Optimal utilization strategy of the LiFePO4 battery storage

Timur Sayfutdinov and Petr Vorobev

Applied Energy, 2022, vol. 316, issue C, No S0306261922004688

Abstract: The paper provides a comprehensive battery storage modeling approach, which accounts for operation- and degradation-aware characteristics and can be used in optimization problem formulations. Particularly, Mixed-Integer Linear Programming (MILP) compatible models have been developed for the lithium iron phosphate (LiFePO4) battery storage using the Special Order Sets 2 to represent the nonlinear characteristics, including efficiency, internal resistance growth, and capacity fade. Such formulation can be used in problems related to various applications, i.e., power systems, smart grid, and vehicular applications, and it allows finding the globally optimal solution using off-the-shelf academic and commercial solvers. In the numerical study, the proposed modeling approach has been applied to realistic scenarios of peak-shaving, where the importance of considering the developed models is explicitly demonstrated. Operation- and degradation-aware techno-economic analysis showed that the optimal battery capacity is driven by operating rather than service requirements. Particularly, a considerable battery over-sizing becomes economically feasible when the battery storage is used more extensively. Another finding suggests that to achieve the maximum value from battery storage, its operation strategy needs to be significantly modified during the course of its lifetime. In the scenarios considered, the charging time gradually increased from four to seven hours, while the average SoC decreased by 20%. Such an adaptable scheduling results in reduced battery degradation and a longer lifetime, which may provide as much as 12.1% of savings in the battery storage system project.

Keywords: Battery degradation; Lithium-ion battery; Mixed-integer linear programming; Optimal scheduling; Optimal sizing; Peak-shaving; Techno-economic analysis (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2022.119080

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