Polytope-Based Aggregation of Electric Vehicles for Day-Ahead Bi-Level Scheduling of Active Distribution Network Considering Uncertainties

The integration of large-scale electric vehicles (EVs) into the distribution network can significantly increase the complexity of system management. In this paper, a polytope-based inner approximate aggregation model is developed to effectively describe the feasible region of large-scale EVs. A bi-level model is then developed for the participation of EV aggregators in the optimal scheduling of the distribution network, where the uncertainty in the bi-level model is modeled by chance constraints. The bi-level model is solved using distributionally robust optimization, Strong Duality Theorem, and KKT conditions. A numerical case study of the IEEE 33 bus distribution network is conducted and the results show the effectiveness of the proposed method.