Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Using long term synthetic time series to assess the impact of meteorological extreme events on renewable energy systems: a case study of wind and hydro power in Sweden
Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, Vienna, Austria.
Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, Vienna, Austria.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0002-4597-4082
2017 (English)Conference paper, Poster (Other academic)
Abstract [en]

Synthetic time series of renewable energy generation provide important inputs for energy system models that study the transition to low carbon energy systems. The coverage of national energy statistics is usually too short or temporal resolution too low – in particular if meteorological extreme events should be assessed. These extreme events may put high stress on power systems with very high shares of renewables and therefore have to be studied in detail. We use simulated time series of Swedish wind energy generation for a 35 year period based on MERRA reanalysis datasets. The simulation of hydropower generation is more complex and requires hydrological models that combine precipitation data with spatially explicit information on soil type and land cover to simulate river discharge. For this purpose, we use time series of daily river discharge that have been simulated using the open source model HYPE (HYdrological Predictions for the Environment).

We compared the derived time series for wind and hydropower generation in the four Swedish bidding areas with respect to their long-term correlation, patterns of seasonality, and length and duration of extreme events. Preliminary results show that expanding wind power capacities could significantly reduce the overall variability of renewable energy generation. Furthermore, the frequency and duration of extreme production events in a combined wind-hydropower system is lower than in a hydropower system only. Further work will study the need for backup capacities in a future Swedish power system with very high shares of hydro, wind and solar power (>90%).

Place, publisher, year, edition, pages
2017.
Series
Geophysical Research Abstracts
National Category
Energy Systems Energy Engineering
Research subject
Energy Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-63412OAI: oai:DiVA.org:ltu-63412DiVA: diva2:1096412
Conference
EGU General Assembly 2017, Vienna, Austria, 23-28 April 2017
Funder
Swedish Research Council Formas, 942-2016-118
Available from: 2017-05-17 Created: 2017-05-17 Last updated: 2017-05-23Bibliographically approved

Open Access in DiVA

Poster(899 kB)45 downloads
File information
File name FULLTEXT01.pdfFile size 899 kBChecksum SHA-512
b4429d62b09ede9fd3312d1908b8c20640981283ac5eb641e97402a3380a6e4c0d53efa2e79463516eaa59bc639f759653bf9ddf47346d8a84b2124e03e0273a
Type fulltextMimetype application/pdf

Other links

http://meetingorganizer.copernicus.org/EGU2017/EGU2017-14131.pdf

Search in DiVA

By author/editor
Wetterlund, Elisabeth
By organisation
Energy Science
Energy SystemsEnergy Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 45 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 79 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf