Electric vehicle charging flexibility assessment for load shifting based on real-world charging pattern identification

Abstract

Coordinated charging control for electric vehicles (EVs) can contribute to load balancing and renewable energy utilization. This paper proposes a novel framework for assessing the flexibility of EVs under different charging control strategies through a rule-based identification of charging patterns. First, key categories of EV charging activity chains, characterized by the sequence of parking and charging activities between adjacent trips, are extracted from real-world EV operation data. Simulations are then conducted by switching charging patterns to represent three coordinated charging control methods: delayed charging, reduced-power charging, and smart charging with Time-of-Use (ToU) tariffs. These strategies are applied by modifying the charging time or charging rate within the original charging sessions. Several evaluation metrics are introduced to quantify each strategy's impact on load profile reshaping, flexibility utilization efficiency, user involvement, and energy cost saving. Comparison results show that smart charging with ToU tariffs outperforms the other two strategies, though the effectiveness of each scheme varies with charging patterns. The findings highlight the idle parking time and its ratio to the required charging time as key indicators for identifying potential EV users for coordinated charging control. Additionally, it is shown that shifting 1 % of EV charging load out of peak periods requires at least 4 % of user participation, while at least 3 % is needed for shifting 1 % of EV charging load into valley periods. The proposed pattern-based charging model and evaluation framework offer valuable insights for designing more efficient, cost-effective, and user-friendly EV charging scheduling strategies.