Abstract
Abstract. One of the main factors contributing to wind power forecast inaccuracies is the occurrence of large changes in wind power output over a short amount of time, also called “ramp events”. In this paper, we assess the behaviour and causality of 1183 ramp events at a large wind farm site located in Victoria (southeast Australia). We address the relative importance of primary engineering and meteorological processes inducing ramps through an automatic ramp categorisation scheme. Ramp features such as ramp amplitude, shape, diurnal cycle and seasonality are further discussed, and several case studies are presented. It is shown that ramps at the study site are mostly associated with frontal activity (46 %) and that wind power fluctuations tend to plateau before and after the ramps. The research further demonstrates the wide range of temporal scales and behaviours inherent to intra-hourly wind power ramps at the wind farm scale.
Highlights
Environmental protection and sustainability have become the main incentives to integrate more green energy sources into electrical systems
Based on the methodology above, a total of 1183 ramps are identified in the 29month period. 2.4 Ramp categorisation we present the automatic scheme developed to classify ramps according to their underlying causes
Downward ramps tend to occur in the late afternoon, with a maximum likelihood of occurrence at 15:00 UTC+10. Both upward and downward ramps are more common during warmer months, with a noticeable peak in spring (Fig. 7b)
Summary
Environmental protection and sustainability have become the main incentives to integrate more green energy sources into electrical systems. Since the early 2000s, wind energy has gained significant traction and is currently the fastest-growing mode of electricity production across the globe (EIA, 2019), with up to 51.3 GW of wind power capacity installed worldwide in the year 2018 alone (GWEC, 2019). In emerging markets such as Australia, Canada and the United States, newly built wind farms are installed in large blocks, often exceeding 400 MW (Kariniotakis, 2017). Motivated by the need to enhance management of such events as well as by optimising integration and control of wind farms, there is currently a great incentive to develop accurate and timely short-term (intrahourly) ramp forecasts (Zhang et al, 2017; Cui et al, 2015; Gallego et al, 2015a)
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