[1]朱永胜,杨振涛,丁同奎,等.考虑用户动态充电需求的电动汽车充电站规划[J].郑州大学学报(工学版),2023,44(02):82-91.[doi:10.13705/j.issn.1671-6833.2023.02.001]
　ZHU Yongsheng,YANG Zhentao,DING Tongkui,et al.Electric Vehicle Charging Station Planning Considering Users′ Dynamic Charging Demand[J].Journal of Zhengzhou University (Engineering Science),2023,44(02):82-91.[doi:10.13705/j.issn.1671-6833.2023.02.001]
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考虑用户动态充电需求的电动汽车充电站规划()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44
期数:
2023年02期
页码:
82-91
栏目:
出版日期:
2023-02-27

文章信息/Info

Title:
Electric Vehicle Charging Station Planning Considering Users′ Dynamic Charging Demand
作者:
朱永胜1; 杨振涛1; 丁同奎2;  徐其迎1;  巫付专1;  聂彩静1
1.中原工学院 电子信息学院,河南 郑州 450007, 2.国网郑州供电公司,河南 郑州 450052

Author(s):
ZHU Yongsheng1;  YANG Zhentao1;  DING Tongkui2;  XU Qiying1;  WU Fuzhuan1;  NIE Caijing1
1.Central Plains College of Electronic Information, Henan Zhengzhou 450007, 2.State Grid Zhengzhou Power Supply Company, Henan Zhengzhou 450052

关键词:
电动汽车;  交通路网;  Dijkstra 算法;  动态充电需求;  容量配置;  充电站
Keywords:
electric vehicles;  traffic network;  Dijkstra algorithm;  dynamic charging needs;  capacity configuration;  charging station
分类号:
TM715
DOI:
10.13705/j.issn.1671-6833.2023.02.001
文献标志码:
A
摘要:
为提高电动汽车(EV)充电站规划布局的合理性,避免出现投资高、效率低的局面,提出一种考虑用户动态 充电需求的电动汽车充电站规划方法。 首先,利用出行链理论和起止点(OD)矩阵得到用户出行起讫点,构建交通 拥堵状况随时间变化的动态交通路网模型,并改进 Dijkstra 算法规划 EV 行驶路径,考虑环境温度和车速实时变化 对单位里程功耗的影响,建立考虑用户动态充电需求的充电站选择模型;其次,采用 M / M / c 排队论方法对充电站 进行容量配置,以充电站建设运维成本和 EV 用户经济损失( 包括时间损失和电量损耗) 之和最小为目标函数,建 立充电站规划模型;最后,以某市主城区部分实际道路情况为规划区域,通过迭代排列寻优并结合粒子群算法对模 型求解。 结果表明:区域内规划的 6 座充电站位置分布均匀,减少了用户充电路途成本,且充电桩最优配置数在保 证充电满意度的同时使充电站总经济成本最低,所提规划方法合理有效。
Abstract:
In order to improve the rationality of the planning and layout of electric vehicle (EV) charging stations and avoid the situation of high investment and low efficiency, a planning method of EV charging station that considered users′ dynamic charging demand was proposed. Firstly, the starting and ending points of user were obtained by using the travel theory, start and end point ( origin-destination,OD) matrix method; a dynamic traffic road network model with time-varying traffic congestion was constructed. The Dijkstra algorithm was improved to plan the EV travel path, considering the real-time changes of ambient temperature and vehicle speed. Based on the influence of mileage and power consumption, a charging station selection model considering the dynamic charging needs of users was established; then, the M / M / c queuing theory method was used to configure the capacity of charging stations. The cost of construction, operation and maintenance of charging stations and the economic losses of EV users ( including the sum of time loss and power loss) was minimized as the objective function, and a charging station planning model was established. Finally, taking the actual road conditions in the main urban area of a city as the planning area, the model was solved by iterative arrangement optimization combined with particle swarm algorithm. The results showed that the locations of the six planned charging stations in the area were evenly distributed, which could reduce the cost of users′ charging journeys. And the optimal configuration number of charging piles could ensure charging satisfaction while minimizing the total economic cost of charging stations. The proposed planning method was reasonable and effective.

参考文献/References:

[1] SUN S Y, YANG Q, YAN W J. Optimal temporal-spatial PEV charging scheduling in active power distribution networks[EB / OL] . ( 2017 - 09 - 19) [ 2022 - 03 - 17] . https: / / doi. org / 10. 1186 / 541601-017-0065-x.

 [2] 严干贵, 刘华南, 韩凝晖, 等. 计及电动汽车时空分 布状态的充电站选址定容优化方法[ J] . 中国电机工 程学报, 2021, 41(18) : 6271-6284. 
YAN G G, LIU H N, HAN N H, et al. An optimization method for location and capacity determination of charging stations considering spatial and temporal distribution of electric vehicles[ J] . Proceedings of the Csee, 2021, 41 (18) : 6271-6284.
 [3] 李晓辉, 李磊, 刘伟东, 等. 基于动态交通信息的电 动汽车充电负荷时空分布预测[ J] . 电力系统保护与 控制, 2020, 48(1) : 117-125.
 LI X H, LI L, LIU W D, et al. Spatial-temporal distribution prediction of charging load for electric vehicles based on dynamic traffic information [ J] . Power System Protection and Control, 2020, 48(1) : 117-125. 
[4] YAN J, ZHANG J, LIU Y Q, et al. EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs [ J] . Renewable Energy, 2020, 159: 623-641. 
[5] 张琳娟, 许长清, 王利利, 等. 基于 OD 矩阵的电动汽 车充电负荷时空分布预测[ J] . 电力系统保护与控制, 2021, 49(20) : 82-91. 
ZHANG L J, XU C Q, WANG L L, et al. OD matrix based spatiotemporal distribution of EV charging load prediction[ J] . Power System Protection and Control, 2021, 49(20) : 82-91.
 [6] 邵尹池, 穆云飞, 余晓丹, 等. “ 车-路-网” 模式下电 动汽车充电负荷时空预测及其对配电网潮流的影响 [ J] . 中国电机工程学报, 2017, 37(18) : 5207-5219, 5519. 
SHAO Y C, MU Y F, YU X D, et al. A spatial-temporal charging load forecast and impact analysis method for distribution network using EVs-traffic-distribution model [ J] . Proceedings of the Csee, 2017, 37 ( 18) : 5207 - 5219, 5519.
 [7] 赵书强, 周靖仁, 李志伟, 等. 基于出行链理论的电 动汽车 充 电 需 求 分 析 方 法 [ J] . 电 力 自 动 化 设 备, 2017, 37(8) : 105-112. 
ZHAO S Q, ZHOU J R, LI Z W, et al. EV charging demand analysis based on trip chain theory [ J ] . Electric Power Automation Equipment, 2017, 37(8) : 105-112. 
[8] 宋雨浓, 林舜江, 唐智强, 等. 基于动态车流的电动 汽车充电负荷时空分布概率建模[ J] . 电力系统自动 化, 2020, 44(23) : 47-56. 
SONG Y N, LIN S J, TANG Z Q, et al. Spatial-temporal distribution probabilistic modeling of electric vehicle charging load based on dynamic traffic flow[ J] . Automation of Electric Power Systems, 2020, 44(23) : 47-56. 
[9] 张美霞, 孙铨杰, 杨秀. 考虑多源信息实时交互和用 户后悔心理的电动汽车充电负荷预测[ J] . 电网技术, 2022, 46(2) : 632-645. 
ZHANG M X, SUN Q J, YANG X. Electric vehicle charging load prediction considering multi-source information real-time interaction and user regret psychology[ J] . Power System Technology, 2022, 46(2) : 632-645. 
[10] LIU Y B, XIANG Y, TAN Y Y, et al. Optimal allocation model for EV charging stations coordinating investor and user benefits[ J] . IEEE Access, 2018,6: 36039-36049. 
[11] SADEGHI-BARZANI P, RAJABI-GHAHNAVIEH A, 90 郑 州 大 学 学 报 (工 学 版) 2023 年 KAZEMI-KAREGAR H. Optimal fast charging station placing and sizing[ J] . Applied Energy, 2014, 125: 289 -299. 
[12] 张忠会, 刘故帅, 熊剑峰, 等. 基于谱聚类算法的城 市充换电站分布决策[ J] . 郑州大学学报( 工学版) , 2017, 38(5) : 32-38. 
ZHANG Z H, LIU G S, XIONG J F, et al. The application of spectral clustering algorithm to distributive decision for charging and battery swap station [ J ] . Journal of Zhengzhou University ( Engineering Science) , 2017, 38 (5) : 32-38. 
[13] 臧海祥, 舒宇心, 傅雨婷, 等. 考虑多需求场景的城 市电动汽车充电站多目标规划[ J] . 电力系统保护与 控制, 2021, 49(5) : 67-80. 
ZANG H X, SHU Y X, FU Y T, et al. Multi-objective planning of an urban electric vehicle charging station considering multi demand scenarios[ J] . Power System Protection and Control, 2021, 49(5) :67-80.
 [14] 陈静鹏, 艾芊, 肖斐. 基于用户出行需求的电动汽车 充电站规划[ J] . 电力自动化设备, 2016, 36(6) : 34- 39.
 CHEN J P, AI Q, XIAO F. EV charging station planning based on travel demand [ J] . Electric Power Automation Equipment, 2016, 36(6) : 34-39. 
[15] 姜欣, 冯永涛, 熊虎, 等. 基于出行概率矩阵的电动 汽车充电站规划[ J] . 电工技术学报, 2019, 34( 增刊 1) : 272-281. 
JIANG X, FENG Y T, XIONG H, et al. Electric vehicle charging station planning based on travel probability matrix[ J] . Transactions of China Electrotechnical Society, 2019, 34( S1

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更新日期/Last Update: 2023-02-25