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March 14, 2026Energies2 citationsOpen Access

Review of Degradation Models of Battery Energy Storage for Potential Integration into Unit Commitment Problems

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RMRhianna MaakestadRochester Institute of TechnologyFHFarhan HyderRochester Institute of TechnologyGRGharvin RamnaraseRochester Institute of Technology

Key Points

  • This review aims to assess existing degradation models for battery energy storage within unit commitment frameworks.
  • Survey of degradation models for battery energy storage
  • Comparison of integration approaches into unit commitment
  • Analysis of trade-offs between accuracy and computational tractability
  • Identified gaps in current unit commitment formulations
  • Highlighted the need for accurate battery degradation representation
  • Demonstrated potential for improved energy efficiency and reliability through integration

Abstract

As renewable energy penetration accelerates, battery energy storage systems have become essential for enhancing flexibility, reliability, and economic efficiency in power system operations. For the daily operations of grids, the unit commitment (UC) problem plays a central role in determining the optimized scheduling of generation resources, but current formulations rarely incorporate battery degradation dynamics. The accurate representation of battery aging is crucial, as degradation costs may influence dispatch. This review provides a synthesis of existing approaches for integrating battery degradation into UC formulations. We survey and compare major classes of degradation models and then examine how these models have been embedded into UC frameworks, highlighting trade-offs between modeling accuracy and tractability. This paper concludes with identified research gaps and recommendations for future UC formulations that more faithfully capture battery degradation while maintaining computational efficiency. This review aims to serve as a foundation for researchers and system operators seeking to incorporate realistic battery aging mechanisms into operational decision-making for the evolving low-carbon grid.

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Cite This Study

Maakestad et al. (2026) studied this question.

synapsesocial.com/papers/69b4ba0818185d8a398026b7https://doi.org/10.3390/en19061425
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