Optimal Pricing Algorithm of EV Cluster Demand Response Considering Time-Space Coupling and User Bounded Rationality

In recent years, the significant increase in Electric Vehicles (EVs) connecting to the power grid has posed great challenges to the secure and stable operation of distribution networks. Therefore, this paper proposes a time-space coupled EV cluster adjustable load potential model, taking into account user bounded rationality, and an optimization TOU pricing algorithm for guiding demand response. Firstly, considering the time-space mobility characteristics of EVs, a virtual aggregated load capacity model is established for the time-space coupled EV cluster using trip chains and a sub-group EV orderly charging adjustable model. This model effectively avoids the problem of information redundancy caused by the large-scale integration of EVs. Secondly, by analyzing EV users’ risk aversion and bounded rational behavior, a user demand response model is developed. Next, this paper establishes an EV charging management and price optimization framework, and proposes a dual-layer optimization algorithm that aims to reduce peak-to-valley differences while ensuring social welfare through adjustable capacity aggregation and grid TOU pricing. Finally, the effectiveness of the proposed models and methods is verified through multiple sets of case studies with different EV penetration rates. This provides theoretical support for guiding EV users’ participation in demand response through optimizing day-ahead TOU prices for grid optimization.