IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v230y2018icp845-854.html
   My bibliography  Save this article

On the inter-annual variability of wind energy generation – A case study from Germany

Author

Listed:
  • Jung, Christopher
  • Schindler, Dirk

Abstract

The intermittent and stochastic nature of the wind resource complicates constant electricity supply in countries with high wind energy share in the electricity mix. Therefore, the goal of this study was to quantify the inter-annual variability of wind energy generation on the national scale by estimating upper and lower limits of annual wind energy generation (WEG). A novel methodology was developed and is presented for Germany, where onshore wind energy already accounts for more than 15% of net electricity consumption. First, a comprehensive wind turbine data set was produced including all onshore wind turbines operating in 2017. Next, the wind speed-wind shear model (WSWS) was used to reconstruct the high spatial resolution (200 m × 200 m) annual wind speed distributions in the wind turbine hub height range 30–179 m above ground level in the period 1979–2017. By using wind turbine-specific power curves, the annual wind energy yield was calculated for each wind turbine. It was summed up for the entire country, yielding WEG. Then, 16 theoretical distributions were fitted to WEG. From the fitted distributions, long-term return values of WEG were calculated. In a 100-year period (probability 98%), WEG lies between 67 and 112 TWh/yr and the annual greenhouse gas mitigation potential varies between 45.6 and 76.3 Mio. tCO2-equiv. under current climate. The great WEG-range emphasizes the importance of considering upper and lower WEG-limits for ensuring constant electricity supply at the national scale.

Suggested Citation

  • Jung, Christopher & Schindler, Dirk, 2018. "On the inter-annual variability of wind energy generation – A case study from Germany," Applied Energy, Elsevier, vol. 230(C), pages 845-854.
  • Handle: RePEc:eee:appene:v:230:y:2018:i:c:p:845-854
    DOI: 10.1016/j.apenergy.2018.09.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918313278
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2018.09.019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. McKenna, R. & Hollnaicher, S. & Fichtner, W., 2014. "Cost-potential curves for onshore wind energy: A high-resolution analysis for Germany," Applied Energy, Elsevier, vol. 115(C), pages 103-115.
    2. McKenna, R.C. & Bchini, Q. & Weinand, J.M. & Michaelis, J. & König, S. & Köppel, W. & Fichtner, W., 2018. "The future role of Power-to-Gas in the energy transition: Regional and local techno-economic analyses in Baden-Württemberg," Applied Energy, Elsevier, vol. 212(C), pages 386-400.
    3. González-Aparicio, I. & Zucker, A., 2015. "Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain," Applied Energy, Elsevier, vol. 159(C), pages 334-349.
    4. de Jong, Pieter & Dargaville, Roger & Silver, Jeremy & Utembe, Steven & Kiperstok, Asher & Torres, Ednildo Andrade, 2017. "Forecasting high proportions of wind energy supplying the Brazilian Northeast electricity grid," Applied Energy, Elsevier, vol. 195(C), pages 538-555.
    5. Monforti, Fabio & Gonzalez-Aparicio, Iratxe, 2017. "Comparing the impact of uncertainties on technical and meteorological parameters in wind power time series modelling in the European Union," Applied Energy, Elsevier, vol. 206(C), pages 439-450.
    6. Notton, Gilles & Nivet, Marie-Laure & Voyant, Cyril & Paoli, Christophe & Darras, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2018. "Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 87(C), pages 96-105.
    7. Pes, Marcelo P. & Pereira, Enio B. & Marengo, Jose A. & Martins, Fernando R. & Heinemann, Detlev & Schmidt, Michael, 2017. "Climate trends on the extreme winds in Brazil," Renewable Energy, Elsevier, vol. 109(C), pages 110-120.
    8. Commin, Andrew N. & French, Andrew S. & Marasco, Matteo & Loxton, Jennifer & Gibb, Stuart W. & McClatchey, John, 2017. "The influence of the North Atlantic Oscillation on diverse renewable generation in Scotland," Applied Energy, Elsevier, vol. 205(C), pages 855-867.
    9. Bett, Philip E. & Thornton, Hazel E., 2016. "The climatological relationships between wind and solar energy supply in Britain," Renewable Energy, Elsevier, vol. 87(P1), pages 96-110.
    10. Gallagher, Sarah & Tiron, Roxana & Whelan, Eoin & Gleeson, Emily & Dias, Frédéric & McGrath, Ray, 2016. "The nearshore wind and wave energy potential of Ireland: A high resolution assessment of availability and accessibility," Renewable Energy, Elsevier, vol. 88(C), pages 494-516.
    11. Christopher Jung & Dirk Schindler & Alexander Buchholz & Jessica Laible, 2017. "Global Gust Climate Evaluation and Its Influence on Wind Turbines," Energies, MDPI, vol. 10(10), pages 1-18, September.
    12. Davy, Richard & Gnatiuk, Natalia & Pettersson, Lasse & Bobylev, Leonid, 2018. "Climate change impacts on wind energy potential in the European domain with a focus on the Black Sea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1652-1659.
    13. González-Aparicio, I. & Monforti, F. & Volker, P. & Zucker, A. & Careri, F. & Huld, T. & Badger, J., 2017. "Simulating European wind power generation applying statistical downscaling to reanalysis data," Applied Energy, Elsevier, vol. 199(C), pages 155-168.
    14. Cannon, D.J. & Brayshaw, D.J. & Methven, J. & Coker, P.J. & Lenaghan, D., 2015. "Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain," Renewable Energy, Elsevier, vol. 75(C), pages 767-778.
    15. Leonie Grau & Christopher Jung & Dirk Schindler, 2017. "On the Annual Cycle of Meteorological and Geographical Potential of Wind Energy: A Case Study from Southwest Germany," Sustainability, MDPI, vol. 9(7), pages 1-11, July.
    16. Dupont, Elise & Koppelaar, Rembrandt & Jeanmart, Hervé, 2018. "Global available wind energy with physical and energy return on investment constraints," Applied Energy, Elsevier, vol. 209(C), pages 322-338.
    17. Serri, Laura & Lembo, Ettore & Airoldi, Davide & Gelli, Camilla & Beccarello, Massimo, 2018. "Wind energy plants repowering potential in Italy: technical-economic assessment," Renewable Energy, Elsevier, vol. 115(C), pages 382-390.
    18. Alonzo, Bastien & Ringkjob, Hans-Kristian & Jourdier, Benedicte & Drobinski, Philippe & Plougonven, Riwal & Tankov, Peter, 2017. "Modelling the variability of the wind energy resource on monthly and seasonal timescales," Renewable Energy, Elsevier, vol. 113(C), pages 1434-1446.
    19. Huang, Junling & McElroy, Michael B., 2015. "A 32-year perspective on the origin of wind energy in a warming climate," Renewable Energy, Elsevier, vol. 77(C), pages 482-492.
    20. Katzenstein, Warren & Apt, Jay, 2012. "The cost of wind power variability," Energy Policy, Elsevier, vol. 51(C), pages 233-243.
    21. Christopher Jung, 2016. "High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series," Energies, MDPI, vol. 9(5), pages 1-20, May.
    22. Henckes, Philipp & Knaut, Andreas & Obermüller, Frank & Frank, Christopher, 2018. "The benefit of long-term high resolution wind data for electricity system analysis," Energy, Elsevier, vol. 143(C), pages 934-942.
    23. Coester, Andreas & Hofkes, Marjan W. & Papyrakis, Elissaios, 2018. "An optimal mix of conventional power systems in the presence of renewable energy: A new design for the German electricity market," Energy Policy, Elsevier, vol. 116(C), pages 312-322.
    24. Katinas, Vladislovas & Gecevicius, Giedrius & Marciukaitis, Mantas, 2018. "An investigation of wind power density distribution at location with low and high wind speeds using statistical model," Applied Energy, Elsevier, vol. 218(C), pages 442-451.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christopher Jung & Dirk Schindler, 2023. "Reasons for the Recent Onshore Wind Capacity Factor Increase," Energies, MDPI, vol. 16(14), pages 1-17, July.
    2. Liu, Fa & Sun, Fubao & Liu, Wenbin & Wang, Tingting & Wang, Hong & Wang, Xunming & Lim, Wee Ho, 2019. "On wind speed pattern and energy potential in China," Applied Energy, Elsevier, vol. 236(C), pages 867-876.
    3. Jung, Christopher & Schindler, Dirk, 2022. "A review of recent studies on wind resource projections under climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    4. Liu, Xiong & Liang, Shi & Li, Gangqiang & Godbole, Ajit & Lu, Cheng, 2020. "An improved dynamic stall model and its effect on wind turbine fatigue load prediction," Renewable Energy, Elsevier, vol. 156(C), pages 117-130.
    5. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    6. Shih-Chieh Liao & Shih-Chieh Chang & Tsung-Chi Cheng, 2021. "Managing the Volatility Risk of Renewable Energy: Index Insurance for Offshore Wind Farms in Taiwan," Sustainability, MDPI, vol. 13(16), pages 1-27, August.
    7. Martin, Sean & Jung, Sungmoon & Vanli, Arda, 2020. "Impact of near-future turbine technology on the wind power potential of low wind regions," Applied Energy, Elsevier, vol. 272(C).
    8. Guo, Zijian & Liu, Tanghong & Xu, Kai & Wang, Junyan & Li, Wenhui & Chen, Zhengwei, 2020. "Parametric analysis and optimization of a simple wind turbine in high speed railway tunnels," Renewable Energy, Elsevier, vol. 161(C), pages 825-835.
    9. Jung, Christopher & Schindler, Dirk, 2023. "Introducing a new wind speed complementarity model," Energy, Elsevier, vol. 265(C).
    10. Jung, Christopher & Schindler, Dirk, 2019. "The role of air density in wind energy assessment – A case study from Germany," Energy, Elsevier, vol. 171(C), pages 385-392.
    11. He, J.Y. & Chan, P.W. & Li, Q.S. & Lee, C.W., 2022. "Characterizing coastal wind energy resources based on sodar and microwave radiometer observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    12. Kuang, Zhonghong & Chen, Qi & Yu, Yang, 2022. "Assessing the CO2-emission risk due to wind-energy uncertainty," Applied Energy, Elsevier, vol. 310(C).
    13. Jahns, Christopher & Osinski, Paul & Weber, Christoph, 2023. "A statistical approach to modeling the variability between years in renewable infeed on energy system level," Energy, Elsevier, vol. 263(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Hengxu & Cao, Yongji & Zhang, Yi & Terzija, Vladimir, 2018. "Quantitative synergy assessment of regional wind-solar energy resources based on MERRA reanalysis data," Applied Energy, Elsevier, vol. 216(C), pages 172-182.
    2. Garrido-Perez, Jose M. & Ordóñez, Carlos & Barriopedro, David & García-Herrera, Ricardo & Paredes, Daniel, 2020. "Impact of weather regimes on wind power variability in western Europe," Applied Energy, Elsevier, vol. 264(C).
    3. Christopher Jung & Dirk Schindler & Alexander Buchholz & Jessica Laible, 2017. "Global Gust Climate Evaluation and Its Influence on Wind Turbines," Energies, MDPI, vol. 10(10), pages 1-18, September.
    4. Russell McKenna & Stefan Pfenninger & Heidi Heinrichs & Johannes Schmidt & Iain Staffell & Katharina Gruber & Andrea N. Hahmann & Malte Jansen & Michael Klingler & Natascha Landwehr & Xiaoli Guo Lars', 2021. "Reviewing methods and assumptions for high-resolution large-scale onshore wind energy potential assessments," Papers 2103.09781, arXiv.org.
    5. McKenna, Russell & Pfenninger, Stefan & Heinrichs, Heidi & Schmidt, Johannes & Staffell, Iain & Bauer, Christian & Gruber, Katharina & Hahmann, Andrea N. & Jansen, Malte & Klingler, Michael & Landwehr, 2022. "High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs," Renewable Energy, Elsevier, vol. 182(C), pages 659-684.
    6. Florin Onea & Liliana Rusu, 2018. "Evaluation of Some State-Of-The-Art Wind Technologies in the Nearshore of the Black Sea," Energies, MDPI, vol. 11(9), pages 1-16, September.
    7. Ju-Young Shin & Changsam Jeong & Jun-Haeng Heo, 2018. "A Novel Statistical Method to Temporally Downscale Wind Speed Weibull Distribution Using Scaling Property," Energies, MDPI, vol. 11(3), pages 1-27, March.
    8. Murcia, Juan Pablo & Koivisto, Matti Juhani & Luzia, Graziela & Olsen, Bjarke T. & Hahmann, Andrea N. & Sørensen, Poul Ejnar & Als, Magnus, 2022. "Validation of European-scale simulated wind speed and wind generation time series," Applied Energy, Elsevier, vol. 305(C).
    9. Kena Likassa Nefabas & Lennart Söder & Mengesha Mamo & Jon Olauson, 2021. "Modeling of Ethiopian Wind Power Production Using ERA5 Reanalysis Data," Energies, MDPI, vol. 14(9), pages 1-17, April.
    10. Hayes, Liam & Stocks, Matthew & Blakers, Andrew, 2021. "Accurate long-term power generation model for offshore wind farms in Europe using ERA5 reanalysis," Energy, Elsevier, vol. 229(C).
    11. Xiao, Qing & Zhou, Shaowu, 2018. "Probabilistic power flow computation considering correlated wind speeds," Applied Energy, Elsevier, vol. 231(C), pages 677-685.
    12. Henni, Sarah & Staudt, Philipp & Kandiah, Balendra & Weinhardt, Christof, 2021. "Infrastructural coupling of the electricity and gas distribution grid to reduce renewable energy curtailment," Applied Energy, Elsevier, vol. 288(C).
    13. Matti Koivisto & Kaushik Das & Feng Guo & Poul Sørensen & Edgar Nuño & Nicolaos Cutululis & Petr Maule, 2019. "Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(3), May.
    14. Rabbani, R. & Zeeshan, M., 2020. "Exploring the suitability of MERRA-2 reanalysis data for wind energy estimation, analysis of wind characteristics and energy potential assessment for selected sites in Pakistan," Renewable Energy, Elsevier, vol. 154(C), pages 1240-1251.
    15. Siavash Asiaban & Nezmin Kayedpour & Arash E. Samani & Dimitar Bozalakov & Jeroen D. M. De Kooning & Guillaume Crevecoeur & Lieven Vandevelde, 2021. "Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System," Energies, MDPI, vol. 14(9), pages 1-41, May.
    16. de Jong, Pieter & Barreto, Tarssio B. & Tanajura, Clemente A.S. & Kouloukoui, Daniel & Oliveira-Esquerre, Karla P. & Kiperstok, Asher & Torres, Ednildo Andrade, 2019. "Estimating the impact of climate change on wind and solar energy in Brazil using a South American regional climate model," Renewable Energy, Elsevier, vol. 141(C), pages 390-401.
    17. Wang, Ni & Verzijlbergh, Remco A. & Heijnen, Petra W. & Herder, Paulien M., 2020. "A spatially explicit planning approach for power systems with a high share of renewable energy sources," Applied Energy, Elsevier, vol. 260(C).
    18. Dupré la Tour, Marie-Alix, 2023. "Photovoltaic and wind energy potential in Europe – A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    19. Francisco Haces-Fernandez & Hua Li & David Ramirez, 2022. "Analysis of Wave Energy Behavior and Its Underlying Reasons in the Gulf of Mexico Based on Computer Animation and Energy Events Concept," Sustainability, MDPI, vol. 14(8), pages 1-23, April.
    20. Jung, Christopher & Schindler, Dirk, 2022. "On the influence of wind speed model resolution on the global technical wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:230:y:2018:i:c:p:845-854. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.