Abstract: . . .  Tomorrow’s Energy Storage Solution. 2004. Available at http://www.beaconpower.com/products/ EnergyStorageSystems/DocsPresentations.htm, accessed 04/21/2004. Page 14 14 Figure captions Figure 1. Spectral power density of horizontal wind speed showing the macro- meteorological and micro-meteorological fluctuations (adopted from Rohatgi & Nelson). [1] Figure 2. Demonstration of the effects of increasing t (= energy storage capacity) on the amplitude of the wind power fluctuation for a real wind power case. Figure 3. Classification of ?t = 1s wind power data used in the analysis according to mean power and power integral time scale. Figure 4a. Short-term influence of time constant t on the relative standard deviation of the wind turbine power output. Turbine . . .
. . .  with ?t = 1s and power fluctuation classes A-D. Figure 8. Influence of long-term time constant t on the macro-scale standard deviation of the wind turbine power output. Turbine data set with ?t = 1hr. Figure 9. Influence of time constant t on the relative energy storage capacity per MW wind turbine capacity. Turbine data sets with ?t = 1hr. Figure 10. Relation of long-term energy storage capacity and relative standard deviation of the wind turbine power output. Turbine data set with ?t = 1hr. Page 16 16 List of Tables Table 1. Wind turbine sites and types of datasets used as input in the analysis. Table 2. Statistical characteristics of the datasets with ?t = 1s. Symbols A-D refer to the wind power fluctuation classes defined in Figure 3. Table . . .
. . .  = 1s and power fluctuation classes A-D. Figure 8. Influence of long-term time constant t on the macro-scale standard deviation of the wind turbine power output. Turbine data set with ?t = 1hr. Figure 9. Influence of time constant t on the relative energy storage capacity per MW wind turbine capacity. Turbine data sets with ?t = 1hr. Figure 10. Relation of long-term energy storage capacity and relative standard deviation of the wind turbine power output. Turbine data set with ?t = 1hr. Page 16 16 List of Tables Table 1. Wind turbine sites and types of datasets used as input in the analysis. Table 2. Statistical characteristics of the datasets with ?t = 1s. Symbols A-D refer to the wind power fluctuation classes defined in Figure 3. Table 3. Statistical . . .
. . .  capacities which may improve the power quality of an individual wind power turbine. The concept may be expanded to large wind power systems when the corresponding system data is analyzed. The effects caused by fluctuation with less than 1s causing e.g. flickering, [6] are excluded here. In the paper a methodology is presented to describe the smoothing out of wind turbine power variations when combined with energy storage. The system output is modeled by introducing to the output data a time constant corresponding to energy storage capacity. This method is applied to two sets of data with a time resolution of 1 second and 1 hour, respectively. The method allows to directly relate the size of the energy storage with wind power variation on a short . . .
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