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.
Electrical reliability council of texas, four coincident peak calculations,
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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.