Artificial Intelligence-based Approach For Electric Vehicle Travel Behavior Modeling

Guest Editorial: Situational awareness of integrated energy systems

Many EU countries have used financial incentives to promote electric light commercial vehicles and decarbonise urban freight transport. However, the high costs of electric light commercial vehicles still pose a significant adoption barrier. This paper evaluates the impact of financial incentives on the total cost of ownership of electric light commercial vehicles in the European Union and their role in achieving the cost-competitiveness of electric vehicles. A pairwise comparison is performed for three weight categories of electric and diesel vehicles in twelve countries. Our analysis shows that financial incentives have a high impact on the total cost of ownership and have proven necessary to achieve electric vehicles' cost-competitiveness in most cases. However, in specific cases, financial incentives may no longer be needed as the cost of electric vehicles' ownership would have been lower than diesel vehicles even without them. This paper demonstrates the importance of adjusting government incentives to avoid unnecessary funding allocations while promoting the decarbonisation of light commercial vehicles, particularly as the number of incentives does not directly correlate with electric vehicles penetration.

Social welfare-based charging of electric vehicles in the microgrids fed by renewables

Scheduling model of electric vehicles charging considering inconvenience and dynamic electricity prices

The increasing trend of transportation electrification presents investors the opportunity to provide charging services to Electric Vehicle (EV) owners via the energy purchased from the wholesale electricity market. This will benefit EV owners with the availability of competitive rates compared to the regulated utility time-of-use (TOU) rates. The fundamental questions addressed in this paper are 1) will EV owners benefit from the additional choice of Electric Vehicle Charging Stations (EVCSs) compared to home charging? 2) is there any profitable market opportunity for charging stations while the retail electricity market is regulated? To this end, the strategic bidding problem for EVCSs which purchase electricity from the Day-Ahead Electricity Market (DAM) and sell it to EV owners is presented. The strategic bidding problem is constrained by the market-clearing problem within the DAM as well as EVs' charging cost minimization problem. A bi-level optimization problem formulation and a solution method are presented to address this work's research questions. The effectiveness of the proposed structure in gaining profit for EVCSs is illustrated, and it is shown that EV owners also save on their charging cost with the presence of EVCSs as a choice.

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.

The use of electric vehicles (EVs) are increasing in the transportation world. In the electricity market, if a large number of EVs are connected in the power system, but the EVs charging power demand is scheduled appropriately, then the EV owners, as well as the grid, will make the profit. This work focuses on customer-oriented EV charging in the day-ahead market with the objective of reducing the charging cost for EV owners while also considering the impact on spot prices of EV demand in the day-ahead market. An aggregative game has been proposed for satisfying the EVs charging demand in the dayahead electricity market. In the day-ahead aggregative game approach, we formulate the energy charging problem where each EV chooses its own demand strategy to maximize its own benefit selfishly while taking into account the strategies of other EVs. The Nash equilibrium(NE) for the proposed game model has been proved.

The expectation of a huge number of Electric Vehicles (EVs) into the market creates multiple technical problems. The power system is at risk due to the unsecured and unreliable operation resulting from unplanned charging and discharging. The unplanned EV charging and discharging change the load profile of the electric grid. Hence, it is required to develop an effective scheduling scheme for charging and discharging EVs. This research focuses on the minimization of the charging cost of a large number of Electric Vehicles (EVs) in a residential area and parking station. When a huge number of EVs are aggregated in the residential and parking stations, charging and discharging power should be a significant and viable contributor to the power grid. V2G (Vehicle-to-Grid) technology offers additional potential benefits to improve power quality and also system reliability. EVs can also supply G2V (Grid-to-Vehicle) power through the discharge of the battery, which benefits both EV owners and electric utilities. It is very difficult to schedule the configurations of EV charging and discharging in an optimized way so as to decrease the charging cost of EVs. The usage of renewable energy sources like solar Photovoltaic (PV) power, reduces the burden on the grid. The solar PV systems are likely to have significant surplus power during the daytime. In this research, optimal charging and discharging scheduling schemes for EVs are developed and the proposed work is carried out under the MATLAB environment with and without Photovoltaic (PV) systems. The realistic load data is collected from Transmission Corporation of Andhra Pradesh (APTRANSCO), India and the solar data is taken from the Indian Solar Resource Data website. The effectiveness of the scheduling scheme is rated according to its capability to reduce the total cost. Extensive simulation studies have been performed on the distribution system under consideration and the results have been presented. The results demonstrate that the charging cost of EVs can be reduced significantly with the use of solar PV power.

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.

Increased electric vehicle (EV) penetration in the existing power system necessitates analysis of its impact on power system operations. It requires an extensive study on approaches involved for modelling EV in long-term power system planning problems. This study explores the challenges and opportunities involved in long-term power system modelling with incorporation of EV as a load. Power system Planning involving transmission generation co-optimization with EV integration needs analysis of impact on energy and power demand of the system. It summarizes the various methods incorporated in the modelling of generation, transmission, as well as EV load and constraints involved in formulating power system planning models. The EV load profile correlates with the renewable generation profile hence it becomes necessary to consider co-optimization of transmission and generation expansion plans for suitable transmission network reinforcement as well generation mix required for serving load reliably in future years. Further studies require improvement of existing EV modelling techniques with more granular approach towards considering EV load representation.

This paper investigates the day-ahead optimal reserve management problem of electric vehicle (EV) aggregator. Geographically dispersed EVs are coordinated by the aggregator to participate in the day-ahead reserve market. A bilevel model is proposed to formulate the interaction between the aggregator and the EV owners. In the upper level, the EV aggregator aggregates the reserve capacity provided by the EV owners and then bids in the reserve market. In the lower level, the EV owners decide their optimal energy charging/discharging and reserve capacity based on the reserve price released by the aggregator. Compared with existing works, our proposed bilevel model is more practical. To be specific, we take into account the exclusive right constraint for accessing EV battery, i.e., the battery cannot be simultaneously accessed by the EV owner and the aggregator. This practical model leads to a bilevel mixed integer nonlinear program, which is difficult to solve because the lower level problem incorporates nonconvex integer variables. A novel exact algorithm is developed to solve it and the finite convergence is proved. Comprehensive case studies demonstrate the economic merits of our proposed model to both the aggregator and the EV owners. We also compare the solution optimality with the state-of-the-art approach and thus validate the effectiveness of our proposed exact algorithm.

The rising concerns over global climate change and depleting fossil fuel reserves are two of the main reasons for the ongoing efforts towards the electrification of the transportation sector. While greenhouse gases (GHGs) emissions from other sectors are generally falling, emissions from the road transport have increased over the past few decades, with both full electric vehicles (FEVs) and plug-in hybrid electric vehicles (PHEVs) being recognized as potential alternatives to combat climate change and reduce GHG emissions. However, wide-spread integration of FEVs and PHEVs will substantially increase the load on the power system which will eventually affect the reliability of existing power systems. In this paper, a probabilistic model for integrating FEVs and PHEVs with existing power grids is proposed that incorporates important FEV and PHEV characteristics, such as battery capacity, charge depleting distance, and charging rates. In addition, user behavior is taken into account through time of recharging, arrival and departure times, and daily miles driven. Furthermore, different charging strategies, i.e., opportunistic charging and controlled charging with and without vehicle-to-grid (V2G) scheme have been considered to evaluate the impact of FEVs and PHEVs on the composite power system. IEEE-RTS-79 system is used to examine the proposed probabilistic technique considering different FEV and PHEV penetration levels as well as charging strategies. Simulation results show that even a relatively low penetration level of FEVs or PHEVs might have a significant impact on the system reliability unless a proper charging and/or discharging schemes are utilized.

The project aims at sustainable transportation by offering an environmentally friendly option for users who are looking for alternatives to the traditional fossil fuel powered vehicles and thus helping to reduce the dependence on fossil fuels which are a major source of carbon emissions and environmental pollution. Although electric vehicles (EVs) are gradually gaining popularity, there is a need for systems that can assist in identifying the charging stations, check for available slots, and receive information about the charging status in real time since current EV charging and rental services are often fragmented and complicated. The proposed solution is a mobile application that integrates an EV charging station locator and slot booking service through which an EV owner can easily find the charging stations on Google Maps, check for the availability of slots, book slots for specific time, and even get real time updates on the charging. The system has two main modules: the EV charging station module that uses a smart system to control the charging current according to the vehicle's battery capacity and need, and the slot booking module that encourages the use of sustainable transport by allowing users to make bookings for charging slots. The system is developed based on IoT devices, cloud computing and mobile application technologies and the goal is to minimize the use of fossil fuels, encourage the use of eco friendly modes of transportation and for a comfortable and hassle free long distance travel by offering the locations of EV charging stations and controlling the charging process easily.

The objective of this paper is to help state agencies better understand the impact of electric and hybrid vehicles on the Highway Trust Fund and to develop a method for estimating proper annual registration fees for electric vehicles (EVs). In this study, a comprehensive literature review was conducted to summarize the background on electric and hybrid vehicles, current national and state policies and incentives, the trend of EV market in the U.S., and registration fees on electric and hybrid vehicles. As electric and hybrid vehicles do not contribute to fuel excise tax revenue, to compensate the lost tax revenues, some states charge additional annual registration fees to EV owners. To help the legislators determine the proper annual fees, a method was developed to assess the additional registration fees for EVs and plug-in hybrid electric vehicles (PHEVs) in Alabama. The collected data include number of registered electric and hybrid vehicles, fuel tax per gallon, and annual average mileage traveled by electric and hybrid vehicles in Alabama. The results of this study served as a key reference in the Rebuild Alabama Act that proposed an annual registration fee of $200 and $100 for EVs and PHEVs, respectively, which is effective since January 2020. The method in this study can be applied to other states for developing policies on registration fees for EVs and PHEVs to offset the fuel excise tax revenue loss.

In recent years, the adoption of electric vehicles (EVs) and variable energy resources such as photovoltaic (PV) has increased with the desire to reduce reliance on fossil fuels, decrease emissions, and promote sustainable energy. However, the increasing adoption of EVs and PVs has introduced unprecedented challenges to the reliability of power systems. The challenge lies in the inherent intermittency associated with solar generation and the uncertainty introduced by the charging load of EVs on the demand side of power grids. Therefore, it is indispensable from the perspective of power system operation and planning to consider the uncertainties associated with the output power of these resources in the reliability assessment framework. This paper develops an electric vehicle load model considering diverse charging station locations, EV types, and drivers' behavior. Also, the proposed method integrates the uncertainty of PV generation through interval prediction utilizing the K-Nearest Neighbors regressor. A sequential Monte Carlo simulation is used to analyze the impact of PV interval (forecasted lower and upper generation profile), EV load (hourly and peak), line failures, and demographic characteristics associated with EV on power system reliability. The reliability assessment is extended to sensitivity analysis and evaluation of the impact of EV loads and PV generation profiles on the capacity value of PV generators with different capacities, utilizing the Discrete Convolution approach. The proposed approach is demonstrated on the IEEE Reliability Test System and the results show the effectiveness of the proposed approach in determining the reliability of the power system by explicitly accommodating PV uncertainties and the intricacies of EVs.