A Fast Polytope-Based Approach for Aggregating Large-Scale Electric Vehicles in the Joint Market Under Uncertainty

The aggregation of electric vehicles (EVs) has been advocated as an effective means to manage and control large-scale plug-in EVs. However, the efficient aggregation is challenging due to the heterogeneity and uncertainty of individual EVs. To cope with this challenge, this paper puts forth a fast polytope-based aggregation approach, which could deal with the time-coupled, uncertain, and heterogeneous individual EV models. First, the feasible region of each EV’s power profiles is expressed as a polytope under H-representation considering various operational constraints. To improve the aggregation efficiency, H-representation reconstruction and polytopic projection processes are designed to address the heterogeneity of EVs. The uncertainties of EVs are modeled by the distributionally robust joint chance-constrained programming. The original individual and aggregate feasible region of EVs are subsequently inner approximated by the homothets of a basic prototype. Last, a day-ahead optimal bidding model for charging service provider in a joint market, including energy, peak-shaving and reserve markets, is developed. Numerical simulation results based on real-world dataset demonstrate that the proposed aggregation method can improve both the computation efficiency and accuracy of approximate feasible region.