Electric Vehicle Usage Pattern Analysis Using Nonnegative Matrix Factorization in Renewable EV-Smart Charging Grid Environment

High penetration of renewable energy in the power grid causes serious frequency deviations. Large-scale integrations of electric vehicles (EVs) in the power grid provide considerable vehicle-to-grid potential for frequency regulation. Existing frequency regulation strategies with aggregated EVs realize accurate power control that relies on complete information interaction between the EV aggregator and individual EVs. However, the data collection for all EV parameters is not applicable due to privacy protection and the limited communication environment. Considering the limited data collection from grid-connected EVs, this paper provides a novel frequency regulation strategy and tends to address the uncertain influence from EV users’ charging requirements, the EV aggregator’s power regulation, and the frequency regulation performance. Firstly, considering the influence of the limited data collection by EVs on the users’ requirement of traveling and regulation preference, a probabilistic evaluation model for the available regulation capacity of the EV aggregator and the probabilistic control method for EVs are developed. Then, a frequency regulation strategy with error correction control and progressive regulation recovery is developed to simultaneously guarantee the system frequency regulation performance and the regulation requirements of EV users. Finally, case studies are carried out to validate the effectiveness of frequency regulation strategy for decreasing the uncertain influence from the limited data collection, ensuring the EV users’ requirements, and improving the system frequency stability.

Toward sustainable cities: Where the electric vehicle users reside matters

The charging transaction data of electric vehicle (EV) users is crucial for studying charging market dynamics and formulating effective policies. However, due to factors such as the privacy of EV users and the complex coupling relationships between charging dealers, existing EV charging transaction datasets are plagued by issues such as incompleteness, significant bias, and a lack of real-time information. To address these issues, a real-time charging transaction dataset has been created, comprising 441,077 charging transactions collected from 13 charging stations in China over a 2-year period. The dataset includes detailed data of EV user charging transaction time, price and charging status, as well as the charging termination reasons and weather data for each charging session. This dataset offers references for identifying EV user behaviors and extracting charging fault factors from multiple aspects, supporting research applications in EV charging facility planning, EV charging and discharging management, and charging economic evaluation.

Electric vehicle charging scheduling considering urgent demand under different charging modes

The scaled access of electric vehicles (EVs) exacerbates load fluctuations in distribution networks, which is not conducive to the stable and economic operation of the distribution networks. At present, user satisfaction degree is generally low. To avoid this problem, this paper proposed a two-layer cooperative optimization of flexible interconnected distribution networks considering EV user satisfaction degree. First, the EV user satisfaction degree model is established by considering EV users’ charging waiting time, charging power, and other indicators. At the same time, an EV charging mode switching model is constructed based on the number of EVs entering the network and their battery charge state. On this basis, the Monte Carlo algorithm is used to generate the results of the daily distribution of EV loads taking into account the user satisfaction degree, so as to improve the load ratio of the transformer in the distribution network. Further, a two-layer cooperative optimization of flexible interconnected distribution networks considering electric vehicle user satisfaction degree is developed with the daily operating cost of each network as the optimization objective. Finally, a flexible interconnected power distribution network consisting of three power distribution networks is used as an example for validation. The results show that this method is effective in improving EV user satisfaction degree and reducing the peak-to-valley ratio of the system load while taking into account the safe and economic operation of the distribution network, which greatly improves the reliability and economy of the operation of the flexible interconnected power distribution network.

With the development of intelligent distribution networks and the proposal of the carbon peaking and carbon neutrality goals, the importance of demand‐side management for the improvement of flexible power operation systems is becoming increasingly prominent. To solve the problems of the excessive peak–valley load differences, the insufficient utilization of demand‐side resources, and the unreasonable pricing of aggregators, this paper proposes a three‐stage economy optimization method for the aggregator based on EV(electric vehicle) user response volumes. To gain the advantages of different types of EVs while increasing the dispatchable capacity, the contract mode between the aggregator and EVs is divided into three categories: complete dispatching, rolling reward and punishment mechanism dispatching, and free dispatching. Based on the cloud model, considering loss aversion, the user's own time flexibility, and the impact of the tariff set by the aggregator on the user's decision‐making, we introduced indicator weights to obtain the improved cloud model. Based on the improved cloud model, a user response volume model is obtained. Then, the aggregator performs a three‐stage pricing optimization operation for EVs based on the bid‐winning peak shaving capacity. In the first stage, the attraction between EVs and charging stations is determined based on the law of gravity, and the charging and discharging reward electricity price is set. In the second stage, the dynamic electricity price of three types of EVs and the charging punishment electricity prices of the rolling reward and punishment mechanism dispatching type are determined. In the third stage, the electricity price of the rolling reward and punishment dispatching EVs is further optimized based on their lack of peak shaving capacity. The method proposed in the paper has been verified to be effective in increasing aggregator profits, increasing EV dispatch capacity, and reducing EV charging costs through the example analysis.

The problem of energy and reserve exchange between electric vehicles (EVs) and fast charging station (FCS) is studied using a Stackelberg game. In this game, the FCS operator, who acts as a leader, needs to set its energy and reserve prices to optimize its revenue while ensuring EV users' charging demand. On the other hand, EV users, who act as the followers, needs to decide their charging and reserve strategies to optimize a tradeoff between the benefit from battery charging and reserves provision. It is shown that the proposed game possesses a social optimal Stackelberg equilibrium, in which the FCS operator optimizes its prices and the EV users choose their equilibrium strategies. A mathematical programming with equilibrium constraints (MPEC) reformulation of the game is proposed and can be solved efficiently by commercial software packages. The reformulation enables the FCS operator and EV users to reach the equilibrium and is assessed by extensive simulations.

In recent years, environmental issues have motivated the wide usage of electric vehicles (EVs) due to their zero tailpipe emission. However, this trend can pose severe challenges to power systems, such as decreasing equipment lifetime. Moreover, the CO2 emission of EVs is closer to that of internal combustion engine vehicles in some cases due to the carbon footprint of EV charging. Modeling the uncertain nature of EV users’ behavior is another obstacle due to the complex dynamics of these uncertainties. To overcome these problems, an overarching day-ahead smart charging method is proposed in this paper from the perspective of distribution system operators (DSOs), EV users, and governments simultaneously. The aim of the proposed method is to minimize the operating cost of microgrids, the degradation cost of EV batteries, and emission cost by scheduling the active and reactive power of EV parking lots integrated with photovoltaic (PV) systems as well as finding the optimum network configuration. Previous model-based methods cannot appropriately model uncertainties in EV users’ behavior because of some statistical assumptions. Nevertheless, this paper employs data-driven methods based on generative adversarial networks (GAN) to represent these uncertainties. The performance of the proposed method is evaluated by implementing it on a real reconfigurable microgrid. The results show that using the proposed method, the DSO and emission costs can reduce by 11.96% and 3.37% compared to the uncoordinated charging of EVs, respectively. Furthermore, the share of sustainable energy in EV charging increases by 9% using the proposed method.

Although the rapid progress of the global economy and technology has advanced human civilization, it has also caused tremendous damage to the global ecological environment. Therefore, humans are thinking seriously about the environment and its sustainable development. One of the solutions to environmental problems is new energy vehicles. Since the promulgation of the “Energy Saving and New Energy Vehicle Industry Development Plan (2012–2020)” by the General Office of the State Council, the Chinese government has determined a strategy of pure electric driving technology. The electric vehicle market in China has expanded rapidly, making China the largest electric vehicle market in the world. Hence, research on the situation of electric vehicles in China is highly necessary and of reference value for other countries to develop electric vehicles. As a result, it is a critical issue to develop low-carbon, energy-saving, and intelligent electric vehicles to reduce the environmental impact. This paper establishes a theoretical framework based on the theory of planned behavior (TPB), technology acceptance model (TAM) and innovation diffusion theory (IDT), and explores the key factors influencing consumers’ purchase of electric vehicles. The results show that: The application of the key factor model constructed in this study to consumers’ behavioral intention regarding electric vehicle purchase is acceptable. According to the structural equation modeling (SEM) analysis results, (1) In terms of behavioral intention: Consumers’ control over the resources required to purchase electric vehicles has the highest influence on their behavioral intention, while consultation opinions from consumers’ surroundings also significantly affect their behavioral intention to purchase electric vehicles. In addition, consumers’ environmental awareness and acceptance of technology products will also influence their behavioral intention. (2) In terms of attitude toward behavior: When consumers believe that electric vehicles are more beneficial at the individual, environment or national level, or they believe that the usage of electric vehicles is simpler and more convenient, they will show a more positive attitude towards the purchase of electric vehicles. Consumers consider electric vehicles as forward-looking technology products with similar driving operation and usage cost compared to traditional vehicles. (3) In terms of regulations: The opinions of consumers’ family members, friends, colleagues or supervisors do not significantly affect the attitude or behavior of consumers regarding electric vehicle purchase. The key factors influencing consumers’ purchase of electric vehicles are not only applicable to the design and development of electric vehicles that better suit consumer demands, but also serve as a theoretical basis for the popularization of electric vehicles, and provide a reference for consumers’ choice and purchase. Therefore, the government and relevant manufacturers need to consider increasing the publicity of electric vehicles and launch more attractive battery and charging schemes to attract consumers and promote the sustainable development of the automobile industry.

Deployment of Electric Vehicles (EV) is increasing in recent years due to economic and environmental advantages compared with fossil fuel-based vehicles. As the market of EVs grows, new challenges to the electric grid are emerging to accommodate the EVs demand, especially in the distribution networks. In this paper, we investigate the impact of EVs deployment on the electricity demand and distributed network. We propose a model to generate EV demand profiles that consider the EV users’ driving pattern such as daily energy consumption and charging schedule, in addition to the EV’s charging characteristics. The EV demand model uses data we obtained from a survey to evaluate the model’s parameters. We use the EV demand model to simulate and evaluate the impact of EVs demand on the distribution network. We present a case study with an actual model for a distribution network to evaluate the impact of EVs on the distribution network in Saudi Arabia. We analyze the simulation results and show how EVs impact the demand and the distribution network performance.

A market-based real-time algorithm for congestion alleviation incorporating EV demand response in active distribution networks

The widespread use of electric vehicles (EV) has put a strain on the stable operation of power grid. Therefore, the potential of EV cluster power load regulation has been paid attention. In the cluster, the electric vehicle aggregator (EVA) can gather a large number of EVs and participate in the electricity spot market by optimizing the charging/discharging power. In this study, a bi-objective optimization model for V2G enabled EV cluster operation is proposed to determine the optimal load of EV cluster considering the electricity spot market. First, the scheduling capability of EVs is modelled and aggregated considering the EV user willingness. Then, the demand response and electricity spot trade for EVA are analyzed. Based on the capability constraints and the market rules, an optimization model is established with two objectives of maximizing EVA profits and EV user satisfaction. Finally, a case study in Beijing, China is implemented to prove the feasibility of the proposed model. The results show that the EV user willingness for orderly charging/discharging is distributed in the range of 0.26 and 0.94 with an average value of 0.85. In addition, the proposed EV cluster operation strategy can improve the EVA daily profits by 81.27% and increase the EV user satisfaction by 70% compared with normal charging strategy.

This paper develops a discrete-continuous model to examine the ownership and usage of electric vehicles in the household. The impact of the ownership and usage of ordinary vehicles is taken into consideration. 5766 stated preference data concerning purchasing electric vehicles in the Chukyo region in Japan are utilized as the research sample. The monthly mileages of ordinary and electric vehicles are measured by a Tobit model, respectively. The ordinary vehicle ownership is measured by an ordered probit model, while the electric vehicle ownership is measured by a binary probit model. Gibbs sampler algorithm is used to estimate four jointed equations. The result shows that there is a substitution effect between two types of vehicles in the ownership and usage. The price, capacity, range and charging rate in the gas station impact both the ownership and usage of electric vehicles. Meanwhile, charging time does not affect either the ownership or usage.

In this modern era of cyber–physical–social systems, there is a need of dynamic coordination strategies for electric vehicles (EVs) to enhance the resilience of modern power distribution networks (MPDNs). This paper proposes a two‐stage EV coordination framework for MPDN smart restoration. The first stage is to introduce a novel proactive EV prepositioning model to optimize planning prior to a rare event, and thereby enhance the MPDN survivability in its immediate aftermath. The second stage involves creating an advanced spatial–temporal EV dispatch model to maximize the number of available EVs for discharging, thereby improving the MPDN recovery after a rare event. The proposed framework also includes an information system to further enhance MPDN resilience by effectively organizing data exchange among intelligent transportation system and smart charging system, and EV users. In addition, a novel bidirectional geographic graph is proposed to optimize travel plans, covering a large penetration of EVs and considering variations in traffic conditions. The effectiveness is assessed on a modified IEEE 123‐node test feeder with real‐world transportation and charging infrastructure. The results demonstrate a significant improvement in MPDN resilience with smart restoration strategies. The validation and sensitivity analyses evidence a significant superiority of the proposed framework.

Electric vehicle user classification and value discovery based on charging big data