考虑预警负荷的电动汽车充放电优化策略

预警负荷会严重影响电力系统的安全经济运行。面向参与车辆到电网(vehicle to grid,V2G)服务的电动汽车用户,综合考虑预警负荷、预警电价和充电激励措施对充放电过程的影响,提出基于改进粒子群算法(improved particle sw arm optimization,IPSO)的电动汽车充放电优化策略。通过计算预警负荷发生时的放电奖励,建立预警负荷电价模型、电池容量损耗模型,基于分时电价和放电激励制度建立用户充放电成本模型。此外,引入长短时记忆的概念,提出改进粒子群优化算法。在上述模型和算法的基础上,以最小化用户成本为优化目标,计及用户充电需求和充放电功率等约束,提出不同预警负荷情况下的充放电优化策略。在MATLAB中完成了仿真验证,结果表明,在已知预测预警负荷的前提下,采用文中的充放电优化策略能够提高电动汽车用户V2G参与度,有效降低用户成本,并缓解预警负荷发生时电网压力。

Optimization Method for Electric Vehicle Charging/Discharging Considering Forewarning Load

Forew arning 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 forew arning 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 forew arning load occurs,models of forew arning 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 forew arning 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 forew arning load occurs.