User Willingness-Incorporated V2G Optimization Method for Active Distribution Networks

With the high penetration of distributed photovoltaic (PV) systems and large-scale integration of electric vehicles (EV), distribution networks are confronted with challenges such as intensified load fluctuations and insufficient PV accommodation. Traditional vehicle-to-grid (V2G) optimization methods predominantly focus on grid-side economic dispatch, lacking quantitative modeling of user willingness response and dynamic electricity price coordination mechanisms. A user willingness-incorporated V2G optimization method is proposed in this paper. First, a dynamic time-of-use pricing mechanism is established based on fuzzy logic. Second, the Sigmoid function is employed to quantify the V2G participation willingness of different EV types, using charging duration-travel demand compatibility as the evaluation criteria to determine the maximum schedulable power. Furthermore, a multi-objective optimization model is formulated to simultaneously minimize grid net load variance, maximize collaborative revenue for PV producers and EV aggregators, as well as minimize total user costs. The NSGA-III algorithm is adopted to search for Pareto optimal solutions, which reveals effective trade-offs between grid stability and economic efficiency. This study provides a theoretical foundation for V2G scheduling of EVs in active distribution networks under high renewable energy penetration scenarios.