Restoring the missing high-frequency fluctuations in a wind power model based on reanalysis data

Olauson, Jon; Bergström, Hans; Bergkvist, Magnus

doi:10.1016/j.renene.2016.05.008

Cited by 16 publications

(13 citation statements)

References 21 publications

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“…Although not one of the most common techniques for statistical post-processing of wind power forecasts, it has been previously used with some success [51]. See also [45,52] for examples of the use of GB and EOFs in the wind power field, including more detailed descriptions and references for further reading. By using the advanced post-processing, the SDs of FEs were reduced by 12%-14% and 10%-11% for D + 1 and D + 7, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…In earlier work [35,45], methodologies were developed for modelling hourly, aggregated wind power production based on the MERRA reanalysis dataset. The method proved to be successful; when validating with data from the Swedish TSO, the root mean square error was 3.8% and the correlation coefficient was 0.98.…”

Section: Modelled Generation and "Raw" Forecastsmentioning

confidence: 99%

“…The method proved to be successful; when validating with data from the Swedish TSO, the root mean square error was 3.8% and the correlation coefficient was 0.98. The four basic steps of the method described in [35,45] are:…”

Section: Modelled Generation and "Raw" Forecastsmentioning

confidence: 99%

“…Aggregate for the studied area; 4. Use bias corrections to improve the results and subsequently filter and add high-frequency noise [45] in order to get a spectrum and step change distribution in better agreement with measurements.…”

Section: Modelled Generation and "Raw" Forecastsmentioning

confidence: 99%

“…In [45], the Inverse Fast Fourier Transform (IFFT) was utilized to generate noise with a desired spectrum. Since we wanted to be able to control the correlation of the noise time series for neighbouring farms, a different approach was taken: first, a long noise time series was generated with the IFFT method and subsequently an ARMA model was fitted to this time series.…”

Section: Processing Of Raw Forecastsmentioning

confidence: 99%

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

et al. 2016

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Modelled Generation and "Raw" Forecastsmentioning

confidence: 99%

Section: Modelled Generation and "Raw" Forecastsmentioning

confidence: 99%

Section: Modelled Generation and "Raw" Forecastsmentioning

confidence: 99%

Section: Processing Of Raw Forecastsmentioning

confidence: 99%

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

et al. 2016

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Reviews on characteristic of renewables: Evaluating the variability and complementarity

Yan

Han

et al. 2020

Int Trans Electr Energ Syst

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Simulating intra‐hourly wind power fluctuations on a power system level

2016

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In wind integration studies, sub-hourly, load synchronous wind data are often preferable. These datasets can be generated by a hybrid approach, combining hourly measurements or output from meteorological models with a stochastic simulation of the high-frequency fluctuations. This paper presents a method for simulating aggregated intra-hourly wind power fluctuations for a power system, taking into account the time-varying volatility seen in measurements. Some key elements in the modelling were transformations to stationarity, the use of frequency domain techniques including a search for appropriate phase angles and an adjustment of the resulting time series in order to get correct hourly means. Generation data from Denmark and Germany with 5 and 15 min temporal resolution were used for training models. It is shown that the distribution and non-stationarity of simulated deviations from hourly means closely follow those of measurements. Power spectral densities and step change distributions agree well. Of particular importance is that the results are good also when the training and objective power systems are not the same. The computational cost is low in comparison with other approaches for generating high-frequency data. NOMENCLATUREVariables in continuous time are denoted with the time argument in parentheses, while discrete time is indicated with subscripts. Additional subscripts indicate whether the time series are simulations or measurements used for validation of the model performance, e.g., P t,sim and x t,val . Subscript obj is used for variables that are common for the objective power system (i.e. both simulation and validation). No additional subscript indicates training data or when the variable is discussed in more general terms. The first-order and second-order derivatives are denoted with prim and bis, mean values with a vertical bar and absolute values with horizontal bars.

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Restoring the missing high-frequency fluctuations in a wind power model based on reanalysis data

Cited by 16 publications

References 21 publications

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

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

Reviews on characteristic of renewables: Evaluating the variability and complementarity

Simulating intra‐hourly wind power fluctuations on a power system level

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