Distributed Noncooperative MPC for Energy Scheduling of Charging and Trading Electric Vehicles in Energy Communities
In this paper, we propose a novel control strategy for the optimal scheduling of an energy community constituted by prosumers and equipped with unidirectional vehicle-to-grid (V1G) and vehicle-to-building (V2B) capabilities. In particular, V2B services are provided by long-term parked electric vehicles (EVs), used as temporary storage systems by prosumers, who in turn offer the V1G service to EVs provisionally plugged into charging stations. To tackle the stochastic nature of the framework, we assume that EVs communicate their parking and recharging time distribution to prosumers, allowing them to improve the energy allocation process. Acting as selfish agents, prosumers and EVs interact in a rolling horizon control framework with the aim of achieving an agreement on their operating strategies. The resulting control problem is formulated as a generalized Nash equilibrium problem, addressed through the variational inequality theory, and solved in a distributed fashion leveraging on the accelerated distributed augmented Lagrangian method, showing sufficient conditions for guaranteeing convergence. The proposed model predictive control approach is validated through numerical simulations under realistic scenarios.
This preprint has been accepted for publication in IEEE Transactions on Control Systems Technology.
How to cite: N. Mignoni, R. Carli and M. Dotoli, "Distributed Noncooperative MPC for Energy Scheduling of Charging and Trading Electric Vehicles in Energy Communities," in IEEE Transactions on Control Systems Technology.
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Submitting Author's InstitutionPolitecnico di Bari
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