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Data-Assimilation-Assisted Reinforcement Learning for Power Grid Control under Load Uncertainty
Reliable power grid control requires sequential decisions under transmission constraints, time-varying demand, renewable variability, and imperfect load information. We investigate how load information quality affects control in a customized transmission network simulator inspired by Grid2Op and bas
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