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 perform 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 AMH and PMH programs over a "season." Using PD, we design a menu of MH programs, tailored for each driver type. The menu is presented to the EV drivers before the season starts. LM is used within the season to dynamically manage 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. We use our models and solutions to present managerial insights on jointly designing AMH and PMH programs to achieve an optimal cost, effects of high participation in NMH and/or PMH, trade-off between cost and demand variability, importance of customizing PMH, and effects of charging frequency.

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