Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Snow-induced PV loss modeling using production-data inferred PV system models
RISE Research Institutes of Sweden, Samhällsbyggnad, Energi och resurser.ORCID-id: 0000-0003-0245-7082
SMHI, Sweden.
SMHI, Sweden.
2021 (Engelska)Ingår i: Energies, E-ISSN 1996-1073, Vol. 14, nr 6, artikel-id 1574Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Snow-induced photovoltaic (PV)-energy losses (snow losses) in snowy and cold locations vary up to 100% monthly and 34% annually, according to literature. Levels that illustrate the need for snow loss estimation using validated models. However, to our knowledge, all these models build on limited numbers of sites and winter seasons, and with limited climate diversity. To overcome this limitation in underlying statistics, we investigate the estimation of snow losses using a PV system’s yield data together with freely available gridded weather datasets. To develop and illustrate this approach, 263 sites in northern Sweden are studied over multiple winters. Firstly, snow-free production is approximated by identifying snow-free days and using corresponding data to infer tilt and azimuth angles and a snow-free performance model incorporating shading effects, etc. This performance model approximates snow-free monthly yields with an average hourly standard deviation of 6.9%, indicating decent agreement. Secondly, snow losses are calculated as the difference between measured and modeled yield, showing annual snow losses up to 20% and means of 1.5-6.2% for winters with data for at least 89 sites. Thirdly, two existing snow loss estimation models are compared to our calculated snow losses, with the best match showing a correlation of 0.73 and less than 1% bias for annual snow losses. Based on these results, we argue that our approach enables studying snow losses for high numbers of PV systems and winter seasons using existing datasets. © 2021 by the authors.

Ort, förlag, år, upplaga, sidor
MDPI AG , 2021. Vol. 14, nr 6, artikel-id 1574
Nyckelord [en]
Photovoltaics, PV system modeling, PV system performance, Reanalysis data, Remote sensing, Shading, Snow, Snow losses, Soiling, Climate models, Energy dissipation, Loss estimation, Northern sweden, Performance Model, Photovoltaic energy, Production data, Shading effect, Standard deviation, Winter seasons
Nationell ämneskategori
Energisystem
Identifikatorer
URN: urn:nbn:se:ri:diva-54703DOI: 10.3390/en14061574Scopus ID: 2-s2.0-85107952823OAI: oai:DiVA.org:ri-54703DiVA, id: diva2:1575655
Anmärkning

 Funding details: Energimyndigheten, 38180-2; Funding text 1: Funding: This research was funded by Energimyndigheten (Swedish Energy Agency), project number 38180-2.

Tillgänglig från: 2021-06-30 Skapad: 2021-06-30 Senast uppdaterad: 2023-08-28Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Person

van Noord, Michiel

Sök vidare i DiVA

Av författaren/redaktören
van Noord, Michiel
Av organisationen
Energi och resurser
I samma tidskrift
Energies
Energisystem

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 77 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf