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Modelling the Swedish Wind Power Production Using MERRA Reanalysis Data
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.ORCID iD: 0000-0003-4921-8345
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
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.

Place, publisher, year, edition, pages
2015. Vol. 76, no 4, 717-725 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Engineering Science with specialization in Science of Electricity
Identifiers
URN: urn:nbn:se:uu:diva-225868DOI: 10.1016/j.renene.2014.11.085ISI: 000348955400075OAI: oai:DiVA.org:uu-225868DiVA: diva2:722543
Available from: 2014-06-09 Created: 2014-06-09 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Wind Power and Natural Disasters
Open this publication in new window or tab >>Wind Power and Natural Disasters
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Wind power can be related to natural disasters in several ways. This licentiate thesis gives some background and introduces four papers devoted to two aspects of this relation. The first section looks into how small-scale wind energy converters (WECs) could be used to generate power after a natural disaster. For this application diesel generators are the most common solution today, but there would be several advantages of replacing these systems. A study of off-grid systems with battery storage at 32 sites showed that photovoltaics (PV) were more suitable than WECs. The results were confirmed by a study for the entire globe; PV outperformed WECs at most sites when it comes to small-scale application. This is especially true for areas with a high disaster risk. Hybrid systems comprising both PV and WECs are however interesting at higher latitudes. For the Swedish case, it is shown that gridded data from a freely available meteorological model, combined with a statistical model, give good estimates of the mean wind speed at 10 meters above ground. This methodology of estimating the mean wind speed can be used when there is no time for a proper wind measurement campaign.

The second section is directed towards wind power variability and integration. The results presented in the thesis are intended as a basis for future studies on how a substantially increased wind power capacity affects the electric grid in terms of stability, grid reinforcement requirements, increased balancing needs etc. A review of variability and forecastability for non-dispatchable renewable energy sources was performed together with researchers from the solar, wave and tidal power fields. Although a lot of research is conducted in these areas, it was concluded that more studies on combinations of the sources would be desirable. The disciplines could also learn from each other and benefit from the use of more unified methods and metrics. A model of aggregated hourly wind power production has finally been developed. The model is based on reanalysis data from a meteorological model and detailed information on Swedish WECs. The model proved very successful, both in terms of low prediction errors and in the match of probability density function for power and step changes of power. 

Abstract [sv]

Vindkraft kan relateras till naturkatastrofer på flera olika sätt. Den här licentiat\-avhandlingen ger bakgrund till och introducerar fyra artiklar som beskriver två aspekter av detta samband. I den första avdelningen undersöks hur småskalig vindkraft skulle kunna användas för att generera el efter en naturkatastrof. I dagsläget är det dieselaggregat som används för detta ändamål, men det skulle finnas stora fördelar med att övergå till förnybara system. En studie av 32 platser (myndigheten MSB:s utlandsstationeringar augusti 2012) visade att solceller var mer lämpade än vindkraftverk. Resultaten bekräftades av en studie för hela världen; solceller ger billigare system än småskaliga vindkraftverk för de flesta platser, inte minst om man tittar på områden som är utsatta för naturkatastrofer. Hybridsystem med både solceller och vindkraftverk var dock intressanta på högre breddgrader. För Sverige så visas det att data från en fritt tillgängliga meteorologisk modell tillsammans med en statistisk korrigering beroende på terrängtyp ger bra uppskattningar av medelvinden på 10 meters höjd. Den föreslagna metodiken kan vara användbar som ett komplement till vindmätningar eller om det inte finns tid eller möjlighet till en riktig mätkampanj.

Den andra avdelningen är inriktad mot vindens variabilitet och integrering av vindkraft i kraftsystemet. De resultat som presenteras i denna avhandling är tänkta som en bas för framtida studier av hur en kraftigt ökad andel vindkraft påverkar elsystemet med avseende på stabilitet, nödvändiga nätförstärkningar, ökade krav på balanskraft etc. En översiktsstudie av variabilitet och prognosbarhet för intermittenta förnybara energikällor gjordes tillsammans med forskare inom sol-, våg och tidvattenkraft. Även om mycket forskning pågår inom dessa områden så var en slutsats att mer studier för kombinationer av olika källor skulle vara önskvärt. Forskare inom de olika disciplinerna skulle också kunna lära från varandra och dra fördel av gemensamma metoder och mått. Slutligen har en modell av aggregerad timvis vindkraftproduktion tagits fram. Modellen baseras på data från en meteorologisk modell samt detaljerad information om vindkraftverk i Sverige. Modellen visade sig vara mycket träffsäker, både vad gäller låga prediktionsfel och i överensstämmelse av sannolikhetsfördelning av effekt och stegförändring av timvis effekt.

Place, publisher, year, edition, pages
Uppsala: Uppsala universitet, 2014. 44 p.
Series
UURIE / Uppsala University, Department of Engineering Sciences, ISSN 0349-8352 ; 337-14L
Keyword
Wind power, Natural disasters, Hybrid energy system, Meteorological model, Statistical model, Variability, Wind power integration
National Category
Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-225573 (URN)
Presentation
(English)
Opponent
Supervisors
Available from: 2014-06-09 Created: 2014-06-04 Last updated: 2014-06-09Bibliographically approved
2. 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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Olauson, JonBergkvist, Mikael

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