Joint Price and Power MPC for Peak Power Reduction at Workplace EV Charging Stations
This work addresses cost reduction for commercial EV charging station operators, representing an incremental improvement in control methods for this specific domain.
The paper tackled the problem of high demand charges for workplace EV charging station operators by developing a joint price and power optimization framework using model predictive control, resulting in significant cost reductions compared to a state-of-the-art benchmark in simulations.
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.