Model Predictive Control for Multi-Objective Vehicle-to-Grid Dispatch: Jointly Optimizing Peak Shaving, Renewable Utilization, Battery Degradation, and Economic Revenue in Smart EV Infrastructures

Abstract

Vehicle-to-grid (V2G) technology lets a parked electric vehicle push power back to the network, so a fleet of cars can act as distributed storage. Many studies report large benefits from this, such as peak shaving and better use of renewable energy, but most describe their simulation only in words, name no test network, and compare a single charging behavior against a do-nothing case. It is then hard to separate what V2G delivers from what the control strategy delivers. This paper builds and compares dispatch strategies on one fully specified system: a price-following rule with no knowledge of the network, a stronger randomized time-of-use baseline, and a receding-horizon model predictive control (MPC) strategy that re-plans every hour with the distribution network’s per-line thermal limits and voltage bounds embedded directly in the optimization, not merely checked afterward. All run on the IEEE 33-bus feeder at low (10%), medium (30%), and high (50%) EV shares, with a full AC power flow solved every hour. The central result is a critical-penetration effect. At a 50% share, the naive price rule makes the system peak 23.7% worse than having no V2G at all, because the whole fleet reacts to one price signal at once, while the MPC cuts the peak by 18 to 28% in every case. Embedding the network limits removes the worst-line-loading side-effect a system-peak-only objective creates, bringing high-share worst-line loading down from 86.3% to 81.8% while preserving the full peak reduction. A workplace daytime-charging scenario shows renewable use becoming a real, separating metric (up to 2.8 MWh/day of EV demand met directly by rooftop solar, against zero for overnight charging), a quadratic wear cost smooths the profit-cycling frontier that a linear cost makes step-shaped, the controller is robust to forecast error up to 20%, and its solve time is set by network size rather than fleet size. Results are given as they came out of the model.