Utilities' Managed Home Charging Programs for Electric Vehicles

Abstract

Experts estimate 20 million electric vehicles (EVs) will be on U.S. roads by 2030, and the majority (around 80%) of EV drivers will use home charging. Many utilities are designing managed home charging (MH) programs to centrally manage EV drivers' home charging to reduce cost, avoid new and aggravated peaks and blackouts, and ensure grid stability. An MH program is either an active program (AMH), in which the utility continuously controls the EV charging while the vehicle is plugged in, or a passive program (PMH), in which the participating EV drivers decide when to charge, based on pre-announced low-rate episodes. We present a program-design model (PD) and a load-management model (LM) for jointly designing and executing MH programs. PD designs a menu of MH programs, tailored for each driver type, and LM dynamically manages the load supply to each individual participant. LM consists of a large number of non-homogeneous participants, and it is a large-scale mixed-integer nonlinear stochastic problem. We present an effective approximation method, conduct thorough theoretical and numerical analyses of our approximation, and provide worst-case bounds for its error components. Our methodology provides detailed insights on the amount and timing of the improvements achievable in cost and demand variability by offering AMH, PMH, and both, and by customizing PMH. It also offers detailed insights on the significance of the trade-off between cost and demand variability. We find promoting a culture of charging EVs every night may significantly increase utilities’ total cost if PMH has a high participation level.

Keywords: electric vehicles, managed charging programs, home charging, large-scale optimization, approximation, error analysis