Agent-based modeling on residential coordinated charging with dynamic participation

With the increasing adoption of electric vehicles (EVs), residential EVs face charging resource competition due to the limited transformer capacity of residential communities. Coordinated charging is thought a promising solution for this issue. Existing studies often overlook the dynamic evolution in EV users’ willingness to adopt coordinated charging and fail to examine the effects of coordinated charging under adaptive behavioral responses from a long-term perspective. This study integrates agent-based modeling (ABM) with optimization to construct a willingness-based dynamic feedback mechanism, which allows residential EV owners to dynamically decide whether to participate. By designing three different charging strategies (day-ahead optimization, prioritization strategy, and real-time optimization) and objective functions, the study further explores the evolutionary trends of willingness and the outcomes of coordinated charging under various scenarios. The study finds that: (1) Compared to the baseline scenario, coordinated charging strategies can reduce charging costs by up to 26.62% and peak load by up to 56.46%. The annual average willingness can reach a maximum of 0.8635. Notably, time-of-use (TOU) pricing under smart charging conditions may lead to a maximum increase of 86.19% in charging load compared to the baseline. (2) When charging resources are scarce, willingness may decrease by up to 49.8%, indicating that sole reliance on a single coordinated charging technology is insufficient. (3) When penetration rates exceed 50%, dynamic changes in willingness may cause strategies focusing on reducing peak load and charging costs to fail to achieve their intended goals.