Optimal Threshold Control for Energy Arbitrage with Degradable Battery Storage

Marek Petrik, Xiaojian Wu UMASS
Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:279-288, 2015.

Abstract

Energy arbitrage has the potential to make electric grids more efficient and reliable. Batteries hold great promise for energy storage in arbitrage but can degrade rapidly with use. In this paper, we analyze the impact of storage degradation on the structure of optimal policies in energy arbitrage. We derive properties of the battery degradation response that are sufficient for the existence of optimal threshold policies, which are interpretable and relatively easy to compute. Our experimental results indicate that explicitly considering battery degradation in optimizing energy arbitrage significantly improves solution quality.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR13-petrik15a, title = {Optimal Threshold Control for Energy Arbitrage with Degradable Battery Storage}, author = {Petrik, Marek and UMASS, Xiaojian Wu}, booktitle = {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence}, pages = {279--288}, year = {2015}, editor = {Meila, Marina and Heskes, Tom}, volume = {R13}, series = {Proceedings of Machine Learning Research}, month = {12--16 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r13/main/assets/petrik15a/petrik15a.pdf}, url = {https://proceedings.mlr.press/r13/petrik15a.html}, abstract = {Energy arbitrage has the potential to make electric grids more efficient and reliable. Batteries hold great promise for energy storage in arbitrage but can degrade rapidly with use. In this paper, we analyze the impact of storage degradation on the structure of optimal policies in energy arbitrage. We derive properties of the battery degradation response that are sufficient for the existence of optimal threshold policies, which are interpretable and relatively easy to compute. Our experimental results indicate that explicitly considering battery degradation in optimizing energy arbitrage significantly improves solution quality.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Optimal Threshold Control for Energy Arbitrage with Degradable Battery Storage %A Marek Petrik %A Xiaojian Wu UMASS %B Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2015 %E Marina Meila %E Tom Heskes %F pmlr-vR13-petrik15a %I PMLR %P 279--288 %U https://proceedings.mlr.press/r13/petrik15a.html %V R13 %X Energy arbitrage has the potential to make electric grids more efficient and reliable. Batteries hold great promise for energy storage in arbitrage but can degrade rapidly with use. In this paper, we analyze the impact of storage degradation on the structure of optimal policies in energy arbitrage. We derive properties of the battery degradation response that are sufficient for the existence of optimal threshold policies, which are interpretable and relatively easy to compute. Our experimental results indicate that explicitly considering battery degradation in optimizing energy arbitrage significantly improves solution quality. %Z Reissued by PMLR on 04 October 2026.
APA
Petrik, M. & UMASS, X.W.. (2015). Optimal Threshold Control for Energy Arbitrage with Degradable Battery Storage. Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R13:279-288 Available from https://proceedings.mlr.press/r13/petrik15a.html. Reissued by PMLR on 04 October 2026.

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