Consideration of Wind Speed Variability in Creating a Regional Aggregate Wind Power Time Series

Abstract

For the purposes of understanding the impacts on the electricity network, estimates of hourly aggregate wind power generation for a region are required. However, the availability of wind production data for the UK is limited, and studies often rely on measured wind speeds from a network of meteorological (met) stations. Another option is to use historical wind speeds from a reanalysis dataset, with a resolution of around 40-50 km. Mesoscale models offer a potentially more desirable solution, with a homogeneous set of wind speeds covering a wide area at resolutions of 1-50 km, but they are computationally expensive to run at high resolution. An understanding of the most appropriate choice of data requires knowledge of the variability in time and space and how well that is represented by the choice of model. Here it is demonstrated that in regions offshore, or in relatively smooth terrain where variability in wind speeds is smaller, lower resolution models or single point records may suffice to represent aggregate power generation in a sub-region. The need for high resolution modelling in areas of complex terrain where spatial and temporal variability is higher is emphasised, particularly when the distribution of wind generation capacity is uneven over the region.

Quantifying the increasing sensitivity of power systems to climate variability

“…To this end, well-validated multi-decadal hourly demand and wind-power time series are constructed for GB and the impact of climate variability assessed using six different power-system metrics (total annual energy requirement, peak load, use of 'peaking', 'mid-merit' and 'baseload' plant, and wind power curtailment). This work therefore extends a growing body of energy-climate literature (e.g., [9,10,[30][31][32][33][34][35] for GB) by using multi-decadal meteorological records to provide insight into the operation of an integrated power system for the first time. For simplicity, solar photovoltaics are not included in this present study (in GB the total energy from solar photovoltaic generation in 2014 was less than 15% of that from wind [36]).…”

Identifying and characterising large ramps in power output of offshore wind farms

“…a reversal of some of the smoothing benefits gained by the spatial dispersion of turbines For the UK, Sinden [22] and Earl et al [23] used wind speed data measured at Met Office surface stations to quantify the inter-annual, seasonal and diurnal variability of UK aggregated wind generation. However, these studies did not consider offshore sites and assumed the distribution of wind capacity matched the distribution of weather stations which can lead to large errors [24]. To address this problem, Cannon et al [7] used wind speed data derived from the MERRA reanalysis dataset to determine the characteristics of wind power in Great Britain over a 33 year period.…”

“…However, analysis using this data is unable to quantify the regional power swings or indicate how the variability has been affected by the change in wind farm distribution. Cradden et al [24] used an hourly 11 year hindcast derived using the Weather Research and Forecasting model (WRF) at 3 km resolution to assess the variability of generation from 13 different regions in the UK.…”

“Section to Point” Correction Method for Wind Power Forecasting Based on Cloud Theory

“…One is to forecast wind power by wind speed according to the wind power formula [2][3][4]. The other is to make statistical prediction based on historical data by adding meteorological factors (wind speed, wind direction) as auxiliary forecasting [5,6].…”