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Optimal price-threshold control for battery operation with aging phenomenon: a quasiconvex optimization approach

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  • Yuhai Hu

    (Lehigh University)

  • Boris Defourny

    (Lehigh University)

Abstract

This paper is concerned with grid-level battery storage operations, taking battery aging into consideration. Battery operations under price uncertainty are modeled as a Markov Decision Process with expected cumulated discounted rewards. The structure of the optimal policy is studied. An algorithm that takes advantage of the problem structure and works directly on the continuous state space is developed to maximize the objective over the life of the battery. The algorithm determines an optimal policy by solving a sequence of quasiconvex problems indexed by a battery-life state. Computational results are presented to compare the proposed approach to a standard dynamic programming method, and to evaluate the impact of refinements in the battery model. Error bounds for the proposed algorithm are established to demonstrate its accuracy.

Suggested Citation

  • Yuhai Hu & Boris Defourny, 2022. "Optimal price-threshold control for battery operation with aging phenomenon: a quasiconvex optimization approach," Annals of Operations Research, Springer, vol. 317(2), pages 623-650, October.
  • Handle: RePEc:spr:annopr:v:317:y:2022:i:2:d:10.1007_s10479-017-2505-4
    DOI: 10.1007/s10479-017-2505-4
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    References listed on IDEAS

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    1. Mount, Timothy D. & Ning, Yumei & Cai, Xiaobin, 2006. "Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters," Energy Economics, Elsevier, vol. 28(1), pages 62-80, January.
    2. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    3. Martin L. Puterman & Shelby L. Brumelle, 1979. "On the Convergence of Policy Iteration in Stationary Dynamic Programming," Mathematics of Operations Research, INFORMS, vol. 4(1), pages 60-69, February.
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