Abstract:The uncertainty and long-term prediction of the load fluctuation of electric vehicle (EV) charging stations pose significant challenges to accurately predict the charging load. An EV charging load prediction based on dynamic adaptive graph neural network is proposed. Firstly, a spatiotemporal correlation feature extraction layer for charging load information is constructed. By combining multi-head attention mechanism with adaptive relevance graph, a comprehensive feature representation with spatiotemporal correlation is generated to capture the load fluctuation of EV charging station. Then, the extracted features are input into a spatiotemporal convolutional layer to capture the coupling relationship between time and space. The ability of the model to couple long time series is enhanced by Chebyshev polynomial graph convolution and multi-scale temporal convolution. The effectiveness of the algorithm has been verified using two real datasets. Taking the Palo Alto dataset as an example, compared with existing methods, the average prediction error of this algorithm under 4 volatile conditions is reduced sharply.

Abstract:In order to balance the spatial distribution of fast charging load and improve electric vehicle (EV) users’ charging satisfaction, this paper proposes a dynamic pricing mechanism for fast charging considering the load space balance and the cooperation game of charging stations. First, the travel probability transfer matrix is obtained through inverse geocoding of the actual order data, and the spatial and temporal distribution model of EV charging load is established by combining the road network model and the speed-flow model. Then, the charging station selection decision-making model is established by considering the users’ subjective willingness to take into account users’ preferences, and the rational decision-making behaviors of the users’ charging station selection are simulated. Finally, the dynamic pricing game model in the cooperation mode of fast charging stations in the region is established with the objective of charging station node load balance, and the equilibrium value is solved by an iterative algorithm, while the residual benefits obtained from the cooperative alliance are distributed by using the Shapley value method. The simulation example shows that the proposed dynamic pricing strategy can effectively reduce the load variance of charging station nodes, improve the utilization rate of charging piles and charging station revenues, and reduce the queuing time of users to improve users’ satisfaction.

Abstract:The vehicle to grid can release the potential of electric vehicles (EVs) as flexible resources, effectively improve the peak-shaving capacity of power grids, promote the consumption of a high proportion of renewable energy, and improve the economic benefits of EV users, which has attracted increasing attention from all parties. In recent years, California, USA has done a lot of work in promoting the development of vehicle to grid and has made important progress. Firstly, the latest trends of California’s supporting policy and legislation on vehicle to grid are introduced. Then,the latest progress of California’s basic work on vehicle to grid is introduced from the perspectives of technical standards and market mechanism. Finally, relevant suggestions are put forward based on the actual development of vehicle to grid in China

Abstract:Electric vehicle (EV) is the flexible load that can provide flexibility to the power system. Most of the existing studies modeling the flexibility of EVs only consider the uncertainty of charging behavior and the impact of time-of-use tariffs. The deviation between the day-ahead tariff and real-time tariff is ignored, and the modeling of real-time tariff and charging load multi-timescale time-series characteristics is neglected. Aiming at this problem, this paper summarizes the manifestations and influencing factors of the flexibility of EVs, and proposes a probabilistic modeling method of the flexibility of EVs based on the temporal attention mechanism by considering the uncertainty of tariff-oriented response and the uncertainty of charging behavior. The different timescale weights are extracted by the time-series attention mechanism. A multi-timescale feature extraction network based on the temporal convolutional network is designed to learn the uncertainty of charging behavior and electricity price, and extract multi-timescale flexibility fluctuation features. The cases show that the proposed model can effectively learn charging behavior uncertainty and tariff-oriented response uncertainty, and its probabilistic modeling effect has higher reliability and accuracy.

Abstract:The aggregation schedulable capacity (ASC) of large-scale electric vehicles (EVs) is one of the important technical indicators for virtual power plant containing EVs to participate in multi-level and multi-scenario power balance auxiliary services. However,the existing ASC model of EVs is difficult to adapt to the interactive scenario between large-scale EVs and provincial power dispatching center. Therefore,from the new perspective of multi-level and multi-scenario regulation for peak-shaving, frequency regutalion, and voltage regulation of power systems,this paper proposes a dual-layer clustering modeling method for ASC of EVs based on data-driven and machine learning. By constructing the individual schedulable capacity model of generalized EV-charging pile energy storage unit,this method combines the density space-based clustering algorithm and the improved self-organizing map deep clustering algorithm,which effectively integrates the time distribution of electric quantity of EVs and the spatial distribution characteristics of charging piles,and constructs the ASC model for multi-scenario regulation of peak shaving,frequency regulation and voltage regulation. The proposed method is verified by actual charging records in a province of China, and various charging profiles such as “morning type”, “noon type” and “evening type” are obtained. The self-aggregation of generalized energy storage system with different spatial and temporal distributions of EVs is realized, and the potential evaluation of provincial-level EVs participating in different auxiliary services of power grid is realized. The data foundation is laid for the prediction of ASC.

Abstract:With the large-scale development of electric vehicles, it is of great significance to study how to effectively consider the traveling behavior mechanism of users and formulate rational charging prices for charging stations for the collaborative optimization and scheduling of power-transportation coupling networks. To solve this problem, this paper proposes a pricing strategy for charging stations in the power-transportation coupling network considering the user travel cost budget. Firstly, a transportation user equilibrium model considering the travel cost budget is established, and the equilibrium state is equivalently described through variational inequalities, so as to characterize the traveling demands and charging behaviors of electric vehicles. Secondly, a second-order cone optimization model for distribution networks considering power reduction is constructed. The charging station pricing problem has been transformed into an optimization problem with variational inequality constraints, and an alternating iteration algorithm combined with an extra-gradient algorithm is designed to solve the problem. Finally, the effectiveness of the proposed model and methods is verified through a case, and the results show the necessity of considering the travel cost budget for charging pricing in coupling networks.

Abstract:In order to address the urgent need for flexibility resources in transmission networks and tap into the flexibility support potential of distributed resources in distribution networks, firstly, a vehicle-to-grid response model for electric vehicles in the market environment is constructed to analyze the demand response ability of electric vehicles under different charging-discharging modes, which provides the model basis for electric vehicles to participate in power grid dispatch and control in the market environment. Then, taking into account the aggregation effect of distribution network operators on demand-side flexibility resources, a clearing model of energy-flexibility markets with transmission and distribution coordination is proposed considering charging and discharging modes of electric vehicles with the objective to minimize the total cost of purchasing electricity and flexibility resources. Various flexibility resources in transmission and distribution networks are coordinated and controlled to meet the flexibility demand of the transmission network. Finally, the effectiveness of the proposed clearing method of energy-flexibility markets with transmission and distribution coordination is analyzed and verified by the coupling test system with improved IEEE 33-bus transmission network and two IEEE 33-bus distribution networks. The results show that the proposed method can improve the operation economy and flexibility of power systems.

Abstract:With the large-scale development of renewable energy generation and electric vehicles (EVs), the regulation capacity of the power grid keeps declining, the pressure of accommodating renewable energy increases, and the vulnerability of the distribution network gradually exposes. The promotion and application of the vehicle to grid (V2G) technology are of great significance for the satisfaction of charging demand, the alleviation of the pressure on distribution grid construction, the utilization of EV flexibility resources, and the renewable energy accommodation. This paper comprehensively summarizes the current research status in the field of V2G from the five aspects of the technology research, the related policy and standard formulation, the infrastructure and energy management platforms, the information security, and the demonstration application. And the shortcomings of the existing studies are pointed out. Further, the potential future research directions in the field of V2G are explored.

Abstract:Electric vehicles (EVs) are entities with dual attributes of mobile load and communication users. In order to fully tap into the schedulable potential generated by their participation in demand response and reduce power grid load fluctuations, a demand response strategy is proposed for EVs and 5G base stations in the context of power-cyber-transportation network coupling. Firstly, the multi-network coupling relationship between EVs and 5G base stations is analyzed. Secondly, flexibility models for the EV clusters and 5G base station cluster are established. Based on these models, a two-stage demand response optimization scheduling strategy is proposed: in the first stage, with the objective of minimizing communication costs, the strategy provides charging navigation and route planning for EVs while optimizing the energy consumption mode of base stations; in the second stage, with the objective of minimizing distribution network load fluctuations, the strategy formulates the charging and discharging strategy for EVs. Finally, through the test of a city traffic model, the influence of scheduling strategy on base station operation, distribution network load, power flow and users is analyzed, and the effectiveness of the model and method is verified.

Abstract:As a cross-domain subject with both transportation and energy attributes, electric vehicles (EVs) can exert their spatio-temporal flexibility to help the coordinated operation of the power-transportation coupling network. Therefore, a scheduling strategy for EV clusters considering the comprehensive benefits of power-transportation coupling network is proposed. First, a dynamic transportation network loading model is constructed based on arc impedance function. Then, considering the difference and coupling of the characteristic parameters of EV users, the flexible operation domain model of individual EV is constructed. Based on the Minkowski sum algorithm under the linear approximation of zonotope, the time-varying flexible operation domain of EV clusters is obtained. On this basis, a two-layer model for flexibility scheduling of EV clusters under optimal assignment of dynamic traffic flow is proposed, and the instantaneous travel cost of unit flow composed of the coupling variables of the upper and lower layers is obtained through iterative solution, which can guide the traveling and charging/discharging behaviors of EVs. Finally, the validity of the proposed scheduling strategy for EV clusters is verified by comparing it with the shortest path guidance strategy.

Abstract:With the vigorous development of renewable energy technology in the world,electric vehicles (EVs) and their supporting charging facilities are becoming increasingly popular. The spontaneous fire accidents of EVs and charging safety issues have also attracted much attention. From the perspective of charging safety factors,this paper thoroughly summarizes the research methods of charging safety early warning for EVs and charging equipment in recent years. Firstly,the influencing factors of charging safety are classified in detail,and the maturity of the existing charging safety early warning methods are summarized and discussed. Then,the evaluation indexes such as early warning model accuracy and early warning error are summarized. Next,the existing models are evaluated and compared based on real charging order data and work order data. Finally,the future research work of charging safety early warning for EVs and charging equipment is prospected

Abstract:In order to adapt to the rapid growth of electric vehicles (EVs) and charging demand, this paper proposes an active charging guidance model of EVs based on the Internet of vehicles by using the improved A* path planning algorithm and the queuing theory from the perspective of EV users. Firstly, incorporating the traffic light waiting time and the no-backtracking condition, the A* path planning algorithm is improved to update the spatiotemporal state matrix of the road network using the actual road network state information, which can optimize the EV driving path in real time and obtain the EV traveling time for charging. Secondly, the deep belief network (DBN) is utilized to predict the short-time arrival numbers of EVs at the charging station, and the EV waiting time for charging is predicted based on the M/G/k model using the queuing theory. Finally, the active charging guidance model of EVs is constructed to minimize the traveling time and waiting time of EVs for charging. Taking the central area of Nanjing, China as an example, the effectiveness of the proposed active charging guidance model is verified. The proposed algorithm can improve the utilization rate of charging piles and reduce the comprehensive charging time of EV users.

Abstract:In the context of the rapid development of smart city concepts, the unguided traveling and disorderly charging and discharging behaviors of electric vehicles (EVs) are affecting the safe and efficient operation of the power-transportation coupling network. To address this problem, this paper proposes a two-phase optimal guidance and regulation strategy for charging and discharging behaviors of EVs based on the fusion of road network and power grid information. Firstly, the interactive factors and modes of the power-transportation coupling network are analyzed, and the guidance and regulation architecture of EV charging and discharging travel path are proposed. Secondly, a dynamic updating strategy for the electricity price of EV charging and discharging travel decision is proposed based on the fusion of road network and power grid information, and an optimization model for EV charging and discharging travel path decision-making is established considering the user time-economic cost. Then, the EV charging and discharging regulation model is established considering the interests of multiple stakeholders. Finally, the simulation results show that the proposed strategy can comprehensively consider the real-time operation state of the road network and the power grid, fully tap into the adjustable potential in the process of path guidance and charging and discharging regulation of EVs, and realize the coordinated operation of the system and multi-party win-win.

Abstract:In recent years, the rapid growth of electric vehicles (EVs) and fast charging facilities has two closely coupled complex infrastructure networks, which is the power system and transportation system. With flexibility in charging time and location, EVs become ideal mobile energy storage resources for new power systems, which provide massive spatial and temporal flexible regulation capabilities. However, behind the macro power-transportation coupling is the micro social decision-making of numerous EV users under the guidance of the multi-source information, which forms a complex cyber-physical-social system. The relevant research on collaborative modeling and optimization of power-transportation coupling network from such perspective is reviewed. Firstly, the basic scenarios and key challenges of power-transportation network are introduced. Subsequently, focusing on social and physical layers, the modeling of EV traveling-charging behavior is reviewed, which integrates the micro EV user decision-making and macro network dynamics. Then, further incorporating the cyber layer, the strategy interaction and collaborative optimization among multiple pricing entities with EV drivers are summarized. Finally, prospects are made for the research on modeling and the optimization of power-transportation coupling network.

Abstract:Due to the limited number of charging piles in charging stations and the long charging time for electric vehicles, there is competition for charging station resources among various electric vehicle users who are successively generating charging demand. This not only increases the queuing probability of users, reduces the revenue and utilization rate of the charging station, but also makes the users’ personalized needs in terms of charging station size, price, evaluation not fully satisfied. For this reason, a guidance strategy for electric vehicle charging is proposed that combines the dynamic Huff model with the bilateral matching method. First, the big data mining is performed on real data sets such as charging station passenger flow, charging order, and charging pile profile to analyze the charging station selection preferences and charging behavior characteristics of public charging station users. Then, based on the dynamic Huff model, the probability of users going to different charging stations in different regions is quantified by combining the users’ selection preferences for charging stations, and the charging station recommendation lists are generated. Finally, the prospect theory is combined with the bilateral matching strategy for charging guidance. Case analysis shows that the proposed strategy significantly reduces the queuing probability of users, meeting their personalized charging needs while ensuring the interests of charging stations.

Abstract:With the rapid development of new distribution systems (DSs), the interdependence between distribution networks and communication networks is gradually deepening. After extreme disasters occur, on the one hand, the restoration control relies on the effective support of the communication network after black-out of the distribution network, and the process of using vehicle to grid (V2G) to restore load power supply after disasters cannot be separated from the guiding role of the communication networks. On the other hand, the normal operation of communication networks depends on the power supply of the distribution network. Therefore, to solve the problem of load restoration under the interaction between distribution network and communication network after a disaster occurs, a fast recovery method for distribution network through cyber-physical collaboration based on V2G and emergency communication is proposed. After disasters occur, an emergency communication network is established by deploying unmanned aerial vehicles equipped with wireless communication base stations to the communication-failure area to quickly restore the control of automated switches and guidance to electric vehicles (EVs). Then, EVs are guided to the V2G station to form the distributed generators and control the topology of DSs to restore power to the loads. The proposed method is based on the scheduling constraints of post-disaster unmanned aerial vehicles and EVs, as well as the operational constraints of DSs, in which a cyber-physical integrated DS restoration model is built, and the effectiveness of the proposed method is verified through a DS and transportation network integrated case.

Abstract:The escalating scale of electric vehicles (EVs) and renewable energy introduces uncertainties, posing severe challenges to the safe operation of distribution networks. In order to comprehensively consider multiple uncertainties and balance the operation cost with the system reliability, firstly, an EV-distribution network charging and discharging dispatching model based on the distributionally robust joint chance constrainted model is proposed. This model effectively manages the overall system reliability by jointly constraining nodal voltages, branch power, and reserve demand. Then, to solve the model, the joint chance constraint problem is transformed into a mixed-integer quadratic programming model based on the optimized Bonferroni approximation method. Notably, the risk level is also treated as a decision variable. Subsequently, the effectiveness and scalability of the proposed model are verified across various power systems. The results demonstrate that the proposed model overcomes the problems of classical stochastic and robust optimization, effectively balancing cost and reliability with high computational efficiency and good scalability. The model achieves approximately a 6.5% cost reduction compared to the Bonferroni approximation method.

Abstract:Electric vehicles (EVs), as a link and bridge connecting transportation electrification and grid cleanliness, can achieve deep coupling among electricity, transportation and information, forming an integrated planning and operation architecture of electricity and transportation. In the distribution-transportation integrated system (DTIS), a large number of uncertain factors such as the multiple spatio-temporal dynamic interweaving of EV charging and discharging behaviors, deep integration of electricity flow, traffic flow and information flow, and dynamic game among multiple stakeholders will cause significant changes in the planning and operation optimization boundaries of the distribution network. However, at the same time, it also brings opportunities to explore and utilize the mobile energy storage characteristics of EVs and improve the flexibility of the distribution network. Therefore, based on the analysis of the morphological evolution characteristics of the distribution network under the deep coupling of drivers, vehicles, piles, traffic and networks, the new challenges faced by the collaborative planning and operation optimization of the distribution network in the new form are analyzed. Furthermore, four key technologies such as EV flexibility modeling, efficient construction and prediction of flexible regions, collaborative planning, and operation optimization under the coupling of drivers, vehicles, piles, traffic and networks are discussed, and the research directions of the related technical issues are prospected.