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Battery Storage Systems as Grid-Balancing Measure in Low-Voltage Distribution Grids with Distributed Generation

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  • Bernhard Faessler

    () (Josef Ressel Center for Applied Scientific Computing in Energy, Finance, and Logistics, Vorarlberg University of Applied Sciences, Hochschulstrasse 1, 6850 Dornbirn, Austria
    Illwerke vkw Endowed Professorship for Energy Efficiency, Energy Research Center, Vorarlberg University of Applied Sciences, Hochschulstrasse 1, 6850 Dornbirn, Austria
    Faculty of Engineering and Science, University of Agder, Jon Lilletuns vei 9, 4879 Grimstad, Norway)

  • Michael Schuler

    () (Josef Ressel Center for Applied Scientific Computing in Energy, Finance, and Logistics, Vorarlberg University of Applied Sciences, Hochschulstrasse 1, 6850 Dornbirn, Austria)

  • Markus Preißinger

    () (Illwerke vkw Endowed Professorship for Energy Efficiency, Energy Research Center, Vorarlberg University of Applied Sciences, Hochschulstrasse 1, 6850 Dornbirn, Austria)

  • Peter Kepplinger

    () (Josef Ressel Center for Applied Scientific Computing in Energy, Finance, and Logistics, Vorarlberg University of Applied Sciences, Hochschulstrasse 1, 6850 Dornbirn, Austria
    Illwerke vkw Endowed Professorship for Energy Efficiency, Energy Research Center, Vorarlberg University of Applied Sciences, Hochschulstrasse 1, 6850 Dornbirn, Austria)

Abstract

Due to the promoted integration of renewable sources, a further growth of strongly transient, distributed generation is expected. Thus, the existing electrical grid may reach its physical limits. To counteract this, and to fully exploit the viable potential of renewables, grid-balancing measures are crucial. In this work, battery storage systems are embedded in a grid simulation to evaluate their potential for grid balancing. The overall setup is based on a real, low-voltage distribution grid topology, real smart meter household load profiles, and real photovoltaics load data. An autonomous optimization routine, driven by a one-way communicated incentive, determines the prospective battery operation mode. Different battery positions and incentives are compared to evaluate their impact. The configurations incorporate a baseline simulation without storage, a single, central battery storage or multiple, distributed battery storages which together have the same power and capacity. The incentives address either market conditions, grid balancing, optimal photovoltaic utilization, load shifting, or self-consumption. Simulations show that grid-balancing incentives result in lowest peak-to-average power ratios, while maintaining negligible voltage changes in comparison to a reference case. Incentives reflecting market conditions for electricity generation, such as real-time pricing, negatively influence the power quality, especially with respect to the peak-to-average power ratio. A central, feed-in-tied storage performs better in terms of minimizing the voltage drop/rise and shows lower distribution losses, while distributed storages attached at nodes with electricity generation by photovoltaics achieve lower peak-to-average power ratios.

Suggested Citation

  • Bernhard Faessler & Michael Schuler & Markus Preißinger & Peter Kepplinger, 2017. "Battery Storage Systems as Grid-Balancing Measure in Low-Voltage Distribution Grids with Distributed Generation," Energies, MDPI, Open Access Journal, vol. 10(12), pages 1-14, December.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2161-:d:123413
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Stefan Arens & Sunke Schlüters & Benedikt Hanke & Karsten von Maydell & Carsten Agert, 2020. "Sustainable Residential Energy Supply: A Literature Review-Based Morphological Analysis," Energies, MDPI, Open Access Journal, vol. 13(2), pages 1-28, January.
    2. 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, Open Access Journal, vol. 12(8), pages 1-26, April.
    3. Evgeny Lisin & Galina Kurdiukova & Pavel Okley & Veronika Chernova, 2019. "Efficient Methods of Market Pricing in Power Industry within the Context of System Integration of Renewable Energy Sources," Energies, MDPI, Open Access Journal, vol. 12(17), pages 1-16, August.
    4. Reimuth, Andrea & Prasch, Monika & Locherer, Veronika & Danner, Martin & Mauser, Wolfram, 2019. "Influence of different battery charging strategies on residual grid power flows and self-consumption rates at regional scale," Applied Energy, Elsevier, vol. 238(C), pages 572-581.
    5. Ahmed Alzahrani & Hussain Alharthi & Muhammad Khalid, 2019. "Minimization of Power Losses through Optimal Battery Placement in a Distributed Network with High Penetration of Photovoltaics," Energies, MDPI, Open Access Journal, vol. 13(1), pages 1-16, December.
    6. Salvatore Favuzza & Mariano Giuseppe Ippolito & Fabio Massaro & Rossano Musca & Eleonora Riva Sanseverino & Giuseppe Schillaci & Gaetano Zizzo, 2018. "Building Automation and Control Systems and Electrical Distribution Grids: A Study on the Effects of Loads Control Logics on Power Losses and Peaks," Energies, MDPI, Open Access Journal, vol. 11(3), pages 1-15, March.
    7. João Martins & Sergiu Spataru & Dezso Sera & Daniel-Ioan Stroe & Abderezak Lashab, 2019. "Comparative Study of Ramp-Rate Control Algorithms for PV with Energy Storage Systems," Energies, MDPI, Open Access Journal, vol. 12(7), pages 1-15, April.

    More about this item

    Keywords

    grid balancing; grid simulation; autonomously optimized battery storage; distributed generation; central and distributed energy storage;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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