Optimal Battery Aging: An Adaptive Weights Dynamic Programming Algorithm

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, mostl...

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Bibliographic Details
Published in:Journal of optimization theory and applications Vol. 179; no. 3; pp. 1043 - 1053
Main Authors: Heymann, Benjamin, Martinon, Pierre
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2018
Springer Nature B.V
Springer Verlag
Subjects:
Adaptive algorithms
Algorithms
Applications of Mathematics
Batteries
Calculus of Variations and Optimal Control; Optimization
Computer simulation
Computing time
Distributed generation
Dynamic programming
Energy management
Energy storage
Engineering
Mathematical models
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Optimization and Control
Parallel processing
Technical Note
Theory of Computation
Weight
Control
93A13
49L20
Energy management system
93C15
Aging
90C39
49M29
49M27
Dynamic programming
Dynamic Programing
Energy Management System
ISSN:0022-3239, 1573-2878
Online Access:Get full text
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Summary: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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ISSN:0022-3239
1573-2878
DOI:10.1007/s10957-018-1371-9