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Modelling Wind Power for Grid Integration Studies
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity. (Wind Power)
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 [en]
Wind power, Wind power modelling, Intermittent renewables, Variability, Integration or renewables, Reanalysis data, Power system studies
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-302837ISBN: 978-91-554-9690-6 (print)OAI: oai:DiVA.org:uu-302837DiVA: diva2:970637
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
List of papers
1. Modelling the Swedish Wind Power Production Using MERRA Reanalysis Data
Open this publication in new window or tab >>Modelling the Swedish Wind Power Production Using MERRA Reanalysis Data
2015 (English)In: Renewable energy, ISSN 0960-1481, E-ISSN 1879-0682, Vol. 76, no 4, 717-725 p.Article in journal (Refereed) Published
Abstract [en]

The variability of wind power will be an increasing challenge for the power system as wind penetration grows and thus needs to be studied. In this paper a model for generation of hourly aggregated wind power time series is described and evaluated. The model is based on MERRA reanalysis data and information on wind energy converters in Sweden. Installed capacity during the studied period (2007–2012) increased from around 600 to over 3500 MW. When comparing with data from the Swedish TSO, the mean absolute error in hourly energy was 2.9% and RMS error was 3.8%. The model was able to adequately capture step changes and also yielded a nicely corresponding distribution of hourly energy. Two key factors explaining the good results were the use of a globally optimised power curve smoothing parameter and the correction of seasonal and diurnal bias.

Because of bottlenecks in the Swedish transmission system it is relevant to model certain areas separately. For the two southern areas the MAE were 3.7 and 4.2%. The northern area was harder to model and had a MAE of 6.5%. This might be explained by a low installed capacity, more complex terrain and icing losses not captured in the model.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Engineering Science with specialization in Science of Electricity
Identifiers
urn:nbn:se:uu:diva-225868 (URN)10.1016/j.renene.2014.11.085 (DOI)000348955400075 ()
Available from: 2014-06-09 Created: 2014-06-09 Last updated: 2017-12-05Bibliographically approved
2. Restoring the missing high-frequency fluctuations in a wind power model based on reanalysis data
Open this publication in new window or tab >>Restoring the missing high-frequency fluctuations in a wind power model based on reanalysis data
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.

Keyword
Wind power variability; Statistical modelling; Machine learning; Power spectral density; MERRA reanalysis dataset
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-294346 (URN)10.1016/j.renene.2016.05.008 (DOI)000379271800070 ()
Available from: 2016-05-18 Created: 2016-05-18 Last updated: 2017-11-30Bibliographically approved
3. Simulating intra-hourly wind power fluctuations on a power system level
Open this publication in new window or tab >>Simulating intra-hourly wind power fluctuations on a power system level
2017 (English)In: Wind Energy, ISSN 1095-4244, E-ISSN 1099-1824, Vol. 20, no 6, 973-985 p.Article in journal (Refereed) Published
Abstract [en]

In wind integration studies, sub-hourly, load synchronous wind data are often preferable. These datasets can be generatedby a hybrid approach, combining hourly measurements or output from meteorological models with a stochastic simulationof the high-frequency fluctuations. This paper presents a method for simulating aggregated intra-hourly wind power fluc-tuations for a power system, taking into account the time-varying volatility seen in measurements. Some key elements inthe modelling were transformations to stationarity, the use of frequency domain techniques including a search for appropri-ate phase angles and an adjustment of the resulting time series in order to get correct hourly means. Generation data fromDenmark and Germany with 5 and 15 min temporal resolution were used for training models. It is shown that the distribu-tion and non-stationarity of simulated deviations from hourly means closely follow those of measurements. Power spectraldensities and step change distributions agree well. Of particular importance is that the results are good also when the train-ing and objective power systems are not the same. The computational cost is low in comparison with other approaches forgenerating high-frequency data.

Keyword
Wind power, Sub-hourly fluctuations, Simulation, FFT, Power system studies
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-302826 (URN)10.1002/we.2074 (DOI)000400860700003 ()
Funder
Swedish Civil Contingencies Agency, 2010-2787
Available from: 2016-09-10 Created: 2016-09-10 Last updated: 2017-07-07Bibliographically approved
4. A New Approach to Obtain Synthetic Wind Power Forecasts for Integration Studies
Open this publication in new window or tab >>A New Approach to Obtain Synthetic Wind Power Forecasts for Integration Studies
2016 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 9, no 10, 800Article in journal (Refereed) Published
Abstract [en]

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.

Keyword
wind power forecasting; synthetic forecasts; GEFS reforecast; power system studies; wind power integration; production planning; dispatch
National Category
Environmental Engineering
Identifiers
urn:nbn:se:uu:diva-302835 (URN)10.3390/en9100800 (DOI)000388578800041 ()
Funder
Swedish Civil Contingencies Agency, 2010-2787
Available from: 2016-09-11 Created: 2016-09-11 Last updated: 2017-11-21Bibliographically approved
5. Variability Assessment and Forecasting of Renewables: A Review for Solar, Wind, Wave and Tidal Resources
Open this publication in new window or tab >>Variability Assessment and Forecasting of Renewables: A Review for Solar, Wind, Wave and Tidal Resources
Show others...
2015 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 44, 356-375 p.Article in journal (Refereed) Published
National Category
Energy Engineering Engineering and Technology
Research subject
Engineering Science with specialization in Science of Electricity; Engineering Science with specialization in Solid State Physics
Identifiers
urn:nbn:se:uu:diva-225870 (URN)10.1016/j.rser.2014.12.019 (DOI)000351324300025 ()
Available from: 2014-06-09 Created: 2014-06-09 Last updated: 2017-12-05
6. Correlation between wind power generation in the European countries
Open this publication in new window or tab >>Correlation between wind power generation in the European countries
2016 (English)In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 114, 663-670 p.Article in journal (Refereed) Published
Abstract [en]

The correlations between wind power generation in different countries are important for quantifying the reductions in variability when electrically interconnecting the countries. Hourly, country-wise time series of wind power output were generated for all European countries using MERRA reanalysis data. By comparing the model output with actual measurements, it is shown that this approach is appropriate for studying correlations. In order to deepen the analysis, correlation coefficients were not only computed for these time series, but also for the one hour step changes and for band-pass filtered data. The general pattern is that correlations reduce with separation distance in an exponential fashion and are highest for the long-term components (T > 4 months) and lowest for step changes and short-term components (T < 2 days). Interesting deviations from this pattern however exist. When comparing to earlier results for individual farms, the exponential decay is slower, in particular for step changes.

Keyword
Wind power, Correlation, MERRA reanalysis dataset, Filters
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
urn:nbn:se:uu:diva-301383 (URN)10.1016/j.energy.2016.08.036 (DOI)000387194800052 ()
Available from: 2016-08-22 Created: 2016-08-22 Last updated: 2017-11-28Bibliographically approved
7. Net load variability in Nordic countries with a highly or fully renewable power system
Open this publication in new window or tab >>Net load variability in Nordic countries with a highly or fully renewable power system
Show others...
2016 (English)In: Nature Energy, ISSN 2058-7546, Vol. 1, 1-8 p., 16175Article in journal (Refereed) Published
Abstract [en]

Increasing the share of intermittent renewable energy (IRE) resources such as solar, wind, wave and tidal energy in a power system poses a challenge in terms of increased net load variability. Fully renewable power systems have previously been analysed, but more systematic analyses are needed that explore the effect of different IRE mixes on system-wide variability across different timescales and the optimal combinations of IRE for reducing variability on a given timescale. Here we investigate these questions for the Nordic power system. We show that the optimal mix of IRE is dependent on the frequency band considered. Long-term (>4 months) and short-term (<2 days) fluctuations can be similar to today’s, even for a fully renewable system. However, fluctuations with periods in between will inevitably increase significantly. This study indicates that, from a variability point of view, a fossil- and nuclear-free Nordic power system is feasible if properly balanced by hydropower.

National Category
Materials Engineering
Identifiers
urn:nbn:se:uu:diva-302836 (URN)10.1038/NENERGY.2016.175 (DOI)000394793000001 ()
Funder
StandUpStandUp for Wind
Available from: 2016-09-11 Created: 2016-09-11 Last updated: 2017-11-28

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