A decentralized control strategy for optimal charging of electric vehicle fleets with congestion management

This paper proposes a novel decentralized control strategy for the optimal charging of a large-scale fleet of Electric Vehicles (EVs). The scheduling problem aims at ensuring a cost-optimal profile of the aggregated energy demand and at satisfying the resource constraints depending both on power grid components capacity and EV locations in the distribution network. The resulting optimization problem is formulated as a quadratic programming problem with a coupling of decision variables both in the objective function and in the inequality constraints. The solution approach relies on a decentralized optimization algorithm that is based on a variant of ADMM (Alternating Direction Method of Multipliers), adapted to take into account the inequality constraints and the non-separated objective function. A simulated case study demonstrates that the approach allows achieving both the overall fleet and individual EV goals, while complying with the power grid congestion limits.