Spatio‐temporal analysis and modeling of short‐term wind power forecast errors

Abstract

Forecasts of wind power production are increasingly being used in various management tasks. So far, such forecasts and related uncertainty information have usually been generated individually for a given site of interest (either a wind farm or a group of wind farms), without properly accounting for the spatio‐temporal dependencies observed in the wind generation field. However, it is intuitively expected that, owing to the inertia of meteorological forecasting systems, a forecast error made at a given point in space and time will be related to forecast errors at other points in space in the following period. The existence of such underlying correlation patterns is demonstrated and analyzed in this paper, considering the case‐study of western Denmark. The effects of prevailing wind speed and direction on autocorrelation and cross‐correlation patterns are thoroughly described. For a flat terrain region of small size like western Denmark, significant correlation between the various zones is observed for time delays up to 5 h. Wind direction is shown to play a crucial role, while the effect of wind speed is more complex. Nonlinear models permitting capture of the interdependence structure of wind power forecast errors are proposed, and their ability to mimic this structure is discussed. The best performing model is shown to explain 54% of the variations of the forecast errors observed for the individual forecasts used today. Even though focus is on 1‐h‐ahead forecast errors and on western Denmark only, the methodology proposed may be similarly tested on the cases of further look‐ahead times, larger areas, or more complex topographies. Such generalization may not be straightforward. While the results presented here comprise a first step only, the revealed error propagation principles may be seen as a basis for future related work. Copyright © 2010 John Wiley & Sons, Ltd.

Voltage Control in the Future Power Transmission Systems

“…In fact, there is spatio-temporal correlation in the wind power productions and the wind power forecasting errors [73]- [77]. The wind power forecasting systems considering the spatio-temporal correlations can greatly reduce the forecasting errors, especially for the short-term forecasts that have great influence on the system operations [72]- [74].…”

“…The WPFE are used to correct the future forecasting errors based on the auto regression technique and regime-switching models [74]. For example, the WPFE in Zone 12 in Figure 42, ( ), can be estimated from (4.57)-(4.58), where the regime switches depending on the wind direction.…”

Modelling of wind power forecasting errors based on kernel recursive least-squares method

“…Due to the inertia of meteorological phenomena, the current forecasting error of an upwind wind farm may have a strong correlation with that of a downwind wind farm [25]. For meteorological studies, teleconnection means the stable correlation between synoptic processes that are away from each other spatially and temporally [26,27].…”

Modelling and Simulating the Spatio-Temporal Correlations of Clustered Wind Power Using Copula

“…The smoothing effect brings down the requirement of flexibility, i.e., spinning reserve requirement, ramping rate demand and transmission capacity margin and therefore reduces the system operation cost [7], [8], and the demand for energy storage [9]. Such effect is also found in the forecast errors of wind power [10].…”