EV Charging Scheduling Under Demand Charge: A Block Model Predictive Control Approach

Abstract

This paper studies the online scheduling of electric vehicle charging by a service provider subject to a demand charge in a distribution system. Demand charge imposes a penalty on the peak power consumption over each billing period, representing a substantial cost for the service provider with a large number of clients. Because the demand charge is calculated at the end of the billing period, it poses challenges in real-time scheduling when energy demand forecasts are inaccurate, resulting in either overly conservative power consumption or substantial demand charge. We propose a block model predictive control approach that decomposes the demand charge into a sequence of stage costs. Optimality conditions on demand patterns are also presented and analyzed. Numerical simulations demonstrate the efficacy of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper addresses a significant practical problem of minimizing the demand charge on the real-time scheduling of deferrable demands. In particular, we consider a setting where a commercial electric vehicle (EV) charging service provider has to manage the online scheduling of a large number of arriving EVs at a charging facility subject to a maximum charging power constraint and a tariff with the demand charge. A major practical challenge is to balance the tradeoff between maximizing profit in scheduling as much EV charging as possible and the need to minimize penalty on the peak charging power. We propose a model predictive control strategy that decomposes the overall demand charge into a sequence of terminal costs. Also addressed is the practical constraint arising from the mismatched EV charging decision period and the power measurement period used to compute the demand charge. Using real data collected at the Adaptive Charging Network (ACN) testbed in simulations, the proposed approach yields 8-12% improvement in operational profit over existing benchmarks, while it has yet been tested in actual charging systems. In the future research, we will address the charging scheduling under demand charge over multiple charging stations.