Research On Optimal Scheduling Strategy Of Electric Vehicles For Smart Buildings

In recent years,electric vehicles have shown explosive growth in quantity due to their ability to cope with world energy shortages and environmental pollution.In cities of future,there will be a trend that large-scale electric vehicles park around various buildings,and urban buildings will become an excellent place for electric vehicles to interact with the power grid.Therefore,from the point of view of buildings,this paper takes the building as the unit organization and management of electric vehicles,and promotes the orderly charging and discharging of electric vehicles in its jurisdiction through the unified and centralized dispatch of the building energy management system,so as to achieve power grid peak-cutting and valley filling,new energy consumption and the goal of optimizing the charging cost of electric vehicle users.Based on this,this article studies the building-oriented electric vehicle dispatching strategy from many aspects,such as building types,number of buildings,and scheduling time ways.The thesis mainly does the following research work:1.A micro-grid model of an intelligent building with electric vehicles is established.The building basic load,electric vehicles,distributed power generation and energy storage equipment are integrated into a building microgrid.The key modules of "vehicle-building" interaction in a building are introduced.The analysis deduced the distributed photovoltaic and wind power output of the building.For different types of buildings,the characteristics of the building load and the characteristics of stopping and charging of electric vehicles are analyzed.The Monte Carlo method is used to analyze the travel characteristics of electric vehicles in buildings.The electric vehicle to building mode and two charging methods are introduced.2.The multi-objective optimal double-layer scheduling strategy for electric vehicles in single buildings is studied.A two-layer optimal dispatching model for electric vehicles in a single building is established.The outer layer is multi-objective optimization,taking into account the optimal peak-to-valley difference and the minimum goal of charging fees for electric vehicle users,using the NSGA-Ⅱ algorithm to solve.The inner layer takes the minimum deviation of the charging demand of each electric vehicle and the owner as the goal,and combines the optimization results of the outer layer as constraints,and uses the particle swarm optimization algorithm to solve it to obtain the specific charge and discharge strategy of a single electric vehicle.3.The multi-objective optimal double-layer scheduling strategy for electric vehicles in single buildings is studied.Establish charging priority indicators,clarify the use of fast charging methods and conventional charging methods,and the charging priority of electric vehicles;introduce schedulable duration indicators to schedule discharge according to the schedulable duration;establish a real-time scheduling process model,cluster scheduling mode,and business Building ideal optimal load model.Taking the optimal overall dispatch priority index as the objective function and the influence on the building load and distribution network as the constraint conditions,a real-time charging and discharging strategy for a single electric vehicle is obtained.4.A two-layer group scheduling strategy for multi-type buildings for electric vehicles considering the travel chain is studied.The travel chain model and the shortest path optimization model are considered,and the details of the previous declaration are further detailed.The two-layer multi-type building electric vehicle optimization dispatching model is established.The outer layer uses the power grid to cut peaks and fill valleys and absorb wind power as objective functions to solve the building charge and discharge strategy.The inner layer takes the highest private electric vehicle travel satisfaction in the building group as The objective function is solved to obtain the charging and discharging strategies of each electric vehicle group,and the charging and discharging power is evenly distributed to each electric vehicle in the electric vehicle group,which is the optimal scheduling strategy for electric vehicles in the final building group.The improved genetic algorithm is used to solve the problem.This model reduces the calculation dimension and improves the calculation efficiency.