Potential contributions of wind power to a stable and highly renewable Swiss power supply

Using data from two measurement networks, we analyse the following aspects of wind speeds over Switzerland to assess the possibility of high wind power penetration: spatial correlation, persistent low wind power conditions and the diurnal and seasonal wind speed patterns. We show that correlation amongst speeds as a function of distance is significantly lower compared to values found in literature. This can be attributed to the complex terrain of the Alps, which has a profound influence on meteorological parameters. Secondly, using extreme value analysis we calculate return levels for low wind power periods. Large differences are found, with return levels ranging from 29 to 1017h of no power production for a return period of 10years. No clear spatial pattern was found that can account for these values. However, the length of no-production periods decreases with increasing elevation. Next, we investigate diurnal and seasonal wind speed patterns and show how the different patterns and their intra-annual variation can be explained by local topography. We also find that with increasing elevation mean wind speeds and power production increase, even when accounting for lower air density. Wind speeds are on average higher in winter, and at elevation the relative increase in winter compared to summer is higher. Notable exceptions are explained from topography and carry implications for wind power development. In view of Switzerland’s electricity shortage in winter, these findings make a strong claim for wind power development, especially at higher elevations.

[1]  Anthony C. Davison,et al.  Model misspecification in peaks over threshold analysis , 2010, 1010.1357.

[2]  F. Chow,et al.  Mountain Weather Research and Forecasting: Recent Progress and Current Challenges , 2013 .

[3]  M. Furger,et al.  Climatology of near‐surface wind patterns over Switzerland , 2001 .

[4]  Machteld van den Broek,et al.  Least-cost options for integrating intermittent renewables in low-carbon power systems , 2016 .

[5]  Machteld van den Broek,et al.  Impacts of large-scale Intermittent Renewable Energy Sources on electricity systems, and how these can be modeled , 2014 .

[6]  Joachim Peinke,et al.  Turbulence and wind turbines , 2011 .

[7]  Anthony Lehmann,et al.  Spatial Predictions of Extreme Wind Speeds over Switzerland Using Generalized Additive Models , 2009 .

[8]  Jason Jonkman,et al.  Atmospheric and Wake Turbulence Impacts on Wind Turbine Fatigue Loading: Preprint , 2012 .

[9]  Kasım Koçak,et al.  Practical ways of evaluating wind speed persistence , 2008 .

[10]  S. Coles,et al.  An Introduction to Statistical Modeling of Extreme Values , 2001 .

[11]  Robert J. Brecha,et al.  Analyzing Major Challenges of Wind and Solar Variability in Power Systems , 2014 .

[12]  Peter Lund,et al.  Review of energy system flexibility measures to enable high levels of variable renewable electricity , 2015 .

[13]  Jérôme Dujardin,et al.  Interplay between photovoltaic, wind energy and storage hydropower in a fully renewable Switzerland , 2017 .

[14]  Janet F. Barlow,et al.  A study into the accuracy of using meteorological wind data to estimate turbine generation output , 2013 .

[15]  Jean Palutikof,et al.  A review of methods to calculate extreme wind speeds , 1999 .

[16]  Lion Hirth,et al.  The benefits of flexibility: The value of wind energy with hydropower , 2016 .

[17]  E. Simiu,et al.  Extreme Wind Distribution Tails: A “Peaks over Threshold” Approach , 1996 .

[18]  Marcel Prokopczuk,et al.  The Case of Negative Day-Ahead Electricity Prices , 2011 .

[19]  Anthony C. Davison,et al.  Spatial modeling of extreme snow depth , 2011, 1111.7091.

[20]  Janina Ketterer,et al.  The impact of wind power generation on the electricity price in Germany , 2014 .

[21]  Stefan Emeis.  How Well Does a Power Law Fit to a Diabatic Boundary-Layer Wind Profile? , 2005 .

[22]  R. Smith.  Statistics of Extremes, with Applications in Environment, Insurance, and Finance , 2003 .

[23]  Antonio J. Gutiérrez-Trashorras,et al.  Analytical methods for wind persistence: Their application in assessing the best site for a wind farm in the State of Veracruz, Mexico , 2010 .

[24]  Gary King,et al.  Amelia II: A Program for Missing Data , 2011 .

[25]  L. Haan,et al.  Extreme value theory : an introduction , 2006 .

[26]  Önder Güler,et al.  Investigation of wind shear coefficients and their effect on electrical energy generation , 2011 .

[27]  I. Vergeiner,et al.  Valley winds and slope winds — Observations and elementary thoughts , 1987 .

[28]  C. Draxl,et al.  Meteorological wind energy potential in the Alps using ERA40 and wind measurement sites in the Tyrolean Alps , 2011 .

[29]  Mikhail Kanevski,et al.  Spatial Patterns of Wind Speed Distributions in Switzerland , 2016 .

[30]  Michael Lehning,et al.  Effect of winds in a mountain pass on turbine performance , 2014 .

[31]  Michael Lehning,et al.  snowpack model calculations for avalanche warning based upon a new network of weather and snow stations , 1999 .

[32]  G. Galanis.  Smoothing out the wind power production patterns by connecting different countries within Europe , 2014 .

[33]  B. Hahn,et al.  Reliability of Wind Turbines , 2007 .

[34]  J. Blanchet,et al.  Mapping snow depth return levels: smooth spatial modeling versus station interpolation , 2010 .

[35]  Michael Lehning,et al.  Mapping frequencies of icing on structures in Switzerland , 2012 .

[36]  Hannele Holttinen,et al.  Hourly wind power variations in the Nordic countries , 2005 .

[37]  Machteld van den Broek,et al.  Operational flexibility and economics of power plants in future low-carbon power systems , 2015 .

[38]  Kasım Koçak,et al.  A method for determination of wind speed persistence and its application , 2002 .

[39]  Martin Greiner,et al.  Seasonal optimal mix of wind and solar power in a future, highly renewable Europe , 2010 .

[40]  Jay Apt,et al.  The variability of interconnected wind plants , 2010 .

[41]  Michael Lehning,et al.  Extreme value statistics of snowfall in the Swiss Alpine region , 2009 .

[42]  S. Rehman,et al.  Wind shear coefficients and their effect on energy production , 2005 .

[43]  Eamon McKeogh,et al.  Persistence of low wind speed conditions and implications for wind power variability , 2013 .

[44]  Anthony C. Davison,et al.  Threshold modeling of extreme spatial rainfall , 2013 .

[45]  Annelen Kahl,et al.  Risks and Reliability in a Fully Renewable Switzerland , 2015 .

[46]  Hannele Holttinen,et al.  Current Methods to Calculate Capacity Credit of Wind Power, IEA Collaboration , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[47]  Reza S. Abhari,et al.  Assessment of Wind Turbine Performance in Alpine Environments , 2011 .

[48]  Robert N. Farrugia,et al.  The wind shear exponent in a Mediterranean island climate , 2003 .

[49]  Mikhail Kanevski,et al.  Power spectrum and multifractal detrended fluctuation analysis of high-frequency wind measurements in mountainous regions , 2016 .

[50]  G. A. Demarrais.  WIND-SPEED PROFILES AT BROOKHAVEN NATIONAL LABORATORY , 1959 .