Open-Loop and Model Predictive Control for Electric Vehicle Charging to Manage Excess Renewable Energy Supply in Texas

TL;DR

This paper compares open-loop and model predictive control strategies for EV charging to optimize renewable energy utilization in Texas, highlighting the benefits and challenges of each approach in a weather-dependent grid.

Contribution

It introduces and evaluates open-loop and MPC schemes for EV charging to better utilize excess renewable energy, considering forecast uncertainties and user participation.

Findings

MPC further increases renewable energy usage compared to open-loop control.

Frequent MPC updates can cause customer burden and rebound peaks.

Balancing RES utilization and customer participation is crucial.

Abstract

Modern power grids are evolving to become more interconnected, include more electric vehicles (EVs), and utilize more renewable energy sources (RES). Increased interconnectivity 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 and compare how both open-loop control and model predictive control (MPC) schemes 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 while open-loop control increases RES usage, MPC increases RES usage even further 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.