The Reactive Power Reserve (RPR) holds great significance in the planning of stable and secure operations in power systems. Maintaining a high RPR level is crucial for ensuring the stability and security of the power system. Nevertheless, achieving sufficient RPR in power systems is challenging due to the presence of uncertainties present in the system. This study introduces a complex stochastic RPR optimization strategy aimed to enhance the voltage stability in power systems, particularly in presence of practical uncertain load and wind power. The uncertainties associated with load and wind power penetration are handled explicitly through MCS based mean value approach. A load modeling approach considering the realistic variations in real and reactive power demand is introduced. Further, a newly developed optimization algorithm called ‘Coronavirus Herd Immunity Optimizer (CHIO)’ is employed to optimize RPR. MATLAB programs are developed and tested using the IEEE 30 bus system. The performance of proposed strategy is validated through several case studies.


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    Titel :

    Stochastic Maximization of Reactive Power Reserve in Power Systems with Uncertain Practical Demand and Wind Power Using CHIO Approach


    Beteiligte:
    Rani, Nibha (Autor:in) / Malakar, Tanmoy (Autor:in)


    Erscheinungsdatum :

    31.07.2024


    Format / Umfang :

    878180 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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    La disfatta di Chio (parte seconda)

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