Cen Energy (2-11) On-Farm Renewable Energy Production Survey (2009) Volume 3 • Special Studies • Part 6 AC-07-SS-6 Issued February 2011 United States Department of Agriculture Tom Vilsack, Secretary National Agricultural Statistics Service Cynthia Z.F. Clark, Administrator Contents Page Introduction ................................................................................. V TABLES 1. Farms Reporting Wind Turbines, Capacity, Installation Cost, Percent Funded by Outside Sources, and Year of Installation: 2009 .......................................... 1 2. Farms Reporting Methane Digesters, Number of Digesters, Methane Produced, Installation Cost, Percent Funded by Outside Sources, and Year of Installation: 2009...... 2 3. Farms Reporting Photovoltaic (PV) and Thermal Solar Panels by Type, Capacity, Installation Cost, Percent Funded by Outside Sources, and Year of Installation: 2009...... 3 4. Farms Reporting Energy Savings, Energy Audits, and/or Federal Funding: 2009................ 4 APPENDICES A. Statistical Methodology ................................................................. A-1 B. General Explanation and Report Form ..................................................... B-1 Introduction BACKGROUND The 2009 On-Farm Renewable Energy Production Survey (OREPS) is a follow-on survey to the 2007 Census of Agriculture. It is the first on-farm renewable energy production survey conducted on the national level by the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2009 OREPS provides additional information on energy produced by wind turbines, solar panels, and methane digesters that were in operation on farms in 2009. Wind turbines located on farm operations under a wind rights lease agreement are considered commercial and were excluded from this survey. Methane digesters not owned and operated by the farm operation were not included in the survey. This energy survey provides an inventory of farm-generated energy practices with detailed data relating to the category or type of energy produced (wind, solar, and manure/methane digester), installation cost, percent of cost from outside funding, year installed, and total amount of utility savings from the use of on-farm renewable energy production. USES OF SURVEY DATA Producers, universities, legislators, utility providers, farm businesses, etc. are in need of renewable energy production and economic data in order to assess the growth and viability of new energy sources and make important marketing, business, and policy decisions. The production of on-farm renewable energy can provide cost saving alternatives for America's farmers and ranchers. The information gathered will help in the continued development of this expanding agricultural industry. AUTHORITY The census of agriculture is required by law under the "Census of Agriculture Act of 1997," Public Law 105-113 (Title 7, United States Code, Section 2204g). The law authorizes the Secretary of Agriculture to conduct surveys deemed necessary to furnish annual or other data on the subjects covered by the census. The 2009 On-Farm Renewable Energy Production Survey was conducted under the provisions of this section. RENEWABLE ENERGY DEFINITION Renewable energy is energy that comes from natural sources (sun, wind, water, biomass, and geothermal) and is renewable or naturally replenished. Renewable energy differs not only from fossil energy sources such as petroleum, gas, and coal, but also from nuclear energy. REFERENCE PERIOD Energy production and savings were measured for the 2009 calendar year. Installation costs and outside funding were as of time of installation. TABLES AND APPENDICES Tables. Tables 1 through 4 provide data from the survey at the U.S. and State level. Appendix A. Provides information about data collection and data processing activities and discusses the statistical methodology used in conducting and evaluating the survey. Tables A and B provide statistical precision estimates for selected survey items. Appendix B. Includes definitions of specific terms and phrases used in this publication. It also provides facsimiles of the report form and instruction sheet used to collect data. RESPONDENT CONFIDENTIALITY In keeping with the provisions of Title 7 of the United States Code, no data are published that would disclose information about an individual operation. All tabulated data are subjected to an extensive disclosure review prior to publication. Any tabulated item that identifies data reported by a respondent or allows a respondent's data to be closely estimated or derived is suppressed and coded with a 'D'. The number of operations reporting an item is not considered confidential information and is provided even though other information may be withheld. DATA PRODUCTS AND CUSTOM TABULATIONS The 2009 On-Farm Renewable Energy Production Survey data, as well as the 2007 Census of Agriculture Volume 1 Geographic Area Series and related reports, are available on the NASS website at www.nass.usda.gov/. Custom-designed tabulations may be developed when data are not published elsewhere. These tabulations are developed to individual user specifications on a cost-reimbursable basis and shared with the public. The census Volume 1 on a downloadable desktop query tool and the census Quick Stats are alternative data sources that should be investigated before requesting a custom tabulation. All custom tabulations are subject to a thorough disclosure review prior to release to prevent the disclosure of any individual respondent data. Requests for custom tabulations can be submitted via the internet from the NASS home page, by mail, or by e-mail to: DataLab National Agricultural Statistics Service Room 6436A, Stop 2054 1400 Independence Ave, S.W. Washington, D.C. 20250-2054 or Datalab@nass.usda.gov ABBREVIATIONS AND SYMBOLS The following abbreviations and symbols are used throughout the tables: - Represents zero. (D) Withheld to avoid disclosing data for individual farms. (Z) Less than half of the unit shown. (NA) Not available. (X) Not applicable. W Watt. kW Kilowatt. Table 1. Farms Reporting Wind Turbines, Capacity, Installation Cost, Percent Funded by Outside Sources, and Year of Installation: 2009 ---------------------------------------------------------------------------------------------------------------------------------------- : : : : : Year wind turbines installed 2/ : : : Average per turbine : : (number of turbines) : : :---------------------------: Percent of :----------------------------------------- : : : Rated : :installation : : : : : : generating :Installation : cost funded : : : : : Number of : capacity 1/ : cost 1/ : by outside : Prior to : 2000- : 2005- State : Farms : turbines : (kilowatts) : (dollars) : sources 1/ : 2000 : 2004 : 2009 ---------------------------------------------------------------------------------------------------------------------------------------- ALL WIND, TOTAL : : United States ................: 1,420 1,845 (X) (X) (X) (D) 535 (D) : SMALL WIND (1-100kW) : : United States ................: 1,406 1,831 6 12,972 49 356 532 899 : Alabama ......................: 3 4 (D) (D) - - - 4 Alaska .......................: 8 8 1 4,394 - 3 - 5 Arizona ......................: 63 78 1 2,768 51 15 30 30 Arkansas .....................: 7 11 2 5,800 - 5 (D) (D) California ...................: 134 160 3 13,955 50 38 60 52 Colorado .....................: 98 147 3 4,581 - 26 38 80 Connecticut ..................: 1 (D) (D) (D) - - - (D) Delaware .....................: - - - - - - - - Florida ......................: 5 5 1 5,250 - - - 5 Georgia ......................: 4 4 (D) (D) - - 4 - : Hawaii .......................: 43 67 1 1,799 - 16 23 28 Idaho ........................: 39 44 4 12,778 25 9 12 23 Illinois .....................: 28 36 5 11,448 (D) (D) (D) 28 Indiana ......................: 49 58 2 7,870 (D) 4 9 43 Iowa .........................: 39 48 8 23,840 29 17 8 21 Kansas .......................: 18 20 6 10,084 - 5 5 10 Kentucky .....................: 1 (D) (D) (D) - - - (D) Louisiana ....................: 2 (D) (D) (D) - - - (D) Maine ........................: 32 34 5 17,353 (D) 8 11 15 Maryland .....................: 2 (D) (D) (D) (D) - - (D) : Massachusetts ................: 22 30 7 43,218 45 5 - 25 Michigan .....................: 34 47 2 9,981 (D) 4 10 33 Minnesota ....................: 99 144 20 37,647 51 15 46 83 Mississippi ..................: 3 3 1 4,467 - - - 3 Missouri .....................: 21 28 3 7,779 (D) (D) (D) 20 Montana ......................: 63 78 3 6,293 62 9 33 36 Nebraska .....................: 7 9 1 1,563 - - 5 4 Nevada .......................: 8 10 1 1,455 - (D) 5 (D) New Hampshire ................: 11 11 3 9,850 - 6 (D) (D) New Jersey ...................: 3 3 8 47,518 63 - (D) (D) : New Mexico ...................: 28 34 1 4,807 - 10 5 17 New York .....................: 58 65 5 22,254 64 12 21 32 North Carolina ...............: 12 12 2 12,800 - (D) (D) 7 North Dakota .................: 5 9 (D) 15,778 (D) 5 (D) (D) Ohio .........................: 44 57 2 11,257 39 15 13 29 Oklahoma .....................: 20 22 2 4,156 (D) (D) 10 (D) Oregon .......................: 37 63 1 3,209 (D) 19 22 22 Pennsylvania .................: 26 27 3 7,148 (D) 4 4 15 Rhode Island .................: 2 (D) (D) (D) (D) (D) - - South Carolina ...............: - - - - - - - - : South Dakota .................: 11 13 4 19,064 - (D) (D) 8 Tennessee ....................: 9 13 (D) 8,177 - - 8 5 Texas ........................: 102 148 4 8,493 - 19 46 81 Utah .........................: 31 41 1 2,562 - 7 10 18 Vermont ......................: 43 54 3 16,847 46 3 33 18 Virginia .....................: 11 13 1 2,971 35 - 4 9 Washington ...................: 50 67 19 12,669 (D) 30 12 23 West Virginia ................: 4 4 5 44,400 - (D) - (D) Wisconsin ....................: 46 59 8 15,329 33 22 18 17 Wyoming ......................: 20 35 2 4,648 (D) (D) (D) 29 ---------------------------------------------------------------------------------------------------------------------------------------- See footnote(s) at end of table. --continued Table 1. Farms Reporting Wind Turbines, Capacity, Installation Cost, Percent Funded by Outside Sources, and Year of Installation: 2009 - (continued) ---------------------------------------------------------------------------------------------------------------------------------------- : : : : : Year wind turbines installed 2/ : : : Average per turbine : : (number of turbines) : : :---------------------------: Percent of :----------------------------------------- : : : Rated : :installation : : : : : : generating :Installation : cost funded : : : : : Number of : capacity 1/ : cost 1/ : by outside : Prior to : 2000- : 2005- State : Farms : turbines : (kilowatts) : (dollars) : sources 1/ : 2000 : 2004 : 2009 ---------------------------------------------------------------------------------------------------------------------------------------- LARGE WIND (>100kW) : : United States ................: 14 14 1,035 1,339,143 39 (D) 3 (D) : Iowa .........................: 9 9 1,359 1,784,889 (D) - 3 6 : Other States 3/ ..............: 5 5 453 536,800 (D) (D) - (D) ---------------------------------------------------------------------------------------------------------------------------------------- 1/ Only includes positive reported data. Operations that reported zero or failed to report are not included. 2/ Numbers may not add to total number of turbines. Only includes operations reporting year installed. 3/ Other States include Kansas, Minnesota, and Montana. Table 2. Farms Reporting Methane Digesters, Number of Digesters, Methane Produced, Installation Cost, Percent Funded by Outside Sources, and Year of Installation: 2009 -------------------------------------------------------------------------------------------------------------------------------------------- : : : : : Year methane digesters installed 2/ : : :Average per methane digester : Percent of : (number of digesters) : : Number of :-----------------------------: installation :-------------------------------------------- State : Farms : methane : Methane : Installation : cost funded : : : : : digesters : produced 1/ : cost 1/ : by outside : Prior to : 2000- : 2005- : : : (cubic feet) : (dollars) : sources 1/ : 2000 : 2004 : 2009 -------------------------------------------------------------------------------------------------------------------------------------------- United States ......: 121 140 30,515,800 1,718,562 48 11 22 86 : California .........: 14 14 29,194,314 1,794,444 47 (D) - (D) Michigan ...........: 5 10 36,923,333 1,322,222 (D) - - 9 Minnesota ..........: 5 6 69,105,120 3,123,333 (D) (D) (D) (D) New York ...........: 16 16 18,611,675 1,611,206 36 - 3 10 Pennsylvania .......: 13 16 18,951,843 642,188 58 (D) (D) 9 Vermont ............: 8 8 (D) 1,718,750 31 (D) - (D) Wisconsin ..........: 21 25 (D) 1,608,924 19 - 6 18 : Other States 3/ ....: 39 45 26,034,140 2,181,189 76 4 8 23 -------------------------------------------------------------------------------------------------------------------------------------------- 1/ Only includes positive reported data. Operations that reported zero or failed to report are not included. 2/ Numbers may not add to total number of digesters. Only includes operations reporting year installed. 3/ Other States include Colorado, Connecticut, Florida, Idaho, Illinois, Indiana, Iowa, Kansas, Maryland, Mississippi, Missouri, Montana, Nebraska, North Carolina, Ohio, Oklahoma, Oregon, South Dakota, Tennessee, Texas, Washington, and Wyoming. Table 3. Farms Reporting Photovoltaic (PV) and Thermal Solar Panels by Type, Capacity, Installation Cost, Percent Funded by Outside Sources, and Year of Installation: 2009 -------------------------------------------------------------------------------------------------------------------------------------------- : : : : : Year solar panels installed 2/ : : Farms reporting 1/ : Average per farm : : (number of solar panels) : :---------------------------------------------------: Percent of :------------------------------------ : : : : PV rated : :installation : : : : : : : generating :Installation: cost funded : : : : : PV solar : Thermal :capacity 1/ : cost 1/ : by outside : Prior to : 2000- : 2005- State : Farms : panels :solar panels: (watts) : (dollars) : sources 1/ : 2000 : 2004 : 2009 -------------------------------------------------------------------------------------------------------------------------------------------- United States ..............: 7,968 7,236 1,835 4,449 31,947 44 18,881 45,028 108,532 : Alabama ....................: 33 24 13 865 6,948 47 25 9 96 Alaska .....................: 16 15 1 865 9,134 42 66 (D) 79 Arizona ....................: 255 242 41 2,002 19,992 50 1,200 1,271 1,795 Arkansas ...................: 41 37 5 833 7,502 (D) 238 47 132 California .................: 1,906 1,825 385 11,229 78,910 41 4,242 27,544 64,328 Colorado ...................: 504 445 117 1,654 16,879 47 1,843 1,462 2,520 Connecticut ................: 26 23 10 4,173 29,571 52 (D) 54 428 Delaware ...................: 4 4 - 15,500 101,250 54 - - (D) Florida ....................: 88 69 39 2,162 12,223 40 148 54 488 Georgia ....................: 32 30 4 3,625 28,545 66 22 76 378 : Hawaii .....................: 520 469 213 1,790 16,665 28 1,498 1,790 4,189 Idaho ......................: 131 121 21 916 12,524 55 442 220 396 Illinois ...................: 58 45 18 4,575 39,018 44 344 803 384 Indiana ....................: 127 112 27 543 5,262 - 132 131 323 Iowa .......................: 40 34 19 1,988 17,791 47 42 142 380 Kansas .....................: 116 93 36 408 4,607 55 186 81 172 Kentucky ...................: 67 56 21 776 6,305 29 62 36 387 Louisiana ..................: 13 13 2 876 10,970 (D) (D) (D) 74 Maine ......................: 97 87 27 1,680 13,892 26 330 216 1,161 Maryland ...................: 21 19 11 2,665 24,201 26 115 (D) 298 : Massachusetts ..............: 63 50 22 3,678 27,624 53 154 173 959 Michigan ...................: 75 53 27 841 7,416 30 198 64 278 Minnesota ..................: 73 51 34 1,409 11,178 45 193 133 240 Mississippi ................: 23 23 1 706 11,593 (D) 14 58 48 Missouri ...................: 93 84 22 1,022 9,429 54 114 63 360 Montana ....................: 238 226 35 988 9,180 48 562 565 882 Nebraska ...................: 65 65 7 742 5,632 57 149 232 317 Nevada .....................: 51 51 6 1,832 21,971 33 152 166 542 New Hampshire ..............: 49 35 26 1,641 16,173 (D) 413 - 416 New Jersey .................: 138 138 24 14,081 112,855 64 216 2,204 9,631 : New Mexico .................: 258 241 38 1,261 12,888 39 675 690 1,000 New York ...................: 156 140 29 2,501 21,661 59 315 459 1,867 North Carolina .............: 104 67 55 1,015 10,198 47 159 163 388 North Dakota ...............: 29 28 2 429 5,048 47 17 40 68 Ohio .......................: 130 115 23 1,614 12,122 53 185 781 454 Oklahoma ...................: 187 167 37 428 4,612 45 164 210 351 Oregon .....................: 332 294 97 3,002 22,147 44 793 1,046 3,284 Pennsylvania ...............: 173 160 37 1,750 20,699 44 122 290 2,048 Rhode Island ...............: 12 10 6 (D) 30,960 (D) 18 (D) 236 South Carolina .............: 20 18 5 (D) 5,047 54 42 28 53 : South Dakota ...............: 55 45 12 696 7,470 49 44 109 148 Tennessee ..................: 66 59 9 1,065 8,657 29 199 114 396 Texas ......................: 573 541 67 783 7,692 42 511 806 1,850 Utah .......................: 133 125 20 1,211 14,573 51 183 580 504 Vermont ....................: 110 103 28 1,304 15,510 34 444 272 582 Virginia ...................: 83 70 16 869 12,868 22 104 186 360 Washington .................: 205 188 39 1,547 10,377 16 588 740 872 West Virginia ..............: 27 24 5 521 8,166 (D) 52 56 (D) Wisconsin ..................: 176 134 78 2,484 17,607 27 866 424 1,333 Wyoming ....................: 176 168 18 1,275 10,362 42 287 411 793 -------------------------------------------------------------------------------------------------------------------------------------------- 1/ Only includes positive reported data. Operations that reported zero or failed to report are not included. 2/ Only includes operations reporting year installed. Table 4. Farms Reporting Energy Savings, Energy Audits, and/or Federal Funding: 2009 [Only operations that reported wind turbines, methane digesters, and/or solar panels] ----------------------------------------------------------------------------------------------------------------------------- : Total farms : Average dollars : : : reporting wind : saved on : Performed : Received : turbines, digesters, : 2009 utility bills : energy audit : federal funding State : and/or solar panels : per farm 1/ : (farms) : (farms) ----------------------------------------------------------------------------------------------------------------------------- United States ...........: 8,569 2,406 613 1,101 : Alabama .................: 33 1,006 1 6 Alaska ..................: 17 1,393 2 3 Arizona .................: 259 2,461 8 46 Arkansas ................: 42 1,070 2 2 California ..............: 1,956 4,395 237 282 Colorado ................: 518 1,415 14 48 Connecticut .............: 28 2,061 8 3 Delaware ................: 4 2,375 2 3 Florida .................: 91 1,013 8 5 Georgia .................: 32 872 2 10 : Hawaii ..................: 522 2,125 10 19 Idaho ...................: 147 1,487 5 8 Illinois ................: 76 1,688 3 14 Indiana .................: 152 518 2 7 Iowa ....................: 74 1,573 7 22 Kansas ..................: 125 689 - 11 Kentucky ................: 68 831 3 3 Louisiana ...............: 13 (D) 1 - Maine ...................: 108 1,221 13 9 Maryland ................: 24 1,125 3 4 : Massachusetts ...........: 78 917 37 19 Michigan ................: 94 529 13 8 Minnesota ...............: 157 1,364 18 29 Mississippi .............: 25 490 1 1 Missouri ................: 102 547 3 12 Montana .................: 253 1,737 25 47 Nebraska ................: 68 1,117 1 7 Nevada ..................: 51 2,084 - 1 New Hampshire ...........: 49 1,162 8 2 New Jersey ..............: 139 2,641 30 26 : New Mexico ..............: 260 2,080 1 35 New York ................: 202 5,067 23 40 North Carolina ..........: 107 1,083 6 7 North Dakota ............: 32 803 - 13 Ohio ....................: 148 2,000 7 9 Oklahoma ................: 200 1,037 2 28 Oregon ..................: 342 1,251 21 46 Pennsylvania ............: 196 4,534 10 52 Rhode Island ............: 13 3,138 1 2 South Carolina ..........: 20 (D) - 2 : South Dakota ............: 63 555 1 11 Tennessee ...............: 71 921 2 4 Texas ...................: 624 1,198 14 32 Utah ....................: 133 2,188 4 5 Vermont .................: 128 2,948 11 27 Virginia ................: 85 920 1 8 Washington ..............: 217 1,181 2 14 West Virginia ...........: 31 606 7 9 Wisconsin ...............: 214 1,506 30 49 Wyoming .................: 178 1,330 3 51 ----------------------------------------------------------------------------------------------------------------------------- 1/ Only includes positive reported data. Operations that reported zero or failed to report are not included. Appendix A. Statistical Methodology THE SURVEY POPULATION The target population for the 2009 On-Farm Renewable Energy Production Survey (OREPS) was all farms and ranches that selected yes to the question "At any time during 2007, did this operation generate energy or electricity on the farm using wind or solar technology, methane digester, etc?" on the 2007 Census of Agriculture. Operations that were listed on the U.S. Environmental Protection Agency's AgStar site (http://www.epa.gov/agstar/projects/index.html) as having a methane digester project that was operable in 2009 and earlier were also included in the sample population. DATA COLLECTION Method of Enumeration The 2009 OREPS was conducted primarily by mail. It was supplemented with electronic data reporting via the internet and data collected by telephone and personal enumeration. Report Form A four page report form was designed to capture data for number of on-farm renewable energy production devices, installation costs, sales to the grid, and utility savings. The main focus of the 2009 OREPS was to provide detailed information on wind turbines, solar panels and methane digesters. The "other forms of energy produced" question in Section 5 of the OREPS report form was primarily for clarification and was not summarized or published. The "other solar powered devices (fence chargers)" question in Section 3 was included to prevent misreporting of solar panels versus small solar powered devices. These devices were included in the 2007 Census of Agriculture published count of farms having renewable energy producing devices but were not included in the published OREPS solar panel section. The questions pertaining to energy sales were not summarized or published in this first energy release due to reporting errors. There was confusion between actual sales and net metering. On future energy surveys, sales questions will be clarified and additional net metering questions will be asked. Report Form Mailings and Respondent Follow-up The 2009 On-Farm Renewable Energy Production Survey report form mail packet was mailed from the Census Bureau's National Processing Center (NPC) at Jeffersonville, IN on May 3, 2010. The mail packet included a labeled report form, an instruction sheet, a letter requesting prompt response with electronic data reporting instructions, and a return envelope to NPC for data capture. The report form carried a return due date of May 24, 2010. A second mailing to nonrespondents took place from NPC on June 4, 2010. Telephone follow-up interviews to nonrespondents took place from June 20 to July 9, 2010 from NASS Data Collection Centers. A process was used to exclude operations from receiving a follow-up telephone call if their report form was received in the mail. Data collection for the 2009 OREPS was coordinated with other NASS agricultural surveys. In some cases, if an operation was selected for multiple surveys, NPC mailed the materials to NASS field offices. Field office personnel then were responsible for collecting the data and completing other survey report forms in an effort to reduce the number of contacts and respondent burden. REPORT FORM PROCESSING Data Capture All report forms returned to NPC were immediately checked in using bar codes printed on the mail label and were removed from follow-up mailings. All forms were reviewed prior to data keying to identify inconsistencies and ensure the data could be keyed. Major inconsistencies, respondent remarks, and blank forms were reviewed by analysts and adjusted prior to keying. In some cases, report forms were mailed to field offices for further editing. All forms with any data were scanned and an image was created for each page of the report form. After images were created, the data were keyed directly from the report form. Data Editing and Analysis Data from each report form were processed through a computer edit which flagged inconsistent entries. Each report form with a flagged entry was reviewed by an analyst. Action was required for any record with reported data that were obviously incorrect. In some cases, respondents may have failed to provide all of the information requested, only indicating the presence of an item but not the amount. Only number of wind turbines and methane digesters were coded for machine imputation. Percents and averages of positive reported data were used for all other items. After the initial edit, an automated imputation program supplied missing wind turbine and digester numbers based on State or national averages. A post- imputation computer edit was performed to ensure imputation actions provided acceptable results. Instances where imputed data failed edit checks were referred to analysts for corrective action. The computer edit ensured the data on a report form were internally consistent. An analysis tool was provided to examine the data across records to check for distributional irregularities and data outliers. Analysts corrected suspect data when necessary and re-edited the record. ESTIMATION Nonresponse Weighting While effort was expended to obtain a response from each farm, a complete set of responses was not achieved. Nonresponse can lead to biases in published estimates because the information concerning on-farm energy production of the nonresponding farms could not be factored into the estimates. Estimates of totals, for example, will be biased low. It is necessary to reduce this bias through the use of a procedure called nonresponse weight adjustment. Nonresponse weight adjustment gives more weight to the data reported by responding farms in an effort to account for the data that would have been reported by the nonresponding farms. This will increase the estimates of totals obtained by the respondents and reduce this bias. Most of the estimates published from the 2009 OREPS are ratios of estimated totals. The potential for bias exists for these estimated ratios, although it is difficult to predict whether the bias is upward or downward. Nevertheless, nonresponse adjustments were calculated and factored into the ratio estimates as well. Conceptually, each farm on the mail list begins the survey with a weight of one. In other words, if each farm on the list would provide the requested data, the data could be simply added up to estimate the total. In the presence of nonresponse, adjustments are computed and applied to the initial weights of the responding farms resulting in a nonresponse adjusted weight greater than 1 for these farms. The initial weight of each nonresponding farm is adjusted to zero. The adjustments are computed in a manner that requires the sum of the nonresponse-adjusted weights across the responding farms on the mail list to equal the sum of the initial weights across all farms on the mail list. If the total number of farms on the mail list is N, the sum of the initial weights across all farms on the mail list equals N, because the initial weight for each farm on the list is 1. The sum of the nonresponse- adjusted weights across all responding farms on the mail list must equal N. In fact, the sum of the nonresponse-adjusted weights across all farms on the list would sum to N as well because the nonresponse adjusted weight of nonresponding farms is set to 0. Weight-Adjustment Groups To compute nonresponse adjustments, each record on the mail list is first placed in a weight-adjustment group. The groups are defined in such a way that all farms that reside in the same group appear to share similarities with respect to the characteristics used to define the group. It is necessary that the characteristics by which the weight-adjustment groups are defined be available for responding and nonresponding farms alike. Therefore, it was not possible to define weight-adjustment groups using data collected via the survey. Information used to define the groups was obtained from historical information maintained on the mail list and available for each farm. The information used to create the weight-adjustment groups was a measure of general farm sales (GFS), expressed in total dollars. This measure is available for all farms on the mail list. The basic definition of the weight- adjustment groups is given below: Weight Adjustment Definition Group ID GFS<= $50,000 10 $50,000< GFS <= $250,000 20 $250,000< GFS <=$500,000 30 $500,000< GFS <= $1,000,000 40 $1,000,000 < GFS <= $5,000,000 50 $5,000,000 < GFS 60 Methane Digester Farms 900 Farms on the mail list were placed in mutually exclusive groups based on the farm's GFS. One additional weight-adjustment group was composed of farms believed to possess a methane digester. These farms are generally economically large but were placed in the group 900 without regard to the GFS. Weight-adjustment groups were created and weight adjustments were carried out separately for each state. To ensure there were sufficient numbers of responding farms in each group, some collapsing of weighting groups occurred, resulting in some States having more weight adjustment groups than others. Nonresponse-Adjustment Computation A separate nonresponse adjustment is calculated within each weight-adjustment group. All responding records within each group will receive the same nonresponse adjusted weight. The nonresponse-adjustment is obtained by dividing the total number of farms contained in a group by the number of responding farms in the group. If the total number of farms in the group is 50 and the number of responding farms in the group is 40, the nonresponse- adjustment for the responding farms is 50/40 or 1.25. The nonresponse- adjusted weight for all responding farms in the group is the product of the survey weight and the nonresponse adjustment of 1.25. This is simply (1 x 1.25) or 1.25. Note that 1.25 x 40 = 50, the total number of farms in the group. The assumption being made is that within each weight-adjustment group, the data the nonrespondents would have provided had they responded is similar to the data provided by the respondents. This assumption is made somewhat more plausible by the fact that farms in the same group share similar characteristics with respect to the information used to define the group- the GFS. Coverage Weighting Adjustments The target population for the 2009 OREPS was the set of all farms in the United States producing at least $1,000 worth of raw agricultural commodities and producing on-farm renewable energy in 2009. Realistically, it is a nearly impossible task to compose a list of farms that is complete. Due to this incompleteness of the 2009 OREPS mail list, estimates produced from it, even if perfectly corrected to account for nonresponse, will still be downward biased because farms not on the list would not have any representation. This bias due to list incompleteness is called coverage bias, or more specifically, bias due to under coverage of the list. To reduce the amount of this bias, an additional adjustment was calculated and applied to the nonresponse-adjusted weight for each responding farm. This is called the coverage adjustment. The coverage adjustment was calculated within the same weight-adjustment groups defined above. Coverage Adjustment Computation Each farm on the 2009 OREPS mail list was a respondent to the 2007 Census of Agriculture. The weights of all 2007 census respondents were fully adjusted for nonresponse and coverage error. Coverage adjustment for the 2007 census was made possible through the use of survey data that was based on a list (or frame) of geographic land segments. In theory, every acre of land in the U.S. is contained in one of these segments. This implies that a survey based on such a frame will have complete coverage and represent all farms in the U.S. This survey was used to derive estimates of the magnitude of the coverage error associated with the census mail list. The nonresponse- adjusted weight for each 2007 agricultural census respondent received a coverage adjustment based on the estimated coverage error obtained from the area frame survey. This resulted in a fully coverage-adjusted weight for each census respondent. These weights were pulled forward for every farm on the 2009 OREPS mail list. Summing these weights across every farm on the list produces an estimate of the total number of farms producing on-farm energy in 2007. These estimates account for both 2007 census nonresponse and coverage bias. Information contained in the census weights is used to create a coverage adjustment for the 2009 OREPS. This information is somewhat dated (2007 vs. 2009), but still useful for accounting for the 2009 OREPS list coverage bias The coverage-adjustment for responding farms to the 2009 OREPS was calculated by first summing the 2007 census fully adjusted weight across all farms residing in the weight-adjustment group. This gives an estimate of the total number of energy producing farms in a state that would fall into that group, whether they are contained on the 2009 OREPS mail list or not. This number is divided by the sum of the nonresponse-adjusted weights for all responding farms in the group. This results in the 2009 OREPS coverage adjustment for that group. If the sum of the fully adjusted census weights in a group for all farms in the group is 60 and the sum of the nonresponse-adjusted weight across all responding farms in the group is 50, the 2009 OREPS coverage- adjustment is 60/50 or 1.2. Multiplying the coverage adjustment by the nonresponse adjusted weight results in the fully-adjusted 2009 OREPS weight. In the given example with 40 responding farms, this would be 1.25 x 1.2=1.5. Note that 1.5 x 40=60. This represents the estimated total number of energy- producing farms that would fall into that group, whether on the list or not. All responding farms in a group will have the same fully-adjusted 2009 OREPS weight. Summary Weights Many of the fully adjusted weights for the 2009 OREPS are not whole numbers (integers). Using these weights to create the estimates published in the tables would result in the tables having lots of fractional values. These would be difficult to read or could cause consistency problems between different tables. To avoid some of these problems, summary weights were created by moving the fully adjusted weights randomly up or down to the nearest integer in an unbiased manner by retaining the weighting cell total. This process is called weight integerization. The resulting integer summary weights are used to actually produce the numbers published in the tables. Explanation of Published Ratios Table 1. Wind Turbines - Calculations for columns 3, 4, and 5 in Table 1 include only those surveyed farms reporting positive data values for both the numerator and the denominator. Column 3. Average Rated Generating Capacity Per Turbine - This is computed as the estimated total kilowatts of rated generating capacity, divided by the estimated number of actively generating turbines. Column 4. Average Installation Cost Per Turbine - This is computed as the estimated total system installation cost (includes outside funding) for all turbines, divided by the estimated number of turbines owned and used. Column 5. Percent of Installation Cost Provided by Outside Funding - This is computed as the estimated installation cost received from outside funding, divided by the estimated total installation cost. Table 2. Methane Digesters - Calculations for columns 3, 4, and 5 in Table 2 include only those surveyed farms reporting positive data values for both the numerator and denominator. Column 3. Average Methane Volume Production Per Digester - This is computed as the estimated total amount of methane produced, divided by the estimated total number of methane digesters. Column 4. Average Installation Cost Per Digester - This is computed as the estimated total installation cost of methane digesters, divided by the estimated total number of digesters on farms with positive installation costs. Column 5. Percent of Installation Cost Provided by Outside Funding - This is computed as the estimated installation cost provided by outside funding, divided by the estimated total installation cost. Table 3. Solar Panels - Calculations for columns 4, 5, and 6 in Table 3 include only surveyed farms reporting positive data values for both the numerator and denominator. Column 4. Average Photovoltaic Solar Panel Generating Capacity Per Farm - This is computed as the estimated total generating capacity of all photovoltaic solar panels, divided by the estimated total number of farms utilizing photovoltaic solar panels. Column 5. Average Installation Cost Per Farm - This is computed as the estimated total solar panel system installation cost, divided by the estimated number of farms having positive solar panel installation cost. Column 6. Percent of Installation Cost Provided by Outside Funding - This is computed as the estimated installation cost received from outside funding, divided by the estimated total installation cost. Table 4. Energy Cost Savings - Column 2. Average Dollars Saved on Utility Bills Per Farm - This is computed as the estimated total amount saved on utility bills for farms having wind turbines, solar panels, and/or methane digesters, divided by the estimated total number of farms having positive utility bill savings. MEASURES OF PRECISION AND ACCURACY OF THE ESTIMATES All numbers published in the tables are merely estimates of particular characteristics of the entire population of energy-producing farms. The true values of these characteristics are unknown and unknowable. Even though an attempt was made to contact every farm on the mail list, the estimates produced by the survey will not exactly attain the true values. This is due to a number of factors, such as survey nonresponse, mail list incompleteness, and the weight integerization process. Hypothetically, if the entire survey process was repeated over and over again, each replication of the survey would almost certainly produce a different estimate for the same population value every time. This is because each time the survey is carried out, a different set of respondents would be obtained, response rates would fluctuate, and the estimated coverage rates of the mail list could change. It is possible to obtain an idea of how much this variation would be on average by calculating the estimate's variance. The variance of an estimate gives a measure of the average squared random fluctuation that would be seen in an estimate if the survey was carried out multiple times. This is referred to as the precision of the estimate. Because the variance measures random fluctuation in squared units, the square root of the variance is computed to obtain a random fluctuation measure that is in the same units as the original estimate. This is called the standard error (se) of the estimate. The standard error can then be divided by the estimate itself to show the relative size of the standard error to the estimate. If this ratio is small, the estimate is quite precise. If this ratio is large, the estimate is imprecise. An estimate of 100 with a standard error of 2 would result in a relative standard error of .02 or 2 percent. This would be a very precise estimate. An estimate of 100 with a standard error of 20 would result in a relative standard error of 20 percent. This might be considered to be an imprecise estimate. The idea of precision can be made a little more clear by stating that if the estimate is 100 with a standard error of 2, you could be quite confident that the true population value would be in the interval 96 to 104 (within two standard deviations of the estimate). Unbiased estimates are generally accurate. This is to say that if the survey is hypothetically repeated over and over, the average of the estimates obtained would be very close to the true value being estimated. This does not mean that any particular realization of the estimate will be "close" to the true value. An accurate estimate that is not precise has a good chance of missing the true value of the characteristic being estimated by a significant amount. If the estimate contains some bias, both precision and accuracy are measured by computing the mean squared error (mse) of the estimate. Bias is systematic error that would be about the same for every hypothetical replication of the survey. Bias is not random fluctuation and affects the accuracy of the estimate. The weight adjustments described earlier are used to decrease biases in the estimates. However, the weight integerization process introduces some bias. Ideally, the amount of bias contained in an estimate should be small or non-existent, but in conducting actual surveys, some biases may be hard to avoid. Biased estimates can be precise, but in hypothetical replications of the survey, will tend to be systematically lower or higher than the true population value being estimated. Highly biased estimates are generally quite inaccurate and are not desirable. The mean squared error is computed by adding a term to the variance called the estimated squared bias. The mean squared error can be used to measure the combined effects of random variation and bias contained in an estimate. Like the variance, the mean squared error is measured in squared units, so the square root of the mean squared error is often taken. This results in what is called the root mean squared error (rmse). Like the standard error, the ratio of the root mean squared error to the estimated value can be created. It is often multiplied by 100 and expressed as a percent. This ratio gives a measure of the relative root mean squared error (relative rmse) of the estimate. When this ratio is small (close to 0 percent), the estimate is both very precise and very accurate. A large ratio (20 percent or more) might indicate that the estimate is precise but not very accurate. Another possibility is that the estimate might be accurate but not very precise. A third possibility is that the estimate might reflect only a moderate level of both accuracy and precision. Table A. Reliability Estimates of Operations Reporting Small and Large Wind Turbines: 2009 ------------------------------------------------------------------------------------------------------------------------------------------ : Small wind turbines : Large wind turbines :--------------------------------------------------------------------------------------------------------------- : : Average per turbine : : Average per turbine : :------------------------------------: :-------------------------------------- : : Rated generating : : : Rated generating : : Farms : capacity :Installation cost: Farms : capacity : Installation cost :--------------------------------------------------------------------------------------------------------------- : Number :Relative: kW :Relative:Dollars :Relative: Number :Relative: kW :Relative: Dollars :Relative State : : RMSE : : RMSE : : RMSE : : RMSE : : RMSE : : RMSE ------------------------------------------------------------------------------------------------------------------------------------------ United States ............: 1,406 1.4 6 3.4 12,972 1.7 14 8.4 1,035 9.7 1,339,143 8.9 : Alabama ..................: 3 48.1 (D) (D) (D) (D) - - - - - - Alaska ...................: 8 11.9 1 11.0 4,394 24.9 - - - - - - Arizona ..................: 63 6.9 1 11.0 2,768 16.0 - - - - - - Arkansas .................: 7 8.5 2 57.3 5,800 56.9 - - - - - - California ...............: 134 5.5 3 4.3 13,955 4.1 - - - - - - Colorado .................: 98 6.9 3 12.5 4,581 4.6 - - - - - - Connecticut ..............: 1 16.0 (D) (D) (D) (D) - - - - - - Delaware .................: - - - - - - - - - - - - Florida ..................: 5 12.3 1 13.6 5,250 8.4 - - - - - - Georgia ..................: 4 12.1 (D) (D) (D) (D) - - - - - - : Hawaii ...................: 43 8.7 1 2.8 1,799 5.5 - - - - - - Idaho ....................: 39 7.8 4 8.0 12,778 14.1 - - - - - - Illinois .................: 28 9.3 5 11.7 11,448 9.3 - - - - - - Indiana ..................: 49 7.4 2 10.2 7,870 9.3 - - - - - - Iowa .....................: 39 5.7 8 7.9 23,840 9.3 9 6.0 1,359 5.8 1,784,889 5.6 Kansas ...................: 18 16.5 6 11.3 10,084 7.0 - - - - - - Kentucky .................: 1 60.1 (D) (D) (D) (D) - - - - - - Louisiana ................: 2 27.9 (D) (D) (D) (D) - - - - - - Maine ....................: 32 7.2 5 9.0 17,353 6.9 - - - - - - Maryland .................: 2 29.5 (D) (D) (D) (D) - - - - - - : Massachusetts ............: 22 6.4 7 7.2 43,218 7.9 - - - - - - Michigan .................: 34 10.7 2 11.8 9,981 19.3 - - - - - - Minnesota ................: 99 4.8 20 6.1 37,647 4.5 - - - - - - Mississippi ..............: 3 23.6 1 4.2 4,467 6.6 - - - - - - Missouri .................: 21 11.9 3 38.4 7,779 23.9 - - - - - - Montana ..................: 63 6.2 3 13.4 6,293 5.9 - - - - - - Nebraska .................: 7 8.0 1 6.7 1,563 15.4 - - - - - - Nevada ...................: 8 34.3 1 7.9 1,455 12.4 - - - - - - New Hampshire ............: 11 16.0 3 18.5 9,850 14.1 - - - - - - New Jersey ...............: 3 46.7 8 7.2 47,518 11.9 - - - - - - : New Mexico ...............: 28 14.4 1 5.7 4,807 10.9 - - - - - - New York .................: 58 7.6 5 7.4 22,254 9.9 - - - - - - North Carolina ...........: 12 10.9 2 5.2 12,800 6.8 - - - - - - North Dakota .............: 5 35.7 (D) (D) 15,778 10.8 - - - - - - Ohio .....................: 44 10.6 2 8.4 11,257 11.8 - - - - - - Oklahoma .................: 20 14.7 2 13.5 4,156 9.9 - - - - - - Oregon ...................: 37 7.9 1 4.5 3,209 10.2 - - - - - - Pennsylvania .............: 26 7.0 3 7.7 7,148 20.9 - - - - - - Rhode Island .............: 2 30.6 (D) (D) (D) (D) - - - - - - South Carolina ...........: - - - - - - - - - - - - : South Dakota .............: 11 7.6 4 35.8 19,064 13.1 - - - - - - Tennessee ................: 9 23.7 (D) (D) 8,177 13.0 - - - - - - Texas ....................: 102 6.9 4 9.4 8,493 4.2 - - - - - - Utah .....................: 31 9.0 1 4.7 2,562 9.8 - - - - - - Vermont ..................: 43 4.9 3 2.2 16,847 5.5 - - - - - - Virginia .................: 11 11.0 1 25.5 2,971 34.0 - - - - - - Washington ...............: 50 7.6 19 16.7 12,669 7.9 - - - - - - West Virginia ............: 4 19.9 5 14.8 44,400 16.0 - - - - - - Wisconsin ................: 46 5.4 8 1.2 15,329 1.9 - - - - - - Wyoming ..................: 20 12.8 2 8.2 4,648 10.4 - - - - - - : Other States 1/ ..........: - - - - - - 5 20.0 453 54.0 536,800 55.7 ------------------------------------------------------------------------------------------------------------------------------------------ 1/ Other States include Kansas, Minnesota, and Montana. Table B. Reliability Estimates of Operations Reporting Methane Digesters and Solar Panels: 2009 ------------------------------------------------------------------------------------------------------------------------------------------ : Methane digesters : Solar panels :------------------------------------------------------------------------------------------------------------------- : : Average per methane digester : : Average per farm : :----------------------------------------: :-------------------------------------- : : : : : PV rated : : Farms : Methane produced : Installation cost : Farms :generating capacity:Installation cost :------------------------------------------------------------------------------------------------------------------- : Number :Relative: Cubic :Relative: Dollars :Relative: Number :Relative: Watts :Relative : Dollars :Relative State : : RMSE : feet : RMSE : : RMSE : : RMSE : : RMSE : : RMSE ------------------------------------------------------------------------------------------------------------------------------------------ United States ........: 121 1.5 30,515,800 2.0 1,718,562 1.3 7,968 1.5 4,449 3.8 31,947 2.8 : Alabama ..............: - - - - - - 33 7.7 865 23.3 6,948 12.3 Alaska ...............: - - - - - - 16 8.0 865 7.9 9,134 9.0 Arizona ..............: - - - - - - 255 4.9 2,002 4.9 19,992 2.7 Arkansas .............: - - - - - - 41 6.6 833 14.6 7,502 8.0 California ...........: 14 8.9 29,194,314 20.5 1,794,444 23.3 1,906 5.4 11,229 5.6 78,910 4.5 Colorado .............: - - - - - - 504 5.1 1,654 4.5 16,879 2.7 Connecticut ..........: - - - - - - 26 12.1 4,173 16.4 29,571 17.6 Delaware .............: - - - - - - 4 17.1 15,500 19.3 101,250 18.7 Florida ..............: - - - - - - 88 6.3 2,162 6.6 12,223 4.0 Georgia ..............: - - - - - - 32 6.8 3,625 9.3 28,545 8.3 : Hawaii ...............: - - - - - - 520 6.0 1,790 3.5 16,665 3.2 Idaho ................: - - - - - - 131 6.1 916 3.5 12,524 6.9 Illinois .............: - - - - - - 58 8.2 4,575 19.1 39,018 16.6 Indiana ..............: - - - - - - 127 6.3 543 5.7 5,262 6.4 Iowa .................: - - - - - - 40 6.7 1,988 13.8 17,791 18.6 Kansas ...............: - - - - - - 116 4.1 408 11.4 4,607 5.4 Kentucky .............: - - - - - - 67 9.6 776 5.9 6,305 7.5 Louisiana ............: - - - - - - 13 10.3 876 11.7 10,970 10.1 Maine ................: - - - - - - 97 5.9 1,680 6.0 13,892 7.2 Maryland .............: - - - - - - 21 11.2 2,665 26.8 24,201 15.0 : Massachusetts ........: - - - - - - 63 5.9 3,678 6.5 27,624 2.4 Michigan .............: 5 1.0 36,923,333 (Z) 1,322,222 (Z) 75 6.5 841 9.2 7,416 6.7 Minnesota ............: 5 1.0 69,105,120 (Z) 3,123,333 (Z) 73 6.6 1,409 7.4 11,178 5.2 Mississippi ..........: - - - - - - 23 13.3 706 13.4 11,593 11.2 Missouri .............: - - - - - - 93 6.1 1,022 12.7 9,429 13.2 Montana ..............: - - - - - - 238 4.3 988 3.0 9,180 3.2 Nebraska .............: - - - - - - 65 4.3 742 6.6 5,632 4.8 Nevada ...............: - - - - - - 51 12.6 1,832 13.7 21,971 8.2 New Hampshire ........: - - - - - - 49 8.4 1,641 5.6 16,173 4.7 New Jersey ...........: - - - - - - 138 5.5 14,081 4.4 112,855 4.4 : New Mexico ...........: - - - - - - 258 4.2 1,261 4.2 12,888 4.6 New York .............: 16 1.5 18,611,675 (Z) 1,611,206 (Z) 156 6.2 2,501 4.8 21,661 4.3 North Carolina .......: - - - - - - 104 8.1 1,015 8.1 10,198 3.5 North Dakota .........: - - - - - - 29 9.9 429 6.1 5,048 10.6 Ohio .................: - - - - - - 130 6.5 1,614 10.7 12,122 16.2 Oklahoma .............: - - - - - - 187 5.2 428 22.9 4,612 9.7 Oregon ...............: - - - - - - 332 5.3 3,002 8.0 22,147 6.9 Pennsylvania .........: 13 1.0 18,951,843 (Z) 642,188 (Z) 173 5.6 1,750 4.8 20,699 2.8 Rhode Island .........: - - - - - - 12 7.9 (D) (D) 30,960 28.3 South Carolina .......: - - - - - - 20 10.4 (D) (D) 5,047 6.9 : South Dakota .........: - - - - - - 55 3.3 696 3.9 7,470 2.7 Tennessee ............: - - - - - - 66 6.5 1,065 8.7 8,657 13.4 Texas ................: - - - - - - 573 4.3 783 2.4 7,692 1.9 Utah .................: - - - - - - 133 5.3 1,211 4.2 14,573 5.7 Vermont ..............: 8 1.0 (D) (D) 1,718,750 (Z) 110 6.2 1,304 1.2 15,510 1.7 Virginia .............: - - - - - - 83 5.7 869 10.1 12,868 5.4 Washington ...........: - - - - - - 205 5.6 1,547 2.7 10,377 2.6 West Virginia ........: - - - - - - 27 9.9 521 9.2 8,166 7.4 Wisconsin ............: 21 2.1 (D) (D) 1,608,924 1.8 176 6.0 2,484 13.8 17,607 8.2 Wyoming ..............: - - - - - - 176 5.6 1,275 5.1 10,362 2.7 : Other States 1/ ......: 39 4.9 26,034,140 6.0 2,181,189 5.6 - - - - - - ------------------------------------------------------------------------------------------------------------------------------------------ 1/ Other States include Colorado, Connecticut, Florida, Idaho, Illinois, Indiana, Iowa, Kansas, Maryland, Mississippi, Missouri, Montana, Nebraska, North Carolina, Ohio, Oklahoma, Oregon, South Dakota, Tennessee, Texas, Washington, and Wyoming. Appendix B. General Explanation and Report Form DEVELOPMENT OF THE REPORT FORM The 2009 On-Farm Renewable Energy Production Survey report form was developed through input from other government agencies, special interest groups and each of NASS's field offices. Report form testing was conducted in several States and included various types of producers. Producers were asked to evaluate the report form through cognitive interviews. TERMS AND DEFINITIONS The following definitions and explanations provide a detailed description of specific items and terms used in this publication and on the report form. Copies of the 2009 On-Farm Renewable Energy Production Survey report form and instruction sheet are included in this appendix. Biodiesel. A non-petroleum based diesel fuel consisting of long-chain alkyl esters. Biodiesel is typically made by chemically-reacting lipids (e.g., vegetable oil) and alcohol. It can be used (alone or blended with conventional petrodiesel) in unmodified diesel-engine vehicles. Energy Audit. An audit conducted by a certified energy manager or professional engineer that focuses on potential capital-intensive projects and involves detailed gathering of field data and engineering analysis. The audit will provide detailed project costs and savings information with a high level of confidence sufficient for major capital investment decisions. Ethanol. A fuel produced by converting crops such as corn, sugarcane, or wood into alcohol sugar (CH3CH2OH). This may then be blended with gasoline to enhance octane, reduce exhaust pollution, and reduce reliance on petroleum fuels. Generating Capacity. The ability to generate electricity is measured in watts. Wind turbines currently manufactured have power ratings ranging from 250 watts to 5 megawatts (MW). A 10-kW wind turbine can generate about 10,000 kWh annually at a site with wind speeds averaging 12 miles per hour, or about enough to power a typical household. A 5-MW turbine can produce more than 15 million kWh in a year - enough to power more than 1,400 households. The average U.S. household consumes about 10,000 kWh of electricity each year. Geothermal Energy. Energy, in the form of heat, stored in the earth, which originates from the original formation of the planet, from radioactive decay of minerals, and from solar energy absorbed at the surface. The "Grid". The grid consists of two infrastructures: the high-voltage transmission systems, which carry electricity from the power plants and transmit it hundreds of miles away, and the lower-voltage distribution systems, which draw electricity from the transmission lines and distribute it to individual customers. Hydroelectric Energy. Power produced through use of the gravitational force of falling or flowing water Kilowatt. kW = 1,000 watts, megawatt (MW) = 1 million watts, and gigawatt (GW) = 1 billion watts. Kilowatt Hour. One kilowatt (1,000 watts) of electricity produced or consumed for one hour. For example, one 50-watt light bulb left on for 20 hours consumes one kilowatt-hour of electricity (50 watts x 20 hours = 1,000 watt-hours = 1 kilowatt-hour). Manure/Methane Digester. Anaerobic digestion involves the decomposition of manure and processing of by-products and other materials into effluent and biogas. Microorganisms perform the decomposition process in an anaerobic digester, which can be designed in several ways. Once biogas is harvested from the processed manure, it can be run through an engine to generate electricity, used in place of natural gas, or flared. Methane. A chemical compound with the molecular formula CH4. It is the simplest alkane, and the principal component of natural gas. Outside Funding Sources. Federal, state, and local governments, universities, and private industries provide funds for renewable energy installation costs. Solar Energy. Energy, in the form of heat, generated by the sun. Solar Panel, Photovoltaic. Solar photovoltaic modules use solar cells to convert light from the sun into electricity. Solar Panel, Thermal. Flat plate and evacuated tube solar collectors are typically used to collect heat for space heating or domestic hot water. Wind Power. Winds are caused by the uneven heating of the atmosphere by the sun, the irregularities of the earth's surface, and rotation of the earth. Wind flow patterns are modified by the earth's terrain, bodies of water, and vegetative cover. This wind flow, or motion energy, when "harvested" by modern wind turbines, can be used to generate electricity. The terms "wind energy" or "wind power" describe the process by which the wind is used to generate mechanical power or electricity. The power available in the wind is proportional to the cube of its speed, which means that doubling the wind speed increases the available power by a factor of eight. A turbine operating at a site with an average wind speed of 12 mph could in theory generate about 33 percent more electricity than one at an 11-mph site, because the cube of 12 (1,768) is 33 percent larger than the cube of 11 (1,331). Wind Powered Device. Usually wind turbines, but can also be windmills. If the energy created is used directly by machinery, such as a pump or grinding stones, the machine is a windmill. If the energy is converted to electricity, the machine is called a wind generator, wind turbine, wind power unit (WPU), wind energy converter (WEC), or aero generator. Wind turbines convert wind energy to electricity.