Wind Power Plant Behaviors: Analyses of Long-Term Wind Power Data

Abstract

November 121 0.12% 176 2,990 (2,300) December 105 0.10% 142 2,930 (2,200) January 2002 88 0.09% 129 4,740 (4,420) February 133 0.13% 170 3,770 (2,460) March 97 0.09% 138 3,090 (4,890) April 112 0.11% 152 2,230 (9,290) May 99 0.10% 149 2,260 (4,040) June 89 0.09% 132 2,400 (5,730) Storm Lake Average (kW) Average (% capacity) Standard Deviation (kW) Maximum (+) (kW) Maximum (-) (kW) July 2001 42 0.04% 60 1,089 (5,158) August 43 0.04% 57 1,379 (14,466) September 48 0.04% 69 3,950 (11,333) October 70 0.06% 98 10,209 (1,321) November 63 0.06% 73 955 (3,454) December 45 0.04% 89 971 (807) January 2002 60 0.06% 70 887 (948) February 77 0.07% 172 4,990 (94,138) March 71 0.06% 128 4,444 (75,356) April

Eastern Wind Integration and Transmission Study (EWITS) (Revised)

“…Laboratory's (NREL) mesoscale data only goes down to a 10-minute resolution, actual wind data collected by NREL (Wan 2004) and others was used for the analysis in the quicker time frames.…”

Wind Plant Ramping Behavior

“…The results also gave Xcel operators a better understanding of when and how different wind power ramps were occurring. A complete briefing on characteristics and behavior of wind power on various timescales and sizes can be found in [6].…”

“…Figure 2 shows a contour plot of time of day and month ramping statistics. 6 For ramps that span two time frames, they are binned by the start time of the ramp Notice that the time period of hours 18-23 stands out amongst the four periods. This period holds the largest ramps, the most ramps, and is the most volatile according to the number of false ramps.…”

“…Although fairly weak (r = -0.05), with this much data even very weak correlations are statistically significant. In [6], it was shown that wind power correlations decay exponentially with increasing distances and that the rate of decay increases with decreasing levels of averaging in the time series data. Thus, correlations between ten-minute averaged power outputs, as above, will decay more quickly than for hourly averages, but much more slowly than for one-minute data.…”

Compatibility of wind and solar energy with electricity demand in Argentina

“…It is well known that a wider geographical distribution of wind farms reduce their overall variability [35,46,13]. This smoothing effect is proportional to the size of the catchment area [2], since the correlation between wind patterns decrease as the distance between sites increase [56,47]. In contrast, fewer studies have addressed the effect of the dispersion of solar generation [59].…”