Spatial characterisation of wind resource using a Bayesian approach

M.S. Miranda, G. Shaddick, R.W. Dunn

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The characterisation of the spatial distribution of the wind resource over a given area has been a subject of great interest, particularly considering the increasing participation of wind power generation in modern power systems. Spatial modelling can be an important tool for assessing wind power integration into the power system and can be used, for example, in the analysis of siting/sizing of wind power plants and their influence on generation adequacy and transmission capability. This paper presents a methodology to characterise the spatiotemporal distribution of the wind which uses a Bayesian approach and is implemented using Markov chain Monte Carlo (MCMC) sampling. The model is built using wind speed data from 5 MetOffice (UK) stations. Estimates are made for another 2 locations (also weather stations, for validation purposes). An additional term, which uses the atmospheric pressure gradient from each location, is introduced as a predictor to the wind. The results from the MCMC simulations are presented and compared with the available data for the predicted locations. Although preliminary, the results are very encouraging, making this approach a feasible alternative for spatial modelling of wind resource, especially if no data is available for the locations of interest.
Original languageEnglish
Title of host publicationEuropean Wind Energy Conference and Exhibition 2006, EWEC 2006
Pages2221-2227
Number of pages7
Volume3
Publication statusPublished - 2006
EventEuropean Wind Energy Conference and Exhibition 2006, EWEC 2006 - Athens, Greece
Duration: 27 Feb 20062 Mar 2006

Conference

ConferenceEuropean Wind Energy Conference and Exhibition 2006, EWEC 2006
CountryGreece
CityAthens
Period27/02/062/03/06

Fingerprint Dive into the research topics of 'Spatial characterisation of wind resource using a Bayesian approach'. Together they form a unique fingerprint.

  • Cite this

    Miranda, M. S., Shaddick, G., & Dunn, R. W. (2006). Spatial characterisation of wind resource using a Bayesian approach. In European Wind Energy Conference and Exhibition 2006, EWEC 2006 (Vol. 3, pp. 2221-2227)