Cooperative multi-EV aggregator strategy of V2G-enabled charging stations under shared interconnection limits

The widespread adoption of electric vehicles creates new opportunities for energy management through vehicle-to-grid participation. When multiple electric vehicle aggregators operate behind a shared point of common coupling, uncoordinated scheduling can cause congestion and inefficient use of interconnection capacity. This paper addresses their coordination when schedules are coupled by shared grid limits and aggregate-dependent electricity prices. A key finding is that coordinated scheduling alone cannot sustain cooperation when cooperative gains are unevenly distributed. Settlement mechanisms guaranteeing individual rationality at aggregator and vehicle levels are therefore required for implementable coordination. Accordingly, a cooperative multi-aggregator scheduling framework is proposed for V2G-enabled charging stations under shared grid-connection limits and aggregate-dependent pricing. The scheduling model minimizes energy procurement costs and battery degradation penalties while enforcing shared import and export limits at the point of common coupling. Cooperative gains are allocated through a two-level asymmetric Nash bargaining mechanism that preserves the coordinated physical dispatch whenever EV-level settlement is feasible. Otherwise, the framework returns the business-as-usual outcome, ensuring individual rationality at aggregator and vehicle levels. A distributed alternating direction method of multipliers solution is developed to preserve EV-level privacy with limited inter-aggregator information exchange. Numerical results for 10-vehicle, two-EVA and 80-vehicle, four-EVA systems show cost reductions of 3.7% and 13.9%, respectively, relative to the uncoordinated benchmark, with no-worse-off outcomes. Robustness is confirmed under parameter variations, alternative bargaining weights, and stochastic EV-demand uncertainty. The results demonstrate the value of cooperative multi-EVA strategies in reducing costs while preserving grid-connection constraints and privacy.