摘要
为解决电动汽车用户在出行过程中的充电问题,同时提高充电站运营效益进而促进充电站运营商建设充电站,在对充电站效益需求进行分析的基础上,提出一个以充电站运营商综合效益最大化为优化目标的多目标函数电动汽车充电分配模型。充电站运营商综合效益所考虑的因素包括充电站运营商经济收入、充电站服务水平和充电分布均衡,并且考虑充电站运营特点,分别以用户在充电站的排队时间和充电站拥挤程度方差对充电站服务水平和充电分布均衡进行量化。采用模糊规划法和模糊偏好关系将模型的多个目标函数转化为同时考虑多个优化目标的单目标函数。结合模型特点,设计遗传算法进行求解。基于实际的充电站静态数据,结合依据经验的假设,设计算例以验证模型和算法的可行性和有效性。研究结果表明:遗传算法适用于求解所提出的模型,通过对模型进行求解可以得到运营商最大综合效益以及相应的电动汽车充电分配方案;通过对结果进行比较分析可以得知,考虑多个优化目标的模型所得到的最优电动汽车分配方案下的运营商综合效益要优于只考虑单优化目标的模型。同时,充电站运营商经济收入、用户排队时间和充电拥挤程度对充电分配方案的确定都有着较为显著的影响,并且相比之下,用户排队时间对充电分配的影响相对较大。
In order to solve the charging problem of EV users during trips, and improve the operation benefits of charging stations and then encourage charging station operators to build charging stations, based on analysing the benefit demand of charging stations, an EV charging assignment model with multiple objective functions is proposed to maximize the comprehensive benefits of charging station operators. The comprehensive benefits of charging station operators consider the factors including economic incomes of charging station operators, service level of charging stations, and charging distribution equilibrium. Moreover, considering the operation characteristics of charging stations, the user queuing time at charging stations and variance of crowdedness degree of charging stations are used to quantify the service level of charging stations and charging distribution equilibrium. The fuzzy programming approach and fuzzy preference relations are applied to transform the multiple objective functions into single objective function that considers the multiple optimization objectives. Combining the feature of the model, a genetic algorithm （GA） is designed to solve the proposed model. Based on the actual static data of charging stations, combining the experiential assumptions, a numerical example is designed to verify the feasibility and effectiveness of the model and the algorithm. The result shows that （ 1 ） the GA is suitable for solving the proposed model, the maximum comprehensive benefits of operator and the corresponding optimal scheme of EVs charging assignment can be obtained through solving the model; （2） through comparative analysis of the results, it is observed that the comprehensive benefits of the charging station operator under the optimal assignment scheme of the model considering multiple optimization objectives is superior to that obtained by the model that only considers the single optimization objective. Meanwhile, the economic incomes of charging station operators, users＇ queuing time and
出处
《公路交通科技》
CAS
CSCD
北大核心
2018年第3期117-125,共9页
Journal of Highway and Transportation Research and Development
基金
国家自然科学基金项目(71471014)
山东省重点研究开发项目(2016GGX105004)
关键词
交通工程
充电分配优化
多目标建模
电动汽车
充电站效益
遗传算法
traffic engineering
charging assignment optimization
multi-objective modeling
electric vehicle（EV）
charging station benefit
genetic algorithm
