A New Approach to Obtain Synthetic Wind Power Forecasts for Integration Studies

Abstract

When performing wind integration studies, synthetic wind power forecasts are key elements. Historically, data from operational forecasting systems have been used sparsely, likely due to the high costs involved. Purely statistical methods for simulating wind power forecasts are more common, but have problems mimicking all relevant aspects of actual forecasts. Consequently, a new approach to obtain wind power forecasts for integration studies is proposed, relying on long time series of freely and globally available reforecasts. In order to produce synthetic forecasts with similar properties as operational ditto, some processing (noise addition and error reduction) is necessary. Validations with measurements from Belgium and Sweden show that the method is adequate; and distributions, correlations, autocorrelations and power spectral densities of forecast errors correspond well. Furthermore, abrupt changes when forecasts are updated and the existence of level and phase errors are reproduced. The influence from terrain complexity on error magnitude is promising, but more data is necessary for a proper validation.

Design and Operation of Energy Systems with Large Amounts of Variable Generation: IEA Wind TCP Task 25 (Final Summary Report)

“…As real measured data for power system-wide wind power started to emerge, it was shown in 2016 that estimated data had higher variability than real data: Using wind speed data from reanalysis (Germany) or measurements (Netherlands) resulted in higher hourly variability than actual, measured, large-scale wind power production data, even if using well-dispersed data to simulate large-scale wind power production (Kiviluoma et al, 2016). However, the new European reanalysis ERA5 from the European Centre for Medium-Range Weather Forecasts (ECMWF) performs considerably better than the often-used Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), both for countrywide generation and for individual wind turbines (Olauson et al, 2016). On average, the errors are approximately 20% lower for ERA5, but the reduction varies between countries.…”

“…The equivalent system radius (REQ) is proposed in Olauson et al (2016) for a metric for system size and WPP dispersion. The idea is that a wind power system can be represented by a uniform wind power disk with the same variance as the actual system, assuming an exponential decline of correlation of output with separation distance (Figure 11).…”

“…Note that BPA (Bonneville Power Administration in the US) power plants are concentrated in a relatively small area, which gives a small REQ compared to the total system dimensions. In particular, the small power plant at (-300,100) does not impact REQ much (Source: Olauson et al, 2016).…”

Correlation Characteristic Analysis for Wind Speed in Different Geographical Hierarchies

“…In addition, the correlation characteristics among the wind farm and its wind turbines is also needed to study to explore the internal wind power variation of wind turbines contributing to their wind farm. Without considering the internal relationship introduced above in the wind speed or power forecast model [12,13], it may lead to the bias that causes power imbalances and increases the risk during operation.…”

Recent Advances in Energy Time Series Forecasting

“…On the one hand, an ensemble system with weather-adapted correction [9] and a method combining metaheuristics, spectrum analysis, and neural networks [10] have been proposed for wind speed forecasting. On the other hand, wind power generation forecasting has been analyzed by applying hybrid approaches [11,12], and with a method exhibiting physical coupling to the weather [13].…”