Conclusions
Many power systems around the world now have total installed wind plant capacities in excess of several gigawatts. System operators must manage the inherent uncertainty and variability of the aggregate wind power in their systems in order to maintain reliability and economic efficiency. The characteristics of aggregate wind power can be quite different from individual wind plants, depending on the level of geographic diversity in the system. Tools such as wind power forecast systems, stochastic unit commitment, and resource planning require reasonable and practical probabilistic models of aggregate wind power and wind power variation as inputs.
This chapter demonstrated that the uncertainty and variability exhibited by aggregate wind power can be reasonably represented using parsimonious para- metric models. More specifically, the two-parameter Beta distribution is well suited for modeling instantaneous aggregate wind power and the two-parameter Laplace distribution is well suited for modeling moment-to-moment variation.
Several aspects of geographic diversity were explored. Among the main conclusions are that geographic diversity should not be viewed as a panacea for the challenges of wind integration. Reduction in variability—the smoothing effect—is noticeable, but reduction in uncertainty requires exceedingly large geographic areas. Appreciable correlation of instantaneous power amongst wind plants can exist at distances approaching 1,000 km. Clustering and other practical considerations also limit the amount of geographic diversity that occurs in many systems. Analysis of several systems showed that the effects of geographic diversity, particularly on uncertainty, saturate when installations of wind plants reach several gigawatts in total.