Three-stage optimization framework for managing isolated local energy networks with integrated electric vehicle charging stations

The growing adoption of distributed energy resources (DERs) has facilitated the emergence of local energy markets in which households can trade energy locally. While most existing research has focused on grid-connected local energy markets, less attention has been given to islanded communities, where electric vehicles (EVs) can function as valuable flexible energy resources. To address this gap, this study develops a three-stage optimization framework that enables energy trading between households and nearby electric vehicle charging stations (EVCSs) in an islanded local energy network. In the first stage, each household optimizes its energy resources, incorporates demand response strategies to manage electricity consumption, and determines surplus or deficit energy while accounting for behavioral preferences that influence willingness-to-trade prices. These energy quantities and price bids are then communicated to the EVCS. In the second stage, the EVCS solves a multi-objective optimization problem to either maximize economic returns or increase the volume of local energy trading, with particle swarm optimization used to determine optimal buying and selling prices. Finally, households adjust their energy schedules according to the finalized trading outcomes. Simulation results demonstrate that the proposed framework substantially reduces load shedding, improves energy reliability, and enhances the operational performance of local energy networks.