A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization

Abstract

Electric vehicles’ (EVs) technology is currently emerging as an alternative of traditional Internal Combustion Engine (ICE) vehicles. EVs have been treated as an efficient way for decreasing the production of harmful greenhouse gasses and saving the depleting natural oil reserve. The modern power system tends to be more sustainable with the support of electric vehicles (EVs). However, there have been serious concerns about the network’s safe and reliable operation due to the increasing penetration of EVs into the electric grid. Random or uncoordinated charging activities cause performance degradations and overloading of the network asset. This paper proposes an Optimal Charging Starting Time (OCST)-based coordinated charging algorithm for unplanned EVs’ arrival in a low voltage residential distribution network to minimize the network power losses. A time-of-use (ToU) tariff scheme is used to make the charging course more cost effective. The concept of OCST takes the departure time of EVs into account and schedules the overnight charging event in such a way that minimum network losses are obtained, and EV customers take more advantages of cost-effective tariff zones of ToU scheme. An optimal solution is obtained by employing Binary Evolutionary Programming (BEP). The proposed algorithm is tested on IEEE-31 bus distribution system connected to numerous low voltage residential feeders populated with different EVs’ penetration levels. The results obtained from the coordinated EV charging without OCST are compared with those employing the concept of OCST. The results verify that incorporation of OCST can significantly reduce network power losses, improve system voltage profile and can give more benefits to the EV customers by accommodating them into low-tariff zones.

Proposed Ranked Strategy for Technical and Economical Enhancement of EVs Charging With High Penetration Level

“…Table 5 illustrates a comparison between the proposed strategy AR-IFC and other strategies in some research studies on the same tested system. These four studies in [26], [27], [32], and [33] have been chosen for a fair comparison as they are recent research that investigated the optimal charging of EVs without implementing any renewable resources. The studies applied different charging techniques, [27], [32], and [33] use fixed charging techniques, while [26] use variable charging techniques.…”

“…These four studies in [26], [27], [32], and [33] have been chosen for a fair comparison as they are recent research that investigated the optimal charging of EVs without implementing any renewable resources. The studies applied different charging techniques, [27], [32], and [33] use fixed charging techniques, while [26] use variable charging techniques. Besides, the variety in their implemented objective functions, where [26] and [33] applied a single objective, but [27] and [32] implemented a double objective function.…”

“…The studies applied different charging techniques, [27], [32], and [33] use fixed charging techniques, while [26] use variable charging techniques. Besides, the variety in their implemented objective functions, where [26] and [33] applied a single objective, but [27] and [32] implemented a double objective function. The results the effectiveness of the proposed strategy where both the peak consumed power and the peak power are the least among other researches, although they have implemented more advanced optimization techniques.…”

“…The results the effectiveness of the proposed strategy where both the peak consumed power and the peak power are the least among other researches, although they have implemented more advanced optimization techniques. In addition, [26], [27], and [33] did discuss the charging cost reduction issue in their strategies which are considered a vital concern. In [32], the charging cost reduction has reached 22.4%, which is less than the proposed strategy by 17.7%.…”

On–Off Scheduling for Electric Vehicle Charging in Two-Links Charging Stations Using Binary Optimization Approaches

“…Therefore, integer-variable optimization tools must be used. Examples include BLP [37], BQP [38], MILP [48][49][50] and various metaheuristics with integer variables, including genetic algorithms [51], ant colony optimization (ASO) [13], particle swarm optimization [52,53], tabu search [54], memetic algorithm [55], artificial bee colony algorithm [56], binary evolutionary programming [57], and other greedy algorithms [33,58]. Other approaches to scheduling can also be obtained in the literature, for example, agent-based scheduling [59,60], deep reinforcement learning [61], and deep learning [62].…”