• DocumentCode
    1988534
  • Title

    Operating power reserve quantification through PV generation uncertainty analysis of a microgrid

  • Author

    Xingyu Yan ; Francois, Bruno ; Abbes, Dhaker

  • Author_Institution
    L2EP, EC de Lille, Villeneuve-d´Ascq, France
  • fYear
    2015
  • fDate
    June 29 2015-July 2 2015
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Due to renewable energy sources (RES) variable nature and their wide integration into power systems, setting an adequate operating power reserve is important to compensate unpredictable imbalance between generation and consumption. However, this power reserve should be ideally minimized to reduce system cost with a satisfying security level. Although many forecasting methodologies have been developed for forecasting energy generation and load demand, management tools for decision making of operating reserve are still needed. This paper deals with power reserve quantification through uncertainty analysis with a photovoltaic (PV) generator. Indeed, using an artificial neural network based predictor (ANNs), PV power and load have been forecasted 24 hours ahead, and also forecasting errors have been predicted. Through forecasting uncertainty analysis, the power reserve quantification is calculated according to various risk indexes.
  • Keywords
    distributed power generation; neural nets; photovoltaic power systems; power engineering computing; ANN; PV generation uncertainty analysis; PV generator; RES; artificial neural network based predictor; microgrid; operating power reserve quantification; photovoltaic generator; power reserve quantification; renewable energy sources; uncertainty analysis; Artificial neural networks; Forecasting; Indexes; Load modeling; Probabilistic logic; Reliability; Uncertainty; Artificial Neural Networks; microgrid uncertainty; power reserve quantification; variability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    PowerTech, 2015 IEEE Eindhoven
  • Conference_Location
    Eindhoven
  • Type

    conf

  • DOI
    10.1109/PTC.2015.7232577
  • Filename
    7232577