A Fully Distributed Coordination of Electric Vehicles for Electric Load Management

The charging scheduling of electric vehicles (EVs) is essential in order to mitigate the negative impact of uncoordinated charging load on the secure and economic operation of distribution power systems. To meet both EV owners’ interests and distribution grid constraints, a scalable algorithm for charging coordination of EVs is necessary. This paper proposes a modified distributed dual-proximal algorithm (MDDPA) to solve EVs scheduling problem at each individual grid node using a fully distributed architecture model. The objective of this optimization problem is to take into account a tradeoff between battery degradation cost and charging cost, subject to feeder capacity constraints. The simulation results confirm the effectiveness of the proposed scheduling in minimizing load curve variance while keeping feeder capacity within a determined range.