# Mitigation of Coincident Peak Charges via Approximate Dynamic Programming

## TL;DR

This paper formulates the mitigation of coincident peak electricity charges as an optimization problem and develops a neural policy using approximate dynamic programming to reduce peak-related costs.

## Contribution

It introduces a novel approach to optimize peak charge mitigation using approximate dynamic programming and neural networks, addressing a gap in existing predictive strategies.

## Key findings

- Existence of near-optimal policies for peak mitigation.
- Neural policies effectively curtail peak charges under ramping constraints.
- Potential for significant cost savings in small consumers.

## Abstract

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.