A neural policy trained on Monte Carlo rollouts can reduce coincident peak electricity charges for a small, ramp-constrained consumer better than an equal-amortization baseline, though without a formal near-optimality proof.
Data center demand response: Avoiding the coinci- dent peak via workload shifting and local generation,
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Mitigation of Coincident Peak Charges via Approximate Dynamic Programming
A neural policy trained on Monte Carlo rollouts can reduce coincident peak electricity charges for a small, ramp-constrained consumer better than an equal-amortization baseline, though without a formal near-optimality proof.