Multi-objective Bi-level Optimal Scheduling Of Power Grid Considering Energy Transfer Characteristics Of Electric Vehicles

With the depletion of fossil fuels such as coal and oil and the increasing environmental pressure,electric vehicles have developed rapidly.Compared with traditional fuel vehicles,it is not only green and low-carbon,but also has good energy storage characteristics.By controlling charging and discharging,the energy storage pressure of the power grid system can be effectively alleviated and certain energy support can be provided.However,with the access of large-scale electric vehicles,the threat of disorderly charging load to the power grid becomes more and more obvious.It is of great significance for the power grid and electric vehicle users to formulate a reasonable scheduling scheme,realize the coordinated scheduling of electric vehicles and power grids,and guide the entry of electric vehicles in an orderly manner.In order to reduce the impact of disorderly charging on the safe and stable operation of the power grid,this paper studies the orderly charging and discharging strategy of electric vehicles.Firstly,in view of the influence of users’ electricity consumption behavior on overall satisfaction and power grid operation,in order to fully mobilize the enthusiasm of users to participate in power grid dispatching,this paper establishes a two-layer real-time dispatching model of charging station considering user credit and battery loss,and formulates a reasonable charging and discharging plan to better realize the integration of vehicle-station-network.In the upper layer,considering the influence of the addition of charging stations on the network loss of the distribution network,a two-stage collaborative scheduling model is established.In the first stage,an optimal network loss scheduling plan is obtained to constrain the load of the charging station.In the second stage,the load of each charging station is coordinated to reduce the comprehensive load fluctuation of the system and improve the consumption of renewable energy.In addition,in order to facilitate the solution of the upper multi-objective optimization problem,this paper improves the ideal point method,and proposes a grid domination-ideal point method(GD-IPM)to solve the problem that the ideal point method has a strong dependence on the accuracy of the ideal point.The multi-objective optimization problem is transformed into a singleobjective optimization problem,and then the coordinated scheduling scheme between charging stations is optimized.In the lower layer,in order to reduce battery loss and standardize user behavior,a priority partition scheduling strategy considering user credit is proposed to reasonably allocate the scheduling plan of the upper grid to electric vehicles.Considering the battery loss,the partition scheduling is introduced.The electric vehicle is partitioned according to the amount of electricity,and different scheduling tasks are undertaken respectively.The available capacity of the power battery in each interval is evaluated to facilitate the calculation of priority.Taking user credit as one of the determinants of priority,a priority calculation model is established to reduce credit as a penalty to regulate the user’s electricity consumption behavior and reasonably quantify the charging/discharging sequence of electric vehicles.Through priority partition scheduling,the upper grid instructions are assigned to users in priority order,forming a lower scheduling scheme.Through continuous correction between the upper and lower layers,the final balanced scheduling scheme is formed to guide the charging and discharging of electric vehicles.Then,in order to improve the optimization performance of genetic algorithm,a parallel migration genetic algorithm(PMGA)is proposed.The population is divided into I and Ⅱ in the form of parallel evolution.For I,through K-means clustering selection,single point crossover,re-selection,and improved adaptive mutation probability,a double selection genetic algorithm is formed to improve the operation accuracy.In the evolution of Ⅱ,the hierarchical selection strategy and adaptive coding are introduced,and an adaptive coding genetic algorithm is proposed to solve the problem of low evolution efficiency caused by fixed coding and the optimization rate is improved.The information exchange between them is realized through the migration operator.It increases the diversity of the population,and improves the convergence accuracy,operation speed and stability.Finally,PMGA is applied to the solution of the two-layer equilibrium solution to verify the improvement in accuracy.According to the obtained equilibrium scheduling scheme,the orderly access is guided to reduce the impact of large-scale electric vehicle disorderly charging on the power grid.