Design and parameter optimization of EV frequency regulation packages based on fuzzy theory

Under the new business model brought by vehicle-to-grid (V2G) technology, electric vehicle aggregators (EVAs) need to design personalized packages to incentivize users to participate in frequency regulation services based on their individual needs. Based on the grid-connected model, targeted packages are designed considering the user charging characteristics that affect the frequency regulation capability of electric vehicles, establishing a dynamic gaming relationship between aggregators and users in the wholesale electricity market. Fuzzy sets are employed to characterize packages under multiple criteria, and the users' satisfaction with the packages is calculated using weighted Euclidean distance to simulate user package selection scenarios. Users are aggregated according to their package choices, and a package parameter optimization model is established with the fuzzy assessment-based package selection model as the lower level and the aggregator's participation in the day-ahead energy-frequency regulation market bidding model as the upper level. The case study results show that the optimized packages can maximize the balance between user experience and aggregator benefits.