• DocumentCode
    111202
  • Title

    Operating Strategies for a GB Integrated Gas and Electricity Network Considering the Uncertainty in Wind Power Forecasts

  • Author

    Qadrdan, Meysam ; Jianzhong Wu ; Jenkins, Nick ; Ekanayake, Janaka

  • Author_Institution
    Electr. & Electron. Eng., Cardiff Univ., Cardiff, UK
  • Volume
    5
  • Issue
    1
  • fYear
    2014
  • fDate
    Jan. 2014
  • Firstpage
    128
  • Lastpage
    138
  • Abstract
    In many power systems, in particular in Great Britain (GB), significant wind generation is anticipated and gas-fired generation will continue to play an important role. Gas-fired generating units act as a link between the gas and electricity networks. The variability of wind power is, therefore, transferred to the gas network by influencing the gas demand for electricity generation. Operation of a GB integrated gas and electricity network considering the uncertainty in wind power forecast was investigated using three operational planning methods: deterministic, two-stage stochastic programming, and multistage stochastic programming. These methods were benchmarked against a perfect foresight model which has no uncertainty associated with the wind power forecast. In all the methods, thermal generators were controlled through an integrated unit commitment and economic dispatch algorithm that used mixed integer programming. The nonlinear characteristics of the gas network, including the gas flow along pipes and the operation of compressors, were taken into account and the resultant nonlinear problem was solved using successive linear programming. The operational strategies determined by the stochastic programming methods showed reductions of the operational costs compared to the solution of the deterministic method by almost 1%. Also, using the stochastic programming methods to schedule the thermal units was shown to make a better use of pumped storage plants to mitigate the variability and uncertainty of the net demand.
  • Keywords
    integer programming; linear programming; pipes; power generation dispatch; power generation planning; pumped-storage power stations; stochastic programming; thermal power stations; transmission networks; wind power; GB integrated gas; Great Britain; economic dispatch algorithm; electricity network; gas flow; gas network; gas-fired generating unit; mixed integer programming; multistage stochastic programming; nonlinear characteristics; pumped storage plant; thermal generator; thermal unit; two-stage stochastic programming; wind power forecasting; Compressors; Electricity; Programming; Stochastic processes; Uncertainty; Wind forecasting; Wind power generation; Integrated gas and electricity network; stochastic programming; wind power forecast uncertainty;
  • fLanguage
    English
  • Journal_Title
    Sustainable Energy, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1949-3029
  • Type

    jour

  • DOI
    10.1109/TSTE.2013.2274818
  • Filename
    6589136