Large Scale EVs’ Charging Scheduling Ensuring Secure and Efficient Operation of Traffic and Distribution

Electric vehicles (EVs) are becoming a popular mode of transportation. However, one of the main concerns for EV and CNG owners is the availability and accessibility of charging stations. In this project, we present the development and implementation of an EV and CNG charging station finder web application. The app uses the Google Maps API to display the location of nearby pay stations and provide details such as connection type, availability and price. The app also allows users to filter charging and CNG stations according to their preferences and report charging stations as false or unserviceable. User testing is done to evaluate the usability and performance of the application. The results show that the app is easy to use and provides accurate and up-to date information on payment facilities In this system, the User is able to manage All his EVs inside the app plus he has search/book as lot in advance in the charging station. The User can also search an EV station based on nearby, city or kilometers. The System also provides a Roadmap if you enter source & destination with the charging stations on the way according to the kilometers entered. Admin will manage all the stations and slots. The need for developing infrastructure such as charging stations is undeniable. This EV Charging Station web application has been developed to help EV drivers locate available charging stations near them. After locating a charging station, users can also book a slot at the station to charge their vehicle. EV owners can also use this system to plan their trips more efficiently. Users simply need to specify the source and destination. Based on these two parameters, this system prepares a roadmap with all available charging stations along the journey.

Chapter 4 - Intelligent charging station recommendations for electric vehicles in the charging market: a fuzzy−deep learning approach

Although electric vehicles (EVs) can help alleviate the energy crisis and environmental pollution, their uncoordinated charging schedules could bring significant negative impacts on the secure and economic operation of power and traffic systems. Given this background, a decarbonization dispatching strategy for EVs based on life cycle analysis is proposed. First, life cycle analysis theory is briefly described. A microscopic traffic flow model (MTFM) is next established as a link between the power system and traffic system concerned. Further, according to the difference between urgent and flexible charging characteristics, a corresponding dispatching strategy is formulated for EVs. For fast charging EV users, the charging station (CS) selection and navigation strategy is addressed. For EV users employing slow charging, a bi-level optimal dispatching model considering decarbonization, distribution system security and battery loss is presented based on life cycle analysis. Finally, a coupled system including a 195-node traffic system and a 22-node distribution system is employed to demonstrate the effectiveness of the proposed model and method.

With large-scale integration of intermittent renewable energy sources (RESs), power systems need more ancillary services to guarantee system stability and security. When properly managed, large-scale electric vehicles (EVs), whose markets have been growing rapidly in recently years, can provide frequency regulation service for power systems, benefiting both EVs and the grid. However, interests balance between different parties and regulation signals uncertainty are not well considered when deploying EVs to provide regulation services in previous studies. In this article, a leader–follower game is proposed for individual EVs and their aggregator to determine optimal day-ahead charging and frequency reserve scheduling considering regulation signals uncertainty. Acting as the leader in the game, the EV aggregator prices for its charging service and offers for regulation service to encourage EVs’ participation. EVs act as price-takers and aim at achieving a tradeoff between costs from electricity consumption and revenues from providing regulation service through an aggregative game. The proposed leader–follower game is then converted to a bilevel optimization problem, which can be solved by introducing an aggregate EV model. Furthermore, for the sake of privacy protection, a distributed approach is designed for EVs to optimize their scheduling. Case studies with the proposed scheduling model are carried out to demonstrate its effectiveness.

The recent trends and need for green and sustainable energy tendered automobile industries to switch toward electric vehicles. DC fast charging stations are required for long-distance driving and large vehicles such as buses, trucks. Integration of DC fast charging stations to the existing grid results in congestion and stability problems. These problems can be overcome by deploying small DC microgrid clusters within the microgrid. This paper investigates the effect of massive electric vehicle integration on the operating parameters of DC microgrids, as DC fast charging stations are a necessity. Bidirectional DC/DC buck-boost converters are used for interfacing EVCS into DC microgrid. In addition to the droop controller, the converter control also consists of PI-based voltage and current controllers. Performance and stability analyses are evaluated through mathematical modeling and simulation analysis for the microgrid test model. The DC microgrid provided competent results with large-scale electric vehicle integration. The test results validate that the DC microgrid is a viable solution for integrating electric vehicles considering stability.KeywordsDc microgridElectric vehicleCharging station

Electric vehicle adoption is a trend in many countries, and the demand for charging station infrastructure is at a rapid pace. The placement of charging stations is the key research topic of many researchers, but charging scheduling is also a problem that is going to rise in the near future. The proper charger utilization, maintaining coordination between charging stations, and satisfying users' demands are some of the key challenges. The traffic pattern is uncertain, coordination of distances between charging stations and users is done by Euclidean distance. The traffic-aware fair charging scheduling (TAFCS) strategy is proposed, which will have a balance on charger utilization and user prioritization, and keep the fairness by equal distribution of electric vehicles among all the charging stations having a centralized charging system monitored by an aggregator. The distribution of the traffic pattern of electric vehicles is performed by Monte Carlo simulation. The proposed system is tested on the IEEE 33 bus standard system using the predefined voltage limits of each bus and limiting power loss to lessen its burden. The discharging process of 50 electric vehicles (V2G) is performed by optimal placement by obtaining the weakest buses, which makes it an intelligent distribution system. This proposed charging framework is validated on MATLAB R2020a.

In order to evaluate the influence factors of power loss in distribution system, an useful software is designed to analyze the power loss problems in this paper. First of all, the influence factors in distribution system are classified into device and operation aspects according to the relationship with the status of the distribution system. Then, simplified power loss models of differentiated distribution network are established based on economic radius of power supply area. Next, influence mechanism of each main influence factor is analyzed. On the basis of sensitivity algorithm, the evaluation software is designed and applied to a simulation example of type E distribution network. This designed software provides a powerful tool for power loss analysis in the differentiated and diversified distribution system.

As the world's supply of fossil fuels declines, countries are shifting more and more toward green energy sources that are dependable, efficient, and sustainable. Electric vehicle (EV) components are becoming less expensive, and EV technology is developing quickly. As a result, EVs are becoming more and more popular as greener alternatives to conventional cars, which have a big impact on the environment. The State of Charge (SoC) of EV batteries is continuously monitored by the proposed paper, which also shows the SoC next to a map that shows the locations of local charging stations. Users are redirected to a website where they can reserve a space at the nearest charging station based on recommendations through the EV driver's interface. An effective slot booking system and additional charging infrastructure are becoming necessities as the use of EV s increases. Even though charging an Electric Vehicle (EV) can take a while, the suggested booking model aims to provide a practical and economical alternative.

This research reviewed challenges in electric vehicles (EVs) charging during trips and proposes a novel powertrain architecture that is cost-effective and time-efficient for EVs to instantly increase their range without deviating from their route. The main objective is to study the feasibility and economic analysis of an additional battery for EV applications without disturbing the traction battery under unknown future charging requirement information about EVs, which helps reduce waiting time at charging stations, mitigate the problem of battery swapping, charging 100% through renewables, reduce the negative impact of EVs on the power grid. This study considers an add-on battery of 15kWh capacity offered as battery-as-a-service (BaaS), available at charging stations or delivered at remote locations for users. We conducted an economic analysis, comparing EVs with and without add-on batteries alongside traditional petrol and diesel vehicles. Based on the results obtained, it can be inferred that adopting this framework reduces the travel time by 55 minutes but increases the cost by US$ 0.176 compared to fast charging for a 400 km trip. We can conclude that EVs with main traction and add-on batteries provide a tradeoff between cost and time and are beneficial for both EV user and add-on battery charging and swapping station (ABCSS) operators.

Electrifying transportation in the form of the large-scale development of electric vehicles (EVs) plays a pivotal role in reducing urban atmospheric pollution and alleviating fossil fuel dependence. However, the rising scale of EV deployment is exposing problems that were previously hidden in small-scale EV applications, and the lack of large-scale EV operating data deters relevant explorations. Here, we report several issues related to the battery utilization and energy consumption of urban-scale EVs by connecting three unique datasets of real-world operating states of over 3 million Chinese EVs, operational data, and vehicle feature data. Meanwhile, by incorporating climatic data and EV data outside China, we extend our models to several metropolitan areas worldwide. We find that blindly increasing the battery energy of urban EVs could be detrimental to sustainable development. The impact of changes in the energy consumption of EVs would be exacerbated in large-scale EV utilization, especially during seasonal shifts. For instance, even with a constant monthly driving demand, the average energy consumption of Beijing light-duty EVs would change by up to 21% during winter-spring shifts. Our results may also prove useful for research on battery resources, urban power supply, environmental impacts, and policymaking.

The high penetration of renewable energy incurs serious power fluctuations in the power system. Large scale electric vehicles (EVs) under the centralized control are able to provide considerable regulation capacity for the power system. In existing modeling methods for centralized EVs, the accurate control results for the regulation services were achieved by modeling each EV individually. However, the computational complexity was a serious problem for modeling large scale EVs. In this paper, a reduced modeling method for centralized EVs is developed by describing a population of EVs with a partial differential equation (PDE). This PDE modeling method uses a finite number of state-of-charge (SOC) intervals to describe the flows of charging EVs, idle EVs and discharging EVs. Considering the three connecting states (CNS) of EVs, this PDE modeling method realizes the power regulation for centralized EVs by changing the CNS of EVs located in different SOC intervals. Comparative simulations validate that the PDE modeling method for large scale centralized EVs achieves high control accuracy and high computational efficiency.

Electric vehicles have the potential to improve power distribution system’s operational performance. Integration of EV charging stations could have positive impacts on system losses and environmental emissions. This research presents a methodology to install different capacities of electric vehicle charging stations inside microgrids, integrated in distribution systems. The economic operation of electric vehicles in microgrids has been investigated in terms of system operational costs, system losses, and system environmental emissions. Electric vehicles’ uncertain behaviors creates a level of uncertainty, thus, the system reliability due to electric vehicle operation is also assessed in this research. A well-known PG&E 69-bus distribution system is chosen for simulations and case studies. Since microgrids have different distributed energy resources along with electric vehicles, which may generate uncertainty for distribution system operations, this research considers uncertain calculation modeling to get more accurate results in simulations and case studies. Sensitivity analysis has been performed for demand and price uncertainties and results are presented for corresponding system performances. The results validate the proposed methodology for an efficient operation of electric vehicles in microgrids integrated distribution systems.

Distributed Generations (DGs) are most commonly used in Advanced Power Distribution System (APDS) for improving the voltage level and minimizing the power loss in distribution system. This paper represents a modified Teaching Learning Based Optimization Technique (TLBO) to reconfigure the distribution system and find the optimal placement and sizing of DGs to minimize the total system loss simultaneously. In modified TLBO radial trees are consider as a initial population and possible paths between nodes are considered as design variables for finding the optimal reconfigured route. A shift operator is used to change the switch position in order to find the desired switch combination. A simultaneous reconfiguration with DG placement is done by the proposed method with different sizes of DGs in search of optimal solution. Proposed approach is illustrated on the standard IEEE-33 bus test system considering real-time design practices.

In modern power system, distributed generators (DGs) have become an effective method to reduce active power loss, improve voltage stability, etc.. In this paper, a new Improved Firefly Algorithm (IFA) is proposed to obtain optimal placement and capacity of DGs to improve power flow distribution and voltage stability, reduce active power loss in distribution system. Compared with the standard Firefly Algorithm (FA), the proposed IFA has better convergence speed and can obtain a smaller active power loss. In addition, the power loss sensitivity factors are introduced to steer the algorithm to seek the best position about the DGs. In order to verify the performance of IFA, IFA and FA are tested on the standard IEEE-33 and IEEE-69 systems to solve the problem of joint configuration optimization of multiple generators. The results of simulation show the active power loss of the distribution system decreases and the voltage stability of the system node improves. By comparing the results of the IFA and FA, a conclusion can be reached, the IFA is more effective and more suitable to reduce active power loss by reasonably allocating the capacity and placement of DGs than FA.

In this paper, a hybrid methodology is implemented for optimal allocation of synchronous generator-based dispersed generation, for minimization of power losses in distribution system considering voltage dependent practical loads. The proposed hybrid approach is a combination of equivalent current injection-based sensitivity factor and particle swarm optimization. Sensitivity factors advantages in reducing the search space and optimal allocation of synchronous generator-based DG are obtained by particle swarm optimization for the diminution of power losses. The suggested method is validated on 33-bus and 69-bus radial distribution system. The results show significant improvement in voltage profile of all buses after optimal allocation of DG in distribution system.