Distributed energy resources (DERs), like electric vehicles (EVs), can offer valuable services to power systems, such as enabling renewable energy to the electricity producer and providing ancillary services to the system operator. However, these new DERs may challenge the distribution grid due to insufficient capacity in peak hours. This paper aims to coordinate the valuable services and operation constraints of three actors: the EV owner, the Fleet operator (FO) and the Distribution system operator (DSO), considering the individual EV owner's driving requirement, the charging cost of EV and thermal limits of cables and transformers in the proposed market framework. Firstly, a theoretical market framework is described. Within this framework, FOs who represent their customer's (EV owners) interests will centrally guarantee the EV owners' driving requirements and procure the energy for their vehicles with lower cost. The congestion problem will be solved by a coordination between DSO and FOs through a distribution grid capacity market scheme. Then, a mathematical formulation of the market scheme is presented. Further, some case studies are shown to illustrate the effectiveness of the proposed solutions.

The aggregation of distributed energy resources (DERs) draws much attention since their penetration increases continuously. We derive the mathematical formulation of the exact aggregate feasible region (AFR) of multiple DERs. The derivation is based on analyzing the redundancy of all possible constraints in the Fourier-Motzkin Elimination (FME) process. In the exact AFR model, the number of constraints is regardless of the number of DERs but exponential with the number of time intervals. Although the computational complexity of the exact AFR is dramatically simplified compared with the original FME, there are still too many constraints for the exact AFR model to be applied in practice. Hence, we propose the $k$ th-order approximate models and two types of multi-timescale approximate models. Illustrative cases show the necessity of each constraint in the proposed models compared with the aggregated power and energy boundary model. Numerical simulations verify the accuracy of the exact AFR model. Besides, the second-order approximate model performs best considering the balance between accuracy, economics, and computational complexity.