On the simulation of aggregated solar PV forecast errors

Edgar Nuño Martinez*, Matti Juhani Koivisto, Nicolaos Antonio Cutululis, Poul Sorensen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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The uncertainty arising from high levels of solar photovoltaic (PV) penetration can have a substantial impact on power system operation. Therefore, there is a need to develop models capable of representing PV generation in a rigorous manner. This paper introduces a novel transformation-based methodology to generate stochastic solar area power forecast scenarios; easy to apply to new locations. We present a simulation study comparing day-ahead solar forecast errors covering regions with different geographical sizes, total installed capacities and climatic characteristics. The results show that our model can capture the spatio-temporal properties and match the long-term statistical properties of actual data. Hence, it can be used to characterize the PV input uncertainty in power system studies.
Original languageEnglish
JournalI E E E Transactions on Sustainable Energy
Issue number4
Pages (from-to)1889-1898
Publication statusPublished - 2018


  • Time series analysis
  • Power systems
  • Stochastic processes
  • Production
  • Predictive models
  • Autoregressive processes
  • Uncertainty
  • Solar power generation
  • Power system simulation
  • Forecasting

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