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Dimensioning battery energy storage systems for peak shaving based on a real-time control algorithm

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  • Lange, Christopher
  • Rueß, Alexandra
  • Nuß, Andreas
  • Öchsner, Richard
  • März, Martin

Abstract

In order to reduce power peaks in the electrical grid, battery systems are used for peak shaving applications. Under economical constraints, appropriate dimensioning of the batteries is essential. A dimensioning process is introduced consisting of a simulation environment to determine the behavior of the energy system, a real-time peak shaving control algorithm and an optimization process for detection of battery and algorithm parameters. The dimensioning process is investigated on the basis of four exemplary load profiles and in comparison to a conventional approach. Deviations between -7% and 75% for capacity and up to 43% for discharging power indicate undersized batteries using the conventional approach. The proposed approach relies on 1-min measurement data and does not require prediction data, leading to accurate dimensioning results for a given load profile, as verified in simulation. The practical use and effectiveness of the control algorithm is proven in a real-world laboratory. A battery system of 60 kWh capacity and 65 kW maximum power achieved successful peak load reduction by 50 kW (8%) for an a priori defined limit of 570 kW. The comparison with simulation shows only small deviations below 17 kW (4.1%) for the resulting load profile proving the realistic representation of an energy system in simulation.

Suggested Citation

  • Lange, Christopher & Rueß, Alexandra & Nuß, Andreas & Öchsner, Richard & März, Martin, 2020. "Dimensioning battery energy storage systems for peak shaving based on a real-time control algorithm," Applied Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:appene:v:280:y:2020:i:c:s0306261920314379
    DOI: 10.1016/j.apenergy.2020.115993
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    5. Henni, Sarah & Becker, Jonas & Staudt, Philipp & vom Scheidt, Frederik & Weinhardt, Christof, 2022. "Industrial peak shaving with battery storage using a probabilistic forecasting approach: Economic evaluation of risk attitude," Applied Energy, Elsevier, vol. 327(C).
    6. Ng, Rong Wang & Begam, K.M. & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2022. "A novel dynamic two-stage controller of battery energy storage system for maximum demand reductions," Energy, Elsevier, vol. 248(C).
    7. Alexander Micallef & Cyril Spiteri Staines & Alan Cassar, 2022. "Utility-Scale Storage Integration in the Maltese Medium-Voltage Distribution Network," Energies, MDPI, vol. 15(8), pages 1-20, April.
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    9. Smolenski, Robert & Szczesniak, Pawel & Drozdz, Wojciech & Kasperski, Lukasz, 2022. "Advanced metering infrastructure and energy storage for location and mitigation of power quality disturbances in the utility grid with high penetration of renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).

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