Research On Orderly Charging Strategy And Charging Facilities Planning Of Electric Vehicles

The research content of this topic comes from Shanxi Electric Power Company's project "Study and Key Strategies of Power Distribution Network Planning about the Introduced New Element City Using Greedy Method".The development of electric vehicles is an effective measure taken by the international community to cope with energy,environment,and climate issues.It is also a strategic choice made by China in the face of energy shortages,environmental pollution and many other factors.With the development of electric vehicles pilot work,the proportion of charging load in the power grid is gradually increasing,which is bound to cause impact on the power grid;at the same time,a reasonable charging facilities planning system is a necessary link to support the development of electric vehicles.Therefore,based on the calculation of the charging load,an orderly charging control model is established for the influence of disorderly charging on the distribution network,and the layout of the distributed charging piles and the charging stations is separately performed.The main research contents are as follows:The charging load model based on the Monte Carlo method is established based on the analysis of charging characteristics of various types of electric vehicles.Taking the standard distribution network of the IEEE33 node system as an example,scenarios are set for winter and summer,respectively,when electric vehicle penetration rates are 0,10%,30%,50%,60%,and 100%.Then theIV impact of disorderly charging on the distribution network load,network loss,and voltage is analyzed.The results show that the impact of electric vehicles on the distribution network is not only related to its quantity,but also related to the basic load of the system.The greater number of electric vehicles,the more serious the impact on the distribution network.The same number of base load at higher level,the impact is more serious.The orderly charging strategies guided by two kinds of electricity price mechanism: time-of-use price and real-time electricity price are studied.With the objective of minimizing load fluctuations and user charging costs,a model that takes into account the user's charge demand constraints is established based on real-time electricity price.An improved differential evolution algorithm,SaDE,is proposed.The algorithm can adjust the control parameters according to the problem itself and avoid the adverse effects caused by the fixed parameters.For the different penetration rates of electric vehicles,the distribution load characteristics of the disorderly,the time-of-use price-based and the real-time electricity price-based charging strategy are compared and analyzed.Simulation results show that the impact of disorderly charging on the power grid is the most serious,followed by the time-of-use price,and the minimum on the real-time electricity price.The type of the planning charging piles is defined,and the total number of charging piles is determined according to the charging load of the electric vehicles it serves.The planning area is divided into plots based on the newly divided land types.The charging demand coefficient of each plot is calculated from two parameters: attribute characteristics and traffic congestion index;the appropriate plots are selected,and the number of charging piles of the selected plot is determined according to the proportion of the charging demand coefficient.The method realizes the planning of charging piles in an actual area.The planning results can meet the demand for electric vehicle charging and user convenience.The evaluation index of the charging station site is established,and all the sites are evaluated by the fuzzy analytic hierarchy process.A model is established with the goal of minimizing investment costs,user charging costs,and grid network damage costs.User costs include the time spent on the journey and the time spent in the station.The Dijkstra algorithm is used to solve the shortest distance between the charging demand point and the candidate station site of the charging station.The immune genetic algorithm and the Dijkstra algorithm are combined to solve the mathematical model.The feasibility of the method is verified by an example and the excellent performance of the immune genetic algorithm is also proved.Finally,the location of the charging station within a city's urban area was finalized.The project has passed the acceptance of the science and technology project of Shanxi Electric Power Company,which provides a theoretical basis for the related planning work.