Distributed Noncooperative MPC for Energy Scheduling of Charging and
Trading Electric Vehicles in Energy Communities
Abstract
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.