Three-Stage Optimization Model for Operation of Islanded Local Energy Network with Electric Vehicle Charging Station

The rise in the adoption of distributed energy resources (DERs) has enabled local energy markets where households trade energy locally to enhance efficiency, sustainability, and resilience. The primary focus of the existing literature has been on grid-connected local energy markets. Meanwhile, the penetration of electric vehicles (EVs) is also increasing, and they can serve as flexible energy resources in islanded communities. Therefore, this study proposes a three-step optimization model for the operation of an islanded local energy network through energy trading with nearby EV charging stations (EVS). First, each household performs local optimization based on available resources to determine the shortage and surplus amounts. During this stage, demand response is employed to manage the loads. Additionally, behavior models for each household are considered to determine their willingness-to-trade prices. Next, the surplus and shortage power, along with their respective prices, are communicated to the EVS. In the second stage, a multi-objective optimization model is developed for the EVS, considering to either maximize its profit or enhance power trading with the local energy network. Particle swarm optimization is employed in this stage to determine the optimal selling and buying prices for the EVS. Finally, in the last step, each household reschedules its resources based on the amount of power settled for trade with the EVS. The performance of the proposed method is evaluated for a community with an EVS, and the results show that the method can significantly reduce load shedding and help improve the stability of the local energy network.