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Optimal Component Sizing for Peak Shaving in Battery Energy Storage System for Industrial Applications

Author

Listed:
  • Rodrigo Martins

    (Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Holger C. Hesse

    (Department of Electrical and Computer Engineering, Technical University of Munich (TUM), 80333 Munich, Germany)

  • Johanna Jungbauer

    (Smart Power GmbH & Co KG, 80333 Munich, Germany)

  • Thomas Vorbuchner

    (Department of Electrical and Computer Engineering, Technical University of Munich (TUM), 80333 Munich, Germany)

  • Petr Musilek

    (Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
    Electrical Engineering and Computer Science, VSB-Technical University Ostrava, 70800 Ostrava, Czech Republic)

Abstract

Recent attention to industrial peak shaving applications sparked an increased interest in battery energy storage. Batteries provide a fast and high power capability, making them an ideal solution for this task. This work proposes a general framework for sizing of battery energy storage system (BESS) in peak shaving applications. A cost-optimal sizing of the battery and power electronics is derived using linear programming based on local demand and billing scheme. A case study conducted with real-world industrial profiles shows the applicability of the approach as well as the return on investment dependence on the load profile. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, peak-power tariff, and battery aging. This underlines the need for a general mathematical optimization approach to efficiently tackle the challenge of peak shaving using an energy storage system. The case study also compares the applicability of yearly and monthly billing schemes, where the highest load of the year/month is the base for the price per kW. The results demonstrate that batteries in peak shaving applications can shorten the payback period when used for large industrial loads. They also show the impacts of peak shaving variation on the return of investment and battery aging of the system.

Suggested Citation

  • Rodrigo Martins & Holger C. Hesse & Johanna Jungbauer & Thomas Vorbuchner & Petr Musilek, 2018. "Optimal Component Sizing for Peak Shaving in Battery Energy Storage System for Industrial Applications," Energies, MDPI, vol. 11(8), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2048-:d:162382
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    References listed on IDEAS

    as
    1. Holger C. Hesse & Rodrigo Martins & Petr Musilek & Maik Naumann & Cong Nam Truong & Andreas Jossen, 2017. "Economic Optimization of Component Sizing for Residential Battery Storage Systems," Energies, MDPI, vol. 10(7), pages 1-19, June.
    2. Merei, Ghada & Moshövel, Janina & Magnor, Dirk & Sauer, Dirk Uwe, 2016. "Optimization of self-consumption and techno-economic analysis of PV-battery systems in commercial applications," Applied Energy, Elsevier, vol. 168(C), pages 171-178.
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    5. Holger C. Hesse & Michael Schimpe & Daniel Kucevic & Andreas Jossen, 2017. "Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids," Energies, MDPI, vol. 10(12), pages 1-42, December.
    6. Claudia Rahmann & Benjamin Mac-Clure & Vijay Vittal & Felipe Valencia, 2017. "Break-Even Points of Battery Energy Storage Systems for Peak Shaving Applications," Energies, MDPI, vol. 10(7), pages 1-13, June.
    7. Beck, T. & Kondziella, H. & Huard, G. & Bruckner, T., 2016. "Assessing the influence of the temporal resolution of electrical load and PV generation profiles on self-consumption and sizing of PV-battery systems," Applied Energy, Elsevier, vol. 173(C), pages 331-342.
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