• DocumentCode
    1397392
  • Title

    Distribution System Planning With Incorporating DG Reactive Capability and System Uncertainties

  • Author

    Zou, Kai ; Agalgaonkar, Ashish Prakash ; Muttaqi, Kashem M. ; Perera, Sarath

  • Author_Institution
    Endeavour Energy Power Quality & Reliability Centre, Univ. of Wollongong, Wollongong, NSW, Australia
  • Volume
    3
  • Issue
    1
  • fYear
    2012
  • Firstpage
    112
  • Lastpage
    123
  • Abstract
    Distributed generation (DG) systems are considered an integral part in future distribution system planning. The active and reactive power injections from DG units, typically installed close to the load centers, are seen as a cost-effective solution for distribution system voltage support, energy saving, and reliability improvement. This paper proposes a novel distribution system expansion planning strategy encompassing renewable DG systems with schedulable and intermittent power generation patterns. The reactive capability limits of different renewable DG systems covering wind, solar photovoltaic, and biomass-based generation units are included in the planning model and the system uncertainties such as load demand, wind speed, and solar radiation are also accounted using probabilistic models. The problem of distribution system planning with renewable DG is formulated as constrained mixed integer nonlinear programming, wherein the total cost will be minimized with optimal allocation of various renewable DG systems. A solution algorithm integrating TRIBE particle swarm optimization (TRIBE PSO) and ordinal optimization (OO) is developed to effectively obtain optimal and near-optimal solutions for system planners. TRIBE PSO, OO, and the proposed algorithm are applied to a practical test system and results are compared and presented.
  • Keywords
    distributed power generation; energy conservation; integer programming; nonlinear programming; particle swarm optimisation; power distribution planning; power distribution reliability; power generation economics; power generation reliability; power generation scheduling; probability; reactive power; OO; PSO; TRIBE; active power; biomass based generation units; cost minimization; distributed power generation; energy saving; mixed integer nonlinear programming; optimal allocation; ordinal optimization; particle swarm optimization; power distribution planning; power distribution reliability; power generation scheduling; power system uncertainty; probabilistic models; reactive power; renewable DG systems; solar power generation; wind power generation; Biomass; Load modeling; Planning; Reactive power; Solar radiation; Uncertainty; Wind speed; Optimization; power distribution planning; reactive power; solar power generation; wind power generation;
  • fLanguage
    English
  • Journal_Title
    Sustainable Energy, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1949-3029
  • Type

    jour

  • DOI
    10.1109/TSTE.2011.2166281
  • Filename
    6102294