Impact of probabilistic small-scale photovoltaic generation forecast on energy management systems

Wessam El-Baz, Michael Seufzger, Sandra Lutzenberger, Peter Tzscheutschler, Ulrich Wagner

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Demand-side Management (DSM) algorithms are exposed to several uncertainties due to their dependency on renewable energy generation forecasts. On the large scale, generation and load forecasts can be relatively accurate, yet on the residential scale, forecasting errors increase due to higher uncertainties. One potential solution is to incorporate a probabilistic PV forecast into an optimal DSM algorithm instead of the existing deterministic PV forecasting algorithms. Hence, in this contribution, a numerical analysis that compares the potential of using a probabilistic PV forecast instead of the conventional deterministic algorithms in a DSM algorithm, is presented. Results show that under different household energy system configurations, the DSM algorithm with the probabilistic PV generation forecast leads to an increase in self-sufficiency and self-consumption by 24.2% and 17.7%, respectively, compared to the conventional deterministic algorithms. These results indicate that probabilistic PV forecasting algorithms may indeed have a higher potential compared to the conventional deterministic ones.

Original languageEnglish
Pages (from-to)136-146
Number of pages11
JournalSolar Energy
Volume165
DOIs
StatePublished - 1 May 2018
Externally publishedYes

Keywords

  • Demand side management
  • Energy management system
  • Forecast error
  • Load planning
  • PV forecast
  • Smart home

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