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This work proposes QAMM, a method that turns the critic-derived adjoint signal into supervision for MeanFlow's average velocity, and derives the adjoint MeanFlow target, specify its gradient boundaries, and train it with an offline actor-critic.
The rapid proliferation of Electric Vehicles (EVs) introduces significant spatio-temporal uncertainties into power grids, while Vehicle-to-Grid (V2G) technology offers critical flexibility through bidirectional power flow. However, integrating large-scale EVs into the Optimal Power Flow framework presents substantial c...
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