Abstract:Decarbonization of transport via replacing fossil fueled vehicles with electric ones, has been recognized worldwide as a key measure to tackle the challenges of climate change and environmental pollution due to green house gas emissions. Electric vehicles (EVs), which are considered by many governments as strategic industry of national importance, have seen rapid development and breakthroughs in battery and vehicle technologies over the past few years. This, coupled with preferential state EV purchasing incentives, likely results in accelerated deployment of EVs over the next few years. However, the mass rollout of EVs will pose a significant challenge to the secure and efficient operation of power systems due to temporal and spatial characteristics of EV charging load. On the other hand, the development of EVs can bring many benefits to power system operations as EVs with vehicle to grid (V2G) technologies could participate in power system balancing and reserve operations, peaking shifting as well as increasing system’s ability to integrate high penetration of renewable power. Therefore, it is important to plan and build the suitable infrastructure, and to manage the integration of EVs to ensure future power systems operate more reliably, more flexibly, and more economically, by coordinating actions of all temporally and spatially distributed actors of different natures, with due considerations to the constraints, and uncertainties imposed upon them by highly complex external environments. This special issue contains fifteen original papers to address various issues relating to the EV battery management and grid integration of EVs. In summary, these fifteen papers serve as an introduction to the recent development in addressing various challenging issues in EVs and their integration with the utility grid penetrated with a significant level of renewables. It is hoped that this issue will serve as a catalyst for future research aimed at tackling complex problems decarbonizing the whole energy system from head to tail.

Abstract:Traditional experimental economics methods often consume enormous resources of qualified human participants, and the inconsistence of a participant’s decisions among repeated trials prevents investigation from sensitivity analyses. The problem can be solved if computer agents are capable of generating similar behaviors as the given participants in experiments. An experimental economics based analysis method is presented to extract deep information from questionnaire data and emulate any number of participants. Taking the customers’ willingness to purchase electric vehicles (EVs) as an example, multi-layer correlation information is extracted from a limited number of questionnaires. Multi-agents mimicking the inquired potential customers are modelled through matching the probabilistic distributions of their willingness embedded in the questionnaires. The authenticity of both the model and the algorithm is validated by comparing the agent-based Monte Carlo simulation results with the questionnaire-based deduction results. With the aid of agent models, the effects of minority agents with specific preferences on the results are also discussed.

Abstract:Electric vehicles (EVs) have received significant attention because of the potential energy savings and emission reductions they enable. However, current studies and demonstrations have focused mainly on specific technologies and equipment types, which cannot in themselves solve the predicaments facing EVs. This study points out that EVs will form a large, complex system that needs to be optimized over different aspects to compete with traditional vehicles. Therefore, whole-system thinking is needed to support the development control of EVs, with a broader scope than operational control, and the core issue is the interaction between EVs and power grid, including their coordinated development and operational management. For development control of EVs, a target system and a step-wise optimization method are presented, as well as the basic principles for designing the target system. There are two key barriers in EVs’ development: the research and mass production of high-performance power batteries, and the formulation of a favorable mechanism to capture benefits and encourage development. To address the problems of EV development, a promotion method that combines franchising with moderate competition is proposed. The concepts and methods developed in this study can facilitate the research and development of EVs.

Abstract:The usage of each private electric vehicle (PrEV) is a repeating behavior process composed by driving, parking, discharging and charging, in which PrEV shows obvious procedural characteristics. To analyze the procedural characteristics, this paper proposes a procedural simulation method. The method aggregates the behavior process regularity of the PrEV cluster to model the cluster’s charging load. Firstly, the basic behavior process of each PrEV is constructed by referring the statistical datasets of the traditional private non-electric vehicles. Secondly, all the basic processes are set as a simulation starting point, and they are dynamically reconstructed by several constraints. The simulation continues until the steady state of charge (SOC) distribution and behavior regularity of the PrEV cluster are obtained. Lastly, based on the obtained SOC and behavior regularity information, the PrEV cluster’s behavior processes are simulated again to make the aggregating charging load model available. Examples for several scenarios show that the proposed method can improve the reliability of modeling by grasping the PrEV cluster’s procedural characteristics.

Abstract:Electic vehicles (EVs) show great potential to cope with the intermittency of renewable energy sources (RES) and provide demand side flexibility required by the smart grid. Furthermore, EVs will increase the electricity consumption. Large scale integration of EVs will probably have substantial impacts on power systems. This paper presents a methodology to transform driving behavior of person into one of the cars in order to analyze the driving pattern of EVs based on the National Travel Surveys. In the proposed methodology, a statistical process is used to obtain the driving behavior of cars by grouping the survey respondents according to the driving license number and car number, and mapping the households with similar characteristics. The proposed methodology was used to carry out the driving pattern analysis in the Nordic region. The detailed driving requirements and charging/discharging availability of vehicles along the day were obtained. Two types of EV availabilities were studied in this paper considering different charging/discharging conditions of EVs for the power system integration, i.e. EV availability all day and EV availability at home. The results show that the daily driving requirements of the Nordic region are not very intensive. The driving patterns of vehicles in the Nordic region vary on weekdays and weekends. The two types of EV availabilities are quite different from each other.

Abstract:In recent years, a wide variety of centralised and decentralised algorithms have been proposed for residential charging of electric vehicles (EVs). In this paper, we present a mathematical framework which casts the EV charging scenarios addressed by these algorithms as optimisation problems having either temporal or instantaneous optimisation objectives with respect to the different actors in the power system. Using this framework and a realistic distribution network simulation testbed, we provide a comparative evaluation of a range of different residential EV charging strategies, highlighting in each case positive and negative characteristics.

Abstract:With the large penetration of renewable energy, fulfilling the balance between electricity demand and supply is a challenge to the modern power system. According to the UK government, the wind power penetration will reach 30% by the year 2020. The role of electric vehicles (EVs) contributing to frequency response was investigated. A dynamic frequency control strategy which considers the comfort level of vehicle owners was developed for EVs to regulate their power consumption according to the deviation of system frequency. A simulation model of a population of EVs equipped with such control was implemented in Matlab/Simulink platform. In this paper, a simplified Great Britain power system model is used to study the contribution of EVs to dynamic frequency control. The case study showed that using EVs as a demand response resource can greatly reduce the frequency deviations. And the rapid response from EVs can help reduce the operation cost of conventional generators.

Abstract:In this paper, a dual droop-frequency diving coordinated control strategy is proposed for electric vehicle (EV) applications, where the hybrid energy storage system (HESS) with supercapacitors and batteries is integrated to prolong the life time of storage elements. The dynamic power allocation between the supercapacitor and batteries are obtained through the voltage cascaded control, upon which the high and low frequency power fluctuation are absorbed by the supercapacitors and batteries respectively to fully exploit the advantages of the supercapacitors and batteries. Moreover, the power capacity is scaled up by connecting storage blocks in parallel. A dual droop control scheme for parallel-connected energy storage system and its operation principle is introduced on the aspect of current sharing characteristic and state-of-charging (SOC) management. After detailed analysis and formula derivation, the corresponding loop parameters are designed. Through this control method, the current sharing performance is ensured and each block makes the self-adaptive adjustment according to their SOC. Consequently, the load power can be shared effectively, which helps to avoid the over-charge/over-discharge operation and contributes to the life cycle of the energy storage system. Each module is autonomous controlled without the necessity of communication, which is easy, economic and effective to realize. Finally, the simulation and experimental results are exhibited to verify the effectiveness of the proposed control scheme.

Abstract:In this paper, the microgrid economic scheduling mathematical model considering the integration of plug-in hybrid electric vehicles (PHEVs) is presented and the influence of different charging and discharging modes on microgrid economic operation is analyzed. The generic algorithm is used to find an economically optimal solution for the microgrid and PHEV owners. The scheduling of PHEVs and the microgrid are optimized to reduce daily electricity cost and the potential benefits of controlled charging/discharging are explored systematically. Constraints caused by vehicle utilization as well as technical limitations of distributed generation and energy storage system are taken into account. The proposed economic scheduling is evaluated through a simulation by using a typical grid-connected microgrid model.

Abstract:Renewable energy, such as wind and photovoltaic (PV), produces intermittent and variable power output. When superimposed on the load curve, it transforms the load curve into a ‘load belt’, i.e. a range. Furthermore, the large scale development of electric vehicle (EV) will also have a significant impact on power grid in general and load characteristics in particular. This paper aims to develop a controlled EV charging strategy to optimize the peak-valley difference of the grid when considering the regional wind and PV power outputs. The probabilistic model of wind and PV power outputs is developed. Based on the probabilistic model, the method of assessing the peak-valley difference of the stochastic load curve is put forward, and a two-stage peak-valley price model is built for controlled EV charging. On this basis, an optimization model is built, in which genetic algorithms are used to determine the start and end time of the valley price, as well as the peak-valley price. Finally, the effectiveness and rationality of the method are proved by the calculation result of the example.

Abstract:Electric vehicles (EV) are proposed as a measure to reduce greenhouse gas emissions in transport and support increased wind power penetration across modern power systems. Optimal benefits can only be achieved, if EVs are deployed effectively, so that the exhaust emissions are not substituted by additional emissions in the electricity sector, which can be implemented using Smart Grid controls. This research presents the results of an EV roll-out in the all island grid (AIG) in Ireland using the long term generation expansion planning model called the Wien Automatic System Planning IV (WASP-IV) tool to measure carbon dioxide emissions and changes in total energy. The model incorporates all generators and operational requirements while meeting environmental emissions, fuel availability and generator operational and maintenance constraints to optimize economic dispatch and unit commitment power dispatch. In the study three distinct scenarios are investigated base case, peak and off-peak charging to simulate the impacts of EV’s in the AIG up to 2025.

Abstract:The storage capacity of the batteries in an electric vehicle (EV) could be utilised to store electrical energy and give it back to the grid when needed by participating in vehicle to grid (V2G) schemes. This participation could be a source of revenue for vehicle owners thus reducing the total charging cost of their EVs. A V2G simulator has been developed using MATLAB to find out the potential cost saving from participation of EVs in V2G schemes. A standard IEEE30 network has been modelled in the simulator which uses the MATPOWER engine to undertake power flow analysis. A novel control algorithm has been developed to take advantage of the difference between the selling and buying electricity prices by charging and discharging EVs at the appropriate time. Two scenarios are simulated to compare the total charging cost of EVs with or without the utilisation of V2G technology within the power system assuming a total of 5000 EVs. The results of the simulation show that the applied control strategy with V2G is able to reduce the charging cost of EVs by 13.6 % while satisfying the minimum requirement for state of charge (SoC) of the EV batteries to complete their next journey.

Abstract:In recent years, the Chinese government and State Grid Corporation of China (SGCC) have paid great attention to the technical development and infrastructure construction for electric vehicles (EVs). This paper focuses on the mechanism of Smart Battery Charging and Swapping Operation Service Network for EVs including its overall architecture and operational mode. The overall architecture based on the internet of things is analyzed and clarified through terminal device, station management and management center layers. Then two different types of demonstration projects are presented which expound on the condition of EV’s infrastructure construction. Lastly, performance analysis of the charging behaviors of electric taxis in fast charging station based on the Queuing theory is proposed. The simulation results show that the service time and the number of generators has an influence on the average waiting time and the length of queue.

Abstract:This study presents a centralized control scheme that coordinates parallel operations of power conditioning system (PCS) for the grid interactions of electric vehicles (EVs) in EV charge–discharge and storage integration station. Key issues for the control and operation of PCS under various operation modes are discussed, including vehicle to grid (V2G) mode, stand-alone mode and seamless transfer mode. The intelligent multi-mode charge–discharge method is utilized for the V2G mode, and the parallel control method based on communication network is adopted for the stand-alone mode. In addition, a novel seamless transfer strategy is proposed, which is able to implement PCS transition between V2G mode and stand-alone mode. The detailed process of the seamless transfer between the two modes is illustrated. Experimental results are presented to show the performance and feasibility of this strategy.

Abstract:The aim of this research study is to present a method for analyzing the performance of the wireless inductive charge-while-driving (CWD) electric vehicles, from both traffic and energy points of view. To accurately quantify the electric power required from an energy supplier for the proper management of the charging system, a traffic simulation model is implemented. This model is based on a mesoscopic approach, and it is applied to a freight distribution scenario. Lane changing and positioning are managed according to a cooperative system among vehicles and supported by advanced driver assistance systems (ADAS). From the energy point of view, the analyses indicate that the traffic may have the following effects on the energy of the system: in a low traffic level scenario, the maximum power that should be supplied for the entire road is simulated at approximately 9?MW; and in a high level traffic scenario with lower average speeds, the maximum power required by the vehicles in the charging lane increases by more than 50?%.

Abstract:To improve the low-temperature charge-discharge performance of lithium-ion battery, low- temperature experiments of the charge-discharge characteristics of 35 Ah high-power lithium-ion batteries have been conducted, and the wide-line metal film method for heating batteries is presented. At ?40 °C, heating and charge-discharge experiments have been performed on the battery pack. The results indicate the charge-discharge performance is substantially worse in cold climates, and can be significantly improved by heating the battery pack with a wide-line metal film. Pulse charge-discharge experiments show that at ?40 °C ambient temperature, the heated battery pack can charge or discharge at high current and offer almost 80% power.