Enhancing grid balancing services from electric vehicle aggregators under uncertainty: A probability-guaranteed feasible region approach

The global transition toward low-carbon transportation is driving rapid growth in electric vehicle (EV) adoption. As millions of EVs intergraded to power grids, their aggregated charging and discharging behaviors present both a challenge and an opportunity. However, turning this potential into reliable, dispatchable services remains highly challenging. EV charging patterns are inherently uncertain and diverse. At the same time, distribution networks impose strict physical constraints that must be respected at all times. These factors make it difficult for electric vehicle aggregators (EVAs) to confidently commit to specific regulation capacities, limiting their ability to participate in electricity markets. To bridge this gap, a probability-guaranteed feasible region (PGFR) framework is proposed in this paper to provide a reliable and quantifiable representation of the EVA's admissible power-exchange range under different confidence levels. The proposed framework employs inverse-function analysis to convert probabilistic uncertainties into tractable deterministic constraints. It then incorporates charging complementarity through McCormick envelope relaxation, enabling an explicit representation of the coupling relationships among EV charging and discharging behaviors. Finally, an outer progressive approximation method is adopted to efficiently handle the high dimensionality and temporal dependence inherent in EVA operation. The PGFR provides a balanced view of operational flexibility, avoiding overly conservative or overly optimistic feasible region descriptions that may otherwise cause economic losses for EVAs or security risks for the power grid. Case studies on a modified IEEE 33-bus distribution system and a 141-bus Venezuelan distribution network verify that the proposed approach provides a reliable and practical tool for EVAs to fully utilize their flexibility potential in supporting future power systems with high renewable energy penetration and significant uncertainty.