Abstract
In order to implement accurate models for wind power ramp forecasting, ramps need to be previously characterised. This issue has been typically addressed by performing binary ramp/non-ramp classifications based on ad-hoc assessed thresholds. However, recent works question this approach. This paper presents the ramp function, an innovative wavelet- based tool which detects and characterises ramp events in wind power time series. The underlying idea is to assess a continuous index related to the ramp intensity at each time step, which is obtained by considering large power output gradients evaluated under different time scales (up to typical ramp durations). The ramp function overcomes some of the drawbacks shown by the aforementioned binary classification and permits forecasters to easily reveal specific features of the ramp behaviour observed at a wind farm. As an example, the daily profile of the ramp-up and ramp-down intensities are obtained for the case of a wind farm located in Spain.
Highlights
Nowadays, large-scale wind power integration is conditioned to properly manage large and fast power output variations observed at wind farms and portfolios
In this paper we present the ramp function, an innovative approach to detect and characterise ramp events in wind power time series
The main feature of this approach is that the ramp intensity is obtained by considering large power gradients evaluated under different time scales
Summary
Large-scale wind power integration is conditioned to properly manage large and fast power output variations (ramp events) observed at wind farms and portfolios. A poor characterisation of the ramp performance is likely to hinder the identification of the processes that cause ramp events To this end, a binary ramp definition is usually adopted by setting threshold values related to the absolute power variation and the time duration of the event observed in the wind power time series. 2. A review on the ramp event binary definition The term ramp event refers to a large variation in wind power output that is observed on a wind farm (or in a portfolio) within a short period of time (up to a few hours). In order to detect ramp events within wind power time series, two thresholds are usually adopted concerning the variation in power output and the time period in which this variation takes places These thresholds are referred to as magnitude, ∆P0, and duration, ∆t of the ramp event. 75%PR 65%PR 15 − 20%PR 10%PR 50%PR 20%PR 50%PR 20%PR 50%PR 10 − 12%PR 15 − 20%PR σg 30%PR
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