Research On Electricity Price Guide Of Electric Vehicle Charging Based On Bi-level Optimization

With the increasing pollution and shortage of energy,electric vehicles(EV)that using clean energy will develop rapidly.Large-scale EV charging will have a serious impact on economy and stability of the grid.EVs load can be optimized based on electricity price guide.So this paper studies on the research on electricity price guide of EV charging based on bi-level optimization.The main results and innovations are as follows:Peak and valley period are divided based on membership function and load changing rate.The EV charging load model was established based on the influencing factors analysis of EV charging.And the uncoordinated charging load was simulated by Monte-Carlo method.Finally,electricity price guide model of EV charging was established by price elasticity.This paper proposed the bi-level optimization strategy for EV charging.The concept and solution of bi-level optimization,dispatch frame of EV charging bi-level optimization were introduced,as well as aggregator mechanism and information interaction mechanism.Bi-level optimization model for EV charging are established considering the constraint of the system,the aggregator and the EV user.In the upper model,the variance of the total load of the power system is minimized by optimizing the dispatching plan of the aggregator in each period.In the lower model,the aggregator guides the users to adjust the charging plan by the charging price,to coincide with the upper dispatching plan.A hierarchical iterative method based genetic algorithm were proposed to solve the bi-level optimization model.The bi-level optimization strategy is used to solve the regional system and the dispatching load and the charging price were obtain.The research shows that the variance of system load,the system active power loss and node voltage fluctuation were reduced without increasing the charging cost of EV users after optimization.The economic benefit with bi-level optimization is analyzed from three aspects:the investment cost of power equipment,the cost of system active power loss and the cost of auxiliary service.Finally,the effects of the price constraint and the quantity of EV on the optimization were studied.