• DocumentCode
    5660
  • Title

    Zonal Reduction of Large Power Systems: Assessment of an Optimal Grid Model Accounting for Loop Flows

  • Author

    Doquet, M.

  • Author_Institution
    R&D Div., RTE, Versailles, France
  • Volume
    30
  • Issue
    1
  • fYear
    2015
  • fDate
    Jan. 2015
  • Firstpage
    503
  • Lastpage
    512
  • Abstract
    In development studies carried out on large power systems, a need may arise for a reduction in which whole regions are seen as the nodes of a simplified graph whose edges replace the inter-regional links. The property required from this kind of backbone representation may be that reduced load flows stay close to their physical counterpart on the full grid. In the dc approximation, such a model can consist of a set of impedances attached to each edge, along with a flow characterizing a particular state of the system. The identification of the model accommodating best a large sample of flows, representative of all of their possible diversity, is a nonconvex problem for which two partially convex reformulations are proposed. Simple ad hoc algorithms are introduced as a way to find approximate solutions satisfying first-order optimality conditions.
  • Keywords
    concave programming; convex programming; load flow; power grids; power system planning; DC approximation; ad hoc algorithm; back-bone representation; convex reformulations; first-order optimality; large power system zonal reduction; load flow reduction; loop flow; nonconvex problem; optimal grid model accounting assessment; power grid; power system planning; Approximation algorithms; Linear programming; Load modeling; Nash equilibrium; Optimization; Power systems; Vectors; Grid equivalents; Nash equilibrium; PTDF; Ward equivalents; grid reduction; nonconvex optimization; power system adequacy; power system planning;
  • fLanguage
    English
  • Journal_Title
    Power Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8950
  • Type

    jour

  • DOI
    10.1109/TPWRS.2014.2320414
  • Filename
    6815723