• DocumentCode
    3532407
  • Title

    Recurrent neural network-based control strategy for battery energy storage in generation systems with intermittent renewable energy sources

  • Author

    Capizzi, G. ; Bonanno, F. ; Napoli, C.

  • Author_Institution
    Dept. of Electr., Electron. & Inf. Eng., Univ. of Catania, Catania, Italy
  • fYear
    2011
  • fDate
    14-16 June 2011
  • Firstpage
    336
  • Lastpage
    340
  • Abstract
    The intermittent nature of renewable sources as wind and solar puts a challenge for their use in supply energy to small islands, isolated communities or in developing countries. The integration of battery energy storage system (BESS) or diesel groups is then mandatory. The aim of the paper is to propose a complete recurrent neural networks (RNN) based control strategy of the BESS accounting state of charge (SOC) and terminal voltage and that can be used for their size and to test the use of different type of BESS.
  • Keywords
    battery storage plants; energy storage; power generation control; recurrent neural nets; renewable energy sources; BESS accounting state of charge; battery energy storage; diesel groups; generation systems; intermittent renewable energy sources; isolated communities; recurrent neural network-based control; small islands; solar; terminal voltage; wind; Batteries; Load modeling; Mathematical model; Recurrent neural networks; Renewable energy resources; System-on-a-chip; Battery energy storage systems; RNN based control; integrated generation systems; recurrent neural network; renewable energies; state of charge;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Clean Electrical Power (ICCEP), 2011 International Conference on
  • Conference_Location
    Ischia
  • Print_ISBN
    978-1-4244-8929-9
  • Electronic_ISBN
    978-1-4244-8928-2
  • Type

    conf

  • DOI
    10.1109/ICCEP.2011.6036300
  • Filename
    6036300