• DocumentCode
    135260
  • Title

    Database-assisted load flow simulation for low voltage grids using a model reduction approach

  • Author

    Kattmann, Christoph ; Abdel-Majeed, Ahmad ; Tenbohlen, Stefan

  • Author_Institution
    Inst. of Power Transm. & High Voltage Technol., Univ. of Stuttgart, Stuttgart, Germany
  • fYear
    2014
  • fDate
    27-31 July 2014
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Steady-state load flow simulation is an essential tool to evaluate the impact of distributed generation, demand side management or traditional grid expansion. As these developments mostly affect the low voltage grid, it is worth having a look at the distinctive characteristics of load flow simulation in these grids. This paper presents a database-driven approach to significantly speed up big simulation tasks by exploiting the special topology of low-voltage grids. This is achieved by separating and reducing grids into smaller, uniform grids, which share a topology and differ only in impedances and consumed power. These values can be stored in a database together with the calculated solutions, so that similar grids can be looked up in the future. The algorithm is particularly suitable for speeding up extensive Monte Carlo simulations of low voltage grids.
  • Keywords
    Monte Carlo methods; demand side management; distributed power generation; load flow; power consumption; power grids; power system analysis computing; Monte Carlo simulation; database assisted load flow simulation; demand side management; distributed generation; low voltage grid topology; model reduction approach; power consumption; power system analysis computing; steady state load flow simulation; Databases; Load flow; Load modeling; Low voltage; Mathematical model; Runtime; Topology; Load flow; Power system analysis computing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    PES General Meeting | Conference & Exposition, 2014 IEEE
  • Conference_Location
    National Harbor, MD
  • Type

    conf

  • DOI
    10.1109/PESGM.2014.6939202
  • Filename
    6939202