Nonparametric Kernel Regression for Coordinated Energy Storage Peak Shaving with Stacked Services
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| Format: | Preprint |
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2026
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| _version_ | 1866910026078093312 |
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| author | Logan, Emily Qi, Ning Xu, Bolun |
| author_facet | Logan, Emily Qi, Ning Xu, Bolun |
| contents | Developing effective control strategies for behind-the-meter energy storage to coordinate peak shaving and stacked services is essential for reducing electricity costs and extending battery lifetime in commercial buildings. This work proposes an end-to-end, two-stage framework for coordinating peak shaving and energy arbitrage with a theoretical decomposition guarantee. In the first stage, a non-parametric kernel regression model constructs state-of-charge trajectory bounds from historical data that satisfy peak-shaving requirements. The second stage utilizes the remaining capacity for energy arbitrage via a transfer learning method. Case studies using New York City commercial building demand data show that our method achieves a 1.3 times improvement in performance over the state-of-the-art forecast-based method, achieving cost savings and effective peak management without relying on predictions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_16586 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Nonparametric Kernel Regression for Coordinated Energy Storage Peak Shaving with Stacked Services Logan, Emily Qi, Ning Xu, Bolun Optimization and Control Systems and Control Developing effective control strategies for behind-the-meter energy storage to coordinate peak shaving and stacked services is essential for reducing electricity costs and extending battery lifetime in commercial buildings. This work proposes an end-to-end, two-stage framework for coordinating peak shaving and energy arbitrage with a theoretical decomposition guarantee. In the first stage, a non-parametric kernel regression model constructs state-of-charge trajectory bounds from historical data that satisfy peak-shaving requirements. The second stage utilizes the remaining capacity for energy arbitrage via a transfer learning method. Case studies using New York City commercial building demand data show that our method achieves a 1.3 times improvement in performance over the state-of-the-art forecast-based method, achieving cost savings and effective peak management without relying on predictions. |
| title | Nonparametric Kernel Regression for Coordinated Energy Storage Peak Shaving with Stacked Services |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2602.16586 |