• DocumentCode
    3683297
  • Title

    Modeling renewable energy sources to promote proactivity in the distribution grid

  • Author

    Evangelia Xypolytou;Thomas Leber;Thomas Aichholzer

  • Author_Institution
    Institute of Computer Technology, Vienna University of Technology, Austria
  • fYear
    2015
  • Firstpage
    145
  • Lastpage
    150
  • Abstract
    Due to growing share of renewable energies in the last decades, the power grid has to deal with a great amount of unpredictable and volatile energy. In order to protect it from overloads and hence resulting risks, models to predict a-priori the energy produced from renewable energy sources are of great necessity. Such forecast models serve as important tool for the Distribution System Operators (DSOs). In this work, forecast models for photovoltaic (PV) and hydroelectric power generation are presented, aiming to their utilization from the grid operator as tool for scheduling switching activities in the distribution grid. The models are successfully evaluated through recorded power output data, provided by the DSO of Carinthia, Austria.
  • Keywords
    "Mathematical model","Predictive models","Data models","Solar radiation","Snow","Power generation","Artificial neural networks"
  • Publisher
    ieee
  • Conference_Titel
    Smart Electric Distribution Systems and Technologies (EDST), 2015 International Symposium on
  • Type

    conf

  • DOI
    10.1109/SEDST.2015.7315198
  • Filename
    7315198