Collaborative multi‐aggregator electric vehicle charge scheduling with PV‐assisted charging stations under variable solar profiles

Abstract

Electric vehicles (EVs) are on the path to becoming a solution to the emissions released by the internal combustion engine vehicles that are on the road. EV charging management integration requires a smart grid platform that allows for communication and control between the aggregator, consumer and grid. This study presents an operational strategy for PVassisted charging stations (PVCSs) that allows the EV to be charged primarily by PV energy, followed by the EV station's battery storage (BS) and the grid. Multi-Aggregator collaborative scheduling is considered that includes a monetary penalty on the aggregator for any unscheduled EVs. The impact of the PVCS is compared to the case with no PV/BS is included. A variation in the PV profile is included in the evaluation to assess its impact on total profits. Profit results are compared in cases of minimum, average and maximum PV energy output. The results indicate that the inclusion of penalties due to unscheduled EVs resulted in lowered profits. Further, the profits experienced an increase as the number of EVs scheduled through PV/BS increased, implying that a lesser percentage of EVs are scheduled by the grid when a greater amount of PV and battery energy are available.

Event-Triggered Distributed Model Predictive Control for Platoon Coordination at Hubs in a Transport System

“…In station-based methods, charging stations direct or suggest appropriate sites for electric trucks to charge. The coordination objectives of these methods vary from maximizing the utilization of charging infrastructure [24], mitigating power overloads across stations [25], enhancing the operational flexibility of power systems [26], to minimizing operational costs at charging stations [27], to name only a few. In contrast to approaches that focus on a single optimization objective, some works [28], [29] model the charging coordination problem as non-cooperative games.…”

Validation of optimal electric vehicle charging station allotment on IEEE 15-bus system

“…A multi-agent system (MAS) simulation framework along with evidential reasoning (ER) approach was used for minimizing the charging cost, charging waiting time and charging travel time [8].Optimal allocation of EVCSs for sharing charging level improvement and total charging distance minimization was presented [9]. In [10], a collaborative multi-aggregator EV charging scheduling for PV powered EVCSs for aggregator total profit maximization was accomplished. An optimum distributed energy trading strategy was proposed using game theory on an EVCS incorporated integrated energy system (IES) [11].…”