Model Predictive Control for Electric Vehicle Charging to Manage Excess Renewable Energy Supply in Texas

Modern power grids are evolving to become more interconnected, include more electric vehicles (EV s), and utilize more renewable energy sources (RES). Increased interconnectiv-ity provides an opportunity to manage EVs and RES by using price signaling to shift EV loads towards periods of high RES output. This work uses ERCOT's 2035 RES installation plans and projections for Texas's EV fleet to examine how model predictive control (MPC) can leverage time varying rates for EV charging to utilize excess RES supply that may otherwise be underutilized in a highly weather-dependent grid. The results show that when benchmarked against RES usage with no control and RES usage with open-loop control, MPC increases RES usage by responding to RES outputs that differ from forecasts due to the inherent uncertainty of weather predictions. If MPC is used with time steps that are too frequent, however, difficulties arise; EV owners may find it too onerous to keep up with changing price structures, and frequent over-corrections to charging profiles can lead to a “rebound peak” phenomenon. Therefore, control schemes should balance maximizing RES usage with ensuring customer participation.