Abstract: Forewarning load will seriously affect the safe and economic operation of the power system. In this paper， for the users of electric vehicles who participate in vehicle to grid （V2G） service， considering the impact of forewarning load， early warning electricity price and incentive measures on the charging and discharging process， an optimization strategy for electric vehicle charging and discharging applying improved particle swarm optimization algorithm is proposed. First of all， by calculating the discharge reward when the forewarning load occurs， models of forewarning load price and battery capacity loss are established. On the basis of the time-sharing price and the discharge incentive system， a model of user charge and discharge cost is established. In addition， the concept of long-term and short-term memory is introduced， and an improved particle swarm optimization algorithm is proposed. On the basis of above models and algorithm， taking the minimization of user cost as the optimization goal， considering the constraints of user charging demand and charging and discharging power， charging and discharging optimization strategies under different forewarning loads are proposed. The simulation results in MATLAB show that， under the premise of forecasting the forewarning load， the optimization strategy of charging and discharging in this paper can improve the participation of V2G， effectively reduce the user cost， and alleviate the grid pressure when the forewarning load occurs.

Key words:  electric vehicle, vehicle to grid（V2G）, forewarning load, charge and discharge process optimization, improved particle swarm optimization