2017 Volume 8 Issue 4 Pages 342-357
Wind power has increased rapidly worldwide in recent years. For power system and wind farm operators, it becomes important to understand the smoothing effects of aggregated power, and the temporal and spatial scales at which smoothing is achieved. Here, we propose a new smoothing index for wind power based on the so-called Koopman Mode Decomposition (KMD). KMD decomposes spatio-temporal data on complex wind power into modes oscillating with single frequencies. We show that the proposed smoothing index is regarded as a generalization of a previously proposed index based on power spectral densities. We then look at smoothing of wind power in Japan on a large-scale by incorporating highly-resolved wind prediction data from the Cloud Resolving Storm Simulator (CReSS). In particular, we consider six regions in northern Honshu (the largest island of Japan) as a test case. By applying the proposed index to simulated wind power, we show how the smoothing improves by distributing wind farms over different regions. Our results indicate that by distributing wind farms over only one to three regions, smoothing results vary considerably depending on the choice of regions. However, as the number of considered regions increases, the smoothing improves, and the particular choice of regions matters less for smoothing effects at the investigated time-scales. This highlights the practical importance of deliberately selecting sites for large-scale wind power production to more effectively smooth the aggregated power.