Wind energy is a clean and cost-efficient source of energy that has gained widespread acceptance in power systems due to its numerous benefits. Despite its reliability, the variability of the source poses a challenge to wind energy integration into power grids, which require schedulable sources of electricity. To mitigate this challenge, wind power forecasting tools have been developed to reduce the uncertainty associated with power output. However, they may fail to predict large rapid power variations, known as relevant ramps. To address this limitation, novel tools for detecting and predicting ramps have been recently explored. As these tools are still in their early stages of development, it is essential to conduct further analysis to identify the optimal parameter settings for detecting relevant ramps. Therefore, this manuscript aims to analyze the impact of these parameters on ramp extraction using real-world data from a set of wind farms connected to a portion of the Italian sub-transmission grid. In particular, an interesting emerging aspect is that the number of detected ramp events decreases quadratic with the ramp amplitude definition and this in turn might affect the behavior of dedicated wind power ramp forecasting methods.
Experimental Assessment of Data-driven Methods for Detecting Wind Power Ramps
Astolfi D.;
2023-01-01
Abstract
Wind energy is a clean and cost-efficient source of energy that has gained widespread acceptance in power systems due to its numerous benefits. Despite its reliability, the variability of the source poses a challenge to wind energy integration into power grids, which require schedulable sources of electricity. To mitigate this challenge, wind power forecasting tools have been developed to reduce the uncertainty associated with power output. However, they may fail to predict large rapid power variations, known as relevant ramps. To address this limitation, novel tools for detecting and predicting ramps have been recently explored. As these tools are still in their early stages of development, it is essential to conduct further analysis to identify the optimal parameter settings for detecting relevant ramps. Therefore, this manuscript aims to analyze the impact of these parameters on ramp extraction using real-world data from a set of wind farms connected to a portion of the Italian sub-transmission grid. In particular, an interesting emerging aspect is that the number of detected ramp events decreases quadratic with the ramp amplitude definition and this in turn might affect the behavior of dedicated wind power ramp forecasting methods.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
