Hill, David and Bell, Keith and McMillan, David and Infield, David (2014) A vector auto-regressive model for onshore and offshore wind synthesis incorporating meteorological model information. Advances in Science and Research, 11. pp. 35-39.
asr_11_35_2014.pdf - Final Published Version
Download (427kB) | Preview
The growth of wind power production in the electricity portfolio is striving to meet ambitious targets set, for example by the EU, to reduce greenhouse gas emissions by 20% by 2020. Huge investments are now being made in new offshore wind farms around UK coastal waters that will have a major impact on the GB electrical supply. Representations of the UK wind field in syntheses which capture the inherent structure and correlations between different locations including offshore sites are required. Here, Vector Auto-Regressive (VAR) models are presented and extended in a novel way to incorporate offshore time series from a pan-European meteorological model called COSMO, with onshore wind speeds from the MIDAS dataset provided by the British Atmospheric Data Centre. Forecasting ability onshore is shown to be improved with the inclusion of the offshore sites with improvements of up to 25% in RMS error at 6 h ahead. In addition, the VAR model is used to synthesise time series of wind at each offshore site, which are then used to estimate wind farm capacity factors at the sites in question. These are then compared with estimates of capacity factors derived from the work of Hawkins et al. (2011). A good degree of agreement is established indicating that this synthesis tool should be useful in power system impact studies.
|Keywords:||greenhouse gas emissions, onshore wind farm, offshore wind farm, electrical supply, Electrical engineering. Electronics Nuclear engineering|
|Subjects:||Technology > Electrical engineering. Electronics Nuclear engineering|
|Department:||Faculty of Engineering > Electronic and Electrical Engineering|
|Depositing user:||Pure Administrator|
|Date Deposited:||16 May 2014 10:42|
|Last modified:||31 Mar 2017 19:00|