Robust and learning-augmented algorithms for degradation-aware battery optimization
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914277054480384 |
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| author | Umenberger, Jack Rasmussen, Anna Osguthorpe |
| author_facet | Umenberger, Jack Rasmussen, Anna Osguthorpe |
| contents | This paper studies the problem of maximizing revenue from a grid-scale battery energy storage system, accounting for uncertain future electricity prices and the effect of degradation on battery lifetime. We formulate this task as an online resource allocation problem. We propose an algorithm, based on online mirror descent, that is no-regret in the stochastic i.i.d. setting and attains finite asymptotic competitive ratio in the adversarial setting (robustness). When untrusted advice about the opportunity cost of degradation is available, we propose a learning-augmented algorithm that performs well when the advice is accurate (consistency) while still retaining robustness properties when the advice is poor. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17193 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Robust and learning-augmented algorithms for degradation-aware battery optimization Umenberger, Jack Rasmussen, Anna Osguthorpe Systems and Control This paper studies the problem of maximizing revenue from a grid-scale battery energy storage system, accounting for uncertain future electricity prices and the effect of degradation on battery lifetime. We formulate this task as an online resource allocation problem. We propose an algorithm, based on online mirror descent, that is no-regret in the stochastic i.i.d. setting and attains finite asymptotic competitive ratio in the adversarial setting (robustness). When untrusted advice about the opportunity cost of degradation is available, we propose a learning-augmented algorithm that performs well when the advice is accurate (consistency) while still retaining robustness properties when the advice is poor. |
| title | Robust and learning-augmented algorithms for degradation-aware battery optimization |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2601.17193 |