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A 2020 GB transmission network study using dispersed wind farm power output

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  • Gerber, Annelies
  • Qadrdan, Meysam
  • Chaudry, Modassar
  • Ekanayake, Janaka
  • Jenkins, Nick

Abstract

A large amount of wind power generation capacity is expected to be installed in Great Britain by 2020. A considerable amount of this capacity will be located in Scotland. This will require reinforcement of power transmission capacity between Scotland and England. Analysis of the impact of a large amount of wind generation capacity on the electricity network requires modelling of wind power output. Due to the lack of available wind power output data a method for artificially generating wind power output from real wind speed data was developed. The technique is based on monthly wind speed averages used to generate Weibull distributions. Hourly wind power output time series over one year are generated for 18 onshore and offshore locations in Great Britain. Capacity factors, maxima, minima and hourly output variations, their means and standard deviations are calculated. A comparison of statistical parameters of the generated data with those of real wind power output shows that both have similar statistical properties. The geographically distributed wind power output data is used in conjunction with an optimal load flow model for analysing three network case studies. One case study uses a hypothesised 2020 Great Britain transmission network the other two cases enhance the network with an Eastern and Western HVDC line linking Scotland and England. The simulation results illustrate the effectiveness of both HVDC lines in reducing wind generation curtailments and operational costs.

Suggested Citation

  • Gerber, Annelies & Qadrdan, Meysam & Chaudry, Modassar & Ekanayake, Janaka & Jenkins, Nick, 2012. "A 2020 GB transmission network study using dispersed wind farm power output," Renewable Energy, Elsevier, vol. 37(1), pages 124-132.
    • Handle: RePEc:eee:renene:v:37:y:2012:i:1:p:124-132
    DOI: 10.1016/j.renene.2011.06.004
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    References listed on IDEAS

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    1. Maddaloni, Jesse D. & Rowe, Andrew M. & van Kooten, G. Cornelis, 2009. "Wind integration into various generation mixtures," Renewable Energy, Elsevier, vol. 34(3), pages 807-814.
    2. Qadrdan, Meysam & Chaudry, Modassar & Wu, Jianzhong & Jenkins, Nick & Ekanayake, Janaka, 2010. "Impact of a large penetration of wind generation on the GB gas network," Energy Policy, Elsevier, vol. 38(10), pages 5684-5695, October.
    3. Sinden, Graham, 2007. "Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand," Energy Policy, Elsevier, vol. 35(1), pages 112-127, January.
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    1. Coker, Phil & Barlow, Janet & Cockerill, Tim & Shipworth, David, 2013. "Measuring significant variability characteristics: An assessment of three UK renewables," Renewable Energy, Elsevier, vol. 53(C), pages 111-120.
    2. Hughes, Nick & Strachan, Neil & Gross, Robert, 2013. "The structure of uncertainty in future low carbon pathways," Energy Policy, Elsevier, vol. 52(C), pages 45-54.

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