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2013-05-03 - Article/Dans un journal avec peer-review - Anglais - 10 page(s)

Vallée François , Klonari Vasiliki , Lisiecki Thomas, Durieux Olgan, Moiny Francis , Lobry Jacques , "Development of a Probabilistic Tool Using Monte Carlo Simulation and Smart Meters Measurements for the Long Term Analysis of Low Voltage Distribution Grids with Photovoltaic Generation" in The International Journal of Electrical Power & Energy Systems, Elsevier, 53, December 2013, 468-477

  • Codes CREF : Sciences de l'ingénieur (DI2000), Electricité courants forts (DI2400)
  • Unités de recherche UMONS : Génie électrique (F101), Physique générale (F901)
  • Instituts UMONS : Institut de Recherche en Energétique (Energie)
Texte intégral :

Abstract(s) :

(Anglais) Connections of distributed generation (DG) units based on the use of photovoltaic cells are highly increasing in low voltage distribution grids. In that way, one of the major problems met by the Distribution System Operators (DSO) comes from overvoltage in the neighbourhood of dispersed units. Consequently, it is important for them to have an analysis tool that computes statistical voltage profiles and allows to assess maximal penetration rates of photovoltaic generation (PV) on low voltage (LV) distribution feeders. In previous studies, it has been shown that such a tool could be obtained by using a Probabilistic Load Flow based on analytical techniques or Monte Carlo methods. In this paper, given its simplicity of implementation, a pseudo-chronological Monte Carlo simulation is used and the statistical behaviour of prosumers (consumers with PV units) is directly based on smart meters measurements. Thanks to this tool, and using collected measurements from smart meters that are expected to be massively deployed in the future, it will be possible for the DSO to directly assess voltage profiles at all the nodes of the LV grid. Moreover, in the context of alleviating the impact of photovoltaic generation on the recorded voltage profiles, smart meters data will also be used in order to not only quantify the influence of reactive power flows on the collected results but also to estimate the auto-consumption potential over some critical nodes of the grid.