This study summarizes a critical review on EVs’ optimal charging and scheduling under dynamic pricing schemes. A detailed comparison of these schemes, namely, Real Time Pricing (RTP), Time of Use (ToU), Critical Peak Pricing (CPP), and Peak Time Rebates (PTR), is presented. Globally, the intention is to reduce the carbon emissions (CO2) has motivated the extensive practice of Electric Vehicles (EVs). The uncoordinated charging and uncontrolled integration however of EVs to the distribution network deteriorates the system performance in terms of power quality issues. Therefore, the EVs’ charging activity can be coordinated by dynamic electricity pricing, which can influence the charging activities of the EVs customers by offering flexible pricing at different demands. Recently, with developments in technology and control schemes, the RTP scheme offers more promise compared to the other types of tariff because of the greater flexibility for EVs’ customers to adjust their demands. It however involves higher degree of billing instability, which may influence the customer’s confidence. In addition, the RTP scheme needs a robust intelligent automation system to improve the customer’s feedback to time varying prices. In addition, the review covers the main optimization methods employed in a dynamic pricing environment to achieve objectives such as power loss and electricity cost minimization, peak load reduction, voltage regulation, distribution infrastructure overloading minimization, etc.
Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.
The uncoordinated integration of electric vehicles (EVs) severely deteriorates the operational performance of a distribution network. To optimize distribution network performance in an EV charging environment, this paper presents a two-stage optimization approach, which integrates coordinated EV charging with network reconfiguration. A formulation to minimize system power loss is presented, and an optimal solution is obtained using a binary particle swarm optimization algorithm. The proposed approach is tested on a modified IEEE 33-bus medium-voltage node test system, coupled with a low voltage distribution network. Results of the coordinated and uncoordinated EV charging are compared with those of the developed integrated approach, and the operational performance of the system is studied. The results show that the integration of network reconfiguration with coordinated EV charging significantly decreases network power losses and fairly improves voltage profile. Thus, the proposed strategy can lead to improved operational performance of the system while dealing with the growing penetration of EVs in the network.
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.
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