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Optimal Battery Aging: An Adaptive Weights Dynamic Programming Algorithm

Author

Listed:
  • Benjamin Heymann

    (Université Paris-Saclay
    Universidad de Chile)

  • Pierre Martinon

    (Université Paris-Saclay)

Abstract

We present an algorithm to handle the optimization over a long horizon of an electric microgrid including a battery energy storage system. While the battery is an important and costly component of the microgrid, its aging process is often not taken into account by the energy management system, mostly because of modeling and computing challenges. We address the computing aspect by a new approach combining dynamic programming, decomposition and relaxation techniques. We illustrate this adaptive weight’ method with numerical simulations for a toy microgrid model. Compared to a straightforward resolution by dynamic programming, our algorithm decreases the computing time by more than one order of magnitude, can be parallelized, and allows for online implementations. We believe that this approach can be used for other applications presenting fast and slow variables.

Suggested Citation

  • Benjamin Heymann & Pierre Martinon, 2018. "Optimal Battery Aging: An Adaptive Weights Dynamic Programming Algorithm," Journal of Optimization Theory and Applications, Springer, vol. 179(3), pages 1043-1053, December.
    • Handle: RePEc:spr:joptap:v:179:y:2018:i:3:d:10.1007_s10957-018-1371-9
    DOI: 10.1007/s10957-018-1371-9
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    References listed on IDEAS

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    1. Aurelio Bruzzo, 2018. "Recenti iniziative europee ed italiane per la valorizzazione del patrimonio culturale," Working Papers 2018127, University of Ferrara, Department of Economics.
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    Cited by:

    1. Tristan Rigaut & Pierre Carpentier & Jean-Philippe Chancelier & Michel Lara, 2024. "Decomposition methods for monotone two-time-scale stochastic optimization problems," Computational Management Science, Springer, vol. 21(1), pages 1-37, June.

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