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  • Modified PSO algorithm for real-time energy management in grid-connected microgrids

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    Hossain484151-Accepted.pdf (617.1Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Hossain, Md Alamgir
    Pota, Hemanshu Roy
    Squartini, Stefano
    Abdou, Ahmed Fathi
    Griffith University Author(s)
    Hossain, Md. Alamgir
    Year published
    2019
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    Abstract
    In real-time energy management of a converter-based microgrid, it is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. The complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function ...
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    In real-time energy management of a converter-based microgrid, it is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. The complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function is proposed in order to obtain the best cost function. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 h. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.
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    Journal Title
    Renewable Energy
    Volume
    136
    DOI
    https://doi.org/10.1016/j.renene.2019.01.005
    Copyright Statement
    © 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Electrical engineering
    Electronics, sensors and digital hardware
    Mechanical engineering
    Other engineering
    Science & Technology
    Green & Sustainable Science & Technology
    Energy & Fuels
    Science & Technology - Other Topics
    Publication URI
    http://hdl.handle.net/10072/410805
    Collection
    • Journal articles

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