HPC-RL is a two-level reinforcement learning scheduler that solves grid power-flow constraints at the top and per-car charging deadlines at the bottom, yielding fast near-optimal V2G schedules.
A non-iterative de- coupled solution of the coordinated robust opf in transmission and distribution networks with variable generating units,
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Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling
HPC-RL is a two-level reinforcement learning scheduler that solves grid power-flow constraints at the top and per-car charging deadlines at the bottom, yielding fast near-optimal V2G schedules.