Wind Power Scenario Synthesis With Smoothing Effect Through Spectral Decomposition and Its Application to Flexible Resource Adequacy

Abstract

We synthesize future scenarios of total wind power generation (WPG) of future wind farms by considering the smoothing effect, in which mid-term variability fades out, but periodicity and short-term variability remain as more geographically distributed wind farms are aggregated. First, we decompose the WPG into a periodic component (PC) and non-periodic component (NC). We design the future PC by modifying the standard deviation and mean of the current PC. Then, we assume that the NC can be decomposed into a variable component (VC), which is a source of mid-term variability, and an uncertain component (UC), which is a source of short-term variability. We separate the VC and UC in the frequency domain. We design the VC by modifying the slope of power spectral density (PSD) to represent the smoothing effect and the UC by synthesizing the PSD distribution to represent randomness. Based on our scenarios, we develop a new flexibility index and a novel recursive algorithm to calculate the required amount of future flexible resources and the value of the smoothing effect to maintain the flexible resource adequacy. Finally, we verify the smoothing effect and the amount of flexible resources by simulating the IEEE test network based on actual WPG in Texas and Australia.