Research On Controlled Charging Strategy Of Electric Vehicles Based On Demand Response

In the 21 st century,traditional energy is depleted day by day,and has multiple impacts on the environment during use,the development of renewable energy becomes more urgent.In recent years,automobile exhaust generated by automobile fuel has greatly damaged the environment,accounting for a large proportion.Therefore,it is extremely urgent to apply new energy to automobile power,especially under the support of national preferential policies,new energy vehicles develop rapidly.At the same time,the supporting facilities of electric vehicles charging piles are gradually increasing,and the charging demand of electric vehicles as load can be satisfied in each period.In order to reduce the power supply pressure of power grid in peak period,it is necessary to actively guide electric vehicles to charge at off-load peaks.First of all,this article has deeply studied the foreign status quo of electric vehicle development,from the United States,Europe to Japan,and compared the development of domestic electric vehicles and charging piles.Scholars from various countries have studied this issue to varying degrees.From the charging behavior model of electric vehicles,the consideration factors of guiding dispatching to the connection with the power supply of the grid,they elaborated on the advantages and disadvantages of the current orderly charging of electric vehicles.On this basis,we will further improve and introduce demand-side response.And then,the charging activities and charging capacity of electric vehicles are affected and restricted by many factors,such as charging start time,charging time,etc.At the same time,the owner satisfaction index is introduced to further guide the charging activities of the owners.Aiming at its shortcomings,a multi population parallel search strategy and an adaptive dynamic inertia weight strategy are proposed.The formula description and implementation steps of the two improved methods are described in detail.The improved multi-objective particle swarm optimization algorithm is applied to solve the scheduling model.Finally,the electric vehicle charging scheduling example was model converted,and the basic particle swarm algorithm and the multi-objective particle swarm algorithm were used to optimize the conversion model,and the Nash equilibrium solution based on game theory was obtained.The time-of-use electricity price and data obtained by the two algorithms verify the feasibility and effectiveness of the above optimization strategy.