Optimization of Bidirectional Electric Vehicle Charging Management to Address Domestic Load Imbalance

Abstract

The integration of electric vehicles into residential power systems poses challenges such as phase imbalance, energy cost optimization, and user satisfaction. This study proposes an optimized three-phase EV charging management framework that dynamically distributes charging and discharging loads across all phases to mitigate domestic load imbalance. Additionally, the parking facility is equipped with a photovoltaic system connected to the main grid, enhancing renewable energy utilization and grid interaction. The proposed approach employs a Particle Swarm Optimization (PSO) algorithm in a day-ahead scheduling framework, integrating multiple objectives such as phase balancing, cost minimization, and user satisfaction within a single weighted optimization function. Charged vehicles actively collaborate with the photovoltaic system system to counteract phase imbalance by intelligently managing charging and discharging patterns in synchronization with solar energy availability. The arrival and departure times of EVs, as well as photovoltaic generation and household loads, are predicted using an advanced forecasting system. These forecasts enable more accurate scheduling of energy distribution, ensuring optimal power allocation while minimizing grid dependency. The method maximizes renewable energy utilization to reduce grid dependency. Simulation results demonstrate a 66.61% reduction in average phase imbalance, along with a 23.88% decrease in total charging costs. Additionally, all vehicles achieve at least 80% State of Charge, representing a 50% improvement over an approach where PV power is equally distributed across the three phases.