This study proposes a bounded rational charging guidance strategy based on mental account theory, which guides users to charge in an orderly manner by formulating real‐time charging prices. Firstly, an orderly guidance framework for fast‐charging EVs under the traffic‐grid coupling network is constructed, and the influencing factors of various dimensions when users make charging decisions are analysed. Secondly, considering the bounded rational behaviour of users when making charging decisions, a multifactor bounded rational charging model for EV users based on mental account theory is proposed so as to obtain different charging costs for users when selecting charging stations. On this basis, a real‐time charging price strategy based on the Stackelberg game model is constructed, with the goal of maximising the economic benefits of charging station operators while reducing the charging cost of EV users as much as possible. Finally, the particle swarm optimisation algorithm is used to solve the game model so as to solve the real‐time charging price under various constraints. The simulation of an example verifies the rationality of the proposed real‐time charging price formulation method and the superiority of the bounded rational charging guidance strategy.
This paper proposes the electric vehicle (EV)-station-grid coordination optimization strategy considering user preferences, which regulates the charging behaviors of EV users from the user side to ensure the stable and safe operation of the power grid. Firstly, the spatio-temporal prediction model of charging load based on speed-temperature is developed. The model of EV power consumption per unit mileage affected by temperature and EV speed is constructed, and the shortest path algorithm is applied to determine the driving paths of EVs so as to judge the charging demand in combination with the state of charge (SOC) of the battery and to determine the charging periods and locations of the EVs, thus obtaining the spatio-temporal information of the charging load. Secondly, a multi-attribute charging decision model considering user preferences is constructed. Fuzzy clustering and rough set theory are applied to mine user behavior preferences, combined with behavioral economics to describe users’ irrational charging decision-making psychology. Lastly, a real-time charging price model considering voltage fluctuation index and user charging cost is constructed to analyze the impact of price on guiding charging behaviors. The simulation results verify the effectiveness and performance of the collaborative optimization strategy.
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