Search

A method for analyzing the uncertain adjustability of charging stations (CSs) under the influence of multi-attribute group decision-makings of electric vehicles (EVs) is proposed. Firstly, considering physical constraints of parameters such as charging power and energy stored, a model for characterizing the schedulability of a single EV under different charging modes including rated power charging, adjustable charging, and flexible charging and discharging is constructed. Secondly, since the charging mode selection of EVs is a comprehensive decision considering charging cost, charging time and other attributes, and individual decision-making is often affected by group users, an EV charging mode selection probability model is developed based on the multi-attribute group decision-making theory. Further, scheduling potential boundary parameters of CSs affected by the scheduling potential of all EVs in the station are derived based on the Minkowski sum theory, and their probability characteristics are calculated by combining the charging mode selection probability of single EVs and the schedulability under each mode. Finally, case studies prove the superiority of the proposed theory. • A method for analyzing the uncertain adjustability of charging stations is proposed. • A charging mode selection probability model under effects of group users is studied. • The schedulability of electric vehicles under different charging mode is constructed. • Scheduling potential of a charging station is derived based on the Minkowski sum. • The evaluation of uncertain scheduling potential is much more complete and practical. [ABSTRACT FROM AUTHOR]

Charging resources are scarce due to the limited capacity of charging stations and the congestion of distribution networks. Here, a hierarchical scheduling framework that considers the privacy of participants is established to optimize the charging power with a fair payment mechanism. The charging resource allocation model is established considering the scarcity of charging resources. Furthermore, the model is divided into three sub problems, including the congestion management problem of distribution network operator, the energy management problem of charging station and the charging mode problem of electric vehicle. Then the collaborative optimization algorithms are proposed to ensure the privacy of participants. Finally, a hierarchical payment mechanism based on the opportunity cost theory is proposed. The performance of the proposed hierarchical scheduling framework is evaluated using the Roy Billiton Test System with 2000 EVs. On the premise of ensuring distribution network economic operation, the proposed hierarchical scheduling strategy can satisfy the requirement for privacy protection and eliminate the overload problem caused by electric vehicle charging. The simulation results have shown that the additional capacity cost has an obvious levelling effect. With the application of the payment mechanism, charging resources is equitably allocated. Also, the equilibrium of a market and the stable operation of power system can be guaranteed. [ABSTRACT FROM AUTHOR]

• A novel analytic framework for charging demand of electric vehicles is proposed. • The fundamental cause of charging demand--travel behavior is attached importance. • The transfer rule between activity chains is described by the activity-based analysis. • Users' bounded rationality choices in the transfer on activity chain are emphasized. • Multiple factors have effects on the daily charging demand of electric vehicles. A novel analytic framework is proposed for the charging demand of electric vehicles (EVs), which considers charging demand is primarily determined by the travel behavior. And the bounded rationality of the EV users in travel choices is focused in this paper. The activity-based analysis is expanded to divide the travel behavior of users into the transfer relationship between activity chains and the time-space transfer rule for each activity chain. The transfer relationship between different activity chains is established by the Bayesian method. Based on the cumulative prospect theory, we prioritize the bounded rationality of users in the selection of the travel mode, the departure time and the travel path. And the time-space transfer rule and the charging demand of EVs on each activity chain are described by combining the dynamic traffic assignment model. On this basis, the daily charging demand rule for EVs is revealed. Finally, a test traffic network and a real urban traffic network are used to study the travel behavior and daily charging demand of EVs. The simulation results show that the proposed method can effectively describe dynamic changes in the charging demands of EVs. In addition, the charging demand of EVs will be affected by the ownership rate of EVs, the service capacity of charging stations and the degree of the bounded rationality of users. [ABSTRACT FROM AUTHOR]