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Distributed Energy Storage Control for Dynamic Load Impact Mitigation

Author

Listed:
  • Maximilian J. Zangs

    (School of Systems Engineering, University of Reading, Whiteknights Campus, Reading RG6 6AY, UK
    These authors contributed equally to this work.)

  • Peter B. E. Adams

    (School of Systems Engineering, University of Reading, Whiteknights Campus, Reading RG6 6AY, UK
    These authors contributed equally to this work.)

  • Timur Yunusov

    (School of Systems Engineering, University of Reading, Whiteknights Campus, Reading RG6 6AY, UK)

  • William Holderbaum

    (School of Systems Engineering, University of Reading, Whiteknights Campus, Reading RG6 6AY, UK)

  • Ben A. Potter

    (School of Systems Engineering, University of Reading, Whiteknights Campus, Reading RG6 6AY, UK)

Abstract

The future uptake of electric vehicles (EV) in low-voltage distribution networks can cause increased voltage violations and thermal overloading of network assets, especially in networks with limited headroom at times of high or peak demand. To address this problem, this paper proposes a distributed battery energy storage solution, controlled using an additive increase multiplicative decrease (AIMD) algorithm. The improved algorithm (AIMD+) uses local bus voltage measurements and a reference voltage threshold to determine the additive increase parameter and to control the charging, as well as discharging rate of the battery. The used voltage threshold is dependent on the network topology and is calculated using power flow analysis tools, with peak demand equally allocated amongst all loads. Simulations were performed on the IEEE LV European Test feeder and a number of real U.K. suburban power distribution network models, together with European demand data and a realistic electric vehicle charging model. The performance of the standard AIMD algorithm with a fixed voltage threshold and the proposed AIMD+ algorithm with the reference voltage profile are compared. Results show that, compared to the standard AIMD case, the proposed AIMD+ algorithm further improves the network’s voltage profiles, reduces thermal overload occurrences and ensures a more equal battery utilisation.

Suggested Citation

  • Maximilian J. Zangs & Peter B. E. Adams & Timur Yunusov & William Holderbaum & Ben A. Potter, 2016. "Distributed Energy Storage Control for Dynamic Load Impact Mitigation," Energies, MDPI, vol. 9(8), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:647-:d:76132
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    References listed on IDEAS

    as
    1. Hadley, Stanton W. & Tsvetkova, Alexandra A., 2009. "Potential Impacts of Plug-in Hybrid Electric Vehicles on Regional Power Generation," The Electricity Journal, Elsevier, vol. 22(10), pages 56-68, December.
    2. Matthew Rowe & Timur Yunusov & Stephen Haben & William Holderbaum & Ben Potter, 2014. "The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction," Energies, MDPI, vol. 7(6), pages 1-24, May.
    3. Dallinger, David & Wietschel, Martin, 2012. "Grid integration of intermittent renewable energy sources using price-responsive plug-in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3370-3382.
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    Cited by:

    1. Dimitar Bozalakov & Mohannad J. Mnati & Joannes Laveyne & Jan Desmet & Lieven Vandevelde, 2019. "Battery Storage Integration in Voltage Unbalance and Overvoltage Mitigation Control Strategies and Its Impact on the Power Quality," Energies, MDPI, vol. 12(8), pages 1-26, April.
    2. Timur Yunusov & Maximilian J. Zangs & William Holderbaum, 2017. "Control of Energy Storage," Energies, MDPI, vol. 10(7), pages 1-5, July.
    3. Andrés Henao-Muñoz & Andrés Saavedra-Montes & Carlos Ramos-Paja, 2018. "Optimal Power Dispatch of Small-Scale Standalone Microgrid Located in Colombian Territory," Energies, MDPI, vol. 11(7), pages 1-20, July.
    4. Bartłomiej Mroczek & Paweł Pijarski, 2021. "DSO Strategies Proposal for the LV Grid of the Future," Energies, MDPI, vol. 14(19), pages 1-19, October.

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