Electric vehicle charging station siting method based on spatial semantics and individual activities

doi:10.11772/j.issn.1001-9081.2022091421

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (9): 2819-2827.DOI: 10.11772/j.issn.1001-9081.2022091421

• Advanced computing • Previous Articles Next Articles

Electric vehicle charging station siting method based on spatial semantics and individual activities

Maozu GUO¹^,², Yazhe ZHANG¹^,², Lingling ZHAO³()

^1.School of Electrical and Information Engineering，Beijing University of Civil Engineering and Architecture，Beijing 100044，China
^2.Beijing Key Laboratory of Intelligent Processing for Building Big Data （Beijing University of Civil Engineering and Architecture），Beijing 100044，China
^3.Faculty of Computing，Harbin Institute of Technology，Harbin Heilongjiang 150001，China

Received:2022-09-26 Revised:2023-01-30 Accepted:2023-02-01 Online:2023-02-28 Published:2023-09-10
Contact: Lingling ZHAO
About author:GUO Maozu， born in 1966， Ph. D.， professor. His research interests include machine learning， smart city and intelligent construction.
ZHANG Yazhe， born in 1997， M. S. candidate. Her research interests include urban computing， artificial intelligence.
Supported by:
National Natural Science Foundation of China(61871020);High Level Innovation Team Construction Program of Beijing Municipal Universities(HT20190506)

基于空间语义和个体活动的电动汽车充电站选址方法

郭茂祖¹^,², 张雅喆¹^,², 赵玲玲³()

^1.北京建筑大学电气与信息工程学院, 北京 100044
^2.建筑大数据智能处理方法研究北京市重点实验室(北京建筑大学), 北京 100044
^3.哈尔滨工业大学计算学部, 哈尔滨 150001

通讯作者: 赵玲玲
作者简介:郭茂祖（1966—），男，山东夏津人，教授，博士，CCF会员，主要研究方向：机器学习、智慧城市与智能建造
张雅喆（1997—），女，陕西韩城人，硕士研究生，主要研究方向：城市计算、人工智能；
基金资助:
国家自然科学基金资助项目(61871020);北京市属高校高水平科研创新团队建设支持计划项目(HT20190506)

Abstract

Abstract:

To address the issue of siting for Electric EVCS （Vehicle Charging Station）， an urban charging station siting method based on spatial semantics and individual activities was proposed. First， according to the urban planning， unsupervised learning was used to cluster the Point Of Interests （POIs） out of the service radius to determine the number of new charging stations. Then， Constrained Two-Archive Evolutionary Algorithm （CTAEA） was used to solve the objective function to optimize the electric vehicle siting scheme under the constraints of maximizing the distance between stations and covering the most POIs with new charging stations. The trajectory data and POIs of taxis in the second-ring road of Chengdu were used as the experimental samples， and siting scheme with 15 charging stations was planned. Experimental results show that compared with NSGA2 （Non-dominated Sorting Genetic Algorithm 2） and SPEA2 （Strength Pareto Evolutionary Algorithm 2）， CTAEA improves 22.9 and 20.6 percentage points on POI coverage， and reduces 18.9% and 25.5% on driver’s average selected distance， which illustrates the convenience and rationality of the method in electric vehicle charging station siting.

Key words: charging station siting, electric vehicle, demand forecasting model, cluster analysis, two-archive evolutionary algorithm

摘要：

针对电动汽车充电站（EVCS）的选址问题，提出一种基于空间语义和个体活动模式的城市充电站选址方法。首先，根据城市规划，采用无监督学习对非服务半径内兴趣点（POI）进行聚类，以确定新建充电站个数；然后，采用受约束的双存档进化算法（CTAEA）求解目标函数，在站间距最大化以及新充电站覆盖POI最多的约束条件下优化电动汽车选址方案。以成都市二环路内出租车的轨迹数据和POI为实验样本，并规划了15个充电站的选址方案。实验结果表明，相较于NSGA2（Non-dominated Sorting Genetic Algorithm 2）和SPEA2（Strength Pareto Evolutionary Algorithm 2），CTAEA的POI覆盖率指标提高了22.9和20.6个百分点，司机平均选择距离缩短了18.9%和25.5%，验证了所提方法在电动汽车选址方面的便利性与合理性。

关键词: 充电站选址, 电动汽车, 需求预测模型, 聚类分析, 双存档进化算法

CLC Number:

TP391

Maozu GUO, Yazhe ZHANG, Lingling ZHAO. Electric vehicle charging station siting method based on spatial semantics and individual activities[J]. Journal of Computer Applications, 2023, 43(9): 2819-2827.

郭茂祖, 张雅喆, 赵玲玲. 基于空间语义和个体活动的电动汽车充电站选址方法[J]. 《计算机应用》唯一官方网站, 2023, 43(9): 2819-2827.

Figures/Tables 15

References 22

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特征		描述
基本信息	时间（Time）	时间戳转换为北京时间
	经度（Lng）	某一个活动点的经度
	纬度（Lat）	某一个活动点的纬度
	司机（ID）	出租车司机ID
高级信息	需求司机	根据EV占比以及需求率和车流密度产生
	需求区域	根据现有充电站服务半径筛选未被覆盖区域
	POI信息	活动点的名称

特征		描述
基本信息	时间（Time）	时间戳转换为北京时间
	经度（Lng）	某一个活动点的经度
	纬度（Lat）	某一个活动点的纬度
	司机（ID）	出租车司机ID
高级信息	需求司机	根据EV占比以及需求率和车流密度产生
	需求区域	根据现有充电站服务半径筛选未被覆盖区域
	POI信息	活动点的名称

司机编号	订单编号	时间戳	经度/（°E）	纬度/（°N）
8f20c9188561b796ef8e26196de30be4	39a096b71376b82f35732eff6d95779b	1477969198	104.075 76	30.723 08
1d6a00f022a7c78fd8e5f652557296ac	992578041ce98f9956d0c22a4bfc566a	1477989864	104.101 86	30.685 23
D0c7229ae9f6501dbd5c1237c1a31d1	d9d4e03abd6439d7149356c89e52f710	1477960321	104.073 03	30.684 55

司机编号	订单编号	时间戳	经度/（°E）	纬度/（°N）
8f20c9188561b796ef8e26196de30be4	39a096b71376b82f35732eff6d95779b	1477969198	104.075 76	30.723 08
1d6a00f022a7c78fd8e5f652557296ac	992578041ce98f9956d0c22a4bfc566a	1477989864	104.101 86	30.685 23
D0c7229ae9f6501dbd5c1237c1a31d1	d9d4e03abd6439d7149356c89e52f710	1477960321	104.073 03	30.684 55

序号	需求司机	最近充电站
0	［104.065°E，30.669°N］	［104.067°E，30.663°N］
1	［104.100°E，30.686°N］	［104.095°E，30.696°N］
2	［104.046°E，30.693°N］	［104.039°E，30.689°N］

Electric vehicle charging station siting method based on spatial semantics and individual activities

基于空间语义和个体活动的电动汽车充电站选址方法

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Figures/Tables 15

References 22

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Metrics

经度/（°E）	纬度/（°N）	充电站被选择次数
30.671 2	104.124 4	53
30.697 8	104.111 4	128
30.689 8	104.069 1	188
30.678 6	104.060 6	127
30.675 9	104.091 2	293
30.697 2	104.081 5	230
30.711 0	104.101 3	145
30.652 1	104.048 3	86
30.668 5	104.059 3	403
30.688 5	104.053 7	247
30.659 7	104.115 1	227
30.700 0	104.060 3	130
30.721 2	104.067 8	85
30.712 5	104.055 1	65
30.708 1	104.064 8	58

算法	POI覆盖数	选择原有充电桩次数	选择新建充电桩次数	建站后POI覆盖率/%	新建站被选择率/%	新建充电站平均选择率/%
建站前	4 488	4 602	—	—	—	—
CTAEA	6 632	2 466	2 488	72.8	48.7	3.25
NSGA2	4 545	3 076	1 622	49.9	31.7	2.12
SPEA2	4 752	3 988	633	52.2	12.4	0.83

算法	最小距离和/km	平均选择距离/km
现有充电站	14 550.8	2.85
CTAEA	5 674.1	1.11
NSGA2	6 992.2	1.37
SPEA2	7 629.0	1.49

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