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Application of statistical wind models for system impacts

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5 Author(s)
Hill, D. ; Dept. of Electron. & Electr. Eng., Univ. of Strathclyde, Glasgow, UK ; McMillan, D. ; Bell, K. ; Infield, D.
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The UK government has provided an incentive mechanism for renewable electricity that is delivering a significantly increasing penetration of wind power in the electricity supply mix, and this growth is likely to continue in the near to medium term. There is a real and pressing need to assess the impacts of increasing amounts of wind power on the UK power system. Statistical models are presented that characterize the temporal and spatial nature of windspeeds across the UK in a more comprehensive way than hitherto expressed. Auto-Regressive Moving Average models (ARMA), often used for predictive purposes on shorter time-scales, are developed to characterize the windspeed field. A detrending method to allow for non-stationarity of the data is presented, developed specifically to model annual trends and a seasonally dependent diurnal effect, noted to be present across sites studied. Vector auto-regressive (VAR) models extend the work by incorporating spatiotemporal correlations between the different sites. Results are presented demonstrating the effectiveness of the proposed approach to wind modelling and synthesis. In future work, these wind synthesis procedures will be used as input to wind and power system time domain modeling with a view to an improved understanding of how a substantial UK wind penetration will impact on grid operation, thus providing a powerful tool for operational and planning purposes.

Published in:

Universities Power Engineering Conference (UPEC), 2009 Proceedings of the 44th International

Date of Conference:

1-4 Sept. 2009