With the increase in the number of distributed energy resources (DERs) and electric vehicles (EVs), it is particularly important to solve the problem of EV charging station siting and capacity determination under the distribution network considering a large proportion of DERs. This paper proposes a bi-level planning model for EV charging stations that takes into account the characteristics of the spatial–temporal distribution of charging loads under an uncertain environment. First, the Origin–Destination (OD) matrix analysis method and the real-time Dijkstra dynamic path search algorithm are introduced and combined with the Larin Hypercube Sampling (LHS) method to establish the EV charging load prediction model considering the spatial and temporal distribution characteristics. Second, the upper objective function with the objective of minimizing the cost of EV charging station planning and user charging behavior is constructed, while the lower objective function with the objective of minimizing the cost of distribution network operation and carbon emission cost considering the uncertainty of wind power and photovoltaics is constructed. The constraints of the lower-layer objective function are transformed into the upper-layer objective function through Karush–Kuhn–Tucker (KKT) conditions, the optimal location and capacity of charging stations are finally determined, and the model of EV charging station siting and capacity determination is established. Finally, the validity of the model was verified by planning the coupled IEEE 33-node distribution network with the traffic road map of a city in southeastern South Dakota, USA.

With the rapid development of electric vehicles (EVs) and charging facilities, EV charging guidance is currently mainly based on charging incentives. Without an in-depth exploration of the superimposed benefits to charging guidance caused by discharging incentives, it is difficult to maximize the benefits of charging station operators and to stimulate the enthusiasm of users to participate in the guidance. In this study, firstly, a traffic network model based on the Logit model is established, and the spatiotemporal distribution characteristics of EV users’ traveling demand based on the O-D matrix and the Monte Carlo Markov method are proposed. Secondly, we analyze the impact of charging and discharging incentive levels on users’ psychological responses to charging guidance. We assess battery degradation during irregular discharging processes of electric vehicles (EVs) while considering users’ personalized travel needs and anxiety levels. We propose a dual-incentive mechanism for charging and discharging to enhance users’ active participation in charging guidance. Then, we construct a model that incorporates users’ travel and waiting time costs, as well as the economic costs of charging and discharging. Subsequently, we consider the economic benefits for users under the discharging incentive mechanism and establish a user charging decision model based on prospect theory. Finally, considering the goal of maximizing the revenue of the charging station, a charging guidance strategy considering users’ participation in the charging and discharging incentive mechanism during the traveling process is proposed. The effectiveness of the EV charging guidance strategy under three different incentive scenarios is verified with comparative results. The proposed guidance strategy enhances operator revenue while taking user interests into account, achieving a 7% increase in operator revenue compared to a strategy that only considers charging incentives. This dual-incentive mechanism promotes operators’ enthusiasm for participating in vehicle-to-grid interactions.

As the penetration rate of electric vehicles (EVs) increases, how to reasonably distribute the ensuing large charging load to various charging stations is an issue that cannot be ignored. This problem can be solved by developing a suitable charging guidance strategy, the development of which needs to be based on the establishment of a realistic EV charging behaviour model and charging station queuing system. Thus, in this paper, a guidance and pricing strategy for fast charging that considers different types of EV users’ willingness to charge is proposed. Firstly, the EVs are divided into two categories: private cars and online ride-hailing cars. These categories are then used to construct charging behaviour models. Based on this, a charging decision model for EV users is constructed. At the same time, a first-come-first-served (FCFS) charging station queuing system is constructed to model the real-time charging situation in the charging station in a more practical way. Finally, a dynamic tariff updating model is used to obtain the optimal time-of-use tariff for each charging station, and then the tariffs are used to guide the fast-charging demand. By comparing the spatial and temporal distribution of charging demand loads at charging stations under different scenarios and considering whether the tariffs at each charging station play a guiding role, it is verified that the proposed strategy effectively optimises the balanced distribution of EV charging loads and alleviates the congestion at charging stations.

With the rapid development of the electric vehicle (EV) industry, various issues, such as the suboptimal deployment of charging facilities and inadequate distribution, hinder improvements in user satisfaction with EVs. Moreover, the interests of multiple stakeholders, including power grid companies and transportation departments, are not sufficiently addressed. To tackle these challenges, this paper proposes a comprehensive evaluation method for EV charging network service quality that integrates user satisfaction. First, considering the coupled dynamics of the EV charging network, we construct a service quality evaluation index system. Then, using a combination of the entropy weight method (EWM) and the analytic hierarchy process (AHP) with game theory, we determine the composite weights for the index system. Finally, we use the improved R-method evaluation model to theoretically verify the charging network in a particular area. The results show that the evaluation method proposed in this paper realizes differentiated evaluation for different planning schemes, highlights the weak links in the operation process of the charging network, and provides a theoretical reference for decision making about charging station planning.

The current uneven deployment of charging stations for electric vehicles (EVs) requires a reliable prediction solution for smart grids. Existing traffic prediction assumes that users’ charging durations are constant in a given period and may not be realistic. In fact, the actual charging duration is affected by various factors including battery status, user behavior, and environment factors, leading to significant differences in charging duration among different charging stations. Ignoring these facts would severely affect the prediction accuracy. In this paper, a Transformer-based prediction of user charging durations is proposed. Moreover, a data aggregation scheme with privacy protection is designed. Specifically, the Transformer charging duration prediction dynamically selects active and reliable temporal nodes through a truncated attention mechanism. This effectively eliminates abnormal fluctuations in prediction accuracy. The proposed data aggregation scheme employs a federated learning framework, which centrally trains the Transformer without any prior knowledge and achieves reliable data aggregation through a dynamic data flow convergence mechanism. Furthermore, by leveraging the statistical characteristics of model parameters, an effective model parameter updating method is investigated to reduce the communication bandwidth requirements of federated learning. Experimental results show that the proposed algorithm can achieve the novel prediction accuracy of charging durations as well as protect user data privacy.

China’s electric vehicle (EV) fleet has reached 30 million, and the effective utilization of their charging and discharging capabilities can provide substantial regulation support to restore supply–demand balance. A prerequisite to achieving this benefit is the proper estimation and utilization of the regulation potential inherent in EVs. This paper focuses on charging stations and introduces a station-aggregator response resource trading mechanism considering energy–power coupling of response capability. First, the response capacity of charging stations is estimated from both energy and power perspectives. Next, trading behavior models are developed separately for charging stations and aggregators. Finally, a joint resource trading mechanism based on a Stackelberg game is proposed to coordinate responses between these entities. Simulation results validate the effectiveness of the proposed estimation method and the economic advantages of the trading mechanism, thereby realizing a win–win outcome.