摘要
由于接入电动汽车充电桩的负荷增多,导致充电线路过载,增加能量损耗,使得电网安全风险增高。为解决上述问题,提出基于混合遗传的电动汽车充电桩容量测试方法。分析车辆运行机制、电池特征、充电方式等因素对充电桩容量的影响,计算车辆充电定点需求与路径需求,设置合理的约束条件;将充电桩容量看作一个满足多个约束条件的可行空间,在满足发电容量、功率平衡、潮流平衡、电压稳定等约束条件下,构建容量测试模型;针对遗传算法收敛速度慢的缺陷,使用粒子群算法对其改进,通过个体寻优过程求解模型,获得测试值。实验结果表明,所提方法在峰荷、腰荷和基荷场景下的测量结果与真实值基本一致,且测量耗时低于6s,算法收敛速度快。
At present,the increased load connected to electric vehicle charging stations may lead to overloading of charging lines,increased energy loss,and increased risks to the power grid.To solve this problem,a method for measuring the capacity of electric vehicle charging piles was proposed hybrid genetic algorithm.First,the impact of some factors such as vehicle operating mechanism,battery characteristics,and charging mode on the capacity of the charging pile was analyzed.Then,vehicle charging point demand and path demand were calculated.Meanwhile,reasonable constraints were determined.Considering the charging pile capacity as a feasible space that satisfied multiple constraints,we built a model for capacity test under the constraints of power generation capacity,power balance,power flow balance and voltage stability.In order to address the issue of slow convergence speed in genetic algorithm,a particle swarm optimization algorithm was introduced to improve it.Moreover,the model was solved through individual optimization.Finally,we got the test values.Experimental results show that the measurement results of the proposed method under peak load,medium load,and base load are basically consistent with the actual values,and the measurement time is less than six seconds.This algorithm converges much faster.
作者
万娜娜
李红英
詹慧贞
WAN Na-na;LI Hong-ying;ZHAN Hui-zhen(School of Artificial Intelligence,Jiangxi University of Technology,Nanchang Jiangxi 330098,China;Jiangxi Normal University,Nanchang Jiangxi 330098,China)
出处
《计算机仿真》
2024年第3期88-92,共5页
Computer Simulation
基金
2022年教育部产学合作协同育人项目(220605211173802)。
关键词
混合遗传算法
电动汽车
充电桩
容量测试
粒子群算法
Hybrid genetic algorithm
Electric vehicle
Charging pile
Capacity test
Particle Swarm Optimization
