Optimization Of Dynamic Electricity Pricing For Charging Stations Based On User Charging Decision Behavior

Electric vehicles(EVs)have advantages such as high energy efficiency,cleanliness,and low carbon emissions.In recent years,with the continuous development of EV technology and supportive policies,along with the improvement of supporting infrastructure,the number of EVs has been increasing annually,leading to a growing demand for charging.At the same time,the emergence of high-power charging equipment has brought potential safety risks and efficiency problems to the power grid.Charging stations,as the link between EVs and the power grid,play a key role in coordinating EV charging demands and the operational status of the grid.Therefore,this study starts from the perspective of charging stations and deeply investigates the relationship between the safe and stable operation of the power grid and user charging behavior,aiming to explore patterns and optimization strategies.It holds practical significance for addressing the potential pressure on the power grid due to the anticipated increase in future charging demands.Firstly,based on real-time operational data of EVs and charging station operations,this study analyzes the current charging behavior and patterns of EV users,as well as the operational situation of corresponding charging stations.Next,it evaluates the current user travel behavior patterns and predicts the future electricity grid load based on electricity demand.Combining the development of modern technology,a comprehensive solution for dynamic pricing of electric vehicle charging is formulated.Secondly,to address the charging demand of large-scale electric vehicles and the prediction of grid burden,this study constructs a simulation model for the coordination of two networks.Different stages of user travel are modeled,including travel behavior modeling,charging decision-making behavior modeling incorporating electricity prices,and travel time modeling,which are then converted to the power grid through power flow calculations.The simulation efficiency is improved by simulating the user’s switching process in different states using state transitions.The established simulation model can simulate the charging scenarios of electric vehicles with arbitrary scales,road networks,and charging station configurations.Combined with basic grid data and basic residential loads,the simulation can replicate the operational status of the power grid at any given time.Finally,the optimization of the power grid is achieved through the guidance of charging station dynamic pricing.The problem of formulating dynamic pricing for charging stations is modeled as an optimization problem,with charging station prices as decision variables and the simulation from demand to the power grid as the objective function.Objective functions from different perspectives,including travel and the power grid,are constructed,and an incremental solving method is employed to solve for the optimal pricing.The effectiveness of the method is validated for different parameters and scales.Simulation results demonstrate an average increase in node voltage of 1.26% and 6.59% for different demand scales.