absorbed in the form of sensible heat gain in the 

ambient air.
Data Compilation and Quality Assurance 11

area or below a dam. Leave-one-out cross-validation (Breiman 

and Spector, 1992) was used to identify possible errors in 

water temperature at remaining sites and a site was flagged 

if its error was greater than two standard deviations from the 

mean error of the cross-validated estimates. Flagged sites were 

then visually evaluated to determine their appropriateness for 

the dataset. 

Plant-reported, monthly average intake water 

temperatures collected by EIA were initially filtered based on 

the method of measurement. Only plants that indicated water 

temperature was measured at intervals (for example, daily) 

with a thermometer or measured continuously with a thermometer were retained. Furthermore, only plants that reported 

once-through freshwater cooling systems were retained. 

Remaining sites were then screened for extreme values and 

irregular seasonal patterns and subjected to the same crossvalidation process described above. There were 133 plants 

with monthly mean intake water temperature after filtering. 

Monthly temperature data from the USGS were combined 

with plant-reported EIA data and the water temperature dataset 

used for input determination contained 755 sites with at least 1 

month of water temperature in 2010. The geographic distribution of water temperature sites varies by month and is not 

uniform across the U.S., particularly in the Southwest, where 

water temperature information is sparse (fig. 12).

Methods of Determination for Environmental Input 

Variables

For each plant, climate inputs were determined by interpolation among nearby weather stations with dry bulb and wet 

bulb observations (fig. 10) or wind speeds (fig. 11). Using a 

100-mile search radius, the distances from each plant to the 

nearest three weather stations were determined and used to 

compute a weighted mean of climate variables based on the 

inverse of distance from the respective plant.

Figure 6. Intake screen and pumps used for once-through cooling at the Tennessee Valley Authority, Browns Ferry 

Nuclear Plant, Alabama. The discharge outlet is not shown. In once-through cooling, water is withdrawn from a source, 

circulated through the condenser, and then returned to a body of water at a higher temperature (Kenny and others, 2009). 

The heat is rejected as latent and sensible heat (Stultz and Kitto, 1992).
12 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Empirical Bayesian kriging (Krivoruchko, 2012; Pilz 

and Spock, 2007) produced an interpolated water-temperature 

surface for the continental U.S. by month based on both USGS 

and EIA data. Water-temperature inputs for plants located on 

rivers, lakes, and reservoirs were determined based on their 

location. An inverse-distance weighted average was not used 

for water temperature because the geographic distribution of 

water temperature sites was not uniform across the U.S. 

(fig. 12). Empirical Bayesian kriging was used because it 

accounts for the error introduced by the selection of an 

underlying model of variation (semivariogram), providing 

smaller standard errors of prediction than other kriging methods (Esri, 2012). Additionally, a subsetting process allows for 

accurate predictions of moderately nonstationary data (Esri, 

2012). Using tools included in the ArcGIS 10.1 Geostatistical 

Analyst Toolbar and Toolbox (Esri, 2012), observed temperature data were divided into local subsets. For each subset, a 

semivariogram model was estimated. Using this semivariogram, estimates of water temperature were simulated for each 

of the locations in the subset. The empirical Bayesian kriging 

tool iterated through this computation a specified number of 

times to produce a distribution of semivariograms for each 

subset. Local models were then derived from those distributions to produce a final predictive model (surface map) of 

average water temperature by month. 

Empirical Bayesian kriging did not work well for 

plants located on the Great Lakes. An initial trial using a 

kriged surface to determine water temperatures for the Great 

Lakes resulted in large differences from observed water 

temperatures. Therefore, remotely sensed water-temperature 

Figure 7. The approximately 2.5-mile-long cooling pond for the Entergy Texas, Lewis Creek Generating Plant, Texas. Water 

is withdrawn and returned to Lewis Creek Reservoir.
Data Compilation and Quality Assurance 13

information (GLSEA2) provided by the NOAA GLERL was 

used to determine water temperatures. Daily average water 

temperatures at the location of each plant on the Great Lakes 

were retrieved from satellite imagery and aggregated to a 

monthly mean.

Uncertainty Analysis

The uncertainty associated with the three input determination methods described above was assessed by comparison 

to a validation dataset of sites that were dropped from model 

development because of missing data. Using the same basic 

approach applied to power plants, dry bulb temperature, 

wet bulb temperature, and wind speed were estimated for 

each validation weather station and compared to observed 

record to determine estimation error. Based on that analysis, 

approximately 95 percent (two standard deviations) of dry 

bulb validation temperatures were within plus or minus 3.2 

degrees Celsius (°C) (5.8 degrees Fahrenheit or °F). Similarly, 

approximately 95 percent of wet bulb validation temperatures 

were within plus or minus 2.0°C (3.6°F). Wind speeds were 

typically within plus or minus 3.7 miles per hour (mph). The 

dry bulb/wet bulb validation dataset consisted of 598 weather 

stations that had fewer than 12 months of 2010 observations, 

which accounted for 4,551 monthly observations across the 

validation dataset. The wind speed validation set included 

424 weather stations missing 1 or more months of data, which 

accounted for 1,800 monthly observations across the validation dataset. 

Uncertainty associated with water-temperature estimates 

derived from kriged surfaces was assessed using leave-one-out 

Figure 8. The approximately 8-mile-long cooling-water reservoir for the Southwestern Electric Power, Lieberman Power 

Plant, Louisiana. Water is withdrawn and returned to Caddo Lake. In once-through cooling, water is withdrawn from a 

source, circulated through the heat exchangers, and then returned to a body of water at a higher temperature (Kenny and 

others, 2009). In once-through cooling, the heat is rejected to a body of water as latent and sensible heat (Stultz and Kitto, 

1992).
14 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

cross-validation within ArcGIS. Kriged water temperatures 

were within plus or minus 3.6°C (6.5°F) of those observed. 

Remotely sensed water temperatures were assessed for uncertainty by comparison to observed temperatures at plants on 

the Great Lakes. Approximately 95 percent of remotely sensed 

water temperatures were within plus or minus 6.3°C (11.3°F) 

of the observed temperatures. The greater uncertainty of 

remotely sensed data on the Great Lakes may result from the 

recirculation of heated discharge water in the vicinity of plant 

intakes. This effect may occur at such a small scale that it is 

not captured in satellite imagery. 

Supplemental Methods

For a few plants, environmental conditions could not 

be determined using the methods described above and the 

uncertainty in these estimates is difficult to evaluate. Based 

on the quality of nearby records, dry bulb or wet bulb temperatures could not be estimated at six plants, and because 

the kriged surface did not extend to Alaska and Hawaii, water 

temperatures could not be estimated for 19 plants. For the 

six plants at which the previously described methods did not 

yield estimates of dry bulb and wet bulb temperatures, the 

nearest weather stations that were missing 1 or more months 

of observations were used. Values for the the missing months 

at these stations were estimated by averaging the preceding 

and following monthly observations. Also, in a few instances, 

estimated wet bulb temperatures were higher than dry bulb 

temperatures so the two were set to equal dry bulb temperatures. For plants in Alaska and Hawaii, records from the nearest USGS water temperature stations were used to estimate 

water temperatures. 

Computing Heat and Water Budgets

Water consumption at most power plants was modeled by 

heat and water budgets constructed in three independent steps: 

(1) the organization of systems within the plant was analyzed, 

and the plant was disaggregated where possible into modeling 

units containing a single generation type or cooling type, 

(2) condenser duty was estimated for groups of boilers and 

generators, and (3) water consumption was estimated for 

groups of boilers, generators, and cooling systems, using 

estimated condenser duty as input. Water consumption at solar 

thermal power plants was calculated on the basis of consumption coefficients found in the literature, because reported plant 

data were insufficient for construction of heat budgets. 

To simplify computation of heat and water budgets, 

plants were disaggregated into groupings of boilers, generators, and cooling systems for which energy can be budgeted. 

For estimation of condenser duty, this required identifying 

groups of boilers and generators that are connected by the 

physical transfer of steam such that the fuel used by boilers 

in a particular boiler-generator group can be associated with 

the electricity generated by the generators in the same group. 

At nearly all plants, it was possible to construct boiler-generator groups with a single generation type (table 1). Because 

reported information on plant characteristics, the types and 

amounts of fuel consumed by a given plant, and electrical 

generation vary among generation types, separate heat-budget 

models were developed for each major generation type.

The water evaporated for cooling was estimated for each 

cooling-system group at each plant using a heat and water 

budget model with estimated condenser duty as input. A 

cooling-system group has one or more cooling systems and the 

associated boiler-generator groups that provide condenser duty 

to these cooling systems. Evaporation was modeled differently 

for cooling towers and surface-water cooling systems independently of generation type.

Disaggregation below the plant scale reduced the need to 

model mixed systems including multiple types of generation 

or cooling. Most thermoelectric water consumption was modeled as a single generation type transferring condenser duty to 

a single cooling type. 

Many plants contain only a single cooling-system group, 

and others did not provide sufficient information to distinguish 

more than one cooling-system group. Nearly 500 plants 

reported no cooling system or operations information and so 

were necessarily treated as plants with a single cooling-system 

group. Where cooling-system groups included multiple types 

of cooling, the condenser duty was allocated between the two 

cooling models using professional judgement.

Error analysis established the upper and lower limit 

values for evaporation associated with cooling. It was assumed 

that errors from different sources were independent. Error 

from some sources such as environmental variables could be 

quantified, but other sources such as the estimation of exhaust 

heat at combined-cycle power plants contribute unknown 

amounts of error. 

Figure 9. The distribution of cooling-water types for modeling 

evaporation for 1,284 thermoelectric plants. Complicated refers 

to multiple cooling types for a single plant. Tower refers to wet 

recirculating cooling tower.
Computing Heat and Water Budgets 15Figure 10. Geographic distribution of the 554 weather stations containing 12 months of monthly mean dry bulb and wet bulb temperatures in 2010.
16 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Figure 11. Geographic distribution of the 727 weather stations containing 12 months of monthly mean wind speed in 2010.
Computing Heat and Water Budgets 17Figure 12. Geographic distribution of the 755 water temperature stations (U.S. Geological Survey stations and greater than 100 megawatt power plants reporting intake temperature) with at least one month of monthly mean water temperature in 2010.
18 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Estimation of Condenser Duty by Generation 

Type

Generator-boiler groups were assigned to five generationtype categories (table 1) based on energy source and prime 

mover:

1.Combustion steam groups have steam turbines (prime 

mover type ST; Appendix 2, table 2) and burn a variety 

of fuels as discussed below.

2.Combined-cycle groups have either a combination of 

combustion turbines and steam turbines driven by heat 

from the combustion turbines’ exhaust, or a combinedcycle single-shaft turbine (prime mover types CT, CA, 

and CS, respectively). Nearly all of them burn primarily natural gas. 

3. Nuclear groups have steam turbines and a nuclear 

energy source (energy source type NUC; Appendix 2, 

table 3).

4. Geothermal groups have steam turbines and a geothermal energy source (energy source type GEO).

5. Solar thermal groups have steam turbines and a solar 

energy source (energy source type SUN). 

Condenser duty for boiler-generator groups of the first four 

types was estimated using heat budgets; condenser duty could 

not be estimated at solar-thermal plants. 

The heat budget of a boiler-generator group can be 

described by the following equation:

CD = TH − SL − NE − AL − XH (1) 

where 

CD = condenser duty; 

TH = total heat introduced into the plant, from 

fuel or other sources; 

SL = heat lost through the stack (exhaust); 

NE = net electrical generation; 

AL = heat lost to the air from plant equipment; 

and 

XH = heat exported from combined heat and 

power plants. 

All terms are normally in units of million British thermal units 

(MMBtu) in conformity with EIA reporting.

Although equation (1) is broadly applicable to thermoelectric plants in general, methods for calculating the terms 

in this equation differ among generation types, reflecting 

differences in physical characteristics and available information. Whereas reasonable values of TH are generally reported 

for combustion-steam and combined-cycle boiler-generator 

groups, reported values of TH for nuclear or geothermal 

boiler-generator groups are nominal, so TH had to be estimated for these groups. Values of SL were estimated by 

different methods for combustion-steam and combined-cycle 

boiler-generator groups, whereas SL was zero for nuclear and 

geothermal boiler-generator groups. Reported values of NE

were used for all types of boiler-generator groups.

AL was estimated at all plants by assuming that heat loss 

to the atmosphere is a constant 2 percent of total heat:

AL = TH *0.02 (2)

Although values of AL are reported to range from 1 percent to 

4 percent for individual plants (U.S. Department of Energy, 

National Energy Technology Laboratory, 1999, 2007a; International Atomic Energy Agency, 2009; International Energy 

Agency, 2008; Electric Power Research Institute, 2011; U.S. 

Department of Energy, Energy Information Administration, 

2013), there is not a strong basis for assigning different values 

of AL to individual boiler-generator groups or to different 

generation types. 

Combined heat and power plants report total fuel and 

electric fuel data. Total fuel is the actual fuel use, and the difference between total and electric fuel is the monthly amount 

of heat exported from the power plant to the associated heatusing process it supports. The exported heat was deducted 

from the condenser duty because this heat is dissipated by a 

second, non-thermoelectric process (fig. 13). 

At all plants with exported heat, the exported heat XH is 

defined by

XH = TH − EH (3) 

where TH is the total heat introduced into the plant and EH is 

the reported electric heat. 

At several power plants, this deduction resulted in a budget with the total heat leaving the plant exceeding the total fuel 

heat entering the plant. This discrepancy indicates either misreported data or an inappropriately constructed heat budget. 

Due to lack of plant-specific data that would allow construction of a more detailed and accurate model, it was assumed 

that such plants export all their waste heat to the associated 

heat-using process.

Combustion-Steam Generation

Plants categorized as combustion steam use a variety of 

fuels or, less commonly, waste heat from outside the plant 

to power steam turbines. Combustion steam plants transfer a 

substantial amount of heat to the atmosphere in the exhaust 

gases. Most of these plants report heat production by boiler 

and electric generation by generator, allowing construction 

of heat budgets for individual boiler-generator associations. 

Reported boiler efficiencies appear realistic at most plants, 

allowing estimation of exhaust-gas heat on a plant by plant 

basis (fig. 14). For combustion steam boiler-generator groups, 
Computing Heat and Water Budgets 19

the reported value of TH was used. Stack losses, SL, were 

estimated from reported or estimated boiler efficiency: 

SL = TH *(1− BE) (4)

where BE is the reported or estimated boiler efficiency at 100 

percent load. 

Boiler efficiency was censored to range between 75 

percent and 94 percent, based on the assumptions that (1) 

efficiencies above 94 percent are unlikely, because a minimum 

of about 6 percent of heat is lost up the stack (Sathyanathan, 

2010), and (2) that a reported efficiency less than 75 percent 

more likely represents overall thermal efficiency (net electric 

generation divided by fuel heat) rather than boiler efficiency 

(steam heat divided by fuel heat).

Of the 1,997 steam-turbine (ST) boilers at 809 plants in 

the study set, 1,511 reported a boiler efficiency at 100 percent 

load, and 1,489 of these were between 75 percent and 94 

percent. For each of these boilers, steam heat was estimated 

as total annual fuel heat times the reported boiler efficiency 

at 100 percent power. There were 1,413 boilers with reported 

efficiencies between 75 percent and 94 percent and non-zero 

fuel heat. At all of these, reported efficiency was used to calculate steam heat.

Of the various fuels that power steam turbines, 10 fuels 

were determined to be dominant: bituminous coal (BIT), black 

liquor (BLQ), distillate fuel oil (DFO), lignite (LIG), natural 

gas (NG), petroleum coke (PC), residual fuel oil (RFO), 

subbituminous coal (SUB), waste, other coal (WC), and 

wood, wood-waste solids (WDS); 1,311 boilers had at least 

75 percent of their fuel heat coming from one of these fuels. 

A weighted average boiler efficiency was calculated for each 

of these 10 fuels as the total estimated steam heat in the fuel 

category divided by the total reported fuel heat. This weighted 

efficiency was dominated by the boilers with the most fuel 

heat. Unweighted average efficiencies differ from weighted 

efficiencies for each fuel, but except for RFO these differences 

were less than 1 percent (table 3). 

There were 484 ST boilers with fuel heat but no valid 

reported boiler efficiency. At 316 of these boilers, the dominant fuel was one for which an average efficiency had been 

determined, and the boiler efficiency was assumed to be 

the average value for the dominant fuel. At 168 boilers that 

primarily burned other fuels—agriculture crop by-products/

straw energy crops (AB), blast-furnace gas (BFG), landfill gas 

(LFG), municipal solid waste-biogenic (MSB), other biomass 

solids (OBS), other gas (OG), and tires (TDF)—the weighted 

average for all 1,311 boilers was used (table 3). 

Figure 14. Sankey diagram for combustion steam plants.

Figure 13. Sankey diagram for an example combustion 

steam power plant with combined heat and power.
20 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States Table 3. Boiler efficiency, reported fuel heat, and estimated fuel heat by fuel type for combustion steam plants. [%, percent; MMBtu, million British thermal units, BIT, bituminous coal; BLQ, black liquor; DFO, distillate fuel oil; LIG, lignite; NG, natural gas; PC, petroleum coke; RFO, residual fuel oil; SUB, subbitu- minous coal; WC, waste, other coal; WDS, wood, wood-waste solids; NA, not applicable] Reported and estimated boiler efficiency by dominant fuel Weighted average boiler efficiency (%) Standard deviation (%) Difference of fuel-specific average from all- fuels average (%) Number of boilers with reported boiler efficiency for each dominant fuel type Total fuel heat for boilers used to estimate average (MMBtu) Number of boilers with estimated boiler efficiency and this dominant fuel Total fuel heat at boilers with this dominant fuel and estimated efficiency (MMBtu) Ratio of fuel heat with estimated boiler efficiency to total fuel heat for this fuel (%) BIT 89.0 1.7 1.3 560 9,061,680,006 86 124,456,829 1.4 BLQ 85.5 NA -2.1 1 7,631,743 0 0 0.0 DFO 85.6 0.8 -2.0 5 171,209 2 4,604 2.6 LIG 82.2 1.5 -5.4 20 576,523,031 1 3,980,593 0.7 NG 86.0 2.4 -1.6 306 905,289,900 87 28,059,638 3.0 PC 90.2 0.4 2.5 8 86,502,485 8 17,273,125 16.6 RFO 89.2 1.8 1.5 48 64,541,476 11 6,680,303 9.4 SUB 86.7 2.0 -1.0 357 7,859,995,116 23 60,398,686 0.8 WC 86.6 1.3 -1.0 5 67,469,755 19 91,097,729 57.5 WDS 85.2 NA -2.4 1 4,686,306 79 178,471,770 97.4 Other fuels not listed, estimated from “all fuels” average 87.6 2.4 0.0 0 0 168 282,058,716 97.1 All fuels 87.6 2.4 0.0 1,311 18,634,491,025 484 792,481,994 5.5
Computing Heat and Water Budgets 21

One plant (EIA plant code 50271, Appendix 1) did not 

report net electrical generation, but did report heat used for 

electrical generation. At this plant, the heat used for electrical 

generation was divided by the heat rate derived from the 

National Electric Energy Data System (NEEDS) v4.10 (U.S. 

Environmental Protection Agency, 2006; Appendix 3) to estimate net electrical generation.

Reported boiler efficiency at 100 percent load is assumed 

to be accurate, but it may be the nameplate efficiency under 

conditions specified by the boiler supplier, not the actual 

efficiency achieved in the boiler during operation in 2010. 

Reported differences between efficiency at 50 percent and 100 

percent load have a mean of near zero and a standard deviation 

of about 2 percent; most electricity is generated nearer to 100 

percent load than 50 percent load, but plants operated intermittently or at partial load have an error in boiler efficiency that 

may be on the scale of this standard deviation.

For boilers without realistic reported efficiencies, the estimated efficiencies that were used are subject to greater error. 

Fuel categories containing more than 20 boilers had standard 

deviations of efficiency from 1.7 percent to 2.4 percent, suggesting an imprecision of about plus or minus 5 percent in 

addition to imprecision in the reported efficiencies as surrogates for actual efficiencies. There were relatively few plants 

with this additional error, so it has little effect on condenser 

duty aggregated over fuel categories.

Combined-Cycle Generation

Combined-cycle plants include both combustion turbines 

and steam turbines working in tandem. Natural gas provided 

about 99 percent of total fuel heat at combined-cycle plants. 

About two-thirds of a combined-cycle plant’s electric output 

is generated in its combustion turbine(s). Exhaust heat from 

the combustion turbine(s) is used to boil water for the steam 

turbine and generates about one-third of the plant’s electric 

output. The efficiency of combined-cycle plants is generally 

high. The shares of fuel heat leaving the plant as both electricity and heat in the exhaust are higher than in other plants, so 

the share of fuel heat that becomes condenser duty is smaller. 

Fuel use and generation associated with the combustion turbines are reported at the plant level, so the heat budget must 

be constructed for the plant as a whole (fig. 15). At combinedcycle plants, reported values of TH were used. 

The reported boiler efficiencies for combined-cycle plants 

were not used in heat budgets. The boilers of prime mover 

type CA (Appendix 2, table 2) reporting boiler efficiency on 

the EIA 860 form are actually heat-recovery steam generators 

(HRSGs) using exhaust from combustion turbines to generate steam. Some have supplementary burners that add heat 

to this exhaust. Many combined-cycle steam boilers report 

efficiencies greater than 94 percent, whereas many others 

report efficiencies below 75 percent. Attempts to produce heat 

budgets based on these reported efficiencies resulted in contradictory results. In some cases, it was impossible to account for 

all of the fuel heat; in others, the model results in more heat 

leaving the system than the total fuel heat entering the system. 

Rather than use reported boiler efficiency to estimate SL, it 

was assumed that 20 percent of the fuel heat leaves combinedcycle plants in their exhaust gases:

SL = TH *0.2 (5)

This assumption is based on detailed heat budgets for 

three plants (U.S. Department of Energy, National Energy 

Technology Laboratory, 1999, 2007a) and is shared by 

Rutberg and others (2011). The three example plants are large, 

new plants with thermal efficiencies above 50 percent, at the 

upper end of the range for operating combined-cycle plants 

in 2010. Many existing plants are smaller, older, and less 

thermally efficient. Some plants were originally combustion 

turbines without heat recovery to which a HRSG and steam 

turbine have been retrofitted. The accuracy of the estimated 

20-percent stack loss across the full range of combined-cycle 

plants has not been established. 

As at other plants, the amount of heat leaving the plant 

by conduction to the air, most of it representing losses from 

fans and pumps, was assumed to be 2 percent of fuel heat. 

Uncertainty in this number has a larger effect on condenser 

duty at combined-cycle plants than at other plants because 

condenser duty is a smaller percentage of fuel heat at combined-cycle plants than at other plants.

Nuclear Generation

In nuclear power plants, no heat leaves the plant in 

exhaust gases, and more heat leaves the plant as condenser 

duty than in combustion steam plants. The total heat values 

reported by EIA were not used, because the data are evidently 

estimated from net generation using a nominal heat rate of 

10,460 Btu per kilowatt hour, corresponding to a nominal thermal efficiency of 32.6 percent at all plants. The actual reactor 

heat at each plant was estimated by assuming that the plant 

operator keeps the reactor close to its maximum permitted 

thermal power. This assumption results in a unique estimated 

thermal efficiency at each nuclear boiler-generator group 

(fig. 16). 

It was assumed that the permitted thermal reactor outputs 

listed by the U.S. Nuclear Regulatory Commission 

(U.S. Nuclear Regulatory Commission, 2009, 2010, 2011) are 

used as operational targets by operators, and that the operators 

generally stay close to those limits (Joshua Trembley, Exelon, 

oral commun., 2012; James Riley, Nuclear Energy Institute, 

oral commun., 2012). This assumption produces the lowest 

reasonable thermal efficiency. Exceedances of maximum permitted thermal reactor power were assumed not to occur.

Monthly average power was calculated as reported net 

generation divided by the number of hours in each month. 

The ratio of monthly average power divided by the permitted 

maximum thermal output at each unit was calculated, and 

it was assumed that the maximum value of this ratio is the 
22 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

peak thermal efficiency for the unit. These maxima occurred 

in November, December, January, or February at nearly all 

units. Using the assumptions described above, mean thermal 

efficiency for nuclear power plants was estimated to be 32.9 

percent, and thermal efficiency at individual plants ranged 

from 29.4 percent to 35.4 percent.

The monthly average power tended to decrease systematically in the summer, generally reflecting the difference 

between reported winter and summer maximum capacity. It 

was assumed that this decrease was caused by operators reducing reactor power during the summer to accommodate reduced 

cooling system capacity. Following this assumption, total 

reactor heat in each month was estimated by net generation 

divided by the peak efficiency. Estimated condenser duties 

ranged from 63 to 69 percent of total reactor heat and 176 to 

233 percent of net generation. 

An alternative assumption would be that thermal 

efficiency decreases in summer while thermal power remains 

near the permitted maximum. This approach would yield an 

estimated summer decline in thermal efficiency that is specific 

to each plant and based on a reported value. This decline was 

in the range of 0 percent to 6.5 percent from winter to summer, with a median of 2.5 percent. Using this approach would 

increase annual condenser duty (and water consumption) by a 

maximum of 3 percent and a median of 2 percent at individual 

nuclear units. Monthly values of condenser duty would remain 

about the same in winter, but would increase by a maximum 

of 10 percent and a median of 3 percent in the summer. Given 

the lack of specific information about reactor operations and 

the small effect of this more detailed approach at most plants, 

the simpler assumption of constant thermal efficiency was 

retained. At nuclear boiler-generator groups, total heat was 

estimated on the basis of the estimated thermal efficiency: 

TH = NE / TE (6) 

where TE is the thermal efficiency estimated, as discussed 

above. 

Geothermal Generation

Geothermal plants are analytically similar to nuclear 

plants although they use different heat sources (fig. 17). Their 

heat and generation are reported by fuel type at the plant level. 

Total heat reported by the EIA is nominal, evidently based on 

an assumed 35-percent thermal efficiency. In NEEDS v.4.10 

(U.S. Environmental Protection Agency, 2006; Appendix 3), 

the thermal efficiency associated with geothermal plants is 

about 16 percent, lower and more realistic than the 35 percent 

used by EIA. Although actual efficiencies realized at geothermal plants are generally lower and depend on the temperature 

of the geothermal resource (DiPippo, 2004; Dagdas and others, 2005; Golub and others, 2006; Franco and Villani, 2009; 

Figure 16. Sankey diagram for nuclear plants.

Figure 15. Sankey diagram for combined cycle plants.
Computing Heat and Water Budgets 23

Drader and others, 2012), total heat at geothermal plants was 

estimated as net generation divided by the NEEDS thermal 

efficiency: 

TH = NE / 0.16 (7) 

where 0.16 is the estimated thermal efficiency for this type of 

plant and NE is net electrical generation. 

The methods used in this study for geothermal power 

plants are approximate and insensitive to the characteristics 

of individual plants; therefore, estimates of condenser duty 

are imprecise, but are believed to be less biased than if the 

reported total-heat values had been used. As geothermal plants 

are few and generally small, this imprecision has little impact 

on regional sums of consumption. Geothermal electric water 

consumption is about 2 percent of all U.S. thermoelectric 

water consumption.

For geothermal plants, published water-consumption 

coefficients vary over a range of more than three orders of 

magnitude (Ashwood and Bharathan, 2011; Clark and others, 

2010; Dennen and others, 2007; Kagel and others, 2005; 

Larson and others, 2007; Mishra and others, 2011), and 

“definitional noise” (Dziegielewski and Kiefer, 2010) makes 

it difficult to generalize typical values. As an experiment, 

published values of water use were substituted at the plants 

they were derived from and at plants with similar cooling 

technology. These substitutions changed water consumption at 

plants representing 75 percent of the geothermal generation, 

reducing consumption to zero at some plants and increasing 

it by a factor of 6 at others. The net effect on water use by the 

geothermal electric sector was to increase it by 35 percent. 

This approach did not appear to be substantially better than 

using the NEEDS heat rate as described above, and was not 

used to produce the final estimates.

Estimating Thermoelectric Evaporation by 

Cooling-System Type

Heat and water budgets provide a transparent means 

to constrain estimates of evaporation from thermoelectric 

plants within thermodynamically realistic values. The heat 

and water budgets presented in this report require estimates 

of monthly condenser duty and information about cooling 

system characteristics and environmental variables as input. 

Monthly estimates of condenser duty are obtained using the 

method described above, based on a given plant’s generation 

type. Simple heat and water budget models were developed 

for the two cooling-system types that use substantial amounts 

of freshwater: (1) wet cooling towers, and (2) surface-water 

cooling systems. Both cooling system models require monthly 

averages of dry bulb air temperature, wet bulb air temperature, ambient surface-water temperature, and wind speed. For 

surface-water cooling systems, the parameters used to calculate one variable in the model, the wind function, are given 

different values for ponds, lakes, and rivers.

Most thermoelectric plants use evaporative cooling towers in which more than 60 percent (Solley and others, 1998) 

of the rejected heat typically leaves the tower as latent heat in 

evaporated water and the rest as sensible heat that increases 

the temperature of the air passing through the cooling tower. 

Evaporation in wet towers depends on tower design, condenser duty, and wet bulb and dry bulb air temperatures. 

A single heat and water budget model was developed for 

all surface-water cooling systems, with somewhat different 

input data for recirculating ponds and once-through systems. 

The basic model for surface-water cooling systems takes as 

input water-surface area and monthly estimates of condenser 

duty, ambient water temperature, and wind speed. For recirculating ponds, water-surface area is equal to the surface area 

of the pond; for once-through systems, the water-surface area 

nominally represents the surface area of the plume created by 

the return flow of heated water that has passed through the 

condenser. Plants with once-through cooling (including ponds 

and canals without recirculation) typically report low water 

consumption within the plant, but evaporation from the plume 

represents consumption outside the plant boundaries that can 

account for more than half of the condenser duty (Ward, 1980; 

Huston, 1975). With once-through cooling, the receiving water 

body is generally not as hot as a typical cooling pond, so the 

percentage of heat that drives evaporation is lower.

Figure 17. Sankey diagram for geothermal plants.
24 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Many plants have both towers and some form of watersurface cooling. These cooling-system types have different 

consumption rates relative to generation, so allocation of the 

available condenser duty among the available cooling-system 

types influences estimated consumption. At plants where 

the disparate cooling systems were connected to different 

boiler-generator groups, or where single-type cooling systems 

reported operations separately, distributing excess heat among 

different cooling-system types was straightforward.

At plants reporting one cooling system that used multiple 

types of cooling, plants with at least some boilers connected 

to multiple cooling systems of different types, or plants not 

reporting cooling operations, the allocation of condenser duty 

was estimated using professional judgment based on known 

plant characteristics. Estimated consumption at all such plants 

represented 17 percent of total estimated thermoelectric water 

consumption. These estimates were bracketed by estimates 

of maximum and minimum plausible thermoelectric water 

consumption at the plant scale. The maximum plausible 

consumption was estimated allocating all condenser duty to 

the cooling-system type with the most consumption (typically 

towers), and the minimum was estimated by allocating all condenser duty to the type with the least consumption (typically 

once-through systems). 

Estimating Evaporation from Cooling Towers

The method described by Leung and Moore (1970, 1971) 

was used as the primary method for estimating evaporation 

from cooling towers. This method uses a heat balance through 

the tower, with the key assumption that the air leaving the 

tower is saturated with water vapor. Estimates made using 

the Leung and Moore method are likely accurate at baseload 

plants within plus or minus 5–15 percent (Strauss, 1978; Hu 

and others, 1978, 1981). Sensitivity testing confirmed that the 

model results are sensitive to wet bulb and, to a lesser extent, 

dry bulb air temperature and to plant elevation and design 

characteristics, but not to the temperature of water added 

to the tower to replace evaporated water (makeup water). A 

single model was used for all wet towers because the reported 

information on tower characteristics and operations did not 

support finer distinctions or justify using multiple models. To 

allow for the range of performance, a suite of tower characteristics was modeled and the maximum, minimum, and median 

evaporation for this suite were reported. 

About two-thirds of thermoelectric plants in the plant 

Master List use wet recirculating cooling towers, and since 

1980, nearly all new power plants have used wet towers. In 

wet cooling towers, hot water coming from the condenser 

moves slowly downward through a volume of fill, while air 

flows through the same volume. The fill takes many forms, 

but always is a structure of stationary elements that slow the 

descent of water and disperse it to maximize water-surface 

area, while minimizing resistance to air flow. The cooling 

water accumulates in a basin under the fill, from where it is 

pumped back to the condenser.

The air may be moved mechanically by fans at the air 

inlet (forced draft) or outlet (induced draft) or by convection 

(natural draft) (United Nations Environment Programme, 

2006). These three cooling tower types encompass a range 

of consumption rates. Natural-draft (convection draft) towers 

have a wide variability in the mass ratio of water flow to dry 

air flow (L/G), whereas mechanical draft towers have near 

constant L/G. 

The temperature to which the tower can cool the warm 

water from the condenser is limited by the wet bulb temperature of the ambient air. In a tower receiving warmed water, the 

water in the basin under it is warmer than the wet bulb temperature by an amount called the approach (Cheremisinoff and 

Cheremisinoff, 1981). The difference in temperature between 

the hot water entering the tower and the cooler water in the 

basin is called the range. The L/G ratio has a strong influence 

on these temperature characteristics. A dimensionless ratio 

called the Merkel tower characteristic describes the heat transfer process and is proportional to C*(L/G)n

, where C and n are 

empirical constants that depend on tower design. 

The approach, the range, and the three terms that define 

the Merkel tower characteristic influence the results of the 

Leung and Moore model, but values of these variables are not 

reported to the EIA. Rather than select a single “typical” set 

of values for these variables, 34 combinations of values were 

modeled; some of these combinations of values may not correspond to real towers. Tower designs were chosen to cover an 

approach from 5 to 15°F, a range of 10 to 25°F (Cheremisinoff 

and Cheremisinoff, 1981), an L/G of 1 to 2, a coefficient “C” 

of 1.6 to 2.5, and an exponent “n” of -0.6 to -0.8. 

The Merkel tower characteristic was determined for each 

example tower design and was constrained between about 1 

and 2.5. Evaporation ratios under design conditions ranged 

from 87 percent to 99 percent, with a median of 88 percent, 

and were generally higher for towers with low approach, low 

range, and low L/G.

The basic equation for the method is as follows: 

2 1

2 1

p

mu

a a

mu

Q L

H H h

 

=  −    − −  

(8)

where 

Lmu = the mass of the water evaporated;

Qp = the net heat rejected in the cooling tower;

Ha1 = the specific heat of inflow air;

Ha2 = the specific heat of outflow air;

	 ω1 = the specific vapor content of inflow air;

	 ω2 = the specific vapor content of outflow air; 

and

hmu = the specific heat of the makeup water.

The quantity 2 1

2 1

a a

mu

H H h

 

 −    − −  

 is the net heat added per 

mass of water evaporated.
Computing Heat and Water Budgets 25

As both Ha2 and ω2 are unknown, it is necessary to assume that 

the air leaving the cooling tower is saturated with water vapor. 

This allows Ha2 to be defined in terms of the other variables, 

and the equation is solved iteratively to estimate Lmu.

For some plants, winter operations present a special 

case requiring adjustment of the method. When the wet bulb 

temperature is at or below freezing, cooling tower operations 

are changed to prevent ice formation in the fill, usually by 

decreasing air flow or concentrating hot water flow in one area 

of the fill. The effect of these changes is to keep the coldest 

water temperature in the fill at 40°F (4.4°C) or above; published recommendations for minimum water temperature in 

the basin below the tower range from 40°F (4.4°C) to 50°F 

(10°C) (Cheremisinoff and Cheremisinoff, 1981; The Cooling 

Tower Company, L.C., 2005; Cooling Technology Institute, 

2010; Evapco, 2010; Marley, 2012). The best way to simulate 

winter operations within the simple structure of the Leung 

and Moore model was to artificially limit both wet bulb and 

dry bulb temperatures to a minimum of 35°F (1.7°C). The 

choice of 35°F (1.7°C) was conservative in the sense that a 

lower temperature could have been justified (Cheremisinoff 

and Cheremisinoff, 1981) and would have produced lower 

winter evaporation. Without this artificial minimum temperature, evaporation ratios become unrealistically low. This rough 

approximation adds uncertainty to the estimates, but winter 

operational changes vary among tower types and individual 

operators, so adding complexity to the model is unjustified.

The model was run for the suite of tower designs at 

many pairs of dry bulb and wet bulb temperatures spanning 

the range of conditions encountered at U.S. power plants. 

The estimated median, minimum, and maximum evaporation 

ratios changed smoothly over this temperature field, and linear 

interpolation was used to estimate nominal median, minimum, 

and maximum evaporation ratios at sea level, based on 

monthly average dry bulb and wet bulb temperatures for 2010 

at U.S. power plants with wet towers. For each combination 

of dry bulb and wet bulb temperatures, the rates of decrease in 

median, minimum, and maximum evaporation ratios with the 

elevation of the plant were found to be approximately linear. 

These linear rates of decrease were estimated by interpolation 

based on dry bulb and wet bulb air temperatures for each 

month and were used in combination with the plant elevation 

to adjust the median, minimum, and maximum evaporation 

ratio for each month.

The method of Rutberg and others (2011) was used to 

generate an alternative estimate of the maximum evaporation 

ratio at each plant location. Rutberg and others (2011) raised 

the issue that the air leaving the cooling tower may be 

unsaturated during hot weather and supersaturated during 

cold weather, leading the Leung and Moore method to 

underestimate winter evaporation and overestimate summer 

evaporation. They developed a simplified method for estimating cooling-tower evaporation based solely on dry bulb air 

temperature: 

3 2 0 000279 0 00109

1

0 345 26 7 100

( . .

. . ) /

a a

a

T T

ER

T

 − +  = −   − +  

(9)

where ER is the ratio of the heat used to evaporate water to the 

total heat discharged through the tower, and Ta is the monthly 

average dry bulb air temperature in °C.

This equation produces estimates of evaporation that are 

higher than the highest estimates made with the Leung and 

Moore method in cool months. This equation was used as a 

supplemental method for estimating the upper limiting value 

of ER when its prediction exceeded the maximum Leung and 

Moore estimate. With the Rutberg and others estimate incorporated as an alternate maximum, the range between minimum 

and maximum estimated values of evaporation ratio exceeded 

the plus or minus 15 percent cited by Hu and others (1981) as 

the accuracy of the Leung and Moore method. More detailed 

modeling based on tower characteristics that are not reported 

to EIA might yield a different range of actual evaporation 

ratios.

The product of the estimated evaporation ratio and the 

estimated condenser duty is the estimated heat of evaporation for a given month. This is converted to a weight of water 

based on the latent heat of vaporization, then to a volume 

per month. The product of the maximum evaporation ratio 

and the maximum condenser duty gives the maximum likely 

evaporation, and the product of the minimum evaporation ratio 

and the minimum condenser duty gives the minimum likely 

evaporation. 

Estimating Forced Evaporation from Surface 

Water 

A simple heat balance model was used to estimate the 

evaporation ratio for surface-water cooling systems. This 

model, first developed by Harbeck (1964) for cooling ponds, 

was improved by Ward (1980). The model uses monthly data 

that can be estimated at most power plants: average natural 

water temperature, average wind speed, and the water-surface 

area over which heat is dissipated. This model is similar to the 

one presented by Diehl (2011). 

Surface-water cooling systems draw water from lakes, 

rivers, and recirculating and once-through ponds. For the heat 

balance model, these water bodies were classified for analysis 

as lakes, rivers, and ponds, with many plants that reported lake 

to EIA classified in this analysis as pond due to high heat loading. The same model was used for all three types of water bodies, with each type using different parameters in the function 

relating wind to mass transfer away from the water surface. 

Heat loading is estimated as the ratio of condenser duty to 

the area over which heat is dissipated. This area was estimated 

to be the entire area of ponds or small lakes, where this ratio 

produced heat-loading estimates exceeding 0.35 MW per acre 

(about 0.002 calories per square centimeter per second 

[(cal/(cm2/s)]). For lakes that were larger relative to condenser 

duty, a heat loading of 0.1 MW per acre (about 0.0006 
26 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

[cal/(cm2/s)]) was assumed; forced evaporation is not sensitive 

to small differences in heat loading. For rivers, a heat loading 

of 0.2 MW per acre (about 0.001 [cal/(cm2/s)]) was assumed. 

This model does not estimate the non-forced evaporation 

from recirculating cooling ponds or reservoirs that would take 

place in the absence of added heat from the power plant’s condenser; only the forced evaporation is modeled. Consumption 

is sometimes defined as including water withdrawn that is no 

longer available to be returned to a water source or all cooling 

water lost to evaporation (U.S. Government Accountability 

Office, 2009), implicitly including unforced evaporation from 

recirculating cooling ponds. Under such a definition, water 

consumption at plants with recirculating cooling ponds would 

be larger than the forced evaporation, with greater increases 

for ponds and reservoirs with low added heat per area, and in 

hot, dry regions. 

Equations for heat loss were solved for both the natural 

and heated water temperatures, with the estimated heatedwater temperature adjusted iteratively until the difference in 

heat loss at the two temperatures was equal to the added heat 

from the power plant. Monthly average values were used for 

environmental variables and monthly estimates of the percent 

of condenser duty that drives evaporation (evaporation ratio) 

were produced. In the following equations, the units used by 

Ward (1980) are preserved to facilitate comparison to his and 

Harbeck’s (1964) publications. 

The method used in this study for estimating forced 

evaporation is based on that of Ward (1980), with a few key 

revisions:

1. A heat loading (condenser duty per area) is estimated 

or measured, as discussed above.

2. A natural water temperature is estimated based on 

available water-temperature data. 

3.The relevant heat balance equations are solved iteratively to estimate a heated water temperature. 

4.The percent forced evaporation is given by the ratio of 

the difference in evaporation at the two water temperatures to the sum of differences in evaporation, conduction, and radiation at the two water temperatures. 

The total heat loss from a water surface is the sum of heat 

loss through evaporation, conduction, and radiation expressed 

in terms of energy flux per unit area:

H(T ) = E(T ) +C(T ) + R(T ) (10)

where H(T) is heat loss from the water surface, E(T) is heat 

loss through evaporation, C(T) is conduction, and R(T) is 

radiation, all in [cal/(cm2/s)], and T is water temperature in °C.

The difference in heat loss between the natural water 

temperature (T) and heated water temperature (T′), equal to 

the heat loading, is given by the sum of differences in evaporation, conduction, and radiation at these two temperatures:

[ ] [ ]

[ ] [ ]

( ) ( ) ( ) ( )

( ) ( ) ( ) ( )

H T H T E T E T

C T C T R T R T

′ − = ′ − +

′ − + ′ − (11)

or:

∆H = ∆E + ∆C + ∆R (12)

Evaporation is given by:

E(T ) = Lf (W )[e(T ) − ea ] (13)

where ρ is water density in grams per cubic centimeter, L is 

the latent heat of vaporization in calories per gram, e(T) is the 

saturation vapor pressure in millibars at water-surface temperature T, ea

 is the vapor pressure of the overlying atmosphere in 

millibars, and f(W) is the wind function, for example the wind 

function of Ward (1980):

8 f (W ) 7.0*10 (W ) − = (14)

where W is wind speed in miles per hour. The values of ρ and 

L change little from T to T′; the effect of this change on forced 

evaporation can be ignored (Ward, 1980). The difference in 

evaporation from T to T′ is given by:

∆E = Lf (W )[e(T ) − e(T ′)] (15)

Conduction is given by:

( ) ( ) ( ) p

a

pc

C T f W T T 



  =   −

 

(16)

where p is atmospheric pressure in millibars, cp is the specific 

heat of air at a constant pressure, 0.24 calories per gram per 

degrees Kelvin, ε is the molecular weight ratio of water vapor 

to dry air, and Ta is air temperature in degrees Celsius. The difference in conduction from T to T′ is given by:

( ) ( ) p pc

C f W T T 



  ∆ =   ′−

 

(17)

Radiation is given by:

( )

4

( ) 273 R T = r T + (18)

where σ is the Stefan-Boltzman constant (1.17*10-7 calories 

per square centimeter per degrees Kelvin to the fourth power 

per day) and εr is the emissivity of the water surface, 0.97. The 

difference in radiation from T to T′ is given by:

( ) ( ) 4 4

( ) 273.15 273.15 R T = r  T′+ − T +    (19)
Computing Heat and Water Budgets 27

The difference in the heat loss at the two temperatures 

ΔH is set equal to the condenser duty by iteratively adjusting 

the heated water temperature (T′). The ratio of forced evaporation to condenser duty is given by:

ER = ∆E / ∆H (20)

Ward (1980) demonstrated that additional heat losses 

through evaporation, conduction, and radiation are approximately linear functions of an imposed increase in water 

temperature, and based on this approximation, the ratio of 

increased evaporation to the total increase in heat loss is a 

function of only water temperature and wind speed (fig. 18). If 

the imposed heat load is distributed over an assumed area, the 

heated temperature can be solved for iteratively, and the share 

of evaporation in the increased heat dissipation can be calculated directly. This solution is insensitive to air temperature, 

humidity, and the variation in water density and vapor pressure 

between the two temperatures. 

The wind function is a coefficient of vertical mixing, 

and is used to calibrate heat budget models to measured 

evaporation data. A wide variety of wind functions have been 

determined for a variety of settings, including cooling ponds, 

natural lakes and ponds, rivers, and irrigation canals. Even 

for a given setting, experimentally determined coefficients 

vary widely. The choice of wind function strongly influences 

estimated water consumption (fig. 19). 

Estimates of evaporation were based on the wind function 

of Brady and others (1969) for ponds, Webster and Sherman 

(1995) for lakes, and Gulliver and Stefan (1986) for rivers. 

The Brady and others (1969) wind function was developed 

for cooling ponds. The Anderson (1954), Harbeck (1964), 

and Ward (1980) wind functions are also derived from cooling ponds but are inappropriate for low wind speeds; Ward’s 

formula is equation 3 in Diehl (2011), expressed here in different units. The parameter values of Fulford and Sturm (1984) 

and Gulliver and Stefan (1986) were derived from flowing 

water. Webster and Sherman (1995) studied lakes without 

added heat. Other wind functions are discussed in McJannet 

and others (2012), Majewski and Miller (1979), and Edinger 

and others (1974). 

The values of some wind functions, for example those of 

Anderson (1954), Harbeck (1964), and Ward (1980) go to zero 

at a wind speed of zero. In practice, convective vertical mixing 

takes place in the absence of wind due to density differences 

between the water-saturated air in the surface film over the 

warm cooling pond and the overlying air. Wind functions that 

reach zero were not used to estimate water consumption.

The Forced Evaporation from Water Surface (FEWS) 

spreadsheet (Appendix 4, available online in an Excel file at 

http://pubs.usgs.gov/sir/2013/5188/appendix/sir2013-5188_

appendix4_fews_version_3.104.xlsx), implements the heat balance model to estimate forced evaporation from water surfaces 

driven by heat from thermoelectric plants. The user enters 

power plant identification numbers, elevations, pond or lake 

area for lake and pond cooling systems, and mean monthly 

values of condenser duty, water temperature, and wind speed. 

Because of the sensitivity of output to the wind function, and 

the unsettled status of the wind function in published literature, selection of the appropriate wind function is left to the 

user. 

The FEWS spreadsheet includes wind functions derived 

for rivers and lakes and reviewed by McJannet and others 

(2012), and the wind function of Brady and others (1969) 

developed for dedicated cooling lakes. The parameter values 

of Fulford and Sturm (1984) and Gulliver and Stefan (1986) 

were derived from flowing water. Webster and Sherman 

(1995) studied lakes without added heat. Other wind functions 

Figure 19. Evaporation ratio in relation to wind speed at 20 

degrees Celsius for four selected wind functions.

Figure 18. Evaporation ratio in relation to wind speed, estimated 

using Brady and others (1969) wind function.
28 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

reviewed by McJannet and others (2012), Majewski and 

Miller (1979), and Edinger and others (1974) should be 

checked against the original publications before being used.

Natural water temperatures in the spreadsheet were 

derived from measured river temperature upstream from the 

plant, or in nearby lakes and streams, but in principle, a natural 

water temperature could be derived from air temperature and 

wet bulb temperature. The spreadsheet solves the relevant 

heat-loss equations iteratively to estimate a heated water temperature that matches the heat loading, and produces monthly 

and annual estimates of forced evaporation.

The method implemented in this spreadsheet has four 

major sources of uncertainty: natural water temperature, heat 

loading, wind function, and error intrinsic to the model. An 

error in the estimated natural water temperature of 1 produces 

a corresponding error in estimated heated water temperature, 

and an error of about 1 percent in evaporation ratio (ER) 

(fig. 18). Error in estimated heat loading produces errors in the 

estimated ER that are roughly proportional to heat loading; in 

cold water with a low heat loading, an error of 0.1 MW/acre in 

heat loading produces an error in ER of about 1 percent 

(fig. 20). The range in ER between the largest and smallest 

wind function values ranges from about 3 percent for high 

wind speed to over 35 percent for wind speed of 1 mph; however, 90 percent of mean monthly wind speeds are greater than 

4.5 mph, at which speed the range in estimated ER is about 12 

percent between the smallest and largest wind function values. 

Finally, the equations for heat balance give a calculated ER

within at most plus or minus 10 percent to 15 percent error 

from real heat loss (David I. Stannard, U.S. Geological 

Survey, written commun., 2012). Forced evaporation from 

dedicated cooling ponds can be measured using water budgets, 

and could be used to estimate the accuracy of the method.

Error Analysis and the Prediction of Maximum 

and Minimum Likely Consumption

Error analysis took place in two stages—first calculating 

ranges of estimation error for condenser duty and evaporation 

ratio, and then combining these error terms by multiplication. 

Errors in parameter values such as boiler efficiency, auxiliary 

heat loss, and thermal efficiency, were assumed to be independent and normally distributed for the purposes of calculating 

an overall range of error. Many parameters were defined in 

the published literature as ranges or values of plus or minus 

some number. These ranges were assumed to represent the 95 

percent of values within two standard deviations of a mean. 

Skewed distributions were ignored in analyzing error. Most 

parameter values were not defined by statistical analysis of 

measured values. Errors were not calculated for the two small 

generation-type categories of geothermal and solar thermoelectric. Water consumption for these generation types was 

estimated by simplified methods subject to undefined errors.

The maximum and minimum values of condenser duty 

were estimated to be the estimated value plus or minus 10 

percent. For combustion steam units that reported a reasonable boiler efficiency, analysis of combined sources of error 

pointed to actual errors of 5 to 6 percent; 10 percent is considered to be conservative. For the 5 percent of combustion steam 

capacity with estimated boiler efficiency, the actual error is 7 

percent to 10 percent. 

Nuclear plants, despite the error introduced by estimating thermal efficiency, have low estimated error in condenser 

duty relative to other generation types. Calculated error is 

plus or minus 5 percent, but when error in estimated thermal 

efficiency is large, error in estimated condenser duty could 

approach 10 percent.

For combined-cycle plants, the critical assumption that 

heat loss in the exhaust gas is 20 percent is subject to an 

unknown degree of error. If the true range is 17 percent to 23 

percent, then the error in condenser duty is close to 10 percent. 

If the stack losses are actually 15 percent to 25 percent, the 

error in condenser duty could be 15 percent or higher. This is 

another parameter for which errors seem likely to be greater 

in one direction than in the other. Since large, new plants 

have 20-percent stack loss, lower values for smaller and older 

combined-cycle plants seem unlikely, whereas higher values 

of stack losses are entirely plausible.

Despite the differences in calculated errors, the value of 

plus or minus 10 percent was chosen for the sake of simplicity 

in order to present a single value of uncertainty that captures 

typical conditions rather than produce tailored estimates for 

each situation. The level of uncertainty in the uncertainty 

analysis itself weighs against false precision in selecting a 

reasonable range.

The error in tower evaporation ratio was assumed to be 

15 percent in addition to the full range of modeled values 

for the suite of 34 sets of tower-variable values. Thus, the 

maximum consumption was the maximum of the suite of 

Figure 20. Effect of pond heat loading on evaporation ratio. towers plus 15 percent, and the minimum consumption was 
Conclusions 29

the minimum of the suite of towers minus 15 percent. When 

the equation of Rutberg and others (2011) yielded a higher ER

than the maximum calculated from the suite of towers, it was 

substituted as the maximum ER value. 

For evaporation ratio from water surfaces, an error of 18 

percent was selected. The intrinsic error in the method of 15 

percent is increased only slightly by input data uncertainty. 

The 18-percent error range is conservative for most plants, 

except for those with once-through cooling systems on the 

Great Lakes, where added uncertainty in the natural temperature might make plus or minus 21 percent a more appropriate 

value. 

Multiplication of the estimated condenser duty and the 

estimated ER, including errors, resulted in a range in predicted 

water consumption of plus or minus 22 percent from the best 

estimate at plants with surface-water cooling. At plants with 

towers, the range in predicted water consumption is the range 

of results from the suite of towers plus or minus 18 percent. 

Other Types of Water Consumption

Several modes of water consumption at thermoelectric 

plants were not amenable to modeling with heat and water 

budgets. These include evaporation from wet cooling towers 

at solar-thermal plants, flue-gas desulfurization at combustionsteam plants, and inlet cooling and nitrogen-oxides (NOx) 

control at combined-cycle plants.

Solar-Thermal Generation

Solar thermoelectric plants were not modeled with heat 

and water budgets because sufficient data are not available. 

Their fuel heat and generation are reported by fuel type at 

the plant level. However, the EIA-reported total heat use is 

nominal, based on an assumed 35-percent thermal efficiency, 

and no data on the amount of heat entering the steam turbine 

are available.

Parabolic-trough solar plants appear to consume 900 to 

1,000 gallons per megawatt hour of net electric generation 

(gal/MWhe), given that wash water and cooling-tower blowdown are evaporated (Cohen and others, 1999; Kelly, 2006). 

Solar-power towers have consumption comparable to (Dahle, 

2008) or substantially less than (U.S. Department of Energy, 

2009) parabolic-trough plants. All solar thermoelectric plants 

in the study set had wet cooling towers, like the example 

plants in the studies cited.

An arbitrary consumption coefficient of 900 gal/MWhe 

was assigned to all solar thermoelectric plants. Solar thermoelectric plants are even smaller than geothermal plants 

and there are fewer of them, so use of this estimation method 

has little effect on regionally aggregated water consumption. 

Estimated water consumption at solar-thermal plants was 

about 0.05 percent of total thermoelectric water consumption.

Flue-Gas Desulfurization

Flue-gas desulfurization at coal- and waste-burning 

plants consumes water by incorporating the water into sulfurbearing minerals and by contributing water vapor to the stack 

gases. Based on average differences between water consumption rates with no FGD, dry FGD, and wet FGD presented in 

Appendix D of U.S. Department of Energy, National Energy 

Technology Laboratory (2010), FGD water consumption was 

estimated as 64 gal/MWhe for wet FGD and 40 gal/MWhe for 

dry FGD. For boilers burning some fuel that does not contain 

sulfur (for example, natural gas), the consumption was multiplied by the ratio of heat from sulfurous fuels to total fuel heat. 

The water consumed by FGD was about 5 percent of total 

thermoelectric water consumption.

Minor Water Consumption at Combined-Cycle 

Plants

Minor types of water consumption at combined-cycle 

plants include inlet cooling and NOx control in the combustion turbine part of the plant (Texas Water Development 

Board, 2003; Maulbetsch and DiFilippo, 2006). Inlet cooling 

can consume about 20 gal/MWhe and NOx reduction can consume about 50 gal/MWhe. No publicly available data indicate 

whether either of these types of consumption takes place at 

each combustion turbine. Exclusion of these types of consumption introduces an unknown downward bias into water 

consumption estimates at some combined-cycle plants. Also, 

some combustion turbines that are not part of combined-cycle 

plants consume water, perhaps as much as 100 gal/MWhe 

(California Energy Commission, 2005), but are not included in 

the study set for this report.

Conclusions

The use of heat and water budgets to estimate water consumption at individual thermoelectric plants provides a useful 

check on other estimation approaches, and in many cases may 

be the most accurate method available. Constraining estimated 

evaporation at thermoelectric plants based on thermodynamics improves estimates of water consumption at plants where 

direct measurements of water use are absent or unreliable. 

Budgets based on heat and electric data provide an independent validation of consumption where it is measured. On a 

regional or national scale, budget-based consumption estimates could be used to guide policy discussions. For example, 

these estimates respond realistically to environmental change, 

increasing with temperature and wind speed. 

Estimates of condenser duty vary in precision depending 

on what data are reported, and in what form, by the various 

types of thermoelectric plants. These estimates could be 

improved by more detailed reporting of boiler efficiency 

and fuel or heat use at some types of plants and could be 
30 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

superseded by accurately monitoring and reporting condenser 

duty. Nearly all fossil-fuel generation types are required to 

report fuel and generation data sufficient to calculate a heat 

budget; nuclear plant heat budgets can be constructed despite 

the lack of reported reactor heat data, based on maximum 

permitted reactor power. Geothermal and solar-thermal plants 

do not report enough data for a heat budget. Budgets could 

be improved by reporting fuel use by boiler and turbine 

and electricity by generator for combined-cycle plants. The 

quality of reported data is uneven, and quality control requires 

substantial effort. Some plants present ambiguities that have 

to be resolved by professional judgment. Additional information could be gathered at power plants that would eliminate 

the need for estimating condenser duty and tower evaporation 

with heat budgets, but not all of these data are required by 

plant operators. 

Budget models of the evaporation process are more 

complex and less definitive than those that estimate condenser 

duty. The Leung and Moore method gives plausible estimates 

of the evaporation ratio for cooling towers, using available 

data. However, it is not definitive, and could be superseded by 

a better model, or, preferably, better monitoring and reporting 

of the elements of the water budget for cooling towers. In 

contrast, monitoring of evaporation from water surfaces is difficult at best, and cannot supersede modeling of the evaporation ratio for forced evaporation from water surfaces. The heat 

balance method presented in this report is not definitive, but it 

incorporates the main variables to which the evaporation ratio 

responds, and its output is realistically sensitive to them. This 

method would benefit from the development of better wind 

functions, and its precision would be improved by more reliable measurements of environmental variables at power plants.

Acknowledgments

The authors thank David I. Stannard of the USGS for his 

input on the surface-water evaporation model. The authors 

would also like to thank Andrew E. LaMotte and Andrew J. 

Sekellick of the USGS for their work on power plant locations 

and elevations.

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p. 53–64.
Glossary 35

B

boiler efficiency the ratio between the 

amount of heat used to generate steam and the 

total heat content of the fuel that is consumed.

C

combined heat and power generating 

systems that produce both heat and electricity 

from a single heat source and export waste 

heat to other heat-using processes.

condenser duty the amount of waste heat 

delivered to the cooling system through the 

condenser. 

cooling system a system that removes waste 

heat from a power plant condenser and transfers it to the atmosphere. 

cooling-system type the technology used to 

dissipate condenser duty to the atmosphere; 

in this report, wet cooling towers and surfacewater cooling are the types considered. 

D

dry cooling system a cooling system that 

condenses steam and transfers the waste heat 

to the atmosphere without the consumption of 

water.

E

evaporation ratio the ratio of the amount of 

heat transferred to the air as evaporation to the 

condenser duty.

exported heat the waste heat produced in 

electricity generation that is used in other 

heat-using processes, such as for a heating 

system.

F

forced evaporation the increase in evaporation of surface water due to the added heat of 

discharged cooling water.

freshwater water that contains less than 

1,000 milligrams per liter (mg/L) of dissolved 

solids. 

G

generating unit any combination of physically connected generators, reactors, boilers, 

combustion turbines, or other prime movers 

operated together to produce electric power.

generation type the type of technology used 

to generate electricity in a given unit or plant, 

including the energy source and prime mover. 

H

heat budget a summation of all significant 

flows of heat into and out of a system such as 

a power plant.

heat loading an estimate of the ratio of 

condenser duty to the area over which heat is 

dissipated.

heat rate the number of British thermal 

units (Btu) of fuel it takes to produce one 

kilowatt hour (kWh) of electricity.

L

linked heat and water budget a summation 

of all significant flows of heat and water into 

and out of a power plant, linked by converting energy flows embodied in water flow and 

evaporation into their equivalent volume flow 

rates of liquid water.

O

once-through cooling system a cooling system in which the water is withdrawn from a 

surface-water source other than a recirculating 

pond to condense the steam used to generate 

electricity and that discharges the water back 

to surface water at a higher temperature. 

P

plant a facility that generates electricity 

from another source of energy such as fossil 

fuels, nuclear fission, geothermal energy, or 

solar radiation and heat.

prime mover in thermoelectric plants, the 

prime mover is the turbine that converts the 

energy in heated gases to mechanical energy.

Glossary
36 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

R

recirculating cooling system a cooling 

system in which water is circulated through 

condensers, cooled, and then re-used in the 

same process. 

S

surface-water cooling a cooling-system 

type that transfers heat from a condenser to 

the atmosphere through evaporation at the 

free surface of an open body of water. In 

addition to evaporation, some heat leaves 

the water surface through conduction and 

radiation.

T

thermal efficiency the percentage of fuel 

heat used to produce electricity. 

thermoelectric relating to the generation of 

electric power from heat.

thermoelectric water consumption the 

water evaporated or incorporated into byproducts as a result of the production of 

electricity from heat.

thermoelectric water withdrawal the water 

removed from groundwater or surface water 

for use in a thermoelectric power plant.

W

waste heat heat used but not converted to 

electricity in a thermoelectric plant.

water budget a summation of all significant 

flows of water into and out of a system such 

as a power plant.

wet cooling tower a cooling-system type 

that transfers heat from a condenser to the 

atmosphere primarily through evaporation, 

and to a lesser extent through conduction, in a 

natural-draft or mechanical-draft tower.
Appendixes 1–3 37

Appendixes 1–3
38 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Alabama (24 power plants)

Autagua 55440 Tenaska Central Alabama Generating Station 390.1 

Autauga 7897 E B Harris Electric Generating Plant 564 

Autauga 55271 Tenaska Lindsay Hill Generating Station 390.1 

Colbert 47 Colbert 1,350 

Covington 533 McWilliams 217 

Etowah 7 Gadsden 138 

Greene 10 Greene County 568.4 

Houston 6001 Joseph M Farley 1,776.4 

Jackson 50 Widows Creek 1,968.6 

Jefferson 6002 James H Miller Jr 2,822 

Lee 7710 H Allen Franklin Combined Cycle 777.1 

Limestone 46 Browns Ferry 3,494 

Lowndes 7698 General Electric Plastic 14.8 

Mobile 3 Barry 2,161.1 

Mobile 7721 Theodore Cogen Facility 88.4 

Mobile 50407 Mobile Energy Services LLC 43.1 

Mobile 55241 Hog Bayou Energy Center 80 

Morgan 55292 Decatur Energy Center 171 

Morgan 55293 Morgan Energy Center 270 

Shelby 26 E C Gaston 2,012.8 

Tallapoosa 55411 Hillabee Energy Center 306 

Walker 8 Gorgas 1,416.7 

Washington 56 Charles R Lowman 538 

Washington 7697 Washington County Cogeneration Facility 39.9 

Alaska (5 power plants)

Anchorage 6559 George M Sullivan Generation Plant 2 33 

Denali Borough 6288 Healy 28 

Fairbanks North Star 79 Aurora Energy LLC Chena 27.5 

Fairbanks North Star 6285 North Pole 13 

Kenai Peninsula 96 Beluga 62 

Arizona (25 power plants)

Apache 6177 Coronado 821.8 

Apache 8223 Springerville 1,749.6 

Cochise 160 Apache Station 489.6 

Coconino 4941 Navajo 2,409.3 

Maricopa 116 Ocotillo 227.2 

Maricopa 117 West Phoenix 641.6 

Maricopa 141 Agua Fria 390.4 

Maricopa 147 Kyrene 122 
Appendixes 1–3 39

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Maricopa 6008 Palo Verde 4,209.3 

Maricopa 8068 Santan 864.6 

Maricopa 55282 Arlington Valley Energy Facility 317 

Maricopa 55306 Gila River Power Station 1,084 

Maricopa 55372 Harquahala Generating Project 480 

Maricopa 55455 Red Hawk 408 

Maricopa 55481 Mesquite Generating Station 642 

Maricopa 57140 Maricopa Solar -- 

Mohave 55124 Griffith Energy LLC 301.8 

Mohave 55177 South Point Energy Center 236 

Navajo 113 Cholla 1,128.8 

Navajo 56616 Snowflake White Mountain Power LLC 27.2 

Pima 126 H Wilson Sundt Generating Station 504.5 

Pinal 118 Saguaro 250 

Pinal 55129 Desert Basin 272.1 

Yuma 120 Yucca 86.7 

Yuma 54694 Yuma Cogeneration Associates 18.5 

Arkansas (16 power plants)

Benton 6138 Flint Creek 558 

Franklin 201 Thomas Fitzhugh 59 

Hot Spring 170 Lake Catherine 552.5 

Hot Spring 55418 KGen Hot Spring Generating Facility 317 

Hot Spring 55714 Hot Spring Power Project 262 

Independence 6641 Independence 1,700 

Jefferson 6009 White Bluff 1,700 

Jefferson 55075 Pine Bluff Energy Center 56 

Lafayette 169 Harvey Couch 156.2 

Mississippi 55340 Dell Power Station 281 

Mississippi 56456 Plum Point Energy Station 720 

Ouachita 203 McClellan 136 

Pope 8055 Arkansas Nuclear One 1,845 

Pulaski 55221 Harry L Oswald 210 

Union 55380 Union Power Partners LP 1,020 

Woodruff 202 Carl Bailey 120 

California (149 power plants)

Butte 54469 Pacific Oroville Power Inc 18 

Colusa 50293 Wadham Energy LP 28.6 

Contra Costa 228 Contra Costa 718 

Contra Costa 271 Pittsburg Power 1,403.9 

Contra Costa 10342 Foster Wheeler Martinez 33.5 
40 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Contra Costa 10367 East Third Street Power Plant 20.5 

Contra Costa 10368 Loveridge Road Power Plant 20.5 

Contra Costa 10369 Wilbur West Power Plant 20.5 

Contra Costa 10370 Wilbur East Power Plant 20.5 

Contra Costa 10371 Nichols Road Power Plant 20.5 

Contra Costa 55217 Los Medanos Energy Center 280.5 

Contra Costa 55333 Delta Energy Center 306 

Fresno 10156 Fresno Cogen Partners 10 

Fresno 10405 Kingsburg Cogen 13.1 

Fresno 10767 Rio Bravo Fresno 28 

Fresno 10837 Covanta Mendota 28 

Fresno 57564 Algonquin Power Sanger LLC 12.5 

Humboldt 10052 Fairhaven Power 18.8 

Humboldt 10764 Blue Lake Power LLC 13.8 

Imperial 389 El Centro 166.1 

Imperial 10631 J M Leathers 49 

Imperial 10632 A W Hoch 49 

Imperial 10634 J J Elmore 49 

Imperial 10759 Salton Sea Unit 3 53.9 

Imperial 10763 Geo East Mesa III 20 

Imperial 10878 Salton Sea Unit 1 10 

Imperial 10879 Salton Sea Unit 2 20 

Imperial 50210 Vulcan 39.6 

Imperial 50762 Ormesa IH 14.4 

Imperial 50764 Ormesa IE 14.4 

Imperial 50766 Ormesa I 31.2 

Imperial 54038 Geo East Mesa II 20 

Imperial 54689 Heber Geothermal 52 

Imperial 54724 Ormesa II 22.8 

Imperial 54996 Salton Sea Unit 4 51 

Imperial 55983 Salton Sea Unit 5 49.9 

Imperial 55984 CE Turbo 11.5 

Inyo 10873 Coso Finance Partners 92.2 

Inyo 10874 Coso Power Developers 90 

Inyo 10875 Coso Energy Developers 90 

Kern 10768 Rio Bravo Jasmin 38.2 

Kern 10769 Rio Bravo Poso 38.2 

Kern 10840 Delano Energy 57 

Kern 10850 Mojave Cogen 16 

Kern 54626 Mt Poso Cogeneration 62 
Appendixes 1–3 41

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Kern 55151 La Paloma Generating LLC 1,200 

Kern 55182 Sunrise Power LLC 270 

Kern 55400 Elk Hills Power LLC 225 

Kern 55656 Pastoria Energy Facility LLC 275 

Kern 56943 Ausra Kimberlina Solar Generation 5 

Kings 10373 Hanford 27 

Lake 510 Sonoma California Geothermal 78 

Lake 902 Bottle Rock Power 55 

Lake 10199 West Ford Flat Power Plant 38 

Lake 10469 Bear Canyon Power Plant 24.4 

Lake 50066 Calistoga Power Plant 176.4 

Lassen 10777 HL Power 36.2 

Lassen 50964 Amedee Geothermal Venture I 3 

Lassen 54468 Mt Lassen Power 11.4 

Los Angeles 315 AES Alamitos LLC 1,922 

Los Angeles 330 El Segundo Power 684 

Los Angeles 356 AES Redondo Beach LLC 1,316.4 

Los Angeles 377 Grayson 163 

Los Angeles 399 Harbor 67 

Los Angeles 400 Haynes 1,410.3 

Los Angeles 404 Scattergood 823.2 

Los Angeles 408 Valley 311 

Los Angeles 420 Broadway 75 

Los Angeles 10090 Commerce Refuse-to-Energy 12 

Los Angeles 10169 Carson Cogeneration 10.5 

Los Angeles 10471 Spadra Landfill Gas to Energy 10.6 

Los Angeles 10472 Puente Hills Energy Recovery 50 

Los Angeles 10473 Palos Verdes Gas to Energy 13 

Los Angeles 10478 Pitchess Cogen Station 7.4 

Los Angeles 50541 Harbor Cogen 25.1 

Los Angeles 50837 Southeast Resource Recovery 35.6 

Los Angeles 50876 Wheelabrator Norwalk Energy 7.7 

Los Angeles 54015 BKK Landfill 6.8 

Los Angeles 56041 Malburg 58.8 

Los Angeles 56046 Magnolia Power Project 188.7 

Los Angeles 57323 Sierra SunTower Solar Generating Station 7.5 

Madera 56706 Ampersand Chowchilla Biomass LLC 12.5 

Merced 56707 El Nido Facility 12.5 

Monterey 260 Dynegy Moss Landing Power Plant 1,870.0 

Monterey 10294 King City Power Plant 42.4 
42 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Orange 335 AES Huntington Beach LLC 888 

Orange 10395 Coyote Canyon Steam Plant 20 

Placer 10772 Rio Bravo Rocklin 27.9 

Placer 56298 Roseville Energy Park 80 

Riverside 10300 Mecca Plant 55.5 

Riverside 55295 Blythe Energy LLC 227 

Riverside 56356 Clearwater Power Plant 8 

Riverside 57154 Imperial Valley Resource Recovery 18.1 

Riverside County 55853 Inland Empire Energy Center 819 

Sacramento 7527 Carson Ice-Gen Project 17.5 

Sacramento 7551 SCA Cogen 2 49.8 

Sacramento 7552 SPA Cogen 3 55.2 

Sacramento 55970 Cosumnes 190 

San Bernardino 329 Coolwater 387.3 

San Bernardino 331 Etiwanda Generating Station 666 

San Bernardino 358 Mountainview Power LLC 428.9 

San Bernardino 10002 ACE Cogeneration Facility 108.0 

San Bernardino 10437 SEGS I 13.8 

San Bernardino 10438 SEGS II 30 

San Bernardino 10439 SEGS III 34.2 

San Bernardino 10440 SEGS IV 34.2 

San Bernardino 10441 SEGS V 34.2 

San Bernardino 10442 SEGS VI 35 

San Bernardino 10443 SEGS VII 35 

San Bernardino 10444 SEGS VIII 92 

San Bernardino 10446 SEGS IX 92 

San Bernardino 50850 OLS Energy Chino 7.3 

San Bernardino 55518 High Desert Power Plant 333 

San Diego 302 Encina 982 

San Diego 310 Dynegy South Bay Power Plant 272 

San Diego 360 San Onofre Nuclear Generating Station 2,254 

San Diego 10810 NTC/MCRD Energy Facility 2.6 

San Diego 10811 Naval Station Energy Facility 16.8 

San Diego 10812 North Island Energy Facility 4 

San Diego 54749 Goal Line LP 10.2 

San Diego 55985 Palomar Energy 229 

San Diego 57584 University of California San Diego 3 

San Francisco 273 Potrero Power 226 

San Joaquin 10502 Thermal Energy Dev Partnshp LP 23 

San Joaquin 10640 Stockton Cogen 60 
Appendixes 1–3 43

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

San Luis Obispo 259 Dynegy Morro Bay LLC 718 

San Luis Obispo 6099 Diablo Canyon 2,323 

Santa Clara 10034 Gilroy Power Plant 40 

Santa Clara 50748 Agnews Power Plant 7.6 

Santa Clara 54561 Jefferson Smurfit Santa Clara Mill 3 

Santa Clara 55393 Metcalf Energy Center 235 

Santa Clara 56026 Donald Von Raesfeld Power Plant 54 

Shasta 7307 Redding Power 26.8 

Shasta 10652 Burney Forest Products 31 

Shasta 50881 Wheelabrator Shasta 62.7 

Shasta 54219 Burney Mountain Power 11.4 

Sonoma 286 Geysers Unit 5-20 1,163 

Sonoma 7368 Geothermal 1 110 

Sonoma 7369 Geothermal 2 110 

Sonoma 52158 Aidlin Geothermal Power Plant 25 

Stanislaus 7266 Woodland 37.7 

Stanislaus 50632 Covanta Stanislaus Energy 24 

Stanislaus 56078 Walnut Energy Center 110.8 

Sutter 10350 Greenleaf 1 Power Plant 20 

Tuolumne 50560 Pacific-Ultrapower Chinese Station 25 

Ventura 345 Mandalay 436 

Ventura 350 Ormond Beach 1,612 

Ventura 50851 OLS Energy Camarillo 7.6 

Yolo 10836 Woodland Biomass Power Ltd 28 

Colorado (25 power plants)

Adams 469 Cherokee 801.3 

Boulder 477 Valmont 191.7 

Denver 465 Arapahoe 158 

Denver 478 Zuni 75 

Denver 55200 Arapahoe Combustion Turbine Project 51.8 

El Paso 492 Martin Drake 257.0 

El Paso 493 George Birdsall 59.6 

El Paso 8219 Ray D Nixon 207 

Fremont 462 W N Clark 43.7 

Garfield 10755 Rifle Generating Station 39 

Jefferson 10003 Colorado Energy Nations Company 35 

Larimer 6761 Rawhide 293.6 

Logan 57134 OREG 4 Peetz -- 

Moffat 6021 Craig 1,427.6 

Montrose 527 Nucla 113.8 
44 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Morgan 6248 Pawnee 552.3 

Morgan 10682 Brush Generation Facility 165 

Prowers 508 Lamar Plant 18.5 

Pueblo 460 Pueblo 22.5 

Pueblo 470 Comanche 1,635.3 

Routt 525 Hayden 465.4 

Weld 6112 Fort St Vrain 342.6 

Weld 50676 Thermo Power & Electric 16.4 

Weld 50707 TCP 272 94 

Weld 55835 Rocky Mountain Energy Center 334.9 

Connecticut (17 power plants)

Fairfield 548 NRG Norwalk Harbor 326.4 

Fairfield 568 Bridgeport Station 563 

Fairfield 50883 Wheelabrator Bridgeport 67 

Fairfield 55042 Bridgeport Energy Project 180 

Hartford 10567 Algonquin Windsor Locks 16 

Hartford 50648 Covanta Bristol Energy 16.3 

Hartford 54945 Covanta Mid-Connecticut Energy 90 

Middlesex 562 Middletown 767.9 

New Haven 6156 New Haven Harbor 460 

New Haven 50664 Covanta Wallingford Energy 11 

New Haven 55126 Milford Power Project 578 

New London 546 Montville Station 489.9 

New London 566 Millstone 2,162.9 

New London 10646 American Ref-Fuel of SE CT 16.9 

New London 10675 AES Thames 213.9 

New London 54758 Wheelabrator Lisbon 14.6 

Windham 50736 Exeter Energy LP 31.3 

Delaware (5 power plants)

Kent 599 McKee Run 151.2 

Kent 10030 NRG Energy Center Dover 18 

New Castle 593 Edge Moor 697.8 

New Castle 7153 Hay Road 395 

Sussex 594 Indian River Generating Station 782.4 

Florida (68 power plants)

Alachua 663 Deerhaven Generating Station 325.7 

Alachua 664 John R Kelly 75 

Bay 643 Lansing Smith 553.3 

Bay 10250 Bay Resource Management Center 13.6 

Brevard 609 Cape Canaveral 804 
Appendixes 1–3 45

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Brevard 55318 Indian River 609 

Broward 613 Lauderdale 302.4 

Broward 617 Port Everglades 1,300 

Broward 50572 CSL Gas Recovery 2.2 

Broward 54033 Wheelabrator North Broward 67.6 

Citrus 628 Crystal River 3,333.1 

Duval 207 St Johns River Power Park 1,358 

Duval 667 Northside Generating Station 1,158.7 

Duval 7846 Brandy Branch 228.1 

Duval 10672 Cedar Bay Generating Company LP 291.6 

Escambia 641 Crist 1,135.1 

Hardee 7380 Midulla Generating Station 189 

Hardee 50949 Hardee Power Station 95.8 

Hernando 10333 Central Power & Lime 125 

Hillsborough 645 Big Bend 1,822.5 

Hillsborough 7873 H L Culbreath Bayside Power Station 685.1 

Hillsborough 50858 Hillsborough County Resource Recovery 47 

Hillsborough 50875 McKay Bay Facility 22.1 

Indian River 693 Vero Beach Municipal Power Plant 117 

Jackson 642 Scholz 98 

Lake 50629 Covanta Lake County Energy 15.5 

Lake 54423 Lake Cogen Ltd 31.1 

Lee 612 Fort Myers 592.3 

Lee 52010 Lee County Solid Waste Energy 59 

Leon 688 Arvah B Hopkins 334.2 

Liberty 50774 Telogia Power 14 

Manatee 6042 Manatee 2,198.4 

Martin 6043 Martin 2,823.7 

Martin 50976 Indiantown Cogeneration LP 395.4 

Miami-Dade 621 Turkey Point 2,796 

Miami-Dade 10062 Miami Dade County Resource Recovery Fac 77 

Orange 564 Stanton Energy Center 1,058.8 

Orange 7294 Central Energy Plant 8.5 

Orange 54466 Orlando Cogen LP 122.4 

Orange 55821 Curtis H Stanton Energy Center 281.9 

Osceola 672 Hansel 20 

Osceola 7238 Cane Island 122.5 

Palm Beach 673 Tom G Smith 36.5 

Palm Beach 50071 North County Regional Resource 62.3 

Palm Beach 54627 Okeelanta Cogeneration 128.9 
46 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Palm Beach 56407 West County Energy Center 1,054 

Pasco 8048 Anclote 1,112.4 

Pasco 50666 Pasco Cnty Solid Waste Resource Recovery 31.2 

Pasco 54424 Pasco Cogen Ltd 26.5 

Pinellas 634 P L Bartow 421 

Pinellas 50884 Pinellas County Resource Recovery 76.5 

Polk 675 Larsen Memorial 25 

Polk 676 C D McIntosh Jr 609.8 

Polk 7242 Polk 133.4 

Polk 7302 Hines Energy Complex 796.3 

Polk 7699 Tiger Bay 82.9 

Polk 54365 Orange Cogeneration Facility 28.6 

Polk 54426 Mulberry Cogeneration Facility 49.5 

Polk 54529 Wheelabrator Ridge Energy 45.5 

Polk 54658 Auburndale Power Partners 52 

Polk 55412 Osprey Energy Center 260 

Putnam 136 Seminole 1,429.2 

Putnam 6246 Putnam 240 

St Lucie 6045 St Lucie 1,700 

St Lucie 56400 Treasure Coast Energy Center 191.8 

Suwannee 638 Suwannee River 147 

Volusia 620 Sanford 1,028.4 

Wakulla 689 S O Purdom 137 

Georgia (22 power plants)

Appling 6051 Edwin I Hatch 1,721.8 

Bartow 703 Bowen 3,498.6 

Burke 649 Vogtle 2,320 

Chatham 733 Kraft 333.9 

Cobb 710 Jack McDonough 598.4 

Coweta 728 Yates 1,487.3 

Dougherty 727 Mitchell 163.2 

Effingham 6124 McIntosh 177.6 

Effingham 55406 Effingham County Power Project 197.8 

Effingham 56150 McIntosh Combined Cycle Facility 563.8 

Floyd 708 Hammond 953 

Glynn 715 McManus 143.7 

Heard 6052 Wansley 1,904 

Heard 7917 Chattahoochee Energy Facility 187.7 

Heard 7946 Wansley Unit 9 226 

Heard 55965 Wansley Combined Cycle 426.6 
Appendixes 1–3 47

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Houston 55040 Mid-Georgia Cogeneration Facility 110 

Monroe 6257 Scherer 3,564 

Murray 55382 KGen Murray I and II LLC 604 

Putnam 709 Harllee Branch 1,746.2 

Rabun 50201 Rabun Gap Cogen Facility 20 

Worth 753 Crisp Plant 12.5 

Hawaii (12 power plants)

Hawaii 772 W H Hill 37.1 

Hawaii 6478 Shipman 15 

Hawaii 52028 Puna Geothermal Venture I 35 

Hawaii 55369 Hamakua Energy Plant 20 

Honolulu 764 Honolulu 104.4 

Honolulu 765 Kahe 609.7 

Honolulu 766 Waiau 372 

Honolulu 54646 Kalaeola Cogen Plant 61 

Kauai 6474 Port Allen 10 

Maui 6056 Kahului 34 

Maui 6504 Maalaea 36 

Oahu 10673 AES Hawaii 203 

Idaho (4 power plants)

Adams 50099 Tamarack Energy Partnership 6.2 

Benewah 55090 Plummer Cogen 6.2 

Kootenai 55179 Rathdrum Power LLC 122.1 

Minidoka 54579 Rupert Cogen Project 10.4 

Illinois (37 power plants)

Christian 876 Kincaid Generation LLC 1,319 

Cook 867 Crawford 597.4 

Cook 886 Fisk Street 374 

Cook 972 Winnetka 28.2 

Cook 55174 Geneva Energy LLC 22 

Crawford 863 Hutsonville 150 

De Witt 204 Clinton Power Station 1,138.3 

Douglas 55245 Tuscola Station 12 

Fulton 6016 Duck Creek 441 

Grundy 869 Dresden Generating Station 2,018.6 

Grundy 55216 Morris Cogeneration LLC 62 

Jackson 862 Grand Tower 199.3 

Jasper 6017 Newton 1,234.8 

Kendall 55131 Kendall County Generation Facility 536 

La Salle 6026 LaSalle Generating Station 2,340 
48 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Lake 883 Waukegan 681.7 

Madison 898 Wood River 500.1 

Mason 891 Havana 488 

Massac 887 Joppa Steam 1,099.8 

Montgomery 861 Coffeen 1,005.4 

Morgan 864 Meredosia 564 

Ogle 6023 Byron Generating Station 2,449.8 

Peoria 856 E D Edwards 780.3 

Pike 6238 Pearl Station 22 

Putnam 892 Hennepin Power Station 306.3 

Randolph 889 Baldwin Energy Complex 1,894.1 

Rock Island 880 Quad Cities Generating Station 2,018.6 

Rock Island 55188 Cordova Energy 191.2 

Sangamon 963 Dallman 667.7 

Shelby 55334 Holland Energy Facility 345.1 

Tazewell 879 Powerton 1,785.6 

Vermilion 897 Vermilion 182.3 

Will 384 Joliet 29 1,320 

Will 874 Joliet 9 360.4 

Will 884 Will County 897.6 

Will 6022 Braidwood Generation Station 2,449.8 

Williamson 976 Marion 272 

Indiana (31 power plants)

Cass 1032 Logansport 43 

Dearborn 988 Tanners Creek 1,100.1 

Dearborn 55502 Lawrenceburg Energy Facility 536 

Dubois 6225 Jasper 2 14.5 

Floyd 1008 R Gallagher 600 

Gibson 6113 Gibson 3,339.5 

Hamilton 1007 Noblesville 100 

Jasper 6085 R M Schahfer 1,943.4 

Jefferson 983 Clifty Creek 1,303.8 

Knox 1004 Edwardsport 144.2 

La Porte 997 Michigan City 540 

Lake 981 State Line Energy 613.8 

Lake 55259 Whiting Clean Energy 213 

Marion 990 Harding Street 785.6 

Marion 992 CC Perry K 23.4 

Miami 1037 Peru 34.5 

Montgomery 1024 Crawfordsville 24.1 
Appendixes 1–3 49

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Morgan 991 Eagle Valley 393.6 

Pike 994 AES Petersburg 2,146.7 

Pike 1043 Frank E Ratts 233.2 

Porter 995 Bailly 603.5 

Porter 55096 Portside Energy 25.6 

Posey 6137 A B Brown 530.4 

Spencer 6166 Rockport 2,600 

Sullivan 6213 Merom 1,080 

Vermillion 1001 Cayuga 1,062 

Vigo 1010 Wabash River 972.7 

Vigo 55364 Sugar Creek Power 237.3 

Warrick 1012 F B Culley 368.9 

Warrick 6705 Warrick 777.6 

Wayne 1040 Whitewater Valley 93.9 

Iowa (22 power plants)

Allamakee 1047 Lansing 312 

Black Hawk 1131 Streeter Station 51.5 

Cerro Gordo 8031 Emery Station 244.6 

Clay 1217 Earl F Wisdom 33 

Clinton 1048 Milton L Kapp 218.4 

Des Moines 1104 Burlington 212 

Dubuque 1046 Dubuque 66.2 

Linn 1060 Duane Arnold Energy Center 679.5 

Linn 1073 Prairie Creek 221.7 

Louisa 6664 Louisa 811.9 

Marion 1175 Pella 38 

Marshall 1077 Sutherland 119.1 

Muscatine 1167 Muscatine Plant #1 293.5 

Muscatine 1218 Fair Station 62.5 

Polk 7985 Greater Des Moines 195.5 

Pottawattamie 1082 Walter Scott Jr Energy Center 1,778.9 

Scott 1081 Riverside 141 

Story 1122 Ames Electric Services Power Plant 108.8 

Union 1206 Summit Lake 22.5 

Wapello 6254 Ottumwa 726 

Woodbury 1091 George Neal North 1,046 

Woodbury 7343 George Neal South 640 

Kansas (22 power plants)

Barton 1235 Great Bend 81.6 

Cherokee 1239 Riverton 87.5 
50 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Clay 1270 Clay Center 8 

Coffey 210 Wolf Creek Generating Station 1,235.7 

Cowley 7013 East 12th Street 26.5 

Douglas 1250 Lawrence Energy Center 566 

Finney 108 Holcomb 348.7 

Finney 1336 Garden City 97.9 

Ford 1233 Fort Dodge 149 

Labette 1243 Neosho 69 

Linn 1241 La Cygne 1,578 

Montgomery 1271 Coffeyville 58.7 

Pottawatomie 6068 Jeffrey Energy Center 2,160 

Pratt 1317 Pratt 19 

Reno 1248 Hutchinson Energy Center 172 

Sedgwick 1240 Gordon Evans Energy Center 526 

Sedgwick 1242 Murray Gill 349 

Seward 1230 Cimarron River 50 

Shawnee 1252 Tecumseh Energy Center 232 

Sumner 1330 Wellington 1 20 

Wyandotte 1295 Quindaro 239.1 

Wyandotte 6064 Nearman Creek 261 

Kentucky (20 power plants)

Boone 6018 East Bend 669.3 

Carroll 1356 Ghent 2,225.9 

Clark 1385 Dale 216 

Daviess 1374 Elmer Smith 445.3 

Hancock 1381 Kenneth C Coleman 602 

Henderson 1382 HMP&L Station Two Henderson 405 

Jefferson 1363 Cane Run 644.6 

Jefferson 1364 Mill Creek 1,717.2 

Lawrence 1353 Big Sandy 1,096.8 

Mason 6041 H L Spurlock 1,608.5 

McCracken 1379 Shawnee 1,750 

Mercer 1355 E W Brown 757.1 

Muhlenberg 1357 Green River 188.6 

Muhlenberg 1378 Paradise 2,558.2 

Ohio 6823 D B Wilson 566.1 

Pulaski 1384 Cooper 344 

Trimble 6071 Trimble County 566.1 

Webster 1383 Robert A Reid 96 

Webster 6639 R D Green 586 

Woodford 1361 Tyrone 75 
Appendixes 1–3 51

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Louisiana (27 power plants)

Acadia 55173 Acadia Energy Center 528 

Caddo 1416 Arsenal Hill 125 

Caddo 1417 Lieberman 278 

Caddo 56565 J Lamar Stall Unit 256 

Calcasieu 1393 R S Nelson 1,596.8 

Calcasieu 10593 Agrilectric Power Partners Ltd 12.1 

De Soto 51 Dolet Hills 720.7 

Evangeline 1396 Coughlin Power Station 356.7 

Iberville 1394 Willow Glen 2,178 

Iberville 1455 Plaquemine 44 

Iberville 55404 Carville Energy LLC 196 

Jefferson 1403 Nine Mile Point 2,141.5 

Lafayette 1443 Louis Doc Bonin 340.9 

Orleans 1409 Michoud 959.2 

Ouachita 1404 Sterlington 348.7 

Ouachita 55467 Ouachita 366 

Ouachita 55620 Perryville Power Station 237 

Pointe Coupee 1464 Big Cajun 1 227.2 

Pointe Coupee 6055 Big Cajun 2 1,871 

Rapides 6190 Brame Energy Center 1,707.3 

St. Charles 1402 Little Gypsy 1,250.6 

St. Charles 4270 Waterford 3 1,199.8 

St. Charles 8056 Waterford 1 & 2 891 

St. Mary 1400 Teche 427.8 

St. Mary 1449 Morgan City 58.3 

Terrebonne 1439 Houma 78.9 

West Feliciana 6462 River Bend 1,035.9 

Maine (14 power plants)

Androscoggin 10354 Boralex Beaver Livermore Falls 39.6 

Aroostook 7513 Boralex Fort Fairfield 37.5 

Aroostook 10356 Boralex Ashland 39.6 

Cumberland 1507 William F Wyman 846 

Cumberland 50225 Regional Waste Systems 13.3 

Cumberland 55294 Westbrook Energy Center 195.5 

Franklin 50650 Boralex Stratton Energy 45.7 

Oxford 10495 Rumford Cogeneration 102.6 

Oxford 55100 Rumford Power Associates 91.9 

Penobscot 10766 Indeck West Enfield Energy Center 27.5 

Penobscot 55068 Maine Independence Station 194.6 
52 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Piscataquis 54852 Greenville Steam 15.6 

Washington 10765 Indeck Jonesboro Energy Center 27.5 

York 10338 Maine Energy Recovery 22 

Maryland (17 power plants)

Allegany 10678 AES Warrior Run Cogeneration Facility 229 

Anne Arundel 602 Brandon Shores 1,370 

Anne Arundel 1554 Herbert A Wagner 1,042.5 

Baltimore 1552 C P Crane 399.8 

Baltimore 1559 Riverside 72.2 

Baltimore 10485 RG Steel Sparrows Point, LLC 120 

Baltimore City 1553 Gould Street 103.5 

Baltimore City 10629 Wheelabrator Baltimore Refuse 64.5 

Calvert 6011 Calvert Cliffs Nuclear Power Plant 1,828.7 

Charles 1573 Morgantown Generating Plant 1,252 

Dorchester 1564 Vienna Operations 162 

Montgomery 1572 Dickerson 588 

Montgomery 50657 Montgomery County Resource Recovery 67.8 

Prince George’s 1571 Chalk Point LLC 2,046 

Prince George’s 54832 Panda Brandywine LP 91.4 

Prince George’s 56038 UMCP CHP Plant 5.4 

Washington 1570 FirstEnergy R Paul Smith Power Station 109.5 

Massachusetts (25 power plants)

Barnstable 1599 Canal 1,165 

Berkshire 50002 Pittsfield Generating LP 60 

Bristol 1619 Brayton Point 1,124.6 

Bristol 1682 Cleary Flood 123.3 

Bristol 52026 Dartmouth Power Associates 32 

Essex 1626 Salem Harbor 805.1 

Essex 50877 Wheelabrator North Andover 40.3 

Essex 50880 Wheelabrator Saugus 53.7 

Hampden 1606 Mount Tom 136 

Hampden 1642 NAEA Energy Massachusetts LLC 113.6 

Hampden 6081 Stony Brook 105 

Hampden 9864 Cabot Holyoke 20 

Hampden 10726 Masspower 80.9 

Hampden 50273 Pioneer Valley Resource Recovery 9.4 

Hampden 55041 Berkshire Power 289 

Middlesex 1588 Mystic Generating Station 1,247 

Middlesex 1595 Kendall Square Station 67.4 

Middlesex 10802 Lowell Cogen Plant 8.5 
Appendixes 1–3 53

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Norfolk 1660 Potter Station 2 25 

Norfolk 55211 ANP Bellingham Energy Project 578 

Plymouth 1590 Pilgrim Nuclear Power Station 670 

Worcester 50878 Wheelabrator Millbury Facility 47.6 

Worcester 54805 Milford Power LP 49.1 

Worcester 55079 Millennium Power 130 

Worcester 55212 ANP Blackstone Energy Project 578 

Michigan (42 power plants)

Alcona 50772 Viking Energy of Lincoln 18 

Allegan 1880 Claude Vandyke 23 

Baraga 1772 John H Warden 18.7 

Bay 1702 Dan E Karn 1,946.3 

Bay 1720 J C Weadock 312.6 

Berrien 6000 Donald C Cook 2,285.3 

Crawford 10822 Grayling Generating Station 38 

Delta 1771 Escanaba 23 

Eaton 1832 Erickson Station 154.7 

Genesee 54751 Genesee Power Station LP 39.5 

Hillsdale 4259 Endicott Station 55 

Huron 1731 Harbor Beach 121 

Ingham 1831 Eckert Station 375 

Jackson 55270 Kinder Morgan Power Jackson Facility 210 

Kent 10819 Ada Cogeneration LP 10.1 

Manistee 50835 TES Filer City Station 70 

Marquette 1769 Presque Isle 450 

Marquette 1843 Shiras 65 

Mason 54915 Michigan Power LP 58 

Midland 10745 Midland Cogeneration Venture 423.4 

Missaukee 50770 Viking Energy of McBain 18 

Monroe 1723 J R Whiting 345.4 

Monroe 1729 Fermi 1,217.0 

Monroe 1733 Monroe 3,279.6 

Montmorency 10346 Hillman Power LLC 20 

Muskegon 1695 B C Cobb 312.6 

Ontonagon 10148 White Pine Electric Power 40 

Ottawa 1710 J H Campbell 1,585.9 

Ottawa 1825 J B Sims 80 

Ottawa 1830 James De Young 62.8 

Ottawa 55087 Zeeland Generating Station 213.3 

St. Clair 1743 St Clair 1,547 
54 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

St. Clair 6034 Belle River 1,395 

St. Clair 6035 Greenwood 815.4 

Van Buren 1715 Palisades 811.8 

Van Buren 55297 New Covert Generating Facility 441 

Washtenaw 50431 University of Michigan 37.5 

Wayne 1740 River Rouge 933.2 

Wayne 1745 Trenton Channel 775.5 

Wayne 1866 Wyandotte 54 

Wayne 55088 Dearborn Industrial Generation 250 

Wexford 54415 Cadillac Renewable Energy 44 

Minnesota (32 power plants)

Blue Earth 1934 Wilmarth 25 

Blue Earth 56104 Mankato Energy Center 320 

Brown 2001 New Ulm 21 

Cook 10075 Taconite Harbor Energy Center 252 

Dakota 1904 Black Dog 430.4 

Dakota 55598 Pine Bend 6.6 

Goodhue 1925 Prairie Island 1,186.2 

Goodhue 1926 Red Wing 23 

Hennepin 1927 Riverside 165 

Hennepin 10013 Covanta Hennepin Energy 39.5 

Itasca 1893 Clay Boswell 1,072.5 

Itasca 10686 Rapids Energy Center 26.5 

Kandiyohi 2022 Willmar 18 

Martin 1888 Fox Lake 93.1 

McLeod 1980 Hutchinson Plant #1 16.0 

McLeod 6358 Hutchinson Plant #2 11.5 

Mower 1961 Austin Northeast 31.9 

Olmsted 2008 Silver Lake 99 

Otter Tail 1943 Hoot Lake 129.4 

Ramsey 1912 High Bridge 250 

Ramsey 56643 St Paul Cogeneration 37 

Rice 56164 Faribault Energy Park 122 

Scott 57119 Koda Biomass Plant 23.4 

Sherburne 2039 Elk River 38.8 

Sherburne 6090 Sherburne County 2,430.6 

St. Louis 1891 Syl Laskin 116 

St. Louis 1897 M L Hibbard 72.8 

St. Louis 1979 Hibbing 35.9 

St. Louis 2018 Virginia 30.2 
Appendixes 1–3 55

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Washington 1915 Allen S King 598.4 

Washington 55010 LSP-Cottage Grove LP 106.2 

Wright 1922 Monticello 631.2 

Mississippi (23 power plants)

Attala 55220 Attala 180 

Benton 55451 Magnolia Power Plant 468 

Bolivar 2051 Delta 225 

Choctaw 55076 Red Hills Generating Facility 513.7 

Choctaw 55694 Choctaw Gas Generation Project 310.5 

Claiborne 6072 Grand Gulf 1,372.5 

Coahoma 2059 L L Wilkins 39 

Desoto 55269 TVA Southaven Combined Cycle 366 

Forrest 2046 Eaton 77.7 

Harrison 2049 Jack Watson 1,173.6 

Hinds 2053 Rex Brown 339.2 

Hinds 55218 Hinds Energy Facility 188 

Jackson 6073 Victor J Daniel Jr 1,487 

Jones 2070 Moselle 177 

Lamar 6061 R D Morrow 400 

Lauderdale 2048 Sweatt 95 

Leflore 2062 Henderson 32.6 

Leflore 2063 Wright 17.5 

Lowndes 55197 Caledonia 318 

Panola 55063 Batesville Generation Facility 337.5 

Warren 2050 Baxter Wilson 1,327.6 

Washington 8054 Gerald Andrus 781.4 

Yazoo 2067 Yazoo 12.6 

Missouri (25 power plants)

Boone 2123 Columbia 73.5 

Buchanan 2098 Lake Road 150.5 

Callaway 6153 Callaway 1,235.8 

Cass 55178 Dogwood Energy Facility 265 

Clay 2171 Missouri City 46 

Dunklin 7604 St Francis Energy Facility 614 

Franklin 2103 Labadie 2,389.4 

Greene 2161 James River Power Station 253 

Greene 6195 Southwest Power Station 194 

Henry 2080 Montrose 564 

Jackson 2079 Hawthorn 737.1 

Jackson 2094 Sibley 524 
56 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Jackson 2132 Blue Valley 115 

Jasper 2076 Asbury 231.5 

Jasper 7296 State Line Combined Cycle 206.5 

Jefferson 6155 Rush Island 1,242 

New Madrid 2167 New Madrid 1,200 

Osage 2169 Chamois 59 

Platte 6065 Iatan 1,640 

Randolph 2168 Thomas Hill 1,135 

Saline 2144 Marshall 22.5 

Scott 6768 Sikeston Power Station 261 

St. Charles 2107 Sioux 1,099.4 

St. Louis 2104 Meramec 923 

St. Louis 56309 Trigen St. Louis 36.8 

Montana (7 power plants)

Big Horn 55749 Hardin Generator Project 115.7 

Richland 6089 Lewis & Clark 50 

Roosevelt 56833 OREG 1 Inc -- 

Roosevelt 56880 OREG 2 Inc -- 

Rosebud 6076 Colstrip 2,272 

Rosebud 10784 Colstrip Energy LP 46.1 

Yellowstone 2187 J E Corette Plant 172.8 

Nebraska (14 power plants)

Adams 60 Whelan Energy Center 76.3 

Dodge 2240 Lon Wright 130 

Douglas 2291 North Omaha 644.7 

Gage 8000 Beatrice 93.9 

Gosper 2226 Canaday 108.8 

Hall 59 Platte 109.8 

Hall 2241 C W Burdick 98 

Jefferson 2236 Fairbury 19 

Lancaster 2277 Sheldon 228.7 

Lancaster 7887 Terry Bundy Generating Station 27 

Lincoln 6077 Gerald Gentleman 1,362.6 

Nemaha 8036 Cooper 801 

Otoe 6096 Nebraska City 1,389.6 

Washington 2289 Fort Calhoun 502 

Nevada (22 power plants)

Churchill 52015 Caithness Dixie Valley 60.5 

Churchill 52174 Soda Lake Geothermal No I II 26.1 

Churchill 55991 Brady 32.9 
Appendixes 1–3 57

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Clark 2322 Clark 208.8 

Clark 2324 Reid Gardner 636.8 

Clark 2326 Sunrise 81.6 

Clark 10761 Las Vegas Cogeneration LP 11.5 

Clark 54271 Saguaro Power 37 

Clark 54349 Nevada Cogen Associates 2 Black Mountain 29.7 

Clark 54350 Nevada Cogen Assoc#1 GarnetVly 29.7 

Clark 55952 Las Vegas Cogeneration II LLC 55.6 

Clark 56405 Nevada Solar One 75.7 

Clark 57684 Goodsprings Waste Heat Recovery 7.5 

Eureka 10287 Beowawe Power 17 

Eureka 56224 TS Power Plant 242 

Humboldt 8224 North Valmy 567 

Humboldt 50760 Empire 4.8 

Lyon 2330 Fort Churchill 230 

Lyon 10018 Desert Peak Power Plant 15 

Lyon 55988 Wabuska 2.2 

Storey 2336 Tracy 607 

Washoe 50654 Steamboat Hills LP 14.6 

New Hampshire (13 power plants)

Carroll 50739 Pinetree Power Tamworth 25 

Coos 10839 DG Whitefield LLC 19.9 

Grafton 10290 Bridgewater Power LP 20 

Grafton 50208 Pinetree Power 17.5 

Merrimack 2364 Merrimack 459.2 

Merrimack 50873 Wheelabrator Concord Facility 14 

Rockingham 2367 Schiller 150 

Rockingham 6115 Seabrook 1,242 

Rockingham 8002 Newington 414 

Rockingham 55170 Granite Ridge 300 

Rockingham 55661 NAEA Newington Power 234.3 

Sullivan 10838 Springfield Power LLC 16 

Sullivan 50872 Wheelabrator Claremont Facility 4.5 

New Jersey (29 power plants)

Bergen 2398 Bergen Generating Station 545.2 

Bergen 50852 Elmwood Energy Holdings LLC 24 

Camden 10435 Camden Resource Recovery Facility 35 

Camden 10751 Camden Plant Holding LLC 61.8 

Cape May 2378 B L England 475.6 

Cumberland 2434 Howard Down 25.0 
58 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Essex 10643 Covanta Essex Company 69.8 

Essex 50385 Newark Bay Cogeneration Partnership LP 40 

Gloucester 10043 Logan Generating Company LP 242.3 

Gloucester 50561 Eagle Point Cogeneration 45 

Gloucester 50885 Wheelabrator Gloucester LP 14 

Hudson 2403 PSEG Hudson Generating Station 1,114.4 

Hudson 50497 Bayonne Plant Holding LLC 61.4 

Hunterdon 2393 Gilbert 135 

Mercer 2408 PSEG Mercer Generating Station 652.8 

Middlesex 2411 PSEG Sewaren Generating Station 431 

Middlesex 10308 Sayreville Cogeneration Facility 143.4 

Middlesex 50799 Parlin Power Plant 48 

Middlesex 55239 AES Red Oak LLC 330 

Middlesex 56119 Middlesex Generating Facility 10.5 

Ocean 2388 Oyster Creek 550 

Salem 2384 Deepwater 73.5 

Salem 2410 PSEG Salem Generating Station 2,340 

Salem 6118 PSEG Hope Creek Generating Station 1,170 

Salem 10099 Pedricktown Cogeneration Company LP 42.4 

Salem 10566 Chambers Cogeneration LP 285 

Union 2406 PSEG Linden Generating Station 542 

Union 10805 Kenilworth Energy Facility 6.8 

Warren 10012 Covanta Warren Energy 13.5 

New Mexico (11 power plants)

Bernalillo 2450 Reeves 154 

Dona Ana 2444 Rio Grande 266.5 

Dona Ana 55210 Afton Generating Station 110 

Lea 2446 Maddox 113.6 

Lea 2454 Cunningham 265.4 

Luna 55343 Luna Energy Facility 300 

McKinley 87 Escalante 257 

San Juan 2442 Four Corners 2,269.6 

San Juan 2451 San Juan 1,848 

San Juan 2465 Animas 15 

San Juan 55977 Bluffview 27 

New York (61 power plants)

Albany 2539 Bethlehem Energy Center 310.2 

Albany 10725 Selkirk Cogen 148.4 

Allegany 7784 Allegany Cogen 25 

Broome 2526 AES Westover 75 
Appendixes 1–3 59

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Cattaraugus 54076 Indeck Olean Energy Center 44.6 

Chautauqua 2554 Dunkirk Generating Plant 627.2 

Chautauqua 2682 S A Carlson 49.0 

Dutchess 10305 Dutchess Cnty Resource Recovery Facility 9.2 

Erie 2549 C R Huntley Generating Station 436 

Erie 50451 Indeck Yerkes Energy Center 19.3 

Franklin 50277 Boralex Chateaugay Power Station 19.7 

Genesee 54593 Batavia Power Plant 18.5 

Jefferson 10464 Black River Generation 55.5 

Kings 54914 Brooklyn Navy Yard Cogeneration 80 

Lewis 10617 CH Resources Beaver Falls 42.3 

Lewis 54526 Lyonsdale Biomass LLC 21.1 

Nassau 2511 E F Barrett 376.0 

Nassau 2514 National Grid Glenwood Energy Center 228.0 

Nassau 10642 Covanta Hempstead 78.6 

Nassau 50292 Bethpage Power Plant 52.2 

Nassau 52056 Trigen Nassau Energy 12 

New York 2493 East River 716.2 

Niagara 6082 AES Somerset LLC 655.1 

Niagara 50202 WPS Power Niagara 56 

Niagara 50472 American Ref-Fuel of Niagara 50 

Niagara 54041 Lockport Energy Associates LP 75.2 

Niagara 54131 Fortistar North Tonawanda 17 

Oneida 50744 Sterling Power Plant 16.5 

Onondaga 50651 Trigen Syracuse Energy 101.1 

Onondaga 50978 Carr Street Generating Station 25 

Orange 2480 Danskammer Generating Station 532 

Orange 8006 Roseton Generating Station 1,242 

Oswego 2589 Nine Mile Point Nuclear Station 1,901.1 

Oswego 2594 Oswego Harbor Power 1,803.6 

Oswego 6110 James A Fitzpatrick 882 

Oswego 50450 Indeck Oswego Energy Center 16.2 

Oswego 54547 Sithe Independence Station 409 

Queens 2500 Ravenswood 1,907 

Queens 2513 Far Rockaway 100 

Queens 8906 Astoria Generating Station 1,330 

Queens 54114 Kennedy International Airport Cogen 27 

Rensselaer 10190 Castleton Energy Center 25 

Rensselaer 54034 Rensselaer Cogen 39 

Rensselaer 56259 Empire Generating Co LLC 295.7 
60 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Richmond 2490 Arthur Kill Generating Station 911.7 

Rockland 2625 Bowline Point 1,242 

Saratoga 50458 Indeck Corinth Energy Center 55 

St. Lawrence 54592 Massena Energy Holdings LLC -- 

Suffolk 2516 Northport 1,548 

Suffolk 2517 Port Jefferson 376 

Suffolk 7314 Richard M Flynn 56 

Suffolk 50649 Covanta Babylon Inc 17 

Suffolk 56188 Pinelawn Power LLC 32 

Tompkins 2535 AES Cayuga 322.5 

Washington 10503 Wheelabrator Hudson Falls 14.4 

Wayne 6122 R E Ginna Nuclear Power Plant 614 

West Chester 8907 Indian Point 3 1,012 

Westchester 2497 Indian Point 2 1,299 

Westchester 50882 Wheelabrator Westchester 59.7 

Wyoming 50449 Indeck Silver Springs Energy Center 17.2 

Yates 2527 AES Greenidge LLC 112.5 

North Carolina (29 power plants)

Brunswick 6014 Brunswick 2,003.2 

Brunswick 10378 CPI USA NC Southport 135 

Buncombe 2706 Asheville 413.6 

Catawba 2727 Marshall 1,996 

Chatham 2708 Cape Fear 358.5 

Cleveland 2721 Cliffside 780.9 

Craven 10525 Craven County Wood Energy LP 50 

Cumberland 1016 Butler-Warner Generation Plant 73 

Duplin 10381 Coastal Carolina Clean Power 44.1 

Edgecombe 10384 Edgecombe Genco LLC 114.8 

Gaston 2718 G G Allen 1,155 

Gaston 2732 Riverbend 466 

Halifax 50555 Rosemary Power Station 54 

Halifax 54035 Roanoke Valley Energy Facililty I 182.3 

Halifax 54755 Roanoke Valley Energy Facility II 57.8 

Mecklenburg 6038 McGuire 2,440.6 

New Hanover 2713 L V Sutton 671.6 

New Hanover 50271 New Hanover County WASTEC 6.3 

Person 2712 Roxboro 2,558.2 

Person 6250 Mayo 735.8 

Person 10379 CPI USA NC Roxboro 67.5 

Richmond 7805 Richmond 195.3 
Appendixes 1–3 61

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Robeson 2716 W H Weatherspoon 165.5 

Rockingham 2723 Dan River 290 

Rowan 2720 Buck 370 

Rowan 7826 Rowan 195 

Stokes 8042 Belews Creek 2,160.2 

Wake 6015 Harris 950.9 

Wayne 2709 Lee 402.4 

North Dakota (8 power plants)

McLean 6030 Coal Creek 1,210 

Mercer 2817 Leland Olds 656 

Mercer 2824 Stanton 190.2 

Mercer 6469 Antelope Valley 869.8 

Mercer 8222 Coyote 450 

Morton 2790 R M Heskett 115 

Morton 57172 Glen Ullin Station 6 5.3 

Oliver 2823 Milton R Young 734 

Ohio (34 power plants)

Adams 2850 J M Stuart 2,440.8 

Adams 6031 Killen Station 660.6 

Ashtabula 2835 FirstEnergy Ashtabula 256 

Ashtabula 55990 Ashtabula 1.6 

Belmont 2864 FirstEnergy R E Burger 103.4 

Butler 2917 Hamilton 110.6 

Clermont 2830 Walter C Beckjord 1,221.3 

Clermont 6019 W H Zimmer 1,425.6 

Coshocton 2840 Conesville 1,890.8 

Cuyahoga 2838 FirstEnergy Lake Shore 256 

Fulton 54974 Sauder Power Plant 7.2 

Gallia 2876 Kyger Creek 1,086.5 

Gallia 8102 General James M Gavin 2,600 

Hamilton 2832 Miami Fort 1,278 

Jefferson 2828 Cardinal 1,880.4 

Jefferson 2866 FirstEnergy W H Sammis 2,455.6 

Jefferson 55611 Mingo Junction Energy Center 32 

Lake 2837 FirstEnergy Eastlake 1,257 

Lake 2936 Painesville 38.5 

Lake 6020 Perry 1,311.6 

Lawrence 55736 Hanging Rock Energy Facility 634.2 

Lorain 2836 Avon Lake 766 

Lucas 2878 FirstEnergy Bay Shore 639.4 
62 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Montgomery 2848 O H Hutchings 414 

Ottawa 6149 Davis Besse 925.2 

Pickaway 2843 Picway 106.2 

Richland 2943 Shelby Municipal Light Plant 32 

Scioto 56848 Haverhill North Cogeneration Facility 67 

Trumbull 2861 Niles 265.6 

Tuscarawas 2914 Dover 19.5 

Washington 2872 Muskingum River 1,529.4 

Washington 55397 Washington Energy Facility 317.1 

Washington 55503 AEP Waterford Facility 399 

Wayne 2935 Orrville 72 

Oklahoma (24 power plants)

Caddo 2964 Southwestern 483 

Caddo 3006 Anadarko Plant 315.3 

Canadian 2953 Mustang 531 

Choctaw 6772 Hugo 446 

Comanche 8059 Comanche 120 

Kay 7546 Ponca City 19.8 

Leflore 10671 AES Shady Point LLC 350 

Mayes 165 GRDA 1,010 

Mayes 7757 Chouteau 181.9 

McClain 55457 McClain Energy Facility 168.3 

Muskogee 2952 Muskogee 1,716 

Noble 6095 Sooner 1,138 

Oklahoma 2951 Horseshoe Lake 826 

Oklahoma 50558 PowerSmith Cogeneration Project 49.9 

Oklahoma 55463 Redbud Power Plant 638 

Payne 3000 Boomer Lake Station 22.7 

Pittsburg 55501 Kiamichi Energy Facility 634.4 

Rogers 2963 Northeastern 1,589 

Seminole 2956 Seminole 1,701 

Tulsa 2965 Tulsa 340 

Tulsa 4940 Riverside 946 

Tulsa 55146 Green Country Energy LLC 366 

Wagoner 55225 Calpine Oneta Power LLC 510 

Woodward 3008 Mooreland 305 

Oregon (12 power plants)

Columbia 8073 Beaver 176.4 

Columbia 56227 Port Westward 171 

Douglas 50993 Co-Gen II LLC 7.5 
Appendixes 1–3 63

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Grant 50921 Co-Gen LLC 7.5 

Jackson 10869 Biomass One LP 30 

Klamath 55103 Klamath Cogeneration Plant 178.5 

Marion 50630 Covanta Marion Inc 13.1 

Morrow 6106 Boardman 601 

Morrow 7350 Coyote Springs 80.6 

Morrow 7931 Coyote Springs II 117 

Umatilla 54761 Hermiston Generating Plant 212.2 

Umatilla 55328 Hermiston Power Partnership 264.4 

Pennsylvania (60 power plants)

Allegheny 3096 Brunot Island 144 

Allegheny 8226 Cheswick Power Plant 637 

Armstrong 3136 Keystone 1,872 

Armstrong 3178 FirstEnergy Armstrong Power Station 326.4 

Beaver 6040 Beaver Valley 1,846.8 

Beaver 6094 FirstEnergy Bruce Mansfield 2,741.1 

Beaver 10676 AES Beaver Valley Partners Beaver Valley 149 

Berks 3115 Titus 225 

Berks 55193 Ontelaunee Energy Center 228 

Bucks 7701 Fairless Hills 60 

Bucks 54746 Wheelabrator Falls 53.3 

Bucks 55298 Fairless Energy Center 542.4 

Cambria 10143 Colver Power Project 118 

Cambria 10603 Ebensburg Power 57.6 

Cambria 10641 Cambria Cogen 98 

Carbon 50776 Panther Creek Energy Facility 94 

Chester 3159 Cromby Generating Station 417.5 

Clarion 54144 Piney Creek Project 36.2 

Clearfield 3131 Shawville 626 

Dauphin 8011 Three Mile Island 975.6 

Dauphin 10118 Harrisburg Facility 24.1 

Delaware 3161 Eddystone Generating Station 1,489.2 

Delaware 10746 American Ref-Fuel of Delaware Valley 90 

Delaware 55231 Liberty Electric Power Plant 242 

Delaware 55801 FPL Energy Marcus Hook LP 271.5 

Fayette 55516 Fayette Energy Facility 317.1 

Greene 3179 Hatfields Ferry Power Station 1,728 

Indiana 3118 Conemaugh 1,872 

Indiana 3122 Homer City Station 2,012 

Indiana 3130 Seward 585 
64 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Lackawanna 50279 Archbald Power Station 23.2 

Lancaster 50859 Lancaster County Resource Recovery 35.7 

Lawrence 3138 New Castle Plant 348 

Lebanon 55337 AES Ironwood LLC 259.2 

Luzerne 6103 PPL Susquehanna 2,596 

Lycoming 10731 Koppers Susquehanna Plant 12.5 

Montgomery 6105 Limerick 2,277 

Montour 3149 PPL Montour 1,641.7 

Northampton 3113 Portland 427.0 

Northampton 3148 PPL Martins Creek 1,701.0 

Northampton 50888 Northampton Generating Company LP 114.1 

Northampton 55690 Bethlehem Power Plant 460 

Northampton 55667 Lower Mount Bethel Energy 232 

Northumberland 10343 Foster Wheeler Mt Carmel Cogen 47.3 

Northumberland 50771 Viking Energy of Northumberland 18 

Philadelphia 3169 Schuylkill Generating Station 190.4 

Philadelphia 54785 Grays Ferry Cogeneration 57.6 

Schuylkill 10113 John B Rich Memorial Power Station 88.4 

Schuylkill 50611 WPS Westwood Generation LLC 36 

Schuylkill 50879 Wheelabrator Frackville Energy 48 

Schuylkill 54634 St Nicholas Cogen Project 99.2 

Snyder 3152 Sunbury Generation LP 437.9 

Venango 50974 Scrubgrass Generating Company LP 94.7 

Washington 3098 Elrama Power Plant 510 

Washington 3181 FirstEnergy Mitchell Power Station 373.9 

Washington 55710 Allegheny Energy Units 3 4 & 5 188 

York 3140 PPL Brunner Island 1,558.7 

York 3166 Peach Bottom 2,319.4 

York 50215 York County Resource Recovery 36.5 

York 54693 York Generation Company LLC 19 

Rhode Island (6 power plants)

Newport 55048 Tiverton Power Plant 93.2 

Providence 3236 Manchester Street 140.0 

Providence 51030 Ocean State Power 88.6 

Providence 54056 Pawtucket Power Associates 27 

Providence 54324 Ocean State Power II 88.6 

Providence 55107 Rhode Island State Energy Partners 204 

South Carolina (21 power plants)

Aiken 3295 Urquhart 250 

Aiken 7652 US DOE Savannah River Site (D Area) 78.2 
Appendixes 1–3 65

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Anderson 3264 W S Lee 355 

Anderson 7834 John S Rainey 190 

Berkeley 130 Cross 2,390.1 

Berkeley 3298 Williams 632.7 

Berkeley 3319 Jefferies 445.6 

Calhoun 55386 Columbia Energy Center 274.5 

Charleston 7737 Cogen South 99.2 

Cherokee 55043 Cherokee County Cogen 41.2 

Colleton 3280 Canadys Steam 489.6 

Darlington 3251 H B Robinson 975.2 

Fairfield 6127 V C Summer 1,029.6 

Georgetown 6249 Winyah 1,260 

Horry 3317 Dolphus M Grainger 163.2 

Jasper 55927 Jasper 405 

Lexington 3287 McMeekin 293.6 

Oconee 3265 Oconee 2,666.7 

Orangeburg 7210 Cope 417.3 

Richland 3297 Wateree 771.8 

York 6036 Catawba 2,410.2 

South Dakota (2 power plants)

Grant 6098 Big Stone 456 

Pennington 3325 Ben French 25 

Tennessee (10 power plants)

Anderson 3396 Bull Run 950 

Hamilton 6152 Sequoyah 2,441 

Hawkins 3405 John Sevier 800 

Haywood 7845 Lagoon Creek 257.6 

Humphreys 3406 Johnsonville 1,485.2 

Rhea 7722 Watts Bar Nuclear Plant 1,269.9 

Roane 3407 Kingston 1,700 

Shelby 3393 Allen Steam Plant 990 

Stewart 3399 Cumberland 2,600 

Sumner 3403 Gallatin 1,255.2 

Texas (111 power plants)

Atascosa 6183 San Miguel 410 

Bastrop 3601 Sim Gideon 639 

Bastrop 55154 Lost Pines 1 Power Project 204 

Bastrop 55168 Bastrop Energy Center 285 

Bexar 3609 Leon Creek 188.7 

Bexar 3611 O W Sommers 892 
66 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Bexar 3612 V H Braunig 894 

Bexar 6181 J T Deely 932 

Bexar 7097 J K Spruce 1,444 

Bexar 7512 Arthur Von Rosenberg 200 

Bosque 55172 Bosque County Peaking 345 

Brazoria 54676 Oyster Creek Unit VIII 200.9 

Brazos 6243 Dansby 105 

Cameron 3559 Silas Ray 27 

Chambers 3460 Cedar Bayou 1,530 

Chambers 55327 Baytown Energy Center LLC 275 

Chambers 56806 Cedar Bayou 4 178.5 

Cherokee 3504 Stryker Creek 703.4 

Collin 3576 Ray Olinger 345 

Dallas 3452 Lake Hubbard 927.5 

Dallas 3453 Mountain Creek 852.2 

Denton 4266 Spencer 126.5 

Ector 55215 Odessa Ector Generating Station 448.4 

Ector 56349 Quail Run Energy Center 250 

El Paso 3456 Newman 405 

Ellis 55091 Midlothian Energy Facility 1,734 

Ellis 55223 Ennis Power Company LLC 133 

Fannin 3508 Valley NG Power Company LLC 594.9 

Fayette 6179 Fayette Power Project 1,690 

Fort Bend 3470 W A Parish 3,992.1 

Fort Bend 55357 Brazos Valley Generating Facility 275.6 

Freestone 3497 Big Brown Power Company LLC 1,186.8 

Freestone 55226 Freestone Power Generation LLC 369.2 

Frio 3630 Pearsall 66 

Galveston 52088 Texas City Cogeneration LLC 141 

Goliad 6178 Coleto Creek 622.4 

Gregg 3476 Knox Lee 501 

Grimes 6136 Gibbons Creek 453.5 

Grimes 55062 Tenaska Frontier Generation Station 390.1 

Guadalupe 55137 Rio Nogales Power Project 373.2 

Guadalupe 55153 Guadalupe Generating Station 403.8 

Harris 3464 Greens Bayou 446.4 

Harris 3468 Sam Bertron 826.2 

Harris 3469 T H Wharton 226.2 

Harris 10670 AES Deepwater 184 

Harris 10741 Clear Lake Cogeneration Ltd 78.2 
Appendixes 1–3 67

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Harris 50815 Optim Energy Altura Cogen LLC 129.2 

Harris 55047 Pasadena Cogeneration 270 

Harris 55187 Channelview Cogeneration Plant 149.9 

Harris 55299 Channel Energy Center LLC 285 

Harris 55464 Deer Park Energy Center 276 

Harrison 7902 Pirkey 721 

Harrison 55176 Eastman Cogeneration Facility 127.7 

Harrison 55664 Harrison County Power Project 230 

Hays 55144 Hays Energy Project 989 

Henderson 3507 Trinidad 239.3 

Hidalgo 55098 Frontera Energy Center 185 

Hidalgo 55123 Magic Valley Generating Station 267 

Hidalgo 55545 Hidalgo Energy Center 198.1 

Hood 55139 Wolf Hollow I LP 280 

Howard 52176 C R Wing Cogen Plant 75 

Hunt 4195 Powerlane Plant 84.7 

Johnson 54817 Johnson County 104.4 

Kaufman 55480 Forney Energy Center 765 

Lamar 50109 Paris Energy Center 90 

Lamar 55097 Lamar Power Project 404 

Lamb 3485 Plant X 434.4 

Lamb 6194 Tolk 1,136 

Limestone 298 Limestone 1,867.2 

Llano 4937 Thomas C Ferguson 446 

Lubbock 3482 Jones 496 

Lubbock 3602 Ty Cooke 97.6 

Lubbock 3604 J Robert Massengale 44 

Marion 3478 Wilkes 882 

Matagorda 6251 South Texas Project 2,708.6 

Milam 6648 Sandow No 4 590.6 

Montgomery 3457 Lewis Creek 542.8 

Moore 3483 Moore County 49 

Morris 3477 Lone Star 40 

Newton 55358 Cottonwood Energy Project 624 

Nueces 3441 Nueces Bay 351 

Nueces 4939 Barney M Davis 703 

Nueces 55206 Corpus Christi Cogeneration LLC 195.5 

Orange 3459 Sabine 2,051.2 

Orange 55104 Sabine Cogen 27 

Orange 55120 SRW Cogen LP 145 
68 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Palo Pinto 3628 R W Miller 366 

Potter 3484 Nichols 474.7 

Potter 6193 Harrington 1,080 

Robertson 6180 Oak Grove 916.8 

Robertson 7030 Twin Oaks Power One 349.2 

Rusk 6146 Martin Lake 2,379.6 

Rusk 55132 Tenaska Gateway Generating Station 390 

San Patricio 55086 Gregory Power Facility 100 

Somervell 6145 Comanche Peak 2,430 

Tarrant 3491 Handley 1,314.8 

Tarrant 55309 Air Products Port Arthur 23.5 

Titus 6139 Welsh 1,674 

Titus 6147 Monticello 1,980 

Travis 3548 Decker Creek 726 

Travis 7900 Sand Hill 190 

Victoria 3443 Victoria 180 

Victoria 3631 Sam Rayburn 67 

Ward 3494 Permian Basin 535.5 

Wharton 56350 Colorado Bend Energy Center 220.1 

Wichita 50127 Signal Hill Wichita Falls Power LP 20 

Wilbarger 127 Oklaunion 720 

Wise 55230 Jack County 300 

Wise 55320 Wise County Power LLC 262 

Yoakum 55065 Mustang Station 172.6 

Young 3490 Graham 634.7 

Utah (11 power plants)

Beaver 299 Blundell 38.1 

Carbon 3644 Carbon 188.6 

Carbon 50951 Sunnyside Cogen Associates 58.1 

Davis 55302 Wasatch Energy Systems Energy Recovery 1.6 

Emery 6165 Hunter 1,472.2 

Emery 8069 Huntington 996 

Millard 6481 Intermountain Power Project 1,640 

Salt Lake 3648 Gadsby 251.6 

Uintah 7790 Bonanza 499.5 

Utah 56177 Nebo Power Station 75 

Utah 56237 Lake Side Power Plant 225.9 

Vermont (3 power plants)

Caledonia 51026 Ryegate Power Station 21.5 

Chittenden 589 J C McNeil 59.5 

Windham 3751 Vermont Yankee 563.4 
Appendixes 1–3 69

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Virginia (25 power plants)

Alexandria 3788 Potomac River 514 

Campbell 10773 Altavista Power Station 71.1 

Chesapeake 3803 Chesapeake 649.5 

Chesterfield 3797 Chesterfield 1,506.5 

City of Richmond 54081 Spruance Genco LLC 229.6 

Fairfax 50658 Covanta Fairfax Energy 124 

Fluvanna 3796 Bremo Bluff 254.2 

Fluvanna 55439 Tenaska Virginia Generating Station 396.2 

Giles 3776 Glen Lyn 337.5 

Halifax 7213 Clover 848 

Harrisonburg City 56006 Harrisonburg Power Plant 2.5 

Hopewell City 10377 James River Genco LLC 114.8 

Hopewell City 10771 Hopewell Power Station 71.1 

King George 54304 Birchwood Power 258.3 

Louisa 6168 North Anna 1,959.4 

Mecklenburg 52007 Mecklenburg Power Station 139.8 

Pittsylvania 52118 Multitrade of Pittsylvania LP 90 

Portsmouth City 10071 Portsmouth Genco LLC 114.8 

Prince George 10633 Hopewell Cogeneration 96 

Prince William 3804 Possum Point 1,499.9 

Richmond 50966 Bellmeade Power Station 110 

Russell 3775 Clinch River 712.5 

Southampton 10774 Southampton Power Station 71.1 

Surry 3806 Surry 1,695 

York 3809 Yorktown 1,257 

Washington (13 power plants)

Benton 371 Columbia Generating Station 1,200 

Cowlitz 55700 Mint Farm Generating Station 133 

Grays Harbor 7999 Grays Harbor Energy Facility 300 

Klickitat 55482 Goldendale Generating Station 114.3 

Lewis 3845 Transalta Centralia Generation 1,539.8 

Pierce 55818 Frederickson Power LP 126.3 

Skagit 54268 March Point Cogeneration 27 

Snohomish 7627 Everett Cogen 42 

Spokane 50886 Wheelabrator Spokane 26 

Stevens 550 Kettle Falls Generating Station 50.7 

Whatcom 7870 Encogen 58.2 

Whatcom 54476 Sumas Power Plant 37.7 

Whatcom 54537 Tenaska Ferndale Cogeneration Station 71.8 
70 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

West Virginia (15 power plants)

Grant 3954 Mt Storm 1,662.4 

Harrison 3944 FirstEnergy Harrison Power Station 2,052 

Kanawha 3936 Kanawha River 439.2 

Marion 3945 FirstEnergy Rivesville 109.7 

Marion 10151 Grant Town Power Plant 95.7 

Marshall 3947 Kammer 712.5 

Marshall 3948 Mitchell 1,632.6 

Mason 3938 Philip Sporn 1,105.5 

Mason 6264 Mountaineer 1,300 

Monongalia 3943 FirstEnergy Fort Martin Power Station 1,152 

Monongalia 10743 Morgantown Energy Facility 68.9 

Pleasants 3946 FirstEnergy Willow Island 213.2 

Pleasants 6004 FirstEnergy Pleasants Power Station 1,368 

Preston 3942 FirstEnergy Albright 278.2 

Putnam 3935 John E Amos 2,932.6 

Wisconsin (26 power plants)

Ashland 3982 Bay Front 67.2 

Brown 4072 Pulliam 350.2 

Buffalo 4140 Alma 181 

Buffalo 4271 John P Madgett 387 

Columbia 8023 Columbia 1,023 

Dane 3992 Blount Street 177.5 

Dane 7991 West Campus Cogeneration Facility 61.3 

Grant 4054 Nelson Dewey 200 

Grant 4146 E J Stoneman Station 53 
Appendixes 1–3 71

Appendix 1. The 1,284 thermoelectric plants one megawatt nameplate capacity or greater with water-cooling systems in the United 

States, 2010.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; MW, megawatt; initials associated with a power plant are part of the plant name, 

and not spelled out; --, nameplate capacity is not reported for the steam side of the combined-cycle power plant]

County EIA 

plant code Plant name Nameplate capacity (MW)

Jefferson 55011 LSP-Whitewater LP 106.2 

Kenosha 6170 Pleasant Prairie 1,233 

Kewaunee 8024 Kewaunee 560.1 

La Crosse 4005 French Island 30.4 

Manitowoc 4046 Point Beach Nuclear Plant 1,047.6 

Manitowoc 4125 Manitowoc 127.4 

Marathon 4078 Weston 1,087.1 

Milwaukee 4041 South Oak Creek 1,191.6 

Milwaukee 4042 Valley 272 

Milwaukee 7549 Milwaukee County 11 

Milwaukee 56068 Elm Road Generating Station 701.3 

Outagamie 56031 Fox Energy Center 250 

Ozaukee 4040 Port Washington Generating Station 538 

Rock 55641 Riverside Energy Center 299.7 

Sheboygan 4050 Edgewater 770 

Vernon 4143 Genoa 345.6 

Winnebago 4127 Menasha 28 

Wyoming (8 power plants)

Albany 6204 Laramie River Station 1,710 

Campbell 4150 Neil Simpson 21.7 

Campbell 7504 Neil Simpson II 80 

Campbell 56319 Wygen 2 95 

Campbell 56596 Wygen III 116.2 

Converse 4158 Dave Johnston 816.7 

Lincoln 4162 Naughton 707.2 

Sweetwater 8066 Jim Bridger 2,317.7 
72 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 2. Guide to data contained in the U.S. Department of Energy, Energy Information Administration (EIA) 2010 Annual Electric 

Generator Data, Form-860, and the 2010 Power Plant Operations Report, Form EIA-923, used for the classification of thermoelectric 

plants and consumption estimation model input. 

Evaporation at thermoelectric plants depends on the method by which electricity is generated (generation type) and the 

method by which waste heat is transferred to the atmosphere (cooling-system type). Because generation type and cooling-system 

type vary independently of one another and represent two distinct stages in the overall process of thermoelectric water consumption, it is useful to consider them separately. Such consideration leads to a two-tiered classification of thermoelectric plants that 

provides the analytical framework for the estimation methods presented in this report. 

Appendix 2, table 1 shows the data fields used in the classification scheme and consumption estimation model from the 

2010 Annual Electric Generator Data database, which is the repository for data from Form EIA-860, and the 2010 Power Plant 

Operations Report, which is the repository for data from Form EIA-923. The Description column describes the corresponding 

field name in Field Name; Form, data field source, either Form EIA-860 or Form EIA-923; File, database spreadsheet name, 

see file names below; Tab, spreadsheet tab associated with a corresponding spreadsheet file; Field Name, field name descriptor. 

Accessed on August 7, 2013 at http://www.eia.gov/electricity/data/eia860/index.html and http://www.eia.gov/electricity/data/

eia923/.

Survey Form EIA-860 collects generator-level specific information about existing and planned generators and associated 

environmental equipment at electric power plants with one megawatt or greater of combined nameplate capacity. Survey Form 

EIA-923 collects detailed electric power data—monthly and annually—on electricity generation, fuel consumption, fossil fuel 

stocks, and receipts at the power plant and prime mover level. Data fields shown in Appendix 2, table 1 are grouped by power 

plant technology type—generator, boiler, flue gas desulfurization, and cooling system—and by plant characteristics and boilerunit associations.

Selected Form EIA-860 survey files and tabs:

• LayoutY2010—Provides a directory of all (published) data elements collected on the Form EIA-860 together with the 

related description, specific file location(s), and, where appropriate, an explanation of codes (Appendix 2, tables 1–4).

• GeneratorY2010—Contains generator-level data for the surveyed generators, split into three tabs. The Exist tab includes 

those generators that are currently operating, out of service, or on standby. 

• PlantY2010—Contains plant-level data for the generators surveyed in all available years. 

• EnviroAssocY2010—Contains boiler association data for the environmental equipment data collected on the Form 

EIA-860. The Boiler_Gen identifies which boilers are associated with each generator; the Boiler_Cool tab shows which 

cooling systems are associated with each boiler. 

• EnviroEquipY2010—Contains environmental equipment data for the surveyed generators. The Boiler tab collects boiler 

data as collected on Schedule 6, Parts B, and C of the Form EIA-860; the Cooling tab collects cooling system data as collected on Schedule 6, Part F; the FGD tab collects FGD data as collected on Schedule 6, Part H. 

Specific Form EIA-923 survey files and tabs: 

• EIA923 SCHEDULES 2_3_4_5 Final 2010—contains monthly and annual operational data as collected on Schedules 

2, 3, 4, and 5. Tab Page 1 Generation and Fuel Data contains the quantity of fuel consumed, the heat content of fuels, and 

the net generation of electricity data as collected on Schedules 3 and 5, parts A. Tab Page 3 Boiler Fuel Data contains the 

quantity of fuel consumed in the boiler and the heat, sulfur, and ash content of the fuels for steam-electric, organic-fueled 

electric power plants as collected on Schedule 3, Part A.

• EIA923 SCHEDULE 3A 5A 8A 8B 8C 8D 8E 8F 2010 on NOV 30 2011—contains monthly and annual operational 

data as collected on Schedules 3, 5, and 8. Tab Cooling Operations contains monthly cooling water data that includes 

diversion, withdrawal, discharge, and consumption rates as well as water temperature data as collected on Schedule 8, 

Part D. Tab FGD Operations contains annual FGD data as collected on Schedule 8, Part F. 

Appendix 2, tables 2, 3, and 4 detail the codes and descriptions of the specific prime mover technologies and energy 

sources by generator type, and the cooling-system types contained in the files and tabs outlined in Appendix 2, table 1.
Appendixes 1–3 73

Appendix 2. Table 1. Data used in the two-tiered classification system for thermoelectric plants and heat and water budget models 

to estimate water consumption from the 2010 Annual Electric Generator Data, EIA Form-860 and 2010 Power Plant Operations Report, 

Form EIA-923.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; FERC, Federal Energy Regulatory Commission; Btu, British thermal unit; blue highlighted field name detailed in Appendix 2, tables 2, 3, and 4]

Description Form File Tab Field name

Plant descriptors

EIA-assigned plant code 860 and 923 All files All tabs PLANT_CODE

Name of plant 860 and 923 All files All tabs PLANT_NAME

State location of plant 860 and 923 All files All tabs STATE

Combined heat and power system status of 

the generator 860 GeneratorY2010 Exist COGENERATOR

FERC-qualifying cogenerator status of the 

plant 860 PlantY2010 FERC_COGEN

North American Industry Classification 

System (NAICS) code indicating the 

primary purpose of the reporting plant

860 PlantY2010 PRIMARY_PURPOSE

Plant-level sector name and number, 

designated by the plant’s NAICS code, 

regulatory status and FERC-cogen status

860 PlantY2010 SECTOR_NAME and SECTOR_NUMBER

Generator technology

Generator identification code 860 GeneratorY2010 Exist GENERATOR_ID

Operating status of the generator 860 GeneratorY2010 Exist STATUS

Prime mover code (turbine that converts 

the energy in heated gases to mechanical 

energy)

860 GeneratorY2010 Exist PRIME_MOVER

The generator nameplate capacity in 

megawatts

860 GeneratorY2010 Exist NAMEPLATE

Fuels used to power the generator 860 GeneratorY2010 Exist ENERGY_SOURCE

Net generation of electricity in megawatt 

hours (MWh) per month and for the 

year. Note: This is total electrical output 

net of station service. In the case of combined heat and power plants, this value 

is intended to include internal consumption of electricity for the purposes of a 

production process, as well as power put 

on the grid.

923 EIA923 SCHEDULES 

2_3_4_5 Final 2010

Page 1 Generation 

and Fuel Data Electricity Net Generation

Boiler Technology

Boiler identification code 860 EnviroEquipY2010 Boiler BOILER_ID

Operating status of the boiler 860 EnviroEquipY2010 Boiler BOILER_STATUS

Fuels used to power the boiler 860 EnviroEquipY2010 Boiler PRIMARY_FUEL

Boiler efficiency when burning at 100 

percent load (nearest 0.1 percent) 860 EnviroEquipY2010 Boiler EFFICIENCY_100_PCT_LOAD

Boiler efficiency when burning at 50 

percent load (nearest 0.1 percent) 860 EnviroEquipY2010 Boiler EFFICIENCY_50_PCT_LOAD
74 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 2. Table 1. Data used in the two-tiered classification system for thermoelectric plants and heat and water budget models 

to estimate water consumption from the 2010 Annual Electric Generator Data, EIA Form-860 and 2010 Power Plant Operations Report, 

Form EIA-923.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; FERC, Federal Energy Regulatory Commission; Btu, British thermal unit; blue highlighted field name detailed in Appendix 2, tables 2, 3, and 4]

Description Form File Tab Field name

Total consumption of the fuel, in millions 

of Btus, per month and for the year. 

Note: this is the total quantity consumed 

for both electricity and, in the case of 

combined heat and power plants, process 

steam production (nominal for nuclear, 

geothermal, and solar plants).

923 EIA923 SCHEDULES 

2_3_4_5 Final 2010

Page 1 Generation 

and Fuel Data Total Fuel Consumed

Consumption of fuel in millions of Btus for 

the purpose of generating electricity, per 

month and for the year. Note: These data 

are relevant to combined heat and power 

plants. 

923 EIA923 SCHEDULES 

2_3_4_5 Final 2010

Page 1 Generation 

and Fuel Data

Quantity Consumed for Electricity

Cooling system technology

Cooling system identification code 860 EnviroEquipY2010 Cooling COOLING_ID

Operating status of the cooling system 860 EnviroEquipY2010 Cooling COOLING_STATUS

Cooling system code for the type of 

cooling system(s) at the plant (multiple codes for primary, secondary, and 

tertiary cooling systems)

860 EnviroEquipY2010 Cooling COOLING_TYPE

Name of the principal source from which 

cooling water is directly obtained. 860 EnviroEquipY2010 Cooling COOLING_WATER_SOURCE

Cooling water flow rate at 100 percent load 

at intake (cubic feet per second) 860 EnviroEquipY2010 Cooling INTAKE_RATE_AT_100_PCT

Total surface area of cooling pond (in 

acres) 860 EnviroEquipY2010 Cooling POND_SURFACE_AREA

The hours that the cooling system operated 

in the month. 923

SCHEDULE 3A 5A 

8A 8B 8C 8D 8E 8F 

2010 on NOV 30 2011

Cooling Operations Hours in Service

Average rate of diversion, withdrawal, 

discharge, and consumption of cooling 

water per cooling unit each month (0.1 

cubic feet per second)

923 Cooling Operations Diversion Rate

923 Cooling Operations Withdrawal Rate

923 Cooling Operations Discharge Rate

923 Cooling Operations Consumption Rate

Method used to measure or estimate flow 

rates

923 Cooling Operations Method for Flow Rates

Cooling water average and maximum 

temperature at cooling-water intake and 

discharge outlet (Fahrenheit to the nearest whole number)

923

SCHEDULE 3A 5A 

8A 8B 8C 8D 8E 8F 

2010 on NOV 30 2011

Cooling Operations Intake AverageTemperature

923 Cooling Operations Intake Maximum Temperature

923 Cooling Operations Discharge Average Temperature

923 Cooling Operations Discharge Maximum Temperature

Method used to measure or estimate water 

temperatures

923 Cooling Operations Method for Temperatures
Appendixes 1–3 75

Appendix 2. Table 1. Data used in the two-tiered classification system for thermoelectric plants and heat and water budget models 

to estimate water consumption from the 2010 Annual Electric Generator Data, EIA Form-860 and 2010 Power Plant Operations Report, 

Form EIA-923.—Continued

[EIA, U.S. Department of Energy, Energy Information Administration; FERC, Federal Energy Regulatory Commission; Btu, British thermal unit; blue highlighted field name detailed in Appendix 2, tables 2, 3, and 4]

Description Form File Tab Field name

Flue gas desulfurization (FGD) technology

Flue gas desulfurization (FGD) unit 

identification code 923 SCHEDULE 3A 5A 

8A 8B 8C 8D 8E 8F 

2010 on NOV 30 2011

FGD Operations FGD ID

Operating status of the FGD unit 923 FGD Operations FGD Unit Status

FGD unit code for the type of FGD unit 

used 860 EnviroEquipY2010 FGD FGD_TYPE

Type of fuel used for generation. Only 

certain fuels are associated with FGD 

systems.

923 EIA923 SCHEDULES 

2_3_4_5 Final 2010

Page 3 Boiler Fuel 

Data Reported Fuel Type Code

Boiler unit associations

EIA-assigned plant code 860 EnviroAssocY2010 Boiler_Gen; and, 

Boiler_Cool PLANT_CODE

Boiler identification code; this database 

provides tables that associate plant boiler 

units with their paired generator and 

cooling system units.

860 EnviroAssocY2010 Boiler_Gen; and, 

Boiler_Cool BOILER_ID

Generator identification code 860 EnviroAssocY2010 Boiler_Gen GENERATOR_ID

Code indicating whether the boiler and 

generator associations during the year 

were actual (A) or theoretical (T).

860 EnviroAssocY2010 Boiler_Gen GENERATOR_ASSOCIATION

Cooling system identification code 860 EnviroAssocY2010 Boiler_Cool COOLING_ID

Appendix 2. Table 2. Prime mover types used to classify plants according to generation type [U.S. Department of Energy, Energy 

Information Administration (EIA), 2010 Annual Electric Generator Report, LayoutY2010. See Appendix 2, table 1, Generator Technology, 

PRIME_MOVER].

[Prime mover is the turbine that converts the energy in heated gases to mechanical energy.]

EIA prime mover code Generation type 

Prime mover description

Combustion steam, nuclear, geothermal, solar thermal

ST Steam turbine

Combined cycle

CA Combined cycle steam part 

CS Combined cycle single shaft (combustion turbine and steam turbine share a single generator.)

CT Combined cycle combustion turbine part 

Geothermal

BT Turbines used in a binary cycle 
76 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 2. Table 3. Energy sources used to classify plants by generation type [U.S. Department of Energy, Energy Information 

Administration (EIA), 2010 Annual Electric Generator Report, LayoutY2010. See Appendix 2, table 1, Generator Technology, ENERGY_

SOURCE].

EIA energy source code Generation type 

Energy source description

Combustion steam

AB Agriculture crop by-products/straw/energy crops

BFG Blast-furnace gas

BIT Bituminous coal

BLQ Black liquor

DFO Distillate fuel oil (all diesel, and no. 1, no. 2, and no. 4 fuel oils)

LFG Landfill gas

LIG Lignite

MSB Municipal solid waste - biogenic

NG Natural gas

OBS Other biomass solids (animal manure and waste, solid by-products, and other solid biomass not specified)

OG Other gas (coke-oven, coal processes, butane, refinery, other process)

PC Petroleum coke

RFO Residual fuel oil (includes no. 5, and no. 6 fuel oil, and bunker C fuel oil)

SUB Subbituminous coal

TDF Tires

WC Waste/other coal (culm, gob, coke, and breeze)

WDS Wood/wood waste solids (paper pellets, railroad ties, utility poles, wood chips, and other wood solids)

Combined cycle

BFG Blast-furnace gas

BIT Bituminous coal

DFO Distillate fuel oil (all diesel, and no. 1, no. 2, and no. 4 fuel oils)

JF Jet fuel

KER Kerosene

LFG Landfill gas

NG Natural gas

OG Other gas (coke-oven, coal processes, butane, refinery, other process)

OTH Other (batteries, chemicals, hydrogen, pitch, sulfur, miscellaneous technologies)

RFO Residual fuel oil (includes no. 5, and no. 6 fuel oil, and bunker C fuel oil)

WH Waste heat

WO Oil—other, and waste oil (butane, liquid), crude oil, liquid by-products, propane (liquid), oil waste, re-refined 

motor oil, sludge oil, tar oil)

Nuclear

NUC Nuclear (uranium, plutonium, thorium)

Geothermal

GEO Geothermal

Solar thermal

SUN Solar (thermal)
Appendixes 1–3 77

Appendix 2. Table 4. Cooling-sytem types used to classify plants by cooling system technology [U.S. Department of Energy, Energy 

Information Administration (EIA), 2010 Annual Electric Generator Report, LayoutY2010. See Appendix 2, table 1, Cooling System 

Technology, COOLING_TYPE].

EIA cooling type code Cooling-system type 

Cooling type description

Dry (air) cooling system

DC Air cooling systems

Hybrid cooling systems

HRC Hybrid: recirculating cooling pond(s) or canal(s) with dry cooling

HRF Hybrid: recirculating with forced draft cooling tower(s) with dry cooling

HRI Hybrid: recirculating with induced draft cooling tower(s) with dry cooling

Once-through cooling systems

OC Once through with cooling pond(s) or canal(s)

OF Once through, freshwater

OS Once through, saline water

Recirculating cooling systems

RC Recirculating with cooling pond(s) or canal(s)

RF Recirculating with forced draft cooling tower(s)

RI Recirculating with induced draft cooling tower(s)

RN Recirculating with natural draft cooling tower(s)

Other cooling system

OT Other
78 Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States

Appendix 3. Databases accessed for thermoelectric plant classification and modeling data.

ECHO is the U.S. Environmental Protection Agency’s Enforcement & Compliance History Online database. ECHO contains 

location latitude/longitude coordinates in decimal degrees for power plants with regulated water discharge data. ECHO database 

water data come from two data sets: the Permit Compliance System (PCS) for Clean Water Act permitted dischargers (under the 

National Pollutant Discharge Elimination System, or NPDES), and the Integrated Compliance Information System (ICIS) for 

Clean Water Act permitted dischargers (under the NPDES). Accessed January 22, 2013 at http://www.epa-echo.gov/echo. 

eGRID is the U.S. Environmental Protection Agency’s Emissions & Generation Resource Integrated Database. The eGRID 

database provides data on electric generation and air emissions of U.S. power plants and includes plant locations in latitude/

longitude decimal degrees. Accessed January 22, 2013 at http://www.epa.gov/cleanenergy/energy-resources/egrid/index.

html#download.

Form EIA-860 is the Department of Energy’s Energy Information Administration electricity database for the Annual Electric 

Generator Report. The database provides generator-level information on existing and proposed generators and includes design 

parameter data on associated environmental equipment. Accessed January 29, 2013 at http://www.eia.gov/electricity/data/

eia860/index.html.

Form EIA-923 is the Department of Energy’s Energy Information Administration electricity database for the Power Plant 

Operations Report. The database provides monthly and annual operational data on electricity generation, fuel consumption, and 

environmental equipment. Accessed January 29, 2013 at http://www.eia.gov/electricity/data/eia923/.

GLSEA2 is the National Oceanic and Atmospheric Administration’s Great Lakes Surface Environmental Analysis, which is a 

daily digitial map of lake surface temperatures and ice cover that is produced by the Great Lakes Environmental Research 

Laboratory. Accessed March 8, 2013, at http://coastwatch.glerl.noaa.gov/glsea/doc/.

HSIP Gold is the Department of Homeland Security’s Homeland Security Infrastructure Program database. The HSIP provides 

power plant information from several databases including latitude and longitude coordinates. Accessed January 24, 2013 at 

https://www.hifldwg.org/public/HSIP%20Gold%20Freedom%20One%20Pager_July%202012.pdf.

NEEDS is the National Electric Energy Data System database, and it contains the generation unit records used to construct the 

“model” plants that represent existing and planned/committed units in USEPA modeling applications of the Integrated Planning 

Model (IPM). NEEDS includes basic geographic, operating, air emissions, and other data on these generating units. NEEDS 

was completely updated for Base Case v.4.10. For a description of the sources used in preparing NEEDS v.4.10, see Base Case 

v.4.10 Documentation, Chapter 4: Generating Resources. Accessed August 1, 2013 at http://www.epa.gov/airmarkt/progsregs/

epa-ipm/BaseCasev410.html#needs.

NETL-CPPDB is the Department of Energy’s National Energy Technology Laboratory, Coal Power Plant Database. NETL’s 

CPPDB provides electric generation and emissions information for all coal power plants in the U.S. and includes locations in 

latitude/longitude decimal degrees. Accessed January 13, 2013 at http://www.netl.doe.gov/energy-analyses/hold/technology.html.

NHDPlus is an integrated suite of application-ready geospatial data products, incorporating many of the best features of the 

National Hydrography Dataset (NHD), the National Elevation Dataset (NED), and the National Watershed Boundary Dataset 

(WBD). NHDPlus includes a stream network based on the medium resolution NHD (1:100,000 scale), improved networking, 

feature naming, and “value-added attributes” (VAA). NHDPlus also includes elevation-derived catchments produced using a 

drainage enforcement technique. This technique involves enforcing the 1:100,000-scale NHD drainage network by modifying 

the NED elevations to fit with the network via trenching and using the WBD, where certified WBD is available, to enforce 

hydrologic divides. The resulting modified digital elevation model (DEM) was used to produce hydrologic derivatives that 

closely agree with the NHD and WBD. An interdisciplinary team from the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA) found this method to produce the best-quality agreement among the ingredient datasets 

among the various methods tested. Accessed January 29, 2013 at http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.

php.

NWIS is the U.S. Geological Survey’s National Water Information System database. NWIS provides surface-water and groundwater resource data collected at streamgaging stations throughout the United States. It includes current and historical daily mean 

surface water temperature data. Accessed March 8, 2013, at http://waterdata.usgs.gov/nwis/sw.

QCLCD is the National Oceanic and Atmospheric Administration National Climatic Data Center, Quality Controlled Local 

Climatological Data database, and it consists of hourly, daily, and monthly summaries of meteorological observations. Accessed 

March 8, 2013, at http://www.ncdc.noaa.gov/land-based-station-data/quality-controlled-local-climatological-data-qclcd. 

QCLCD documentation accessed March 8, 2013, at http://cdo.ncdc.noaa.gov/qclcd/qclcddocumentation.pdf. 
Prepared by USGS West Trenton Publishing Service Center.

Edited by Valerie M. Gaine.

Graphics and layout by Timothy W. Auer.

Sankey diagrams by Timothy H. Diehl.

For additional information, contact:

Eric J. Evenson

Coordinator—National Water Census

U.S. Geological Survey

3450 Princeton Pike, Suite 110

Lawrenceville, NJ 08648

or visit the National Water Census Web site at:

http://water.usgs.gov/watercensus
Diehl, T.H., and others—Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States—Scientific Investigations Report 2013–5188

Printed on recycled paper

ISSN 2328-031X (print)

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20135188