## 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

For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment, visit http://www.usgs.gov or call 1-888-ASK-USGS For an overview of USGS information products, including maps, imagery, and publications, visit http://www.usgs.gov/pubprod To order this and other USGS information products, visit http://store.usgs.gov

Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. 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

A bstract

............................................................................................................................................................................. 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

........................................................................................... 2 3 18. Graph showing evaporation ratio in relation to wind speed, estimated using Brady and others (1969) wind function

................................................................................. 2 7 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

.............................................. 2 8



# 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 (m 2 )

acre 0.4047 hectare (ha)

acre 0.4047 square hectometer (hm 2 )

acre 0.004047 square kilometer (km 2 ) square centimeter (cm 2 ) 0.001076 square foot (ft 2 ) square centimeter (cm 2 ) 0.1550 square inch (ft 2 ) square meter (m 2 ) 0.0002471 acre square meter (m 2 ) 10.76 square foot (ft 2 ) Volume gallon (gal) 3.785 liter (L) gallon (gal) 0.003785 cubic meter (m 3 ) gallon (gal) 0.1337 cubic foot (ft 3 ) 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 (ft 3 ) 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/cm 3 ) 62.4220 pound per cubic foot (lb/ft 3 ) 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 10 6 joule (J) kilowatt hour (kWh) 3,412.14 British thermal unit (Btu) megawatt (MW) 1.0 x 10 6 joule per second (J/s) megawatt (MW) 8.6 x 10 8 calories per hour (cal/h) megawatt hour (MWh) 3.6 x 10 9 joule (J) megawatt hour (MWh) 3.4 x 10 6 British thermal unit (Btu) million British thermal unit

(MMBtu) 1.055 x 10 9 joule (J) 

# Conversion Factors and Datum

### vi

**Multiply By**  **To obtain** Rate calorie per centimeter squared

degrees Kelvin per day (cal/cm 2 °K/d) 0.484259 kilogram degrees kelvin per cubic second (kg°K/s 3 ) calorie per centimeter squared per

second [cal/(cm 2 /s)] 41,840 watts per meter squared (W/m 2 ) 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/(cm 2 /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 repre

sents 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 coef

ficients 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 genera

tion-type categories; data at solar thermal plants were insuf

ficient 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 consumption 1 

### is the water evapo

rated or incorporated into by-products as a result of the pro

duction of electricity from heat. Evaporation from the cooling system 1 

### accounts 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 thermo

electric water withdrawals , which are the largest single water withdrawal category (Kenny and others, 2009), thermoelec

tric 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, thermo

electric 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 tech

niques 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 gen

eration 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 con

sumption 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 evapo

ration 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 scien

tific 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 recom

mended 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 con

sumptive 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 toestimate 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 con

sumption values that are likely to be consistent with thermo

dynamic constraints. Reported values of consumption outside this range are unlikely to be correct based on apparent viola

tion of the thermodynamic constraints. Where the reported amount of water consumption is consistent with the estimated consumption, the reported number can be considered vali

dated. 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 evapora

tion cannot exceed the difference between the energy con

tained in consumed fuel and that contained in the plant’s elec

trical 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 consump

tion, 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 environ

mental 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 tech

nology 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 con

denser duty with different techniques for each generation type. The share of condenser duty that goes to produce evapo

ration depends on cooling-system type as well as a number of environmental conditions, such as plant elevation, air tempera

ture, 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 cool

ing-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 approxi

mately 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 radia

tion 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 tem

perature 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 inde

pendent of the type of cooling system; (2) model the evapora

tion 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 informa

tion, 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 esti

mated 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 cooling

system 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-charac

teristic 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 PowerPlant 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 (associa

tions) between boilers and generators, and flue-gas desul

furization (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 gen

eration 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 transmit

ted 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 comput

ing 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 cool

ing 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 imag

ery, 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 catch

ments (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 cre

ated. 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, how

ever, 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 publi

cally 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 dis

played. 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 ver

sion 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 genera

tion 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 loca

tions 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 towerstructure (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 evapora

tion 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 determi

nations. LCD consists of hourly, daily, and monthly summa

ries 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, tem

perature measurements of lakes and rivers from USGS water temperature stations and plant-reported intake temperatures provided by the EIA were used for water-temperature deter

minations. 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 obser

vations 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 determi

nation 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 t he 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 ther

mometer were retained. Furthermore, only plants that reported once-through freshwater cooling systems were retained. Remaining sites were then screened for extreme values andirregular 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 distri

bution 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 inter

polation 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 meth

ods (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 tempera

ture data were divided into local subsets. For each subset, a semivariogram model was estimated. Using this semivario

gram, 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 distribu

tions 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 determi

nation 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 valida

tion 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 uncer

tainty 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 tem

peratures 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 tempera

tures. For plants in Alaska and Hawaii, records from the near

est 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 consump

tion 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, genera

tors, 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-gener

ator 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 indepen

dently 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 mod

eled 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 **15 Figure 10.** Geographic distribution of the 554 weather stations containing 12 months of monthly mean dry bulb and wet bulb temperatures in2010.

### 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 **17 Figure 12.** Geographic distribution of the 755 water temperature stations (U.S. Geological Survey stations and greater than 100 megawattpower plants reporting intaketemperature) 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 heatfrom the combustion turbines’ exhaust, or a combinedcycle single-shaft turbine (prime mover types CT, CA, and CS, respectively). Nearly all of them burn primar

ily 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 geother

mal 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:

C

D T H S L N E A L X H = − − − − (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 thermo

electric plants in general, methods for calculating the terms in this equation differ among generation types, reflecting differences in physical characteristics and available informa

tion. 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 esti

mated 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:

0 0 2 *

. A L T H = (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; Inter

national 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 dif

ference between total and electric fuel is the monthly amountof 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

X H T H E H = − (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 bud

get with the total heat leaving the plant exceeding the total fuel heat entering the plant. This discrepancy indicates either mis

reported data or an inappropriately constructed heat budget. Due to lack of plant-specific data that would allow construc

tion 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:

1 * ( ) S

L T H B E = − (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 calcu

late 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 domi

nant 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 (%)** BIT89.01.71.35609,061,680,00686124,456,8291.4BLQ85.5NA-2.117,631,743000.0DFO85.60.8-2.05171,20924,6042.6LIG82.21.5-5.420576,523,03113,980,5930.7NG86.02.4-1.6306905,289,9008728,059,6383.0PC90.20.42.5886,502,485817,273,12516.6RFO89.21.81.54864,541,476116,680,3039.4SUB86.72.0-1.03577,859,995,1162360,398,6860.8WC86.61.3-1.0567,469,7551991,097,72957.5WDS85.2NA-2.414,686,30679178,471,77097.4Other fuels notlisted, estimatedfrom “all fuels”average87.62.40.000168282,058,71697.1All fuels87.62.40.01,31118,634,491,025484792,481,9945.5

### 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 esti

mate 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 intermit

tently 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 esti

mated 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, sug

gesting an imprecision of about plus or minus 5 percent in addition to imprecision in the reported efficiencies as surro

gates 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 electric

ity 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 tur

bines are reported at the plant level, so the heat budget mustbe 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 gener

ate 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 contra

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

0 2 *

. S

L T H = (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 com

bined-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 ther

mal 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 per

mitted 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 system

atically in the summer, generally reflecting the difference between reported winter and summer maximum capacity. It was assumed that this decrease was caused by operators reduc

ing 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 sum

mer, 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:

/ T

H N E T E = (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 geother

mal plants are generally lower and depend on the temperature of the geothermal resource (DiPippo, 2004; Dagdas and oth

ers, 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:

0 1 6 /

. T

H N E = (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 tempera

ture, ambient surface-water temperature, and wind speed. For surface-water cooling systems, the parameters used to calcu

late one variable in the model, the wind function, are given different values for ponds, lakes, and rivers. Most thermoelectric plants use evaporative cooling tow

ers 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, con

denser 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 recir

culating 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 con

denser 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 character

istics 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 tempera

ture of the ambient air. In a tower receiving warmed water, the water in the basin under it is warmer than the wet bulb tem

perature 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 trans

fer 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 cor

respond 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
m u

a a m u 

### Q L H H h   =  

−

−  

−   (8)

where L mu 

### = the mass of the water evaporated; Q

p 

### = the net heat rejected in the cooling tower; H

a1 

### = the specific heat of inflow air; H

a2 

### = the specific heat of outflow air;
ω 1 

### = the specific vapor content of inflow air;
ω 2 

### = the specific vapor content of outflow air; and h
mu 

### = the specific heat of the makeup water. The quantity 2 1

2 1 a a m u 

### H H h

   

−

−  

−   is the net heat added per mass of water evaporated.

### Computing Heat and Water Budgets **25**

As both H a2 

### and ω 2 

### are unknown, it is necessary to assume that the air leaving the cooling tower is saturated with water vapor. This allows H a2 

### to be defined in terms of the other variables, and the equation is solved iteratively to estimate L mu 

### . 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; pub

lished 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 tempera

ture, 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 estimat

ing cooling-tower evaporation based solely on dry bulb air temperature: 3 2 

### 0 0 0 0 2 7 9 0 0 0 1 0 9 1 0 3 4 5 2 6 7 1 0 0 (

. .

. . ) / a a

a 

### T T E

R T  

− +

= −  

− +   (9)

where ER is the ratio of the heat used to evaporate water to the total heat discharged through the tower, and T a 

### 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 incor

porated 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 evapora

tion 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 load

ing. The same model was used for all three types of water bod

ies, 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/(cm 2 

### /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/(cm 2 

### /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/(cm 2 

### /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 con

denser; 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 naturaland 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 itera

tively 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 tempera

tures to the sum of differences in evaporation, conduc

tion, 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/(cm 2 

### /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 evapora

tion, 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: 

# [ ] ( ) ( ) ( ) a 

### E T L f W e T e  = − (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 temper

ature T , e a 

### 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 

### 7 0 1 0 ( )

. * ( ) f W 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 L f W e T e T  ′ ∆ = − (15) Conduction is given by:


## ( ) ( ) ( ) p
a 

### p c C T f W T T 
  

= −     (16)

where p is atmospheric pressure in millibars, c p 

### 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 T a 

### is air temperature in degrees Celsius. The dif

ference in conduction from T to T′ is given by:


## ( ) ( ) p 

### p c C f W T T 
  
′ ∆ = −     

### (17) Radiation is given by: 

## ( ) 4 

### 2 7 3 ( ) r 

### R T 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 ε r 

### is the emissivity of the water surface, 0.97. The difference in radiation from T to T′ is given by: 

# ( ) ( ) 4 4 

### 2 7 3 1 5 2 7 3 1 5 ( )

. . r 

### R T T T     ′ = +

− +   (19)

### Computing Heat and Water Budgets **27**
The difference in the heat loss at the two temperatures Δ H is set equal to the condenser duty by iteratively adjusting the heated water temperature ( T′ ). The ratio of forced evapora

tion to condenser duty is given by:

/ E

R E H

= ∆ ∆ (20) Ward (1980) demonstrated that additional heat losses through evaporation, conduction, and radiation are approxi

mately 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 calcu

lated 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 cool

ing ponds but are inappropriate for low wind speeds; Ward’s formula is equation 3 in Diehl (2011), expressed here in differ

ent 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 bal

ance 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 litera

ture, 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 tem

perature 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; how

ever, 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 indepen

dent 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 thermo

electric. 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 reason

able boiler efficiency, analysis of combined sources of error pointed to actual errors of 5 to 6 percent; 10 percent is consid

ered 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 estimat

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

### 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 tempera

ture 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 towersat 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 blow

down 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 ther

moelectric 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-burningplants consumes water by incorporating the water into sulfurbearing minerals and by contributing water vapor to the stack gases. Based on average differences between water consump

tion 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 multi

plied 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 combus

tion 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 con

sume 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 con

sumption 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 con

sumption 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 thermodynam

ics 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 indepen

dent validation of consumption where it is measured. On a regional or national scale, budget-based consumption esti

mates 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 informa

tion 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 dif

ficult at best, and cannot supersede modeling of the evapora

tion 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 reli

able 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.


# Selected References

Anderson, E.R., 1954, Water-loss investigations—Lake Hefner studies, technical report: U.S. Geological Survey Professional Paper 269, 158 p. Ashwood, A., and Bharathan, D., 2011, Hybrid cooling sys

tems for low-temperature geothermal power production: Denver, Colorado, National Renewable Energy Laboratory, NREL/TP-5500-48765, 62 p. Brady, D.K., Graves, W.L., and Geyer, J.C., 1969, Surface heat exchange at power plant cooling lakes, report 5: Baltimore, Maryland, The Johns Hopkins University Department of Geography and Engineering, 154 p. Breiman, L., and Spector, P., 1992, Submodel selection and evaluation in regression: the x-random case: International Statistical Review, v. 60, no. 3, p. 291–319. California Energy Commission, 2005, A preliminary environ

mental profile of California’s imported electricity: Energy Commission staff report prepared in support of the 2005 Integrated energy policy report, 71 p., accessed April 23, 2013 at http://www.energy.ca.gov/2005publications/CEC700-2005-017/CEC-700-2005-017.PDF

. Cheremisinoff, N.P., and Cheremisinoff, P.N., 1981, Cooling

towers selection, design and practice: Ann Arbor, Michigan, Ann Arbor Science Publishers, 347 p. Clark, C.E., Harto, C.B., Sullivan, J.L., and Wang, M.Q.,

2010, Water use in the development and operation of geo

thermal power plants: Argonne, Illinois, Argonne National Laboratory, ANL/EVS/R-10/5, 74 p. Cohen G.E., Kearney, D.W., and Kolb, G.J., 1999, Final

report on the operation and maintenance improvement program for concentrating solar power plants: Albuquerque, New Mexico, Sandia National Laboratories (SNL), and Livermore, California, Lawrence Livermore National Laboratory, No. SAND99-1290, 186 p. Cooling Technology Institute, 2010, Recommendations for winter operations of water cooling towers, chap. 4 of

Cooling Tower Manual: Houston, Texas, Cooling Technology Institute, 16 p. Dagdas, A., Erdem, H.H., and Sevilgen, S.H., 2005, Performance analysis of gas turbine integrated geothermal power plant in Turkey—the proposed Kizildere project: Antalya, Turkey, Proceedings,World Geothermal Congress, 2005, p. 1–5. Dahle, D. Sr., 2008, Solar Technology BLM—Arizona Lands Training (June 25, 2008), accessed February 16, 2013 at http://www.blm.gov/pgdata/etc/medialib/ blm/wo/MINERALS__REALTY__AND_RESOURCE_ PROTECTION_/energy/renewable_references.Par.63109. File.dat/Solar percent20Energy—AZ percent20Lands percent20Training percent2006.25.08.pdf

. Dennen, B., Larson, D., Lee, C., Lee, J., and Tellinghuisen, S., 2007, California’s energy-water nexus—water use in electricity generation: Santa Barbara, California, University of California, Bren School of Environmental Management and Science, 194 p.

### Selected References **31**
Diehl, T.H., 2011, Estimating forced evaporation from surface

water, in Proceedings of the Third Thermal Ecology and Regulation Workshop, October 11–12, 2011, Maple Grove, Minnesota: Palo Alto, California, Electrical Power Research Institute, p. 23-1–23-10. DiPippo, Ronald, 2004, Second law assessment of binary

plants generating power from low-temperature geothermal fluids: Geothermics, v. 33, no. 5, p. 565–586, accessed July 28, 2013 at http://www.sciencedirect.com/science/article/ pii/S0375650504000094

. Drader, D., Jonsson, M.T., and Palsson, H., 2012, Improved

power production efficiency of hydrothermal reservoirs using downhole pumps, in Proceedings of the ThirtySeventh Workshop on Geothermal Reservoir Engineering, January 30–February 1, 2012, Stanford, California: Stanford, California, Stanford University, SGP-TR-194, accessed April 23, 2013 at http://www.geothermal-energy. org/pdf/IGAstandard/SGW/2012/Drader.pdf

. Dziegielewski, Ben, and Kiefer, J.C., 2010, Water conserva

tion measurement metrics—guidance report: American Water Works Association Water Conservation Division Subcommittee Report, submitted to The American Water Works Association Water Conservation Division Subcommittee: Denver, Colorado, 62 p., accessed July 28, 2013 at http://www.awwa.org/Portals/0/files/resources/ resource%20dev%20groups/tech%20and%20educ%20 program/documents/WaterConservationMeasurementMetric sGuidanceReport.pdf

. Eaton, Joe, 2012, Record heat, drought pose problems for

U.S. electric power: National Geographic News, accessed April 25, 2013 at http://news.nationalgeographic.com/news/energy/2012/08/120817-record-heat-drought-poseproblems-for-electric-power-grid/

. Edinger, J.E., Brady, D.K., and Geyer, J.C., 1974, Heat exchange and transport in the environment: Baltimore, Maryland, The Johns Hopkins University, Department of Geography and Environmental Engineering, Report No. 14 (No. NP-2900127), 137 p. Electric Power Research Institute (EPRI), 2011, Program on

technology innovation: Electricity use in the electric sec

tor: Opportunites to enhance electric energy efficiency in the production and delivery of electricity, 2011 Technical Report: Palo Alto, California, EPRI, Final Report 1024651, accessed February 11, 2013 at http://www.pserc.wisc.edu/ documents/publications/special_interest_publications/ EPRI_Electricity_Use_Report_Final_1024651.pdf

. Esri, 2012, ArcGIS: Release 10.1: Redlands, California, Environmental Systems Research Institute. Evapco, 2010, Cooling Towers—Free Cooling Operation. Evapco Engineering bulletin EB23D, April 16, 2010, 28 p., Taneytown, Maryland, accessed August 20, 2013 at http://www.evapco.eu/sites/evapco.eu/files/white_papers/23-FreeCooling-for-Cooling-Towers.pdf

. Franco, Alessandro, and Villani, Marco, 2009, Optimal design of binary cycle power plants for water-dominated, medium-temperature geothermal fields: Geothermics, v. 38, no. 4, p. 379–391, http://dx.doi.org/10.1016/j. geothermics.2009.08.001 , accessed July 28, 2013 at http://www.sciencedirect.com/science/article/pii/ S0375650509000480

. Fulford, J.M., and Sturm, T.W., 1984, Evaporation from flow

ing channels: Journal of Energy Engineering, ASCE, v. 110, no. 1, p. 1–10. Golub, Miroslav, Kurevija, Tomislav, and Pravica, Zdravko,

2006, Maximum energy output of geopressured geothermal reservoirs in Croatia, in Frankovic, Bernard, ed., Rijeka: Hrvatski savez za energiju, 2006 International Congress Energy and Environment October 25–27, 2006, v. 2, p. 121–130, accessed August 20, 2013 at http://bib.irb.hr/ datoteka/264772.MAXIMUM_ENERGY_OUTPUT_OF_ GEOPRESSURED_GEOTHERMAL_RESERVOIRS_IN_ CROATIA.pdf

. Gulliver, J.S., and Stefan, H.G., 1986, Wind function for a sheltered stream: Journal of Environmental Engineering, v. 112, no. 2, p. 387–399. Harbeck, G.E., Jr., 1964, Estimating forced evaporation from cooling ponds: Journal of the Power Division, ASCE, v. 90, no. 3, p. 1–10. Hu, M.C., Pavlenco, G.F., and Englesson, G.A., 1978, Water consumption and costs for various steam electric power plant cooling systems: Research Triangle Park, North Carolina, U.S. Environmental Protection Agency, Industrial Environmental Research Laboratory, EPA-600/7-78-157, 129 p. Hu, M.C., Pavlenco, G.F., and Englesson, G.A., 1981, Executive summary for power plant cooling system water consumption and nonwater impact reports: Research Triangle Park, North Carolina, U.S. Environmental Protection Agency, Industrial Environmental Research Laboratory, EPA-600/S7-81-112, 19 p. Huston, R.J., 1975, An overview of water requirements for electric power generation, in Gloyna, E.F., Herbert, H.W., and Howard, R.D., eds., Water management by the elec

tric power industry: Austin, Texas, University of Texas at Austin, Center for Research in Water Resources, p. 39–49.

### 32 **Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States**
Hutson, S.S., Barber, N.L., Kenny, J.F., Linsey, K.S., Lumia, D.S., and Maupin, M.A., 2004, Estimated use of water in the United States in 2000: U.S. Geological Survey Circular 1268, 46 p. (also available online at http://pubs.usgs.gov/ circ/2004/circ1268/ ). Hutson, S.S., comp., 2007, Guidelines for preparation of state water-use estimates for 2005: U.S. Geological Survey Techniques and Methods Book 4, Chap. E1, 36 p. (also available online at http://pubs.usgs.gov/tm/2007/tm4e1/ ). International Atomic Energy Agency, 2009, Nuclear technol

ogy review 2009, 133 p., International Atomic Energy Agency, Vienna, accessed August 20, 2013 at http://www. iaea.org/Publications/Reports/ntr2009.pdf

. International Energy Agency, 2008, Combined heat and power—Evaluating the benefits of greater global investment, OECD/IEA, Paris, accessed February 27, 2013 at http://www.iea.org/publications/freepublications/ publication/chp_report.pdf

. Kagel, A., Bates, D., and Gawell, K., 2005, A guide to geo

thermal energy and the environment: Washington, D.C., Geothermal Energy Association, 75 p. Kelly, B., 2006, Nexant parabolic trough solar power plant systems analysis—Task 2—comparison of wet and dry Rankine cycle heat rejection: Denver, Colorado, National Renewable Energy Laboratory, NREL/SR-550-40163, 28 p. Kenny, J.F., Barber, N.L., Hutson, S.S., Linsey, K.S., Lovelace, J.K., and Maupin, M.A., 2009, Estimated use of water in the United States in 2005: U.S. Geological Survey Circular 1344, 52 p. (also available online at http://pubs. usgs.gov/circ/1344/ ). Krivoruchko, Konstantin, 2012, Empirical bayesian kriging:

ArcUser, v. 15, no. 4, p. 6–10. Larson, D., Lee, C., Tellinghuisen, S., and Keller, A., 2007, California’s energy-water nexus: Water use in electricity generation: Southwest Hydrology, v. 6, no. 5, p. 20–22. Leung, Paul and Moore, R.E., 1970, Water consumptive deter

mination for steam power plant cooling towers: a heat and mass balance method: Combustion, v. 42, no. 5, p. 14–23. Leung, Paul and Moore, R.E, 1971, Water consumption study for Navajo plant: Journal of the Power Division, v. 97, no. 4, p. 749–766.McJannet, D.L., Webster, I.T., and Cook, F.J., 2012, An areadependent wind function for estimating open water evapora

tion using land-based meteorological data: Environmental Modelling & Software, v. 31, p. 76–83, http://dx.doi. org/10.1016/j.envsoft.2011.11.017 , accessed July 30, 2013 at http://www.sciencedirect.com/science/article/pii/ S1364815211002805

. MacKichan, K.A., 1951, Estimated water use in the United States, 1950: U.S. Geological Survey Circular 115, 13 p. (available online only at http://pubs.er.usgs.gov/publication/ cir115 ). MacKichan, K.A., 1957, Estimated water use in the United States, 1955: U.S. Geological Survey Circular 398, 18 p. (available online only at http://pubs.er.usgs.gov/publication/ cir398 ). MacKichan, K.A., and Kammerer, J.C., 1961, Estimated use of water in the United States, 1960: U.S. Geological Survey Circular 456, 44 p. (available online only at http://pubs. er.usgs.gov/publication/cir456 ). Macknick, J., Newmark, R., Heath, G., and Hallett, K.C., 2011, A review of operational water consumption and withdrawal factors in electricity generating technologies: Golden, Colorado, National Renewable Energy Laboratory Technical report NREL/TP—6A20-50900, 21 p., accessed August 19, 2013 at http://www.nrel.gov/docs/ fy11osti/50900.pdf

. Majewski, W., and Miller, D.C., 1979, Predicting effects of

power plant once-through cooling on aquatic systems: A state-of-the-art report of IHP Working Group 6.2 on the effects of thermal discharges: Paris, France, UNESCO, Technical papers in hydrology 20, 171 p. Marley, 2012, Operating cooling towers in freezing weather: Bellevue, Washington, Olympic Engineered Sales, Inc., Marley technical report H-003B, 8 p. accessed August 20, 2013 at http://olyengsales.com/techreports.html

. Maulbetsch, J.S., and DiFilippo, M.N., 2006, Cost and

value of water use at combined-cycle power plants: Prepared for California Energy Commission, Public Interest Energy Research Program, PIER Energy-Related Environmental Research, CEC-500-2006-034, [variously paged], accessed July 30, 2013 at http://www.energy. ca.gov/2006publications/CEC-500-2006-034/CEC-500- 2006-034.PDF

. Mishra, G.S., Glassley, W.E., and Yeh, Sonia, 2011, Realizing the geothermal electricity potential—water use and consequences: Environmental Research Letters, v. 6, no. 3, 8 p., accessed July 30, 2013 at http://dx.doi. org/10.1088/1748-9326/6/3/034023

. Murray, C.R., 1968, Estimated use of water in the United States, 1965: U.S. Geological Survey Circular 556, 53 p. (available online only at http://pubs.er.usgs.gov/publication/ cir556 ). Murray, C.R., and Reeves, E.B., 1972, Estimated use of water in the United States, 1970: U.S. Geological Survey Circular 676, 37 p. (available online only at http://pubs.er.usgs.gov/ publication/cir676 ).

### Selected References **33**
Murray, C.R., and Reeves, E.B., 1977, Estimated use of water in the United States, 1975: U.S. Geological Survey Circular 765, 39 p. (available online only at http://pubs.er.usgs.gov/ publication/cir765 ). Pilz, J., and Spöck, G., 2007, Why do we need and how should we implement Bayesian kriging methods: Stochastic Environmental Research and Risk Assessment, v. 22, no. 5, p. 621–632. Rutberg M., Delgado, A., Herzog, H., and Ghoniem, A., 2011, A system-level generic model of water use at power plants and its application to regional water use estimation, in Proceedings of the American Society of Mechanical Engineers, 2011 International Mechanical Engineering Congress and Exposition, Denver, Colorado, 11 p. Sathyanathan, V.T., 2010, Running a coal fired boiler at the

best efficiency: Bright Hub Engineering, accessed March 25, 2013 at http://www.brighthubengineering.com/powerplants/41375-running-a-coal-fired-boiler-at-the-bestefficiency/

. Solley, W.B., Chase, E.B., and Mann, W.B., IV, 1983,

Estimated use of water in the United States in 1980: U.S. Geological Survey Circular 1001, 55 p. (available online only at http://pubs.er.usgs.gov/publication/cir1001 ). Solley, W.B., Merk, C.F., and Pierce, R.R., 1988, Estimated

use of water in the United States in 1985: U.S. Geological Survey Circular 1004, 82 p. (available online only at http:// pubs.er.usgs.gov/publication/cir1004 ). Solley, W.B., Pierce, R.R., and Perlman, H.A., 1998,

Estimated use of water in the United States in 1995: U.S. Geological Survey Circular 1200, 71 p. (available online only at http://pubs.er.usgs.gov/publication/cir1200 ). Stannard, D.I., Rosenberry, D.O., Winter, T.C., and Parkhurst,

R.S., 2004, Estimates of fetch-induced errors in Bowenratio energy-budget measurements of evapotranspiration from a prairie wetland, Cottonwood Lake area, North Dakota, USA: Wetlands, v. 24, no. 3, p. 498–513, accessed July 30, 2013 at http://link.springer.com/ article/10.1672%2F0277-5212(2004)024%5B0498%3AEO FEIB%5D2.0.CO%3B2#

. Strauss, J.B., 1978, Comparison of model predictions and con

sumptive water use of closed cycle cooling system, Final Report, March–July 1978: Springfield, Virginia, Versar, Inc., EPA-600/7-78-206, [variously paged]. Stultz, S.C., and Kitto, J.B., eds., 1992, Steam, its generation

and use (40th ed.): Barberton, Ohio, Babcock & Wilcox, 131 p. Templin, W.E., Herbert, R.A., Stainaker, C.B., Horn, Marilee, and Solley, W.B., 1999, National handbook of recom

mended methods for water data acquisition—Chapter 11J—Thermoelectric power generation: U.S. Geological Survey, accessed August 22, 2013, at http://pubs.usgs.gov/ chapter11/chapter11J.html

. Texas Water Development Board, 2003, Power generation

water use in Texas for the years 2000 to 2060: Final Report: Austin, Texas, 141 p. plus appendixes. The Cooling Tower Company, L.C., 2005, Installation, opera

tion and maintenance manual for Series TCI cooling tower: TCI Cooling, Tower Revision 1, 35 p. United Nations Environment Programme, Division of Technology, 2006, Energy efficiency guide for industry in Asia: United Nations Publications, 17 p., accessed March 25, 2013 at http://www.energyefficiencyasia.org/docs/ ee_modules/Chapter-Cooling%20Towers.pdf

. U.S. Census Bureau, North American Industry Classification System, 2007, accessed August 20, 2013 at http://www. census.gov/cgi-bin/sssd/naics/naicsrch?chart=2007

. U.S. Department of Energy, 2009, Concentrating solar power commercial application study: Reducing water consumption of concentrating solar power electricity generation: Report to Congress: Washington, D.C., U.S. Department of Energy, 24 p. U.S. Department of Energy, Energy Information Administration, 2001, 2000 Form EIA-860 database, accessed August 20, 2013 at http://www.eia.gov/electricity/ data/eia860/index.html

. U.S. Department of Energy, Energy Information Administration, 2009, 2008 Form EIA-860 database, accessed January 22, 2013 at http://www.eia.gov/electricity/ data/eia860/index.html

. U.S. Department of Energy, Energy Information Administration, 2011a, 2010 Form EIA-860 database, Annual electric generator report, accessed January 22, 2013 at http://www.eia.gov/electricity/data/eia860/index.html

. U.S. Department of Energy, Energy Information Administration, 2011b, 2010 Form EIA-923 database, Power plant operations report, accessed January 22, 2013 at http://www.eia.gov/electricity/data/eia923/

. U.S. Department of Energy, Energy Information Administration, 2013, Electric Power Annual 2011, accessed August 21, 2013 at http://www.eia.gov/electricity/ annual/pdf/epa.pdf

.

### 34 **Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States**
U.S. Department of Energy, National Energy Technology Laboratory, May 1999, DOE/FE-0400 Market-based advanced coal power systems: Final Report, 374 p., accessed August 21, 2013 at http://www.netl.doe.gov/ technologies/coalpower/turbines/refshelf/igcc-h2-sygas/ marketbased_systems_report.pdf

. U.S. Department of Energy, National Energy Technology Laboratory, 2007a, Cost and performance baseline for fossil energy plants, DOE/NETL-2007/1281, Volume 1: Bituminous coal and natural gas to electricity final report, accessed February 11, 2013 at http://www.netl.doe.gov/ energy-analyses/pubs/BitBase_FinRep_2007.pdf

. U.S. Department of Energy, National Energy Technology Laboratory, 2007b, Coal power plant database, accessed August 20, 2013 at http://www.netl.doe.gov/energyanalyses/pubs/cppd/XLS%20CPPDB%202005%20-%20 Public.zip

. U.S Department of Energy, National Energy Technology Laboratory, September 30, 2010, Estimating freshwater needs to meet future thermoelectric generation require

ments, 2010 update: U.S. Department of Energy, DOE/ NETL-400/2010/1339, 109 p., accessed August 20, 2013 at http://www.netl.doe.gov/energy-analyses/refshelf/ PubDetails.aspx?Action=View&PubId=335 (index page) or http://www.netl.doe.gov/energy-analyses/pubs/2010_Water_ Needs_Analysis.pdf (manuscript). U.S. Environmental Protection Agency, 2006, National Electric Energy Data System (NEEDS) 2006, accessed March 27, 2013 at http://www.epa.gov/airmarkets/ progsregs/epa-ipm/BaseCase2006.html (index page), http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/ v410/NEEDSv410.zip (Excel spreadsheet database), and http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/ v410/Guide_to_NEEDSv410.pdf , and http://www.epa.gov/ airmarkets/progsregs/epa-ipm/docs/v410/Chapter4.pdf (supporting documentation). U.S. Environmental Protection Agency, 2007a, Emissions & Generation Resource Integrated Database (eGRID), accessed January 22, 2013 at http://www.epa.gov/ cleanenergy/energy-resources/egrid/index.html#download

. U.S. Environmental Protection Agency, 2007b, National Hydrography Dataset Plus-NHDPlus, Version 1.1: Washington D.C., U.S. Environmental Protection Agency, Office of Water (vector/raster digital data.) U.S. Environmental Protection Agency, Enforcement & Compliance History Online (ECHO), [n.d.], accessed January 22, 2013 at http://www.epa-echo.gov/echo

. U.S. Government Accountability Office, 2009, Energy-water nexus: Improvements to Federal water use data would increase understanding of trends in power plant water use: Report to the Chairman, Committee on Science and Technology, House of Representatives, GAO-10-23, 73 p., accessed July 31, 2013 at http://www.gao.gov/new.items/ d1023.pdf

. U.S. Nuclear Regulatory Commission, 2009, 2009–2010 Information Digest, NUREG-1350, 21, 194 p., accessed August 20, 2013 at http://www.nrc.gov/reading-rm/doccollections/nuregs/staff/sr1350/v21/sr1350v21.pdf

. U.S. Nuclear Regulatory Commission, 2010, 2010–2011 Information Digest, NUREG-1350, 22, 191 p., accessed August 21, 2013 at http://pbadupws.nrc.gov/docs/ML1024/ ML102460490.pdf

. U.S. Nuclear Regulatory Commission, 2011, 2011–2012 Information Digest, NUREG-1350, 23, 186 p., accessed August 21, 2013 at http://www.nrc.gov/reading-rm/doccollections/nuregs/staff/sr1350/v23/sr1350v23.pdf

. Ward, G.H., Jr., 1980, Accuracy of Harbeck diagram for forced evaporation: Journal of the Energy Division, v. 106, no. 1, p. 23–31. Webster, I.T., and Sherman, B.S., 1995, Evaporation from fetch-limited water bodies: Irrigation Science, v. 16, 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 trans

fers 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 evapora

tion 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 physi

cally 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 convert

ing energy flows embodied in water flow and evaporation into their equivalent volume flow rates of liquid water.
**O once-through cooling system**  a cooling sys

tem 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** thewater 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 thermoelectr ic 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 consump

tion, 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 powerplant 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 col

lected 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 ener gy 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 high

lighted 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 SEC

TOR_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 com

bined heat and power plants, this value is intended to include internal consump

tion 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 high

lighted 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 Elec

tricity

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 (mul

tiple 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 near

est 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 Tempera

ture
923 Cooling Operations Discharge Maximum Tem

perature 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 high

lighted 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_ASSOCIA

TION 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 dischar ge 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 USEP A 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. Envi

ronmental 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 ground

water 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/watercensusDiehl, T.H., and others**—Methods for Estimating Water Consumption for Thermoelectric Power Plants in the United States—**Scientific Investigations Report 2013–5188

P r inted on recycled p ap e r ISSN 2328-031X (print) ISSN 2328-0328 (online) http://dx.doi.org/10.3133/sir20135188