Intra-hour wind power variability assessment using the conditional range metric : quantification, forecasting and applications
The research presented herein concentrates on the quantification, assessment and forecasting of intra-hour wind power variability. Wind power is intrinsically variable and, due to the increase in wind power penetration levels, the level of intra-hour wind power variability is expected to increase as well. Existing metrics used in wind integration studies fail to efficiently capture intra-hour wind power variation. As a result, this can lead to an underestimation of intra-hour wind power variability with adverse effects on power systems, especially their reliability and economics. One major research focus in this dissertation is to develop a novel variability metric which can effectively quantify intra-hour wind power variability. The proposed metric, termed conditional range metric (CRM), quantifies wind power variability using the range of wind power output over a time period. The metric is termed conditional because the range of wind power output is conditioned on the time interval length k and on the wind power average production l[subscript j] over the given time interval. Using statistical analysis and optimization approaches, a computational algorithm to obtain a unique p[superscript th] quantile of the conditional range metric is given, turning the proposed conditional range metric into a probabilistic intra-hour wind power variability metric. The probabilistic conditional range metric CRM[subscript k,l subscript j,p] assists power system operators and wind farm owners in decision making under uncertainty, since decisions involving wind power variability can be made based on the willingness to accept a certain level of risk [alpha] = 1 - p. An extensive performance analysis of the conditional range metric on real-world wind power and wind speed data reveals how certain variables affect intra-hour wind power variability. Wind power variability over a time frame is found to increase with increasing time frame size and decreasing wind farm size, and is highest at mid production wind power levels. Moreover, wind turbines connected through converters to the grid exhibit lower wind power variability compared to same size simple induction generators, while wind power variability is also found to decrease slightly with increasing wind turbine size. These results can lead to improvements in existing or definitions of new wind power management techniques. Moreover, the comparison of the conditional range metric to the commonly used step-changes statistics reveals that, on average, the conditional range metric can accommodate intra-hour wind power variations for an additional 15% of hours within a given year, significantly benefiting power system reliability. The other major research focus in this dissertation is on providing intrahour wind power variability forecasts. Wind power variability forecasts use pth CRM quantiles estimates to construct probabilistic intervals within which future wind power output will lie, conditioned on the forecasted average wind power production. One static and two time-adaptive methods are used to obtain p[superscript th] CRM quantiles estimates. All methods produce quantile estimates of acceptable reliability, with average expected deviations from nominal proportions close to 1%. Wind power variability forecasts can serve as joint-chance constraints in stochastic optimization problems, which opens the door to numerous applications of the conditional range metric. A practical example application uses the conditional range metric to estimate the size of an energy storage system (ESS). Using a probabilistic forecast of wind power hourly averages and historical data on intra-hour wind power variability, the proposed methodology estimates the size of an ESS which minimizes deviations from the forecasted hourly average. The methodology is evaluated using real-world wind power data. When the estimated ESS capacities are compared to the ESS capacities obtained from the actual data, they exhibit coverage rates which are very close to the nominal ones, with an average absolute deviation less than 1.5%.