Joint Price and Power MPC for Peak Power Reduction at Workplace EV Charging Stations

TL;DR

This paper presents a joint price and power control method using model predictive control to reduce peak power demand and costs at workplace EV charging stations.

Contribution

It introduces a novel joint optimization framework combining pricing and power control for peak demand reduction in EV charging stations.

Findings

The proposed algorithm outperforms existing benchmark strategies.

Significant reduction in station operator costs achieved.

Monte Carlo simulations validate the effectiveness of the approach.

Abstract

Demand charge, a utility fee based on an electricity customer's peak power consumption, often constitutes a significant portion of costs for commercial electric vehicle (EV) charging station operators. This paper explores control methods to reduce peak power consumption at workplace EV charging stations in a joint price and power optimization framework. We optimize a menu of price options to incentivize users to select controllable charging service. Using this framework, we propose a model predictive control approach to reduce both demand charge and overall operator costs. Through a Monte Carlo simulation, we find that our algorithm outperforms a state-of-the-art benchmark optimization strategy and can significantly reduce station operator costs.