IEEE Transactions on Smart Grid · 2021
Learning-Based Predictive Control via Real-Time Aggregate Flexibility
IEEE Transactions on Smart Grid
How can an aggregator communicate flexibility in real time?
Maximum entropy feedback summarizes the feasible actions of controllable loads. Reinforcement learning approximates this feedback for Penalized Predictive Control, reducing information and computation requirements. Charging data demonstrates the coordination approach.
Research themes
Connections
Guarantees meet physical infrastructure
Aggregate flexibility communicates feasible actions for coordinated charging. Robust dispatch and safe water control connect performance guarantees to operational costs and physical constraints.
Open questions
Coordinated low carbon infrastructure
Can distributed energy and water resources adapt together under changing conditions?
Explore open directionsCite this paper
Tongxin Li, Bo Sun, Yue Chen, Zixin Ye, Steven H. Low, Adam Wierman. Learning-Based Predictive Control via Real-Time Aggregate Flexibility. IEEE Transactions on Smart Grid, 2021. https://doi.org/10.1109/TSG.2021.3094719
BibTeX
@article{tongxin-aggregate-flexibility,
title = {{Learning-Based Predictive Control via Real-Time Aggregate Flexibility}},
author = {Tongxin Li and Bo Sun and Yue Chen and Zixin Ye and Steven H. Low and Adam Wierman},
year = {2021},
journal = {IEEE Transactions on Smart Grid},
url = {https://doi.org/10.1109/TSG.2021.3094719},
doi = {10.1109/TSG.2021.3094719}
}
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