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Restoring the missing high-frequency fluctuations in a wind power model based on reanalysis data
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity. (Wind Power)
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
2016 (English)In: Renewable energy, ISSN 0960-1481, E-ISSN 1879-0682, Vol. 96, 784-791 p.Article in journal (Refereed) Published
Abstract [en]

A previously developed model based on MERRA reanalysis data underestimates the high-frequency variability and step changes of hourly, aggregated wind power generation. The goal of this work is to restore these fluctuations. Since the volatility of the high-frequency signal varies in time, machine learning techniques were employed to predict the volatility. As predictors, derivatives of the output from the original “MERRA model” as well as empirical orthogonal functions of several meteorological variables were used. A FFT-IFFT approach, including a search algorithm for finding appropriate phase angles, was taken to generate a signal that was subsequently transformed to simulated high-frequency fluctuations using the predicted volatility. When comparing to the original MERRA model, the improved model output has a power spectral density and step change distribution in much better agreement with measurements. Moreover, the non-stationarity of the high-frequency fluctuations was captured to a large degree. The filtering and noise addition however resulted in a small increase in the RMS error.

Place, publisher, year, edition, pages
2016. Vol. 96, 784-791 p.
Keyword [en]
Wind power variability; Statistical modelling; Machine learning; Power spectral density; MERRA reanalysis dataset
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:uu:diva-294346DOI: 10.1016/j.renene.2016.05.008ISI: 000379271800070OAI: oai:DiVA.org:uu-294346DiVA: diva2:929368
Available from: 2016-05-18 Created: 2016-05-18 Last updated: 2016-09-14Bibliographically approved
In thesis
1. Modelling Wind Power for Grid Integration Studies
Open this publication in new window or tab >>Modelling Wind Power for Grid Integration Studies
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

When wind power and other intermittent renewable energy (IRE) sources begin to supply a significant part of the load, concerns are often raised about the inherent intermittency and unpredictability of these sources. In order to study the impact from higher IRE penetration levels on the power system, integration studies are regularly performed. The model package presented and evaluated in Papers I–IV provides a comprehensive methodology for simulating realistic time series of wind generation and forecasts for such studies. The most important conclusion from these papers is that models based on coarse meteorological datasets give very accurate results, especially in combination with statistical post-processing. Advantages with our approach include a physical coupling to the weather and wind farm characteristics, over 30 year long, 5-minute resolution time series, freely and globally available input data and computational times in the order of minutes. In this thesis, I make the argument that our approach is generally preferable to using purely statistical models or linear scaling of historical measurements.

In the variability studies in Papers V–VII, several IRE sources were considered. An important conclusion is that these sources and the load have very different variability characteristics in different frequency bands. Depending on the magnitudes and correlations of these fluctuation, different time scales will become more or less challenging to balance. With a suitable mix of renewables, there will be little or no increase in the needs for balancing on the seasonal and diurnal timescales, even for a fully renewable Nordic power system. Fluctuations with periods between a few days and a few months are dominant for wind power and net load fluctuations of this type will increase strongly for high penetrations of IRE, no matter how the sources are combined. According to our studies, higher capacity factors, more offshore wind power and overproduction/curtailment would be beneficial for the power system.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 114 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1428
Keyword
Wind power, Wind power modelling, Intermittent renewables, Variability, Integration or renewables, Reanalysis data, Power system studies
National Category
Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-302837 (URN)978-91-554-9690-6 (ISBN)
Public defence
2016-11-04, Polhemsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2016-10-07 Created: 2016-09-11 Last updated: 2016-10-25

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