Reinforcement Learning-based Placement of Charging Stations in Urban Road Networks

Wahl, Leonie von; Tempelmeier, Nicolas; Sao, Ashutosh; Demidova, Elena

doi:10.1145/3534678.3539154

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…In recent years, the charging station location problem (CSLP) has garnered considerable attention from researchers, leading to a multitude of studies exploring different aspects and perspectives [4,6,[10][11][12][13][14][15][16][17][18][19][20][21][22]. Notable contributions include a deployment framework proposed by Zhao et al [16], which considers existing competitors in the planning of PEV fast-charging stations.…”

Section: Related Work 21 Charging Station Location Problemmentioning

confidence: 99%

“…However, as problem size and constraints increase, computational efficiency becomes a significant limitation for these methods. Von Bahr et al [6] presented a reinforcement learning approach to address CSLP, demonstrating its potential advantages in scalability and computational efficiency.…”

Section: Related Work 21 Charging Station Location Problemmentioning

confidence: 99%

“…While the number of charging stations has increased, it remains insufficient to meet the growing charging demands. Optimizing the location and deployment of charging stations, known as the charging station location problem (CSLP) [6], is a critical approach to address this challenge. The CSLP is an application of the facility location problem, aiming to select suitable locations from a candidate set to optimize various objectives.…”

Section: Introductionmentioning

confidence: 99%

“…However, the optimal placement of charging stations in road networks is not without its challenges. Influenced by a myriad of factors, including road network topology, existing charging infrastructure, traffic patterns, and charging duration, determining appropriate placements is complex [6]. Additionally, existing methods often neglect practical constraints, such as the need for fairness in charging station distribution and the requirement for automated decision making in identifying viable new charging station locations.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism

2023

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With the aim of promoting energy conservation and emission reduction, electric vehicles (EVs) have gained significant attention as a strategic industry in many countries. However, the insufficiency of accessible charging infrastructure remains a challenge, hindering the widespread adoption of EVs. To address this issue, we propose a novel approach to optimize the placement of charging stations within a road network, known as the charging station location problem (CSLP). Our method considers multiple factors, including fairness in charging station distribution, benefits associated with their placement, and drivers’ discomfort. Fairness is quantified by the balance in charging station coverage across the network, while driver comfort is measured by the total time spent during the charging process. Then, the CSLP is formulated as a reinforcement learning problem, and we introduce a novel model called PPO-Attention. This model incorporates an attention layer into the Proximal Policy Optimization (PPO) algorithm, enhancing the algorithm’s capacity to identify and understand the intricate interdependencies between different nodes in the network. We have conducted extensive tests on urban road networks in Europe, North America, and Asia. The results demonstrate the superior performance of our approach compared to existing baseline algorithms. On average, our method achieves a profit increase of 258.04% and reduces waiting time by 73.40%, travel time by 18.46%, and charging time by 40.10%.

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Section: Related Work 21 Charging Station Location Problemmentioning

confidence: 99%

Section: Related Work 21 Charging Station Location Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism

2023

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Multi-agent Reinforcement Learning for Online Placement of Mobile EV Charging Stations

Ting,

Lin,

Lin

et al. 2024

Lecture Notes in Computer Science

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A survey on applications of reinforcement learning in spatial resource allocation

Zhang,

Wang,

Mango

et al. 2024

Comput.Urban Sci.

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The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation.

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Reinforcement Learning-based Placement of Charging Stations in Urban Road Networks

Cited by 10 publications

References 12 publications

Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism

Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism

Multi-agent Reinforcement Learning for Online Placement of Mobile EV Charging Stations

A survey on applications of reinforcement learning in spatial resource allocation

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