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Scharff, Richard

Publications and source records attributed to Scharff, Richard.

Variability in large-scale wind power generation: Variability in large-scale wind power generation

The paper demonstrates the characteristics of wind power variability and net load variability in multiple power systems based on real data from multiple years. Demonstrated characteristics include probability distribution for different ramp durations, seasonal and diurnal variability and low net load events. The comparison shows regions with low variability (Sweden, Spain and Germany), medium variability (Portugal, Ireland, Finland and Denmark) and regions with higher variability (Quebec, Bonneville Power Administration and Electric Reliability Council of Texas in North America; Gansu, Jilin and Liaoning in China; and Norway and offshore wind power in Denmark). For regions with low variability, the maximum 1 h wind ramps are below 10% of nominal capacity, and for regions with high variability, they may be close to 30%. Wind power variability is mainly explained by the extent of geographical spread, but also higher capacity factor causes higher variability. It was also shown how wind power ramps are autocorrelated and dependent on the operating output level. When wind power was concentrated in smaller area, there were outliers with high changes in wind output, which were not present in large areas with well-dispersed wind power.

17 WIND ENERGY↗

Index for Wind Power Variability

Variability of large scale wind power generation is dependent on several factors: characteristics of installed wind power plants, size of the area where the plants are installed, geographic dispersion within that area and its weather regime(s). Variability can be described by ramps in power generation, i.e. changes from time period to time period. Given enough data points, it can be described with a probability density function. This approach focuses on two dimensions of variability: duration of the ramp and probability distribution. This paper proposes an index based on these two dimensions to enable comparisons and characterizations of variability under different conditions. The index is tested with real, large scale wind power generation data from several countries. Considerations while forming an index are discussed, as well as the main results regarding what the drivers of variability experienced for different data.

17 WIND ENERGY↗