The importance of forecasting regional wind power ramping: A case study for the UK

Abstract

In recent years there has been a significant change in the distribution of wind farms in Great Britain, with a trend towards very large offshore farms clustered together in zones. However, there are concerns these clusters could produce large ramping events on time scales of less than 6 hours as local meteorological phenomena simultaneously impact the production of several farms. This paper presents generation data from the wind farms in the Thames Estuary (the largest cluster in the world) for 2014 and quantifies the high frequency power ramps. Based on a case study of a ramping event which occurred on 3rd November 2014, we show that due to the large capacity of the cluster, a localised ramp can have a significant impact on the cost of balancing the power system on a national level if it is not captured by the forecast of the system operator. The planned construction of larger offshore wind zones will exacerbate this problem. Consequently, there is a need for accurate regional wind power forecasts to minimise the costs of managing the system. This study shows that state-ofthe-art high resolution forecast models have capacity to provide valuable information to mitigate this impact.

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

“…Another significant issue is that, as a consequence of its chaotic nature, the future state of the atmosphere is very sensitive to small errors at the start of the prediction [35]. This leads to an uncertainty in NWP model forecasts, which increases with the prediction horizon.…”

“…To overcome this problem, a given NWP model is usually run a number of times, with slightly different starting conditions, leading to a set of predictions. The complete set of predictions is called the ensemble [35]. In this methodology, the individual members of the ensemble can be analyzed to get a better idea of which possible weather events may occur.…”

“…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 other hand, ref. [35] is intriguing because it explores the impact of high frequency ramps in a cluster of large offshore wind farms in the UK, and by using a variety of state-of-the-art high resolution NWP models, the authors are able to predict those WPREs that are caused by the wind farm clusters themselves.…”

“…Focused exclusively on the WPRE prediction, at relatively short lead times (minutes to hours), forecasts can be made using simple statistical methods such as ARMA [35,64]. A more elaborate approach, a hybrid ARMA-hidden Markov model approach has been proposed in [65] for the forecast of short-term wind speed, including wind ramp events.…”