This paper focuses on the efficient sizing of tramway electrical infrastructures, involving the determination of optimal specifications for electrical substations, overhead transmission lines, feeders, and equipotential bondings. Sizing aims to establish the number, positions, and technical characteristics for each component, balancing considerations of investment costs, energy efficiency, and power supply quality. Departing from conventional trial-and-error methods, our approach formulates a bi-objective optimization problem and employs the NSGA-II algorithm, incorporating expert knowledge strategically within crossover and mutation operators. Applying this new sizing method to a real case shows significant efficiency improvements, offering a systematic solution to the technical and financial challenges tied to creating new tramway lines or extending existing lines. The results highlight the potential for substantial improvements in efficiency and economic outcomes. This innovative approach positions itself as a valuable tool in the changing landscape of urban public transport electrification projects, providing an optimized solution to the challenges of designing and implementing tramway electrical infrastructures.


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    Titel :

    Genetic Algorithm-Based Optimization for Sizing Tramway Electrical Infrastructures. Application to a Real Study Case


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Belenguer, Enrique (Herausgeber:in) / Beltran, Hector (Herausgeber:in) / Boukir, Anass (Autor:in) / Reinbold, Vincent (Autor:in) / Ossart, Florence (Autor:in) / Bigeon, Jean (Autor:in) / Levy, Paul-Louis (Autor:in)

    Kongress:

    International Conference of the TC Electrimacs Committee ; 2024 ; Castellon, Spain May 27, 2024 - May 31, 2024


    Erschienen in:

    ELECTRIMACS 2024 ; Kapitel : 31 ; 405-416


    Erscheinungsdatum :

    31.01.2025


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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