• DocumentCode
    44655
  • Title

    A Decomposed Solution to Multiple-Energy Carriers Optimal Power Flow

  • Author

    Moeini-Aghtaie, Moein ; Abbaspour, Ali ; Fotuhi-Firuzabad, Mahmud ; Hajipour, Ehsan

  • Author_Institution
    Dept. of Electr. Eng., Sharif Univ. of Technol., Tehran, Iran
  • Volume
    29
  • Issue
    2
  • fYear
    2014
  • fDate
    Mar-14
  • Firstpage
    707
  • Lastpage
    716
  • Abstract
    Presence of energy hubs in the future vision of energy networks creates a great opportunity for system planners and operators to move towards more efficient systems. The role of energy hubs as the intermediate in multi-carrier energy (MCE) systems calls for a generic framework to study the new upcoming technical as well as economical effects on the system performance. In response, this paper attempts to develop a general optimization and modeling framework for coupled power flow studies on different energy infrastructures. This, as a large-scale nonlinear problem, is approached through a robust optimization technique, i.e., multi-agent genetic algorithm (MAGA). The proposed procedure decomposes the multi-carrier optimal power flow (MCOPF) problem into its traditional separate OPF problem in such a way that the major advantages of simultaneous analysis of MCE systems would not be sacrificed. The presented scheme is then applied to an 11-hubs test system and introduces its expected applicability and robustness in the MCE systems analysis.
  • Keywords
    genetic algorithms; load flow; multi-agent systems; power engineering computing; MAGA; MCE systems analysis; coupled power flow studies; energy hubs; energy infrastructures; large-scale nonlinear problem; multiagent genetic algorithm; multicarrier energy systems; multicarrier optimal power flow problem; multiple-energy carriers optimal power flow; robust optimization technique; Electricity; Heating; Mathematical model; Matrix converters; Natural gas; Optimization; Vectors; Energy hubs; multi-agent genetic algorithm (MAGA); multi-carrier optimal power flow (MCOPF);
  • fLanguage
    English
  • Journal_Title
    Power Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8950
  • Type

    jour

  • DOI
    10.1109/TPWRS.2013.2283259
  • Filename
    6626352