Optimal Scheduling of Electric Vehicle Aggregators in Residential Areas: A Cost Minimization Approach

Abstract

This study presents an optimization method for scheduling electric vehicle (EV) charging in residential areas, aimed at minimizing costs associated with peak demand periods. As the adoption of EVs increases, effective management of their charging demands becomes crucial for both utilities and EV owners. This research develops a mathematical model for a combined EV aggregator that coordinates charging and discharging activities among residential, commercial, and industrial users. Using a Multi-Agent Charging and Discharging (MACD) algorithm, the study shifts EV charging from peak to off-peak hours, leveraging time-of-use (TOU) pricing to reduce overall electricity costs. Case studies demonstrate that coordinated charging can decrease costs by up to 15% compared to uncoordinated methods, highlighting the algorithm's efficiency in managing energy demand and enhancing grid stability. The findings underscore the potential for optimized EV scheduling to contribute significantly to smart grid operations and suggest avenues for future research into the algorithm's broader impacts on network performance.