  A significant portion of a consumer's annual electrical costs can be made up
of coincident peak charges: a transmission surcharge for power consumed when
the entire system is at peak demand. This charge occurs only a few times
annually, but with per-MW prices orders of magnitudes higher than non-peak
times. While predicting the moment of peak demand charges over the course of
the entire billing period is possible, optimal cost mitigation strategies based
on these predictions have not been explored. In this paper we cast coincident
peak cost mitigation as an optimization problem and analyze conditions for
optimal and near-optimal policies for mitigation. For small consumers we use
approximate dynamic programming to first show the existence of a near-optimal
policy and second train a neural policy for curtailing coincident peak charges
when subject to ramping constraints.
