The Research Of Spatial-temporal Charging Load Forecast Modeling And V2G Evaluation Method Of Electric Vehicles Considering Traffic Network

Recently electric vehicles(EVs)have attracted widespread attention and support as a green transportation mode that effectively reduces carbon dioxide emissions.However,the charging load of EVs shows spatial and temporal randomness due to the uncertainty of user behavior,the battery capacity,charging facilities and many other factors.In the future,it is necessary to propose a model to forecast the distribution of EVs charging load accurately under the constraint of traffic road network and trip chains.In this paper,a novel charging load forecasting model is proposed to maintain the spatial-temporal characteristic of EVs considering different trip chains and traffic network,where Logit delay function is applied to calculate the road resistance.The normal distribution is used to fit the departure time of each stroke,and the improved Dijkstra algorithm is adopted to select the shortest time-consuming driving path.Finally,the efficiency of proposed model and method is assessed in the road network of test urban area.To start with,the regional traffic road model is established according to the road network topology and regional traffic information.Then,based on the regional traffic road model,the trip chains are divided into two types,the simple chain and the complex chain.Referring to battery capacity and mileage of each EV as well as the power and efficiency of charging facilities,the EVs charging model is proposed.Based on the above three models,the departure and destination of each EV are generated by selecting the trip chains randomly.And the number of EVs is set in different regions according to their different functions.Then the paths are chosen by shortest total travel time.After determining the drive paths and departure time,calculate the charging load of EVs in different regions according to the SOC,charging location and lasting time.Finally,the spatial-temporal information of EV load distribution is obtained.Compared with other methods,the results show that the model can fully reflect the spatial and temporal volatility and randomness of EVs charging load.It can be applied for the prediction of household EV charging power,which is much helpful for charging station planning,and also has good extensibility and scalability on the basis of future travel chain structure changes and travel data updates.In order to study the charging behavior of electric vehicle users under the guidance of electricity price measures,the coordinated charging strategy is established with the optimization target of minimizing total charging cost based on the user's psychology and electricity price guidance.Then a response capacity evaluation method is developed based on the forecasting results.The influence of state of charge(SOC),stay period and time-of-use price on V2G(vehicle-to-grid)responsibility is analyzed using fuzzy control.Finally,the efficiency of proposed model and method is assessed in the road network of test urban area.