WIND RESOURCE ASSESSMENT FOR THE STATE OF WYOMING

Transcription

1 WIND RESOURCE ASSESSMENT FOR THE STATE OF WYOMING Performed by Sriganesh Ananthanarayanan under the guidance of Dr. Jonathan Naughton, Professor, Department of Mechanical Engineering University of Wyoming, Laramie, WY

2 Table of Contents LIST OF FIGURES... 2 LIST OF TABLES... 2 INTRODUCTION:... 3 ANALYSIS... 5 RESULTS... 7 Wind resource reports CONCLUSION: FUTURE WORK: REFERENCES APPENDIX 1: WIND ROSE FOR EACH MONTH FOR LARAMIE APPENDIX 2: WIND RESOURCE ASSESSMENT FOR CHEYENNE APPENDIX 3: WIND RESOURCE ASSESSMENT FOR CASPER APPENDIX 4: WIND RESOURCE ASSESSMENT FOR ROCK SPRINGS APPENDIX 5: WIND RESOURCE ASSESSMENT FOR SHERIDAN APPENDIX 6: WIND RESOURCE ASSESSMENT FOR GILLETTE APPENDIX 7: WIND RESOURCE ASSESSMENT FOR RAWLINS LIST OF FIGURES Figure 1- The state of Wyoming with seven wind sites chosen for South-East Wyoming 4 Figure 2- Probability density function and model distributions for Laramie, WY... 8 Figure 3- Monthly wind speed profile averaged over six years... 9 Figure 4 Wind rose graphics for Laramie, WY during summer Figure 5-Wind rose graphics for Laramie, WY during winter LIST OF TABLES Table 1: Mean wind speed variation with respect to height for Laramie... 8 Table 2: Mean wind speed and Wind power density... 8 Table 3: Wind direction during Winter & Summer Table 4: Annual Energy Production Table 11 2

3 INTRODUCTION: Wyoming wind blows strong and persistently thanks to a combination of elevation, topography and weather conditions. High wind speeds throughout the year is very common in Wyoming, especially South-East Wyoming. Towards the central part of Wyoming there is a low spot in the continental divide. This combined with down-flow on the mountains makes for a rich resource of wind. Wind speeds are depicted by classes ranging from one through seven. Class four (> 7.5 m/s) and higher is considered to be highly attractive for power generation using wind energy [1]. Previous research indicates that most of the locations in the south-east part of Wyoming have average wind speeds in excess of those associated with Class 5 [2]. An important term often associated with wind energy production is the capacity factor, which is defined as the ratio of the actual energy produced in a given period, to the hypothetical maximum possible, i.e. running full time at rated power. With a typical capacity factor of 45% [3], Wyoming contains one of the best on-shore wind resource locations in the United States of America. This is very impressive, but does not mean wind plants will be constructed just anywhere in Wyoming. There are plenty of questions to be answered before going further. For instance, are there large fluctuations in the wind speeds annually? What direction does the wind come from? How much power can be generated during a year? To answer these questions, the wind resource at a prospective development location needs to be assessed in detail. A proper wind resource assessment forms a very important phase in the development of utility scale wind farms. In addition, it provides common people with an awareness of the wind resource possessed by Wyoming and the associated economic potential that could be exploited to generate additional revenue for the state. From a strategic standpoint, in addition to the significant coal and gas reserves possessed by Wyoming, energy generated from wind could also significantly diversify the economic portfolio of the state. Wyoming s phenomenal wind resource has not gone unnoticed. The National Renewable Energy Laboratory and AWS Truepower developed a wind resource map of USA at 80 meters [4], which is the typical height at which utility scale wind turbines nacelles are located. The map from the aforementioned study shows the prominence of Wyoming s wind resource compared to other states. Current wind industry standards show that most of the wind project development is occurring at sites with mean wind speed at hub height greater than 6.5 m/s [5]. All this looks very promising for Wyoming. However there are some important issues we need to address. First and foremost, a readily accessible wind resource report for any chosen location is necessary. A good wind resource report can provide a quick overview of the wind characteristics of the chosen site. In addition, it can point out whether the location needs to be further investigated by installing monitoring stations. The goal of this project was to perform an in-depth analysis of the wind resource for the state of Wyoming. This is done by using the airport wind data recorded for the past few years at chosen locations and using this data to determine the various wind and statistical parameters such as mean wind speed, probability density function, wind power density, and wind rose. As part of this study, all of the above have been incorporated into an 3

4 easily understood wind resource report that is readily available for the general public. The report also determines estimated annual energy production for a typical utility scale wind turbine. Using this report, the public can develop an understanding of the economic potential of wind energy in Wyoming. Furthermore, the procedure developed as part of this study can be used to conduct a similar assessment at any other location in the world, provided wind data exists for that location. For conducting any analysis associated with wind resource, wind data is required. The more data available, the better the analysis results. The National Climatic Data Center (NCDC), which is the world s largest archive for climate data [6], keeps a record of wind speeds measured from meteorological towers at a height of 10 meters above the ground, better known as Automated Surface Observing System (ASOS). In total there are 16 ASOS stations around Wyoming. The measurements at the seven chosen sites were representative of the wind resource needed for commercial wind development, with special emphasis on South-East Wyoming. The wind data from January 1, 2006 to July 7, 2012 for the following sites were analyzed as part of this study: Laramie (KLAR), Cheyenne (KCYS), Casper (KCPR), Rock Springs (KRKS), Gillette (KGCC), Sheridan (KSHR), and Rawlins (KRWL). These locations are depicted in Figure 1 below. Figure 1- The state of Wyoming with seven wind sites chosen for South-East Wyoming wind resource assessment ASOS records the wind speed, direction, temperature, and pressure. For this study, wind speed and direction were the two parameters considered. The raw data was reviewed for errors, and the corrupted data was eliminated. The biggest advantage of collecting large volumes of data is that it provides a good basis for characterizing the wind resource at any given location. For each of these sites, the probability density function (pdf) was developed. After calculating the necessary parameters, the mean wind speed was determined from the pdf. wind power density, average wind speed profile, wind rose, and potential annual energy 4

5 production for a utility scale wind turbine was determined. This procedure has been automated using a MATLAB routine that can handle wind data of any location in the world. ANALYSIS PROBABILITY DENSITY FUNCTION A probability density function is a function associated with wind data that shows the relative frequency with which a particular wind speed occurs or intuitively it is related to the percentage of time that wind blowing at a particular location achieves a certain speed. Hourly wind data implies discrete wind data. The bin spacing is determined by the MATLAB routine according to the data collected. Each set of wind data has its own bin spacing. The number of bins are then used to develop a histogram which provides us with a good distribution of the data. Finally, the histogram is normalized using the empirical formula below, to obtain the probability density function for the location. pdf (u) = histogram(u)/(n*delta_u); where delta_u= bin spacing ; n = total number of bins; The mean wind speed for each location is obtained from the probability density function using the equation. u ( pdf (u ) * u * delta u, u is the mean wind speed. Distributions used for wind speed In order to get a better approximation or fit to the pdf, the pdfs determined here have been compared with two statistical distributions used commonly for wind resource assessment, namely Rayleigh and Weibull probability distributions. The simpler of the two is the Rayleigh distribution f (u ) 2u u / c 2 e c2 0 u, which has a single parameter c. The Weibull distribution f (u ) k u c c k 1 e u / c k 0 u has two parameters k, the shape parameter, and c, the scale parameter. The Rayleigh distribution is actually a special case of the Weibull distribution with k = 2. For both distributions, the minimum value of velocity is zero and the maximum value is infinity. 5

6 WIND SPEED Wind speed is the most important aspect of the wind resource, because there is a direct relationship between wind speed and wind turbine power output. Currently, most of the utility-based wind turbines are constructed with a hub height of 80 meters, so the focus of this analysis is the wind resource at 80 meters above ground. The first step then is to extrapolate the wind speed data collected (by NCDC) for 10 meters. Assuming that the measurements were taken on a flat terrain, the 1/7th Power Law was used for the extrapolation u 80 Z u 0 * Z0 Where u80 stands for velocity of wind at 80 meters, u 0 stands for velocity at a reference height (where the velocity is known), Z is height of interest which is 80 meters, Z0 is the reference height, and α is the shear factor. For this analysis, α is assumed to be 1/7, which is appropriate for neutral stability conditions. [7] The extrapolated wind speed data was used to determine another important parameter associated with wind resource the Wind Power Density (WPD). The wind power density, measured in watts per square meter, indicates how much power in the wind is available at the site for conversion using a wind turbine. Wind speed generally increases with height above ground. The wind power density was determined using = ( u80 ) where u 80 is the velocity at hub height determined using Power Law. The wind power density equation implies that wind power density is proportional to the cube of wind velocity. AVERAGE MONTHLY WIND SPEED PROFILE The average monthly wind speed profile shows the variation of the wind speed for every month throughout the year. This representation of the wind resource will give significant insights on the wind variation over a year. The profile is determined by averaging the wind speed for each month of the monitoring period. The average wind speeds are plotted for each month. WIND ROSE The principal purpose of developing a wind rose is to scope out potential locations for siting wind turbines for commercial use. Obviously, the optimum turbine locations would depend a great deal on the direction from which the wind blows at any particular site. The wind rose is a polar plot that represents the percentage of time that the wind direction falls within each sector of the compass. On the wind rose shown here, the length of each bar is proportional to the fractional frequency at which that particular wind speed (and below) was observed from that direction. Different colors on each bar indicate percentages for different wind speeds. 6

7 ANNUAL ENERGY PRODUCTION The Annual Energy Production was calculated by using the publically available power curve data provided by the manufacturer. The manufacturer s power curve was adjusted for the density of air at each site. The next step was to interpolate the wind speeds with the turbine manufacturer s data, followed by computing the corresponding potential power output. The annual energy production was determined using = ( pdf (u ) * ( P (u ) * delta u ) hours in the year, RESULTS The analysis procedure discussed above was applied to the wind data obtained from NCDC to assess the wind resource of Wyoming. The probability density function and model distributions, namely the Rayleigh and Weibull distributions, generated from the wind data are first described. This is followed by a discussion of the mean wind speed and wind rose plots. Finally, the economic potential of a particular site, which will be of most interest to utility wind developers, common public and other stakeholders, is described using the results obtained for wind power density and potential annual energy production. The only site considered here is Laramie, WY. Resource reports of other sites are presented in the appendices. Probability density function (pdf) and model distributions Figure 2 shows the probability density function and model distributions, both Rayleigh and Weibull, for Laramie. The more the peak of the curves shift to the right, the windier the location is. The vertical axis of this curve is related to the frequency with which a particular wind speed occurs. The majority of the wind speeds are between 4 8 m/s. Rayleigh is a single parameter model for the wind, whereas Weibull is a two parameter model. Both Rayleigh and Weibull distributions give a good fit to the probability density function. Weibull provides more of a conservative approach. 7

8 Figure 2- Probability density function and model distributions for Laramie, WY The table 1 below is a comparison of the mean wind speed at 10 meters, 50 meters and 80 meters. The wind speed at 50 meters and 80 meters was extrapolated using the 1/7 Power Law explained earlier. It is clear that the wind significantly increases with elevation based on the power law. Location Laramie 10m 50m 80m Table 1: Mean wind speed variation with respect to height for Laramie Wind Power Density The wind power density for Laramie at hub height is tabulated below in Table 2. Sites having wind power density greater than 400 W/m2 are considered to be good. Higher values imply that the site is an excellent source for wind power production. This implies that this site is a good candidate for wind energy production. LOCATION Average wind speed at hub height (m) WPD (W/m2) LARAMIE Table 2: Mean wind speed and Wind power density Average monthly wind speed profile Figure 3 below show the average monthly wind speed profile during the monitoring period. The winter has the highest wind speeds, while Spring and Fall have slightly lower winds. During summer there is a significant decrease in the wind speed. 8

9 Figure 3- Monthly wind speed profile averaged over six years Wind Rose Figure 4 shows the wind rose plotted for Laramie during the summer months June through August. Figure 5 shows the wind rose for winter months, September through May. 9

10 Figure 4 Wind rose graphics for Laramie, WY during summer Figure 5-Wind rose graphics for Laramie, WY during winter The typical direction of wind for Laramie obtained from the wind rose graphics above are tabulated in Table 3. The individual wind rose for each month during the monitor period is shown in Appendix 1. Location Laramie Latitude (degrees) Longitude (degrees) Direction of wind (Winter) W/SW Direction of wind (Summer) SW/NW Table 3 Wind direction during Winter & Summer Wind rose provides us with a visual representation of prevailing wind direction. The majority of the time, the distribution of wind direction is strongly bipolar in nature with wind blowing from the south-west or the north-west nearly all of the time. For the winter months from September through May the winds blow from the south-west direction. During the transition period from winter to summer the wind blows from the north-west direction. This implies that siting wind turbines with these directions in mind would best utilize the wind resource. ANNUAL ENERGY PRODUCTION In the context of wind energy production, what is of greatest interest is how the wind characteristics are reflected in the actual performance of a particular wind turbine. In each case, it depends on the shape of the wind turbine s power curve (the amount of electric power the turbine produces at each wind speed). The potential wind energy production from a GE 1.5 MW xle wind turbine was evaluated. The GE 1.5 MW xle is typically employed for commercial wind power generation, and over the years has been widely used in USA [8]. 10

11 The Annual Energy Production was calculated by using the publically available power curve data provided by the manufacturer. The wind speed data was interpolated and the corresponding potential power generation was determined. The results for Laramie are shown in Table 4 below. LOCATION LARAMIE Average wind speed at hub height (m) 7.84 WPD (W/m2) AEP 2006 (MWhrs.) AEP 2007 (MWhrs.) AEP 2008 (MWhrs.) AEP 2009 (MWhrs.) AEP 2010 (MWhrs.) AEP 2011 (MWhrs.) AEP 2012 (MWhrs.) Table 4 Annual Energy Production Table The results obtained for annual energy production are very promising. In addition, a capacity factor for each of the above annual energy production values can be determined. As an example, capacity factor for the year 2008 has been determined below Capacity Factor = (3889.4) / (365*24*1.5) = 29.6% The annual energy production of other chosen wind sites can be found in the appendix. These values confirm that the south-east corner of Wyoming has one of the best wind resources in the country. The capacity factor obtained using the airport wind data provide a reasonable estimate of what was expected. Other wind sites like Laramie valley has a capacity factor of 45% [9]. As part of this study, the maximum value for capacity factor was 45%, obtained for Rawlins in the year Wind resource reports All the results were included in a standard wind resource report for each of the seven locations. The Appendix contains these wind data reports for the monitoring period. The report provides a summary of the wind resource capability of each location with particular emphasis on the following items: pdf and model distributions, Mean wind speed and wind power density, Average monthly wind speed profile, Wind rose for each month averaged over six years, and Annual energy production from a GE 1.5 MW wind turbine. CONCLUSION: Wind resource assessment for Southeast Wyoming has been carried out using the wind data obtained from National Climatic Data Center. One key aspect to keep in mind is that 11

12 the results obtained are based on the wind data collected for every hour. This implies that the wind resource characterization provided here may not be an exact representation of the site, but provides a very good estimate of what can be expected from the chosen site. This study has established that, the South- East corner of Wyoming, especially Laramie, Cheyenne, Casper, Rawlins and Rocksprings possess excellent wind resource that has significant economic potential. Gillette and Sheridan have average wind resource compared to other chosen sites. The resource reports attached in the appendix for the chosen locations indicate that there is lot of potential for both wind energy and economic development. These reports can be accessed by the general public. The results of this study have several important implications. The probability density function and model distributions prove that high quality wind blows steadily throughout the Southeast corner of Wyoming. The high values obtained for wind power density is another indication that Wyoming has one of the best wind resource in the country. Average monthly wind speed profiles, and potential annual energy production for all the locations is a good source of encouragement for both utility scale and residential scale wind developers. In addition, the wind rose graphics provide a precise direction of the wind. Other wind sites in the south-east corner of Wyoming may potentially have wind resource than the sites chosen for this study. However, the potential wind resource at other locations in southeastern Wyoming can only be investigated if data is collected for those sites. FUTURE WORK: These obtained results are highly promising, but further work needs to be performed to obtain a much more accurate representation of these chosen sites. Analysis done using wind speed measurements at various heights would improve our knowledge in modeling the winds more accurately. In addition, on-site measurements and wind data for every five minutes would further verify the results presented herein. Although these are studies that involve long time scales, this study goes a long way in creating an awareness both to the public, and utility scale wind developers about the developable wind resource possessed by Wyoming. 12

13 REFERENCES [1] U.S. Department of Energy, Increasing Wind Energy s contribution to U.S. Electricity Supply; 20% Wind Energy by 2030, July 2008, Page 175 Table B-7 [2] Wind Powering America, Wyoming Wind map and Wind Resource Potential, [3] J. Naughton, J. Baker, and T. Parish, Wind Diversity Enhancement of Wyoming/California Wind Energy Projects, Wyoming Infrastructure Authority report, January 2013, Page 4 [4] U.S. Land based and Offshore Annual Average Wind Speed at 80 meters [5] Wind Resource Assessment Handbook Final Report, New York State Energy Research and Development Authority, October 2010, Page 1-4 [6] National Climatic Data Center [7] S. Wharton, J. K. Lundquist Atmospheric Stability Impacts on Power Curves of Tall Wind Turbines An Analysis of a West Coast North American Wind Farm February 2010, Page 16 [8] General Electric news center, GE wind energy [9] J. Naughton, J. Baker, and T. Parish, Wind Diversity Enhancement of Wyoming/Colorado Wind Energy Projects, Wyoming Infrastructure Authority report, April 2013, Page 21 Dr. Jonathan Naughton can be reached at naughton@uwyo.edu Sriganesh Ananthanarayanan can be reached at sriganesh.ananthanarayanan@gmail.com 13

14 APPENDIX 1: WIND ROSE FOR EACH MONTH FOR LARAMIE January February March April 14

15 May June August July 15

16 September October November December 16

17 APPENDIX 2: WIND RESOURCE ASSESSMENT FOR CHEYENNE Station ID: KCYS Latitude: degrees Longitude: degrees Tower Type: ASOS Sensor Heights: 10 m Monitor Start: Jan 1, 2006 Monitor End: July 7, 2012 Wind speed is the most important aspect of the wind resource, because there is a direct relationship between wind speed and wind turbine power output. A turbine with a 80m hub height was selected, so now we are primarily interested at the wind resource at 80 m. The first step then is to intelligently extrapolate the wind speed data collected at the lower heights. * The mean wind speed at hub height (80m) is = 7.59 m/s 17

18 WIND POWER DENSITY The wind power density, measured in watts per square meter, indicates how much energy is available at the site for conversion by a wind turbine. Wind speed generally increases with height above ground. * The Wind Power Density at hub height (80m) is = W/m^2 PROBABILITY DENSITY FUNCTION A probability density function is a curve associated with the wind data that shows the frequency with which a particular wind speed occurs or the percentage of time that the wind spends at each speed. This provides us with a very good description of nature of the wind in the location. This curve has also been compared with two models used commonly for wind resource assessments, namely Rayleigh and Weibull distributions. WIND ROSE A wind rose is a polar plot that represents the percentage of time that the wind direction falls within each sector of the compass. Figures show an average wind rose for each month measured during the period 1/2006 7/2012. These wind roses are based on the wind vane measurements at 10m, and extrapolated to the hub height of 80 meters. 18

19 January February March April 19

20 May June July August 20

21 September October December November ANNUAL ENERGY PRODUCTION In the context of wind energy production, what is of greatest interest is how the wind characteristics are reflected in the actual performance of a particular wind turbine. There is not a simple or obvious mathematical relationship between the energy contained in the wind and the amount of energy a given wind turbine will produce. In each case, it depends on the shape of the wind turbine s power curve (the amount of electric power the turbine produces at each wind speed). In this section, we examine the productivity of GE 1.5 MW 1) Annual Energy Production for 2006 = MW-hrs. 2) Annual Energy Production for 2007 = 3963 MW-hrs. 3) Annual Energy Production for 2008 = MW-hrs. 4) Annual Energy Production for 2009 = 3894 MW-hrs. 5) Annual Energy Production for 2010 = MW-hrs. 6) Annual Energy Production for 2011 = MW-hrs. 7) Annual Energy Production for 2012(data until 7/7/2012) = MW-hrs. 21

22 APPENDIX 3: WIND RESOURCE ASSESSMENT FOR CASPER Station ID: KCPR Latitude: degrees Longitude: degrees Tower Type: ASOS Sensor Heights: 10 m Monitor Start: Jan 1, 2006 Monitor End: July 7, 2012 Wind speed is the most important aspect of the wind resource, because there is a direct relationship between wind speed and wind turbine power output. A turbine with a 80m hub height was selected, so now we are primarily interested at the wind resource at 80 m. The first step then is to intelligently extrapolate the wind speed data collected at the lower heights. * The mean wind speed at hub height (80m) is = 7.53 m/s 22

23 WIND POWER DENSITY The wind power density, measured in watts per square meter, indicates how much energy is available at the site for conversion by a wind turbine. Wind speed generally increases with height above ground. * The Wind Power Density at hub height (80m) is = W/m^2 PROBABILITY DENSITY FUNCTION A probability density function is a curve associated with the wind data that shows the frequency with which a particular wind speed occurs or the percentage of time that the wind spends at each speed. This provides us with a very good description of nature of the wind in the location. This curve has also been compared with two models used commonly for wind resource assessments, namely Rayleigh and Weibull distributions. WIND ROSE A wind rose is a polar plot that represents the percentage of time that the wind direction falls within each sector of the compass. Figures show an average wind rose for each month measured during the period 1/2006 7/2012. These wind roses are based on the wind vane measurements at 10m, and extrapolated to the hub height of 80 meters. 23

24 January February March April 24

25 May June July August 25

26 September October November December ANNUAL ENERGY PRODUCTION In the context of wind energy production, what is of greatest interest is how the wind characteristics are reflected in the actual performance of a particular wind turbine. There is not a simple or obvious mathematical relationship between the energy contained in the wind and the amount of energy a given wind turbine will produce. In each case, it depends on the shape of the wind turbine s power curve (the amount of electric power the turbine produces at each wind speed). In this section, we examine the productivity of GE 1.5 MW 1) Annual Energy Production for 2006 = MW-hrs. 2) Annual Energy Production for 2007 = MW-hrs. 3) Annual Energy Production for 2008 = MW-hrs. 4) Annual Energy Production for 2009 = MW-hrs. 5) Annual Energy Production for 2010 = MW-hrs. 6) Annual Energy Production for 2011 = MW-hrs. 7) Annual Energy Production for 2012(data until 7/7/2012) = MW-hrs. 26

27 APPENDIX 4: WIND RESOURCE ASSESSMENT FOR ROCK SPRINGS Station ID: KRKS Latitude: degrees Longitude: degrees Tower Type: ASOS Sensor Heights: 10 m Monitor Start: Jan 1, 2006 Monitor End: July 7, 2012 Wind speed is the most important aspect of the wind resource, because there is a direct relationship between wind speed and wind turbine power output. A turbine with a 80m hub height was selected, so now we are primarily interested at the wind resource at 80 m. The first step then is to intelligently extrapolate the wind speed data collected at the lower heights. * The mean wind speed at hub height (80m) is = 7.96 m/s 27

28 WIND POWER DENSITY The wind power density, measured in watts per square meter, indicates how much energy is available at the site for conversion by a wind turbine. Wind speed generally increases with height above ground. * The Wind Power Density at hub height (80m) is = W/m^2 PROBABILITY DENSITY FUNCTION A probability density function is a curve associated with the wind data that shows the frequency with which a particular wind speed occurs or the percentage of time that the wind spends at each speed. This provides us with a very good description of nature of the wind in the location. This curve has also been compared with two models used commonly for wind resource assessments, namely Rayleigh and Weibull distributions. WIND ROSE A wind rose is a polar plot that represents the percentage of time that the wind direction falls within each sector of the compass. Figures show an average wind rose for each month measured during the period 1/2006 7/2012. These wind roses are based on the wind vane measurements at 10m, and extrapolated to the hub height of 80 meters. 28

29 January February March April 29

30 May June July August 30

31 September October November December ANNUAL ENERGY PRODUCTION In the context of wind energy production, what is of greatest interest is how the wind characteristics are reflected in the actual performance of a particular wind turbine. There is not a simple or obvious mathematical relationship between the energy contained in the wind and the amount of energy a given wind turbine will produce. In each case, it depends on the shape of the wind turbine s power curve (the amount of electric power the turbine produces at each wind speed). In this section, we examine the productivity of GE 1.5 MW 1) Annual Energy Production for 2006 = MW-hrs. 2) Annual Energy Production for 2007 = 3760 MW-hrs. 3) Annual Energy Production for 2008 = MW-hrs. 4) Annual Energy Production for 2009 = MW-hrs. 5) Annual Energy Production for 2010 = 3549 MW-hrs. 6) Annual Energy Production for 2011 = MW-hrs. 7) Annual Energy Production for 2012(data until 7/7/2012) = MW-hrs. 31

32 APPENDIX 5: WIND RESOURCE ASSESSMENT FOR SHERIDAN Station ID: KSHR Latitude: degrees Longitude: degrees Tower Type: ASOS Sensor Heights: 10 m Monitor Start: Jan 1, 2006 Monitor End: July 7, 2012 Wind speed is the most important aspect of the wind resource, because there is a direct relationship between wind speed and wind turbine power output. A turbine with a 80m hub height was selected, so now we are primarily interested at the wind resource at 80 m. The first step then is to intelligently extrapolate the wind speed data collected at the lower heights. * The mean wind speed at hub height (80m) is = 6.27 m/s 32

33 WIND POWER DENSITY The wind power density, measured in watts per square meter, indicates how much energy is available at the site for conversion by a wind turbine. Wind speed generally increases with height above ground. * The Wind Power Density at hub height (80m) is = W/m^2 PROBABILITY DENSITY FUNCTION A probability density function is a curve associated with the wind data that shows the frequency with which a particular wind speed occurs or the percentage of time that the wind spends at each speed. This provides us with a very good description of nature of the wind in the location. This curve has also been compared with two models used commonly for wind resource assessments, namely Rayleigh and Weibull distributions. WIND ROSE A wind rose is a polar plot that represents the percentage of time that the wind direction falls within each sector of the compass. Figures show an average wind rose for each month measured during the period 1/2006 7/2012. These wind roses are based on the wind vane measurements at 10m, and extrapolated to the hub height of 80 meters. 33

34 January February March April 34

35 May June July August 35

36 October September November December ANNUAL ENERGY PRODUCTION In the context of wind energy production, what is of greatest interest is how the wind characteristics are reflected in the actual performance of a particular wind turbine. There is not a simple or obvious mathematical relationship between the energy contained in the wind and the amount of energy a given wind turbine will produce. In each case, it depends on the shape of the wind turbine s power curve (the amount of electric power the turbine produces at each wind speed). In this section, we examine the productivity GE 1.5 MW 1) Annual Energy Production for 2006 = MW-hrs. 2) Annual Energy Production for 2007 = MW-hrs. 3) Annual Energy Production for 2008 = MW-hrs. 4) Annual Energy Production for 2009 = MW-hrs. 5) Annual Energy Production for 2010 = MW-hrs. 6) Annual Energy Production for 2011 = MW-hrs. 7) Annual Energy Production for 2012(data until 7/7/2012) = MW-hrs. 36

37 APPENDIX 6: WIND RESOURCE ASSESSMENT FOR GILLETTE Station ID: KGCC Latitude: degrees Longitude: degrees Tower Type: ASOS Sensor Heights: 10 m Monitor Start: Jan 1, 2006 Monitor End: July 7, 2012 Wind speed is the most important aspect of the wind resource, because there is a direct relationship between wind speed and wind turbine power output. A turbine with a 80m hub height was selected, so now we are primarily interested at the wind resource at 80 m. The first step then is to intelligently extrapolate the wind speed data collected at the lower heights. * The mean wind speed at hub height (80m) is = 7.44 m/s 37

38 WIND POWER DENSITY The wind power density, measured in watts per square meter, indicates how much energy is available at the site for conversion by a wind turbine. Wind speed generally increases with height above ground. * The Wind Power Density at hub height (80m) is = W/m^2 PROBABILITY DENSITY FUNCTION A probability density function is a curve associated with the wind data that shows the frequency with which a particular wind speed occurs or the percentage of time that the wind spends at each speed. This provides us with a very good description of nature of the wind in the location. This curve has also been compared with two models used commonly for wind resource assessments, namely Rayleigh and Weibull distributions. WIND ROSE A wind rose is a polar plot that represents the percentage of time that the wind direction falls within each sector of the compass. Figures show an average wind rose for each month measured during the period 1/2006 7/2012. These wind roses are based on the wind vane measurements at 10m, and extrapolated to the hub height of 80 meters. 38

39 January February March April 39

40 May June July August 40

41 September October December November ANNUAL ENERGY PRODUCTION In the context of wind energy production, what is of greatest interest is how the wind characteristics are reflected in the actual performance of a particular wind turbine. There is not a simple or obvious mathematical relationship between the energy contained in the wind and the amount of energy a given wind turbine will produce. In each case, it depends on the shape of the wind turbine s power curve (the amount of electric power the turbine produces at each wind speed). In this section, we examine the productivity of GE 1.5 MW 1) Annual Energy Production for 2006 = MW-hrs. 2) Annual Energy Production for 2007 = MW-hrs. 3) Annual Energy Production for 2008 = MW-hrs. 4) Annual Energy Production for 2009 = MW-hrs. 5) Annual Energy Production for 2010 = MW-hrs. 6) Annual Energy Production for 2011 = MW-hrs. 7) Annual Energy Production for 2012(data until 7/7/2012) = MW-hrs. 41

42 APPENDIX 7: WIND RESOURCE ASSESSMENT FOR RAWLINS Station ID: KRWL Latitude: degrees Longitude: degrees Tower Type: ASOS Sensor Heights: 10 m Monitor Start: Jan 1, 2006 Monitor End: July 7, 2012 Wind speed is the most important aspect of the wind resource, because there is a direct relationship between wind speed and wind turbine power output. A turbine with a 80m hub height was selected, so now we are primarily interested at the wind resource at 80 m. The first step then is to intelligently extrapolate the wind speed data collected at the lower heights. * The mean wind speed at hub height (80m) is = m/s 42

43 WIND POWER DENSITY The wind power density, measured in watts per square meter, indicates how much energy is available at the site for conversion by a wind turbine. Wind speed generally increases with height above ground. * The Wind Power Density at hub height (80m) is = W/m^2 PROBABILITY DENSITY FUNCTION A probability density function is a curve associated with the wind data that shows the frequency with which a particular wind speed occurs or the percentage of time that the wind spends at each speed. This provides us with a very good description of nature of the wind in the location. This curve has also been compared with two models used commonly for wind resource assessments, namely Rayleigh and Weibull distributions. WIND ROSE A wind rose is a polar plot that represents the percentage of time that the wind direction falls within each sector of the compass. Figures show an average wind rose for each month measured during the period 1/2006 7/2012. These wind roses are based on the wind vane measurements at 10m, and extrapolated to the hub height of 80 meters. 43

44 January February March April 44

45 May June July August 45

46 September October November December ANNUAL ENERGY PRODUCTION In the context of wind energy production, what is of greatest interest is how the wind characteristics are reflected in the actual performance of a particular wind turbine. There is not a simple or obvious mathematical relationship between the energy contained in the wind and the amount of energy a given wind turbine will produce. In each case, it depends on the shape of the wind turbine s power curve (the amount of electric power the turbine produces at each wind speed). In this section, we examine the productivity GE 1.5 MW xle wind turbine 1) Annual Energy Production for 2006 = MW-hrs. 2) Annual Energy Production for 2007 = MW-hrs. 3) Annual Energy Production for 2008 = MW-hrs. 4) Annual Energy Production for 2009 = 5282 MW-hrs. 5) Annual Energy Production for 2010 = MW-hrs. 6) Annual Energy Production for 2011 = 5882 MW-hrs. 7) Annual Energy Production for 2012(data until 7/7/2012) = MW-hrs. 46