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Optimization of Operation and Control Strategies for Battery Energy Storage Systems by Evolutionary Algorithms

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Applications of Evolutionary Computation (EvoApplications 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9597))

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Abstract

To support the utilization of renewable energies, an optimized operation of energy systems is important. Often, the use of battery energy storage systems is stated as one of the most important measures to support the integration of intermittent renewable energy sources into the energy system. Additionally, the complexity of the energy system with its many interdependent entities as well as the economic efficiency call for an elaborate dimensioning and control of these storage systems. In this paper, we present an approach that combines the forward-looking nature of optimization and prediction with the feedback control of closed-loop controllers. An evolutionary algorithm is used to determine the parameters for a closed-loop controller that controls the charging and discharging control strategy of a battery in a smart building. The simulation and evaluation of a smart residential building scenario demonstrates the ability to improve the operation and control of a battery energy storage system. The optimization of the control strategy allows for the optimization with respect to variable tariffs while being conducive for the integration of renewable energy sources into the energy system.

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Notes

  1. 1.

    The overall research environment is more closely described in [4].

  2. 2.

    http://www.organicsmarthome.com.

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Authors and Affiliations

  1. Karlsruhe Institute of Technology – Institute AIFB, 76128, Karlsruhe, Germany

    Jan Müller, Matthias März & Hartmut Schmeck

  2. FZI Research Center for Information Technology, 76131, Karlsruhe, Germany

    Ingo Mauser & Hartmut Schmeck

Authors
  1. Jan Müller
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  2. Matthias März
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  3. Ingo Mauser
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  4. Hartmut Schmeck
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Corresponding author

Correspondence to Jan Müller .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

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Müller, J., März, M., Mauser, I., Schmeck, H. (2016). Optimization of Operation and Control Strategies for Battery Energy Storage Systems by Evolutionary Algorithms. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_33

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  • DOI: https://doi.org/10.1007/978-3-319-31204-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31203-3

  • Online ISBN: 978-3-319-31204-0

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