Details
| Original language | English |
|---|---|
| Title of host publication | KDD 2022 |
| Subtitle of host publication | Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 3992-4000 |
| Number of pages | 9 |
| ISBN (electronic) | 9781450393850 |
| Publication status | Published - 14 Aug 2022 |
| Event | 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States Duration: 14 Aug 2022 → 18 Aug 2022 |
Publication series
| Name | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
|---|
Abstract
The transition from conventional mobility to electromobility largely depends on charging infrastructure availability and optimal placement. This paper examines the optimal placement of charging stations in urban areas. We maximise the charging infrastructure supply over the area and minimise waiting, travel, and charging times while setting budget constraints. Moreover, we include the possibility of charging vehicles at home to obtain a more refined estimation of the actual charging demand throughout the urban area. We formulate the Placement of Charging Stations problem as a non-linear integer optimisation problem that seeks the optimal positions for charging stations and the optimal number of charging piles of different charging types. We design a novel Deep Reinforcement Learning approach to solve the charging station placement problem (PCRL). Extensive experiments on real-world datasets show how the PCRL reduces the waiting and travel time while increasing the benefit of the charging plan compared to five baselines. Compared to the existing infrastructure, we can reduce the waiting time by up to 97% and increase the benefit up to 497%.
Keywords
- electromobility, location selection, reinforcement learning
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Information Systems
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KDD 2022 : Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM), 2022. p. 3992-4000 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Reinforcement Learning-based Placement of Charging Stations in Urban Road Networks
AU - Von Wahl, Leonie
AU - Tempelmeier, Nicolas
AU - Sao, Ashutosh
AU - Demidova, Elena
N1 - Funding Information: This work is partially funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK), Germany under the projects “d-E-mand” (grant ID 01ME19009B) and “CampaNeo” (grant ID 01MD19007B), the European Commission (EU H2020, “smashHit”, grant-ID 871477), and by the DFG, German Research Foundation (“WorldKG”, grant ID 424985896).
PY - 2022/8/14
Y1 - 2022/8/14
N2 - The transition from conventional mobility to electromobility largely depends on charging infrastructure availability and optimal placement. This paper examines the optimal placement of charging stations in urban areas. We maximise the charging infrastructure supply over the area and minimise waiting, travel, and charging times while setting budget constraints. Moreover, we include the possibility of charging vehicles at home to obtain a more refined estimation of the actual charging demand throughout the urban area. We formulate the Placement of Charging Stations problem as a non-linear integer optimisation problem that seeks the optimal positions for charging stations and the optimal number of charging piles of different charging types. We design a novel Deep Reinforcement Learning approach to solve the charging station placement problem (PCRL). Extensive experiments on real-world datasets show how the PCRL reduces the waiting and travel time while increasing the benefit of the charging plan compared to five baselines. Compared to the existing infrastructure, we can reduce the waiting time by up to 97% and increase the benefit up to 497%.
AB - The transition from conventional mobility to electromobility largely depends on charging infrastructure availability and optimal placement. This paper examines the optimal placement of charging stations in urban areas. We maximise the charging infrastructure supply over the area and minimise waiting, travel, and charging times while setting budget constraints. Moreover, we include the possibility of charging vehicles at home to obtain a more refined estimation of the actual charging demand throughout the urban area. We formulate the Placement of Charging Stations problem as a non-linear integer optimisation problem that seeks the optimal positions for charging stations and the optimal number of charging piles of different charging types. We design a novel Deep Reinforcement Learning approach to solve the charging station placement problem (PCRL). Extensive experiments on real-world datasets show how the PCRL reduces the waiting and travel time while increasing the benefit of the charging plan compared to five baselines. Compared to the existing infrastructure, we can reduce the waiting time by up to 97% and increase the benefit up to 497%.
KW - electromobility
KW - location selection
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85137148570&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2206.06011
DO - 10.48550/arXiv.2206.06011
M3 - Conference contribution
AN - SCOPUS:85137148570
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 3992
EP - 4000
BT - KDD 2022
PB - Association for Computing Machinery (ACM)
T2 - 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Y2 - 14 August 2022 through 18 August 2022
ER -