Improving EV Aggregate Flexibility with End-to-End Learning

Apoorva Thanvantri, Christopher Yeh, Nicolas Christianson, Adam Wierman
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:624-639, 2026.

Abstract

As the adoption of electric vehicles (EVs) rises, meeting their charging demand efficiently while continuing to ensure reliable power grid operation has become increasingly challenging. One promising avenue for more efficient integration of EV charging demands is leveraging their flexibility. To facilitate this, aggregators—entities that pool energy resources into a single market participant—must combine the constraints encoding each EV’s charging flexibility into an aggregate flexibility set. Computing this set exactly is computationally intractable, motivating the development of methods to approximate this set. However, current methods for approximating this aggregate flexibility set are either unreliable—in that they may contain infeasible power schedules which could lead to grid instability—or they are overly conservative, and may neglect regions of the true aggregate set which are important for optimizing grid-relevant costs. Motivated by these limitations, we develop a novel approach for learning inner approximations of aggregate flexibility sets using Input-Convex Neural Networks (ICNNs). In particular, we propose to train approximate flexibility sets parametrized by ICNNs to minimize a decision cost, while incorporating a feasibility projection at each step of training to ensure the reliability of the learned set. We experimentally validate our methodology on the problem of learning aggregate flexibility sets for a peak power minimization task with real-world load data, showing that our approach enables better performance than decision-agnostic methods while guaranteeing reliability.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-thanvantri26a, title = {Improving EV Aggregate Flexibility with End-to-End Learning}, author = {Thanvantri, Apoorva and Yeh, Christopher and Christianson, Nicolas and Wierman, Adam}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {624--639}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/thanvantri26a/thanvantri26a.pdf}, url = {https://proceedings.mlr.press/v331/thanvantri26a.html}, abstract = {As the adoption of electric vehicles (EVs) rises, meeting their charging demand efficiently while continuing to ensure reliable power grid operation has become increasingly challenging. One promising avenue for more efficient integration of EV charging demands is leveraging their flexibility. To facilitate this, aggregators—entities that pool energy resources into a single market participant—must combine the constraints encoding each EV’s charging flexibility into an aggregate flexibility set. Computing this set exactly is computationally intractable, motivating the development of methods to approximate this set. However, current methods for approximating this aggregate flexibility set are either unreliable—in that they may contain infeasible power schedules which could lead to grid instability—or they are overly conservative, and may neglect regions of the true aggregate set which are important for optimizing grid-relevant costs. Motivated by these limitations, we develop a novel approach for learning inner approximations of aggregate flexibility sets using Input-Convex Neural Networks (ICNNs). In particular, we propose to train approximate flexibility sets parametrized by ICNNs to minimize a decision cost, while incorporating a feasibility projection at each step of training to ensure the reliability of the learned set. We experimentally validate our methodology on the problem of learning aggregate flexibility sets for a peak power minimization task with real-world load data, showing that our approach enables better performance than decision-agnostic methods while guaranteeing reliability.} }
Endnote
%0 Conference Paper %T Improving EV Aggregate Flexibility with End-to-End Learning %A Apoorva Thanvantri %A Christopher Yeh %A Nicolas Christianson %A Adam Wierman %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-thanvantri26a %I PMLR %P 624--639 %U https://proceedings.mlr.press/v331/thanvantri26a.html %V 331 %X As the adoption of electric vehicles (EVs) rises, meeting their charging demand efficiently while continuing to ensure reliable power grid operation has become increasingly challenging. One promising avenue for more efficient integration of EV charging demands is leveraging their flexibility. To facilitate this, aggregators—entities that pool energy resources into a single market participant—must combine the constraints encoding each EV’s charging flexibility into an aggregate flexibility set. Computing this set exactly is computationally intractable, motivating the development of methods to approximate this set. However, current methods for approximating this aggregate flexibility set are either unreliable—in that they may contain infeasible power schedules which could lead to grid instability—or they are overly conservative, and may neglect regions of the true aggregate set which are important for optimizing grid-relevant costs. Motivated by these limitations, we develop a novel approach for learning inner approximations of aggregate flexibility sets using Input-Convex Neural Networks (ICNNs). In particular, we propose to train approximate flexibility sets parametrized by ICNNs to minimize a decision cost, while incorporating a feasibility projection at each step of training to ensure the reliability of the learned set. We experimentally validate our methodology on the problem of learning aggregate flexibility sets for a peak power minimization task with real-world load data, showing that our approach enables better performance than decision-agnostic methods while guaranteeing reliability.
APA
Thanvantri, A., Yeh, C., Christianson, N. & Wierman, A.. (2026). Improving EV Aggregate Flexibility with End-to-End Learning. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:624-639 Available from https://proceedings.mlr.press/v331/thanvantri26a.html.

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