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

TL;DR

This paper addresses how to reduce renewable energy curtailment in a high-RES Texas grid by steering EV charging through price signaling. It compares open-loop day-ahead TVRs with model predictive control that updates pricing as RES forecasts change, using 2035 ERCOT projections for EV penetration and renewable capacity. The results show that MPC improves RES utilization beyond open-loop, with 3-hour MPC delivering the most gains but risking rebound peaks, while 6-hour MPC offers a favorable balance between effectiveness and participant burden. The work has practical implications for grid operators seeking to align EV charging with variable renewables under future ERCOT conditions.

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.

Figures (6)

• Figure 1: When RES generation exceeds load, this can lead to cost effective, carbon free electricity sources such as wind and solar going underutilized. The underutilized RES output is the highlighted region, showing the difference between output and load.
• Figure 2: Grids such as ERCOT can use RES forecasting and price signal based control schemes to manage their EVs through communication with chargepoint operators
• Figure 3: Block diagrams for open-loop and model predictive control for the light duty EV fleet to harness excess renewable energy sources (RES) through price signaling with time varying rates (TVRs) given a business-as-usual (BAU) charging profile. Differences between open-loop and model predictive control (MPC) are shown in green dashed lines and inputs.
• Figure 4: The formulation of the optimization problem used in this study's control schemes.
• Figure 5: TVRs are created and tested on two back to back RES profiles. The differences between open-loop and model predictive control (MPC) are shown in green dashed lines and inputs.