Identifying wind power ramp causes from multivariate datasets: a methodological proposal and its application to reanalysis data

Abstract

Forecasting abrupt variations in wind power generation (the so-called ramps) helps achieve large scale wind power integration. One of the main issues to be confronted when addressing wind power ramp forecasting is the way in which relevant information is identified from large datasets to optimally feed forecasting models. To this end, an innovative methodology oriented to systematically relate multivariate datasets to ramp events is presented. The methodology comprises two stages: the identification of relevant features in the data and the assessment of the dependence between these features and ramp occurrence. As a test case, the proposed methodology was employed to explore the relationships between atmospheric dynamics at the global/synoptic scales and ramp events experienced in two wind farms located in Spain. The achieved results suggested different connection degrees between these atmospheric scales and ramp occurrence. For one of the wind farms, it was found that ramp events could be partly explained from regional circulations and zonal pressure gradients. To perform a comprehensive analysis of ramp underlying causes, the proposed methodology could be applied to datasets related to other stages of the wind-topower conversion chain.

Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

“…To complete this section, we would like to highlight, for different reasons that will be clear later on, two recent works [32,35]. On the one hand, ref.…”

“…On the one hand, ref. [32] is interesting because it uses data from Global Circulation Models (GCMs) (reanalysis data)to identify possible meteorological causes for WPREs and also because it applies a methodology based on wavelets and Principal Component Analysis (PCA) to estimate the best set of features (predictive variables) to estimate WPREs. On the other hand, ref.…”

“…The key point here is that the meteorological process must be always considered, since they are physical precursors of WPREs. Different numerical weather prediction system outputs have been used to obtain these predictive variables, including reanalysis data [32]. This provides a long history record of meteorological variables to be used as predictive variables for WPRE prediction.…”

Wind power variability during the passage of cold fronts across South Africa

“…Variability at longer timescales is also considered to be of greater importance to this study, as it has been found that longer-timescale, synoptic-scale weather patterns (such as cold fronts) drive variability (Kiviluoma et al, 2016). It has been noted that it is difficult to make generalisations on resource variability for a specific region based on studies done for other regions (Kiviluoma et al, 2016;Gallego-Castillo et al, 2015). This is especially true for studies considering the prevailing meteorological processes inherent to a specific region, highlighting the importance of conducting region-specific studies.…”

“…Amongst the most frequent of synoptic systems that affect South Africa's weather are extra-tropical cyclones, and the cold fronts associated with them. Little literature could be found where the underlying causative meteorological phenomena driving wind ramp events were studied (Couto et al, 2015;Gallego-castillo et al, 2015;Lacerda et al, 2017). Furthermore, attempts to find literature in which wind power ramp events associated with specific weather systems had been studied for South Africa were not successful.…”