Utility Energy Optimization for Residential Microgrid With Electric Vehicle Loads

This study presents a Model Predictive Microgrid Control (MPMC)-based energy management framework for reducing utility grid power imports in residential microgrids. The microgrid integrates photovoltaic (PV) generation, an energy storage system (ESS), electric vehicles (EVs) with vehicle-to-home (V2H) capabilities, residential loads, and a backend grid connection. The primary objective is to enhance the energy autonomy and operational reliability by prioritizing local renewable energy utilization while ensuring a continuous power supply under varying operating conditions. The proposed control strategy dynamically schedules power flows over a 24-hour horizon using forecasted PV generation and load profiles, accounting for partial shading effects and evening peak demand. PV generation is prioritized for direct-load supply and EV charging with surplus energy stored in the ESS, whereas grid power is used only as a last resort. A comprehensive set of primary and tertiary operating scenarios was evaluated to assess system performance under different grid, load, and resource availability conditions. MATLAB simulation results demonstrate that the proposed MPMC framework effectively reduces grid dependency, maintains system balance, and enables seamless coordination among PV, ESS, and EV resources. The results confirm the ability of the framework to improve microgrid resilience, energy efficiency, and sustainability.