Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

From local wind energy resource to national wind power production

Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Riccarton, Edinburgh EH14 4AS, Scotland, UK

Special Issues: Wind Power Implementation Challenges

Wind power is one of the most established renewable power resources yet it is also one of the most volatile resources. This poses a key challenge for successfully integrating wind power at a large scale into the power grid. Here we present an analysis of the time scales associated with wind power from hourly to seasonal fluctuations and how combining spatially distributed wind power sources helps to reduce its volatility. The analysis, based on observed wind speeds, is then generalised in a simple statistical model to develop a tool which can estimate the power output profile from a particular consortium of wind power sources. As the estimator only uses the local, or the mean national, wind resource and the mean distance between the sites to estimate the joint power output profile, it can be used by developers to estimate the reliability of their joint power output and to form the most effective consortium.
  Article Metrics

Keywords wind energy resource; wind power production; regional aggregation; Virtual Power Plant

Citation: Wolf-Gerrit Früh. From local wind energy resource to national wind power production. AIMS Energy, 2015, 3(1): 101-120. doi: 10.3934/energy.2015.1.101


  • 1. Adler J, (2010) R in a nutshell. O'Reilly: Beijing Sebastopol.
  • 2. Albadi M H, El-Saadany E F, (2010) Overview of wind power intermittency impacts on power systems. Electric Power Systems Research, 80(6):627-632.
  • 3. Baeyens E, Bitar E Y, Khargonekar P P, Poolla K, (2013) Coalitional aggregation of wind power. IEEE Transactions on Power Systems, 28(4):3774-3784.
  • 4. Bludszuweit H, Dominguez-Navarro J A, Llombart A, (2008) Statistical analysis of wind power forecast error. IEEE Transactions on Power Systems, 23(3):983-991.
  • 5. Chalkiadakis G, Robu V, Kota R, Rogers A, Jennings N R, (2011) Cooperatives of distributed energy resources for eficient virtual power plants. In: The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2011), 2: 787 - 794.
  • 6. Coker P, Barlow J, Cockerill T, Shipworth D, (2013) Measuring significant variability characteristics: An assessment of three UK renewables. Renewable Energy, 53:111-120.    
  • 7. Cradden L C, Harrison G P, Chick J P, (2012) Will climate change impact on wind power development in the UK? Climatic Change, 115(3-4):837-852.
  • 8. Davy T, Woods M, Russell C, Coppin P, (2010) Statistical downscaling of wind variability from meteorological fields. Boundary-Layer Meteorology, 135:161-175.    
  • 9. de Boer H S, Grond L, Moll H, Benders R, (2014) The application of power-to-gas, pumped hydro storage and compressed air energy storage in an electricity system at different wind power penetration levels. Energy, 72(0):360 -370.
  • 10. DECC, (2012) Electricity generation costs. Technical report, UK Department of Energy and Climate Change, October2012.
  • 11. Earl N, Dorling S, Hewston R, von Glasow R, (2013) 1980-2010 variability in UK surface wind climate. Journal of Climate, 26(4):1172-1191.
  • 12. Fabbri A, Gomez T, Roman S, Abbad J R, Quezada V H M, (2005) Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market. IEEE Transactions on Power Systems Transactions on Power Systems, 20(3):1440-1446.
  • 13. Fertig E, Apt J, Jaramillo P, Katzenstein W, (2012) The effect of long-distance interconnection on wind power variability. Environmental Research Letters, 7(3): 034017.
  • 14. Foley A M, Leahy P G, Marvuglia A, and McKeogh A J, (2012) Current methods and advances in forecasting of wind power generation. Renewable Energy, 37:1 - 8.    
  • 15. Fruh W-G, (2013) Long-term wind resource and uncertainty estimation using wind records from Scotland as example. Renewable Energy, 50:1014 - 1026.    
  • 16. Fruh W-G, (2014) How much can regional aggregation of wind farms and smart grid demand management facilitate wind energy integration? In: Proceedings of the World Renewable Energy Congress-XIII "Renewable Energy in the Service of Mankind", 3-8 August, 2014, London, UK.
  • 17. Fruh W-G, (2013) Energy storage requirements to match wind generation and demand applied to the UK network. In: International Conference on Renewable Energies and Power Quality (ICREPQ'13), Renewable Energy and Power Quality Journal 11.
  • 18. Gahleitner G, (2013) Hydrogen from renewable electricity: An international review of power-to-gas pilot plants for stationary applications. International Journal of Hydrogen Energy, 38(5):2039 - 2061.
  • 19. Hasche B, (2010) General statistics of geographically dispersed wind power. Wind Energy, 13(8):773-784.
  • 20. Hornik K, (2011) The R FAQ. ISBN 3-900051-08-9. Available at http://CRAN.R-project.org/doc/FAQ/R-FAQ.html
  • 21. Katzenstein W, Fertig E, Apt J, (2010) The variability of interconnected wind plants. Energy Policy, 38(8):4400 -4410.
  • 22. Liu S, Jian J, Wang Y, Liang J, (2013) A robust optimization approach to wind farm diversification. International Journal of Electrical Power & Energy Systems, 53:409-415.
  • 23. Liu X, (2011) Impact of beta-distributed wind power on economic load dispatch. Electric Power Components and Systems, 39(8):768-779.
  • 24. Marques de S a J P, editor, (2007) Applied Statistics Using SPSS, STATISTICA, MATLAB and R. Springer-Verlag: Berlin Heidelberg New York.
  • 25. National Grid, (2013) Electricity ten year statement (ETYS). Technical report, National Grid.
  • 26. National Grid, (2013) Half-hourly demand data. Available from http://www2.nationalgrid.com/UK/Industryinformation/ Electricity-transmission-operational-data
  • 27. Nolan P, Lynch P, McGrath R, Semmler T, Wang S, (2012) Simulating climate change and its effects on the wind energy resource of Ireland. Wind Energy, 15:593 - 608.    
  • 28. Pand zi c H, Morales J M, Conejo A J, Kuzle I, (2013) Offering model for a virtual power plant based on stochastic programming. Applied Energy, 105(0):282 - 292.
  • 29. Pritchard G, (2011) Short-term variations in wind power: some quantile-type models for probabilistic forecasting. Wind Energy, 14(2):255-269.
  • 30. Sinden G, (2007) Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand. Energy Policy, 35(1):112 - 127.
  • 31. Skittides C, Fruh W-G, (2013) Wind speed forecasting using singular systems analysis. In: International Conference on Renewable Energies and Power Quality (ICREPQ'13), Renewable Energy and Power Quality Journal 11.
  • 32. Sturt A, Strbac G, (2011) Time series modelling of power output for large-scale wind fleets. Wind Energy, 14(8):953-966.
  • 33. Tapiador F J, (2009) Assessment of renewable energy potential through satellite data and numerical models. Energy & Environmental Science, 2(11):1142-1161.
  • 34. Tarroja B, Mueller F, Eichman J D, Brouwer J, Samuelsen S, (2011) Spatial and temporal analysis of electric wind generation intermittency and dynamics. Renewable Energy, 36(12):3424-3432.
  • 35. Tascikaraoglu A, Erdinc O, Uzunoglu M, Karakas A, (1014) An adaptive load dispatching and forecasting strategy for a virtual power plant including renewable energy conversion units. Applied Energy, 119(0):445 - 453.
  • 36. UK Meteorological Ofice, (2011) MIDAS Land Surface Stations data (1853-current). NCAS British Atmospheric Data Centre. Available from http://badc.nerc.ac.uk/view/badc.nerc.ac.uk ATOM dataent ukmo-midas.
  • 37. Watson S J, Kritharas P, Hodgson G J, (2015) Wind speed variability across the UK between 1957 and 2011. Wind Energy, 18(1): 21 - 42.
  • 38. Zhang G, Wan X, (2014) A wind-hydrogen energy storage system model for massive wind energy curtailment. International Journal of Hydrogen Energy, 39(3):1243-1252.
  • 39. Zhang Z-S, Sun Y-Z, Gao D W, Lin J, Cheng L, (2013) A versatile probability distribution model for wind power forecast errors and its application in economic dispatch. IEEE Transactions on Power Systems Transactions on Power Systems, 28(3):3114-3125.


This article has been cited by

  • 1. Merlinda Andoni, Valentin Robu, Wolf-Gerrit Früh, David Flynn, Game-theoretic modeling of curtailment rules and network investments with distributed generation, Applied Energy, 2017, 201, 174, 10.1016/j.apenergy.2017.05.035
  • 2. Andrew N. Commin, Magnus W.H. Davidson, Nicola Largey, Paul P.J. Gaffney, David W. Braidwood, Stuart W. Gibb, John McClatchey, Spatial smoothing of onshore wind: Implications for strategic development in Scotland, Energy Policy, 2017, 109, 36, 10.1016/j.enpol.2017.06.038
  • 3. Jeff Howell, David Forbes, Martin Passmore, A drag coefficient for application to the WLTP driving cycle, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2017, 231, 9, 1274, 10.1177/0954407017704784
  • 4. Andrew N. Commin, Andrew S. French, Matteo Marasco, Jennifer Loxton, Stuart W. Gibb, John McClatchey, The influence of the North Atlantic Oscillation on diverse renewable generation in Scotland, Applied Energy, 2017, 205, 855, 10.1016/j.apenergy.2017.08.126

Reader Comments

your name: *   your email: *  

Copyright Info: 2015, Wolf-Gerrit Früh, licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved