GA-Aided Power Flow Management in a Multi-Vector Energy System

Xiangping Chen, Wenping Cao, Lei Xing

    Research output: Chapter in Book/Published conference outputConference publication


    Utilization of renewable energy (e.g. wind, solar, bio-energy) is high on the governmental agenda globally. In order to tackle energy poverty and increase energy efficiency in energy systems, a hybrid energy system including wind, hydrogen and fuel cells is proposed to supplement to the main power grid. Wind energy is firstly converted into electrical energy while part of the generated electricity is used for water electrolysis to generate hydrogen for energy storage. Hydrogen is used by fuel cells to convert to electricity when electrical energy demand peaks. An analytical model is developed to coordinate the operation of the system involving energy conversion between hydrogen, electrical and mechanical forms. The proposed system is primarily designed to meet the electrical demand of a rural village while the energy storage system can meet the discrepancy between intermittent renewable energy supplies and fluctuated energy demands so as to improve the system efficiency. Genetic Algorithm (GA) is used as an optimization strategy to determine the operational scheme for the multi-vector energy system. In this work, case studies are carried out based on actual measurement data. The test results have confirmed the effectiveness of the proposed methodology and maximizing the wind energy consumption locally. This is an alternative to battery energy storage and can be widely used in wind-rich rural areas.
    Original languageEnglish
    Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
    Number of pages5
    ISBN (Electronic)978-1-7281-2485-8
    ISBN (Print)978-1-7281-2486-5
    Publication statusPublished - 20 Feb 2020
    Event2019 IEEE Symposium Series on Computational Intelligence (SSCI) - Xiamen, China
    Duration: 6 Dec 20199 Dec 2019


    Conference2019 IEEE Symposium Series on Computational Intelligence (SSCI)


    • fuel cells
    • genetic algorithm
    • hydrogen
    • multi-vector energy system
    • wind energy


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