Nicole Mölders^1,2*, Dinah Khordakova², Scott Gende³, Gerhard Kramm⁴

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¹ Geophysical Institute, University of Alaska Fairbanks, Fairbanks, USA.
² Department of Atmospheric Sciences, College of Natural Science and Mathematics, University of Alaska Fairbanks, Fairbanks, USA.
³ National Park Service, Juneau Office, Juneau, USA.
⁴ Engineering Meteorology Consulting, Fairbanks, USA.
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Abstract: This study investigated the uncertainty assessing wind-power production in valleys of complex terrain using Juneau, Alaska as the testbed. The wind-speed data stem from evaluated WRF/Chem simulations for seven tourist seasons (May 15 to September 15). The percentage of wind speeds between cut-in and cutout speed differed up to about 11% among tourist seasons and up to 15% among the examined wind-turbine types. The wind-speed probability density varied the strongest among tourist seasons for wind speeds less than 3 m·sǃ (6 m·sǃ) for wind turbines with hub heights of about 80 m (30 m). At these heights, the interannual differences in the probability density of wind speeds at the rated or higher power were about half or less than those at wind speeds below 3 m·sǃ (6 m·sǃ). The predicted average power output notably differed among tourist seasons. The tall (small) turbines had their highest predicted average production in 2006 (2012). The ranking among wind turbines regarding the predicted average power production was independent of the interannual variability in average power production. Capacity factors differed about 8% (6%) for the tall (small) tubines among tourist seasons. Within the same tourist season, capacity factors differed about 8% (5%) among turbine types. Estimates of capacity and potential power derived from 10 m wind-speed observations by an empirical formula commonly used to estimate wind speeds at hub height, differed up to 40% for 80 m height for some turbine types. Determinating the exponent of the empirical equation by means of WRF/Chem data showed that the traditional empirical approach failed in complex terrain.

Keywords: Wind Energy, WRF/Chem, Southeast Alaska, Uncertainty Assessment, Wind-Speed Prediction

Cite this paper: Mölders, N. , Khordakova, D. , Gende, S. and Kramm, G. (2015) Uncertainty of Wind Power Usage in Complex Terrain—A Case Study. Atmospheric and Climate Sciences, 5, 228-244. doi: 10.4236/acs.2015.53017.

References

[1]   Pirhalla, M.A., Gende, S. and Molders, N. (2014) Fate of Particulate Matter from Cruise-Ship Emissions in Glacier Bay during the 2008 Tourist Season. Journal of Environmental Protection, 4, 1235-1254.
http://dx.doi.org/10.4236/jep.2014.512118

[2]   Molders, N., Bruyère, C.L., Gende, S. and Pirhalla, M.A. (2014) Assessment of the 2006-2012 Climatological Fields and Mesoscale Features from Regional Downscaling of CESM Data by WRF-Chem over Southeast Alaska. Atmospheric and Climate Sciences, 4, 589-613.
http://dx.doi.org/10.4236/acs.2014.44053

[3]   Shulski, M. and Wendler, G. (2007) The Climate of Alaska. University of Alaska Press, Snowy Owl Books, Fairbanks, 216 p.

[4]   Lovich, J.E. and Ennen, J.R. (2013) Assessing the State of Knowledge of Utility-Scale Wind Energy Development and Operation on Non-Volant Terrestrial and Marine Wildlife. Applied Energy, 103, 52-60.
http://dx.doi.org/10.1016/j.apenergy.2012.10.001

[5]   Schirokauer, D., Graw, R. and Faure, A. (2010) Air Pollution Emission Inventory for 2008 Tourism Season Klondike Gold Rush National Heritage Park Skagway, Alaska. National Park Service, Report, 60 p.

[6]   Molders, N., Gende, S. and Pirhalla, M.A. (2013) Assessment of Cruise-Ship Activity Influences on Emissions, Air Quality, and Visibility in Glacier Bay National Park. Atmospheric Pollution Research, 4, 435-445.
http://dx.doi.org/10.5094/apr.2013.050

[7]   ENVIRON (2004) Cold Ironing Cost Effectiveness Study—Executive Summary. Report, 17 p.

[8]   Ross, H.K., Cooney, J., Hinzman, M., Smock, S., Sellhorst, G., Dlugi, R., Moders, N. and Kramm, G. (2014) Wind Power Potential in Interior Alaska from a Micrometeorological Perspective. Atmospheric and Climate Sciences, 4, 100-121.
http://dx.doi.org/10.4236/acs.2014.41013

[9]   Panofsky, H.A. (1963) Determination of Stress from Wind and Temperature Measurements. Quarterly Journal of the Royal Meteorological Society, 89, 85-94.
http://dx.doi.org/10.1002/qj.49708937906

[10]   Kramm, G. and Herbert, F. (2009) Similarity Hypotheses for the Atmospheric Surface Layer Expressed by Dimensional Π Invariants Analysis—A Review. The Open Atmospheric Science Journal, 3, 48-79.
http://dx.doi.org/10.2174/1874282300903010048

[11]   Grell, G.A., Peckham, S.E., Schmitz, R., Mckeen, S.A., Frost, G., Skamarock, W.C. and Eder, B. (2005) Fully Coupled “Online” Chemistry within the WRF Model. Atmospheric Environment, 39, 6957-6975.
http://dx.doi.org/10.1016/j.atmosenv.2005.04.027

[12]   Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X.-Y., Wang, W. and Powers, J.G. (2008) A Description of the Advanced Research WRF Version 3. NCAR Technical Note, NCAR, Boulder, 125 p.

[13]   Peckham, S.E., Fast, J., Schmitz, R., Grell, G.A., Gustafson, W.I., Mckeen, S.A., Ghan, S.J., Zaveri, R., Easter, R.C., Barnard, J., Chapman, E., Salzman, M., Barth, M., Pfister, G., Wiedinmyer, C., Hewson, M. and Freitas, S.R. (2011) WRF/Chem Version 3.3 User’s Guide. NOAA Technical Memo, 98 p.

[14]   Hong, S.-Y. and Lim, J.O.J. (2006) The WRF Single-Moment 6-Class Microphysics Scheme (WSM6). Journal of Korean Meteorological Society, 42, 129-151.

[15]   Grell, G.A. and Dévényi, D. (2002) A Generalized Approach to Parameterizing Convection Combining Ensemble and Data Assimilation Techniques. Geophysical Research Letters, 29, 1693-1696.
http://dx.doi.org/10.1029/2002GL015311

[16]   Chou, M.-D. and Suarez, M.J. (1994) An Efficient Thermal Infrared Radiation Parameterization for Use in General Circulation Models. NASA Technical Memorandum 104606, Volume 3, 85 p.

[17]   Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J. and Clough, S.A. (1997) Radiative Transfer for Inhomogeneous Atmospheres: RRTM, a Validated Correlated-K Model for the Longwave. Journal of Geophysical Research, 102D, 16663-16682.
http://dx.doi.org/10.1029/97JD00237

[18]   Barnard, J., Fast, J., Paredes-Miranda, G., Arnott, W. and Laskin, A. (2010) Technical Note: Evaluation of the WRF-Chem “Aerosol Chemical to Aerosol Optical Properties” Module Using Data from the MILAGRO Campaign. Atmospheric Chemistry and Physics, 10, 7325-7340.
http://dx.doi.org/10.5194/acp-10-7325-2010

[19]   Janjic, Z.I. (2002) Nonsingular Implementation of the Mellor-Yamada Level 2.5 Scheme in the NCEP Meso Model. NCEP Office Note, No. 437, 61 p.

[20]   Chen, F. and Dudhia, J. (2000) Coupling an Advanced Land-Surface/Hydrology Model with the Penn State/NCAR MM5 Modeling System. Part I: Model Description and Implementation. Monthly Weather Review, 129, 569-585.
http://dx.doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2

[21]   Stockwell, W.R., Middleton, P., Chang, J.S. and Tang, X. (1990) The Second-Generation Regional Acid Deposition Model Chemical Mechanism for Regional Air Quality Modeling. Journal Geophysical Research, 95, 16343-16367.
http://dx.doi.org/10.1029/JD095iD10p16343

[22]   Madronich, S. (1987) Photodissociation in the Atmosphere, 1, Actinic Flux and the Effects of Ground Reflections and Clouds. Journal Geophysical Research, 92, 9740-9752.
http://dx.doi.org/10.1029/JD092iD08p09740

[23]   Ackermann, I.J., Hass, H., Memmesheimer, M., Ebel, A., Binkowski, F.S. and Shankar, U. (1998) Modal Aerosol Dynamics Model for Europe: Development and First Applications. Atmospheric Environment, 32, 2981-2299.
http://dx.doi.org/10.1016/S1352-2310(98)00006-5

[24]   Schell, B., Ackermann, I.J., Hass, H., Binkowski, F.S. and Ebel, A. (2001) Modeling the Formation of Secondary Organic Aerosol within a Comprehensive Air Quality Model System. Journal Geophysical Research, 106, 28275-28293.
http://dx.doi.org/10.1029/2001JD000384

[25]   Wesely, M.L. (1989) Parameterization of Surface Resistances to Gaseous Dry Deposition in Regional-Scale Numerical Models. Atmospheric Environment, 23, 1293-1304.
http://dx.doi.org/10.1016/0004-6981(89)90153-4

[26]   Molders, N., Tran, H.N.Q., Quinn, P., Sassen, K., Shaw, G.E. and Kramm, G. (2011) Assessment of WRF/Chem to Capture Sub-Arctic Boundary Layer Characteristics during Low Solar Irradiation Using Radiosonde, Sodar, and Station Data. Atmospheric Pollution Research, 2, 283-299.
http://dx.doi.org/10.5094/APR.2011.035

[27]   Guenther, A. (1997) Seasonal and Spatial Variations in Natural Volatile Organic Compund Emissions. Ecological Applications, 7, 34-45.
http://dx.doi.org/10.1890/1051-0761(1997)007[0034:SASVIN]2.0.CO;2

[28]   Khordakova, D. (2014) Investigation of Potential Wind Power in Southeast Alaska Using Model Data. Bachelor of Sciences, No. 3602036, 64.

[29]   Harris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2013) Updated High-Resolution Grids of Monthly Climatic Observations—The CRU TS3.10 Dataset. International Journal of Climatology, 34, 623-642.
http://dx.doi.org/10.1002/joc.3711

[30]   Zhang, H.-M., Bates, J.J. and Reynolds, R.W. (2006) Assessment of Composite Global Sampling: Sea Surface Wind Speed. Geophysical Research Letters, 33, Article ID: L17714.
http://dx.doi.org/10.1029/2006gl027086

[31]   Zhang, Y., Dubey, M.K., Olsen, S.C., Zheng, J. and Zhang, R. (2009) Comparisons of WRF/Chem Simulations in Mexico City with Ground-Based Rama Measurements During the 2006-Milagro. Atmospheric Chemistry and Physics, 9, 3777-3798.
http://dx.doi.org/10.5194/acp-9-3777-2009

[32]   Kim, J., Waliser, D.E., Mattmann, C.A., Mearns, L.O., Goodale, C.E., Hart, A.F., Crichton, D.J., Mcginnis, S., Lee, H., Loikith, P.C. and Boustani, M. (2013) Evaluation of the Surface Climatology over the Conterminous United States in the North American Regional Climate Change Assessment Program Hindcast Experiment Using a Regional Climate Model Evaluation System. Journal of Climate, 26, 5698-5715.
http://dx.doi.org/10.1175/JCLI-D-12-00452.1