TY - GEN
T1 - Social-aware optimal electric vehicle charger deployment on road network
AU - Liu, Qiyu
AU - Zeng, Yuxiang
AU - Chen, Lei
AU - Zheng, Xiuwen
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/11/5
Y1 - 2019/11/5
N2 - With the increasing awareness towards protecting environment, people are paying more attention to the electric vehicles (EVs). Accompanying the rapid growing number of EVs, challenges raise at the same time about how to place EV chargers (EVC), within a city, to satisfy multiple types of charging demand. To provide a better EVC station deployment plan to benefit the whole society, we propose a problem called Social-Aware Optimal Electric Vehicle Charger Deployment (SOCD) on road network. The SOCD problem is hard and different from existing work in three aspects, 1) we assume that the charging demand should be satisfied not only in urban areas but also in relatively rural areas; 2) our work is the first one that considers an EVC station should have multiple types of charging plugs, which is more reasonable in real world; 3) different from the regional deployment solutions in previous literature, our SOCD directly works on a real road network and EVC stations are placed at appropriate POIs laying on the road network. We show that the SOCD problem is NP-hard. To deal with the hardness, we design two heuristic algorithms whose efficiency and effectiveness can be experimentally demonstrated. Furthermore, we investigate the incremental case, that is, given an existing EVC station deployment plan and extra more budget, we need to decide where and how many to place more chargers. Finally, we conduct extensive experiments on real road network of Shanghai to demonstrate both effectiveness and efficiency of our algorithms.
AB - With the increasing awareness towards protecting environment, people are paying more attention to the electric vehicles (EVs). Accompanying the rapid growing number of EVs, challenges raise at the same time about how to place EV chargers (EVC), within a city, to satisfy multiple types of charging demand. To provide a better EVC station deployment plan to benefit the whole society, we propose a problem called Social-Aware Optimal Electric Vehicle Charger Deployment (SOCD) on road network. The SOCD problem is hard and different from existing work in three aspects, 1) we assume that the charging demand should be satisfied not only in urban areas but also in relatively rural areas; 2) our work is the first one that considers an EVC station should have multiple types of charging plugs, which is more reasonable in real world; 3) different from the regional deployment solutions in previous literature, our SOCD directly works on a real road network and EVC stations are placed at appropriate POIs laying on the road network. We show that the SOCD problem is NP-hard. To deal with the hardness, we design two heuristic algorithms whose efficiency and effectiveness can be experimentally demonstrated. Furthermore, we investigate the incremental case, that is, given an existing EVC station deployment plan and extra more budget, we need to decide where and how many to place more chargers. Finally, we conduct extensive experiments on real road network of Shanghai to demonstrate both effectiveness and efficiency of our algorithms.
KW - Electric Vehicle
KW - Road Network
KW - Social-Aware
UR - https://www.scopus.com/pages/publications/85076967498
U2 - 10.1145/3347146.3359382
DO - 10.1145/3347146.3359382
M3 - 会议稿件
AN - SCOPUS:85076967498
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 398
EP - 407
BT - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
A2 - Banaei-Kashani, Farnoush
A2 - Trajcevski, Goce
A2 - Guting, Ralf Hartmut
A2 - Kulik, Lars
A2 - Newsam, Shawn
PB - Association for Computing Machinery
T2 - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
Y2 - 5 November 2019 through 8 November 2019
ER -