Optimal Battery Aging : an Adaptive Weights Dynamic Programming Algorithm

Benjamin Heymann 1 Pierre Martinon 1
1 Commands - Control, Optimization, Models, Methods and Applications for Nonlinear Dynamical Systems
CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique, Inria Saclay - Ile de France, UMA - Unité de Mathématiques Appliquées
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.
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Submitted on : Monday, June 11, 2018 - 11:52:11 AM
Last modification on : Wednesday, March 27, 2019 - 4:08:29 PM
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Benjamin Heymann, Pierre Martinon. Optimal Battery Aging : an Adaptive Weights Dynamic Programming Algorithm. Journal of Optimization Theory and Applications, Springer Verlag, 2018, ⟨10.1007/s10957-018-1371-9⟩. ⟨hal-01349932v3⟩

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