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As the demand for electric vehicles (EVs) increases in all countries, automobile companies are launching different types of EVs. Installation of EVCS in the present power system network without modification can cause an extra burden on the present network. However, this extra burden can be reduced if EVCS gets a supply of renewable energy resources. This paper proposes energy management schemes by installing a photo-voltaic distributed generator (PV-DG) and energy storage system (ESS) at an EVCS. An IEEE-33 bus system for the Saudi Arabia United Kingdom rural location has been selected to introduce real-time energy management schemes. In this radial distribution network (RDN), three EVCS of different capacities are installed at different locations to maintain the stability of the network. The power flow program is used in the MATLAB environment to check the stability of the network. Accurate solar data, traffic patterns, and EV charging patterns in Saudi Arabia are collected and used for energy management at EVCS. Three cases are used for comparison: EVCS annual charges, EVCS annual benefits, and grid annual fuel charges. All proposed cases are also simulated in MATLAB using MATPOWER6 to check the impact on voltage profile and power line flows.

The primary barrier to the extensive implementation of electric vehicles (EVs) is the lack of necessary and adequate charging infrastructure within a particular area. Moreover, sourcing the energy needed for EVs from the grid that heavily relies on fossil fuels diminishes the environmental advantages of EVs and, further increases strain on grid system in electricity-deficient regions. Therefore, this study designs and simulates six different standalone hybrid renewable energy systems utilizing solar, wind, and biomass sources and a grid-tied system for a selected location of Singaperumal Kovil, Chennai, India. One unique aspect of this research is the use of a single station for charging electric vehicles and hydrogen refueling. These systems are designed to fulfil the energy requirements of 48 battery-electric vehicles and 17 hydrogen vehicles, Using HOMER Pro software, combining a bio-gas generator, wind turbine, and photovoltaic system. The renewable energy sources are identified as the most practical and reliable among the various cases for that location, in terms of technical, economic, and environmental feasibility. Among six different standalone scenarios and a grid-tied system, the biogas generator, wind, and PV configuration model is more cost effective than other configurations. The proposed system generates 2,009,492 kWh/yr of electricity and 30,199 kg/yr of Hydrogen. The reported values for the NPC, LCOE, LCOH, and payback period were ${\$}$ 3.46 million, ${\$}$ 0.493/kWh, ${\$}$ 8.86/kg, and about 3.08 years, in that order. HOMER determines the breakeven grid extension distance as 129.93km. After considering the effective rate after digestate, the NPC, LCOE, and LCOH costs are reduced to ${\$}$ 2.90 million, ${\$}$ 0.412/kWh, and ${\$}$ 7.47/kg, respectively.

The market for Electric Vehicles (EVs) has expanded tremendously as seen in the recent Conference of the Parties 27 (COP27) held at Sharm El Sheikh, Egypt in November 2022. This needs the creation of an ecosystem that is user-friendly and secure. Internet-connected Electric Vehicle Charging Stations (EVCSs) provide a rich user experience and add-on services. Eventually, the EVCSs are connected to a management system, which is the Electric Vehicle Charging Station Management System (EVCSMS). Attacking the EVCS ecosystem remotely via cyberattacks is rising at the same rate as physical attacks and vandalism happening on the physical EVCSs. The cyberattack is more severe than the physical attack as it may affect thousands of EVCSs at the same time. Intrusion Detection is vital in defending against diverse types of attacks and unauthorized activities. Fundamentally, the Intrusion Detection System's (IDS) problem is a classification problem. The IDS tries to determine if each traffic stream is legitimate or malicious, that is, binary classification. Furthermore, the IDS can identify the type of malicious traffic, which is called multiclass classification. In this paper, we address IoT security issues in EVCS by using different machine learning techniques and using the native IoT dataset to discover fraudulent traffic in EVCSs, which has not been performed in any previous research. We also compare different machine learning classifier algorithms for detecting Distributed Denial of Service (DDoS) attacks in the EVCS network environment. A typical Internet of Things (IoT) dataset obtained from actual IoT traffic is used in the paper. We compare classification algorithms that are placed in line with the traffic and contain DDoS attacks targeting the EVCS network. The results obtained from this research improve the stability of the EVCS system and significantly reduce the number of cyberattacks that could disrupt the daily life activities associated with the EVCS ecosystem. [ABSTRACT FROM AUTHOR]

This paper focuses on optimization of charging station capacities and locations. The proliferation of electric vehicles is inextricably linked to the deployment of charging infrastructure. Currently, there are issues with locating and sizing when it comes to the building of electric vehicle charging stations. In order to find out the capacity of charging stations, a mathematical model was developed, which includes a site survey, EV density, electricity demand analysis, load modeling, costing analysis, EV battery charging time and power quality. The proposed approach was tested in MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

The market for Electric Vehicles (EVs) has expanded tremendously as seen in the recent Conference of the Parties 27 (COP27) held at Sharm El Sheikh, Egypt in November 2022. This needs the creation of an ecosystem that is user-friendly and secure. Internet-connected Electric Vehicle Charging Stations (EVCSs) provide a rich user experience and add-on services. Eventually, the EVCSs are connected to a management system, which is the Electric Vehicle Charging Station Management System (EVCSMS). Attacking the EVCS ecosystem remotely via cyberattacks is rising at the same rate as physical attacks and vandalism happening on the physical EVCSs. The cyberattack is more severe than the physical attack as it may affect thousands of EVCSs at the same time. Intrusion Detection is vital in defending against diverse types of attacks and unauthorized activities. Fundamentally, the Intrusion Detection System’s (IDS) problem is a classification problem. The IDS tries to determine if each traffic stream is legitimate or malicious, that is, binary classification. Furthermore, the IDS can identify the type of malicious traffic, which is called multiclass classification. In this paper, we address IoT security issues in EVCS by using different machine learning techniques and using the native IoT dataset to discover fraudulent traffic in EVCSs, which has not been performed in any previous research. We also compare different machine learning classifier algorithms for detecting Distributed Denial of Service (DDoS) attacks in the EVCS network environment. A typical Internet of Things (IoT) dataset obtained from actual IoT traffic is used in the paper. We compare classification algorithms that are placed in line with the traffic and contain DDoS attacks targeting the EVCS network. The results obtained from this research improve the stability of the EVCS system and significantly reduce the number of cyberattacks that could disrupt the daily life activities associated with the EVCS ecosystem.

With the anticipated growth in use of electric vehicles (EVs) due to their benefits in environmental conservation and their potential as an energy storage device, corresponding charging facilities will also observe a growth. EVs require a charging infrastructure that supports a scenario which is capable of handling a large number of scheduling requests. However, this may result in a greater load on the grid. To mitigate the impacts of the increased charging load on the grid, photovoltaic (PV)-assisted charging stations, or PVCS, are proposed here. The benefits of PVCS are evaluated from the point of view of aggregator profits and energy consumption from the grid for a greater number of scheduling requests. With the use of PV at charging stations, it is found that for the period in which PV and battery storage are active, a majority of the charging requests are scheduled through the PV/Battery assist. Thus, reducing the effect on the grid along with an improvement in the total profit of the aggregators.

This paper proposes a flexible framework for scheduling and real time operation of electric vehicle charging stations (EVCS). The methodology applies a multi-objective evolutionary particle swarm optimization algorithm (EPSO) for electric vehicles (EVs) scheduling based on a day-ahead scenario. Then, real time operation is managed based on a rule-based (RB) approach. Two types of consumer were considered: EV owners with a day-ahead request for charging (scheduled consumers, SCh) and non-scheduling users (NSCh). EPSO has two main objectives: cost reduction and reduce overloading for high demand in grid. The EVCS has support by photovoltaic generation (PV), battery energy storage systems (BESS), and the distribution grid. The method allows the selection between three types of charging, distributing it according to EV demand. The model estimates SC remaining state of charge (SoC) for arriving to EVCS and then adjusts the actual difference by the RB. The results showed a profit for EVCS by the proposed technique. The proposed EPSO and RB have a fast solution to the problem that allows practical implementation.

As electric vehicles (EVs) penetration level is increasing, charging infrastructure should expand. This study concentrates on the economic aspects of EV charging stations (EVCSs). When CS capacity is not enough to simultaneously charge all EVs, the EVs will wait in queues to get service. This study presents a model for EVCS and a profit‐based algorithm for charging scheduling (ChSc) of EVs from viewpoint of EVCS owner. The objective function is to maximise long‐term profit of EVCS owner and to minimise delay time of EVs. The charging prices of queues are different from each other, which are determined by the EVCS owner, considering energy price. The time needed to charge an EV from the minimum state of charge to fully charge is considered as a time interval. It is shown that maximising profit at each interval, named as short‐term profit maximisation (STPM), does not necessarily maximise long‐term profit of the EVCS owner. Then, another ChSc strategy, referred to as long‐term profit maximisation (LTPM), is proposed. Simulation results confirm that with LTPM, the owner of EVCS obtains more profit in long term. Also, in comparison to STPM, the incurred average delays of EVs in queues are much less. [ABSTRACT FROM AUTHOR]

Abstract: The electric vehicles (EV) charging in the electric vehicles charging station (EVCS) needs to keep continuous communication with the charging station. As a low cost and low data rate wireless network technology, the ZigBee is suitable for setting up wireless networks in the EVCS. By researching the operation characteristics of the EVCS, we proposed a routing protocol for the EVCS wireless networks based on the AODV routing protocol. Using the network simulation software NS-2 simulates the EVCS wireless networks environment. The simulation is based on different load parameters of wireless networks performance, demonstrated the feasibility of the Zigbee networks to the wireless network of the EVCS networks. [Copyright &y& Elsevier]

The UK government has set a ban on the sale of new petrol and diesel cars and vans by 2030. This will create a shift to electric vehicles. which will present a substantial impact on the grid. Therefore, methods to reduce the charging station's impact on the grid have to be developed. This paper's objective is to evaluate how integrating solar and storage affects a charging station's dependence on the grid. A photovoltaic electric vehicle charging station (PVEVCS) is first designed, and then four charging profiles are selected to assess the station through a simulation using MATLAB. The array produces 3257 MWh/yr which, on average, offsets 40% of the electric vehicle (EV) load experienced by the station. Furthermore, with the integration of storage, the dependence is further reduced by 10% on average. The system also exported energy to the grid, offsetting close to all the energy imported. [ABSTRACT FROM AUTHOR]

This paper proposes a flexible framework for scheduling and real time operation of electric vehicle charging stations (EVCS). The methodology applies a multi-objective evolutionary particle swarm optimization algorithm (EPSO) for electric vehicles (EVs) scheduling based on a day-ahead scenario. Then, real time operation is managed based on a rule-based (RB) approach. Two types of consumer were considered: EV owners with a day-ahead request for charging (scheduled consumers, SCh) and non-scheduling users (NSCh). EPSO has two main objectives: cost reduction and reduce overloading for high demand in grid. The EVCS has support by photovoltaic generation (PV), battery energy storage systems (BESS), and the distribution grid. The method allows the selection between three types of charging, distributing it according to EV demand. The model estimates SC remaining state of charge (SoC) for arriving to EVCS and then adjusts the actual difference by the RB. The results showed a profit for EVCS by the proposed technique. The proposed EPSO and RB have a fast solution to the problem that allows practical implementation. [ABSTRACT FROM AUTHOR]