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.

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.

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.

This paper investigates the impact of tube wells on the discharge and water table of the Quetta Valley aquifer and conducts a financial analysis of the solar photovoltaic water pumping system (SPVWP) in comparison with a typical pumping system for the Water and Sanitation Agency of Quetta’s (WASA) tube wells. Quetta Valley is dependent on groundwater as surface resources are on decline and unpredictable. The population of this city has exponentially increased from 0.26 million in 1975 to 2.2 million in 2017 which has put a lot of pressure on the groundwater aquifer by installing more than 500 large capacity tube wells by WASA and Public Health Engineering (PHE) departments in addition to thousands of low-capacity private tube wells. The unprecedented running of these wells has resulted in drying of the historical Karez system, agricultural activities, and the sharp increase in power tariffs. There are 423 tube wells in operation installed by WASA in addition to PHE, Irrigation and Military Engineering Services (MES), which covers 60% of the city’s water demand. The results will be beneficial for organizations and positively impact the operation of these wells to meet public water demand. For the two zones, i.e., Zarghoon and Chiltan in Quetta Valley, recommendations are given for improved water management.

Doi:10.1093/comjnl/bxaa134 Comp J 2020;00(0): xxxx–xxxx

Today's network intrusion threats are diverse and ever-increasing. The weaknesses and vulnerabilities of software, hardware, network protocols, and network management cause such types of threats. Intruders and criminals have now mastered the new methods to target and circumvent the legitimate ways to damage the devices with the exponential rise in technology. Now the stronger Intrusion Detection System (IDS) are required to classify network traffic with the best performance. We suggest a Deep Neural Network (DNN) based IDS in this study, which tracks traffic from both authorized and non-authorized sources. It reliably defines and extracts malicious traffic with a 99.79 percent accuracy for KDD-99 and 96.53 percent accuracy for NSL-KDD datasets. This study offers an updated version of the Deep Neural Network to solve design algorithm and over-fitting problem, and an evaluation has also been made with current models to show the efficacy, proficiency, and performance of the planned model.