This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems


    Beteiligte:
    Canizes, Bruno (Autor:in) / Vale, Zita (Autor:in) / Soares, João (Autor:in) / Khodr, H. M. (Autor:in)

    Erscheinungsdatum :

    12.04.2013


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




    Electric Vehicle Charging Load Forecasting based on Monte Carlo

    Wang, Peiwen / Shen, Jin | VDE-Verlag | 2022


    Monte‐Carlo Spray Cooling Model

    Kreitzer, Paul J. / Kuhlman, John M. | American Institute of Physics | 2010


    No sweeping curtailment of steel for automobiles

    Engineering Index Backfile | 1918


    Load forecasting of electric vehicles based on Monte Carlo method

    Yong, Chen / YingDa, Jiang / Gang, Xu et al. | IEEE | 2020