Optimal planning for electric vehicle fast charging stations placements in a city scale using an advantage actor-critic deep reinforcement learning and geospatial analysis

Heo, Jae; Chang, Soowon

doi:10.1016/j.scs.2024.105567

Sustainable Cities and Society

2024

DOI: 10.1016/j.scs.2024.105567

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Optimal planning for electric vehicle fast charging stations placements in a city scale using an advantage actor-critic deep reinforcement learning and geospatial analysis

Jae Heo,

Soowon Chang

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Cited by 2 publications

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An improved arithmetic method for determining the optimum placement and size of EV charging stations

Fotis

2024

Computers and Electrical Engineering

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An improved arithmetic method for determining the optimum placement and size of EV charging stations

Fotis

2024

Computers and Electrical Engineering

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Multi-Stage Hybrid Planning Method for Charging Stations Based on Graph Auto-Encoder

Wu,

Lau

2024

Electronics

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To improve the operational efficiency of electric vehicle (EV) charging infrastructure, this paper proposes a multi-stage hybrid planning method for charging stations (CSs) based on graph auto-encoder (GAE). First, the network topology and dynamic interaction process of the coupled “Vehicle-Station-Network” system are characterized as a graph-structured model. Second, in the first stage, a GAE-based deep neural network is used to learn the graph-structured model and identify and classify different charging station (CS) types for the network nodes of the coupled system topology. The candidate CS set is screened out, including fast-charging stations (FCSs), fast-medium-charging stations, medium-charging stations, and slow-charging stations. Then, in the second stage, the candidate CS set is re-optimized using a traditional swarm intelligence algorithm, considering the interests of multiple parties in CS construction. The optimal CS locations and charging pile configurations are determined. Finally, case studies are conducted within a practical traffic zone in Hong Kong, China. The existing CS planning methods rely on simulation topology, which makes it difficult to realize efficient collaboration of charging networks. However, the proposed scheme is based on the realistic geographical space and large-scale traffic topology. The scheme determines the station and pile configuration through multi-stage planning. With the help of an artificial intelligence (AI) algorithm, the user behavior characteristics are captured adaptively, and the distribution rule of established CSs is extracted to provide support for the planning of new CSs. The research results will help the power and transportation departments to reasonably plan charging facilities and promote the coordinated development of EV industry, energy, and transportation systems.

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Optimal planning for electric vehicle fast charging stations placements in a city scale using an advantage actor-critic deep reinforcement learning and geospatial analysis

Cited by 2 publications

References 42 publications

An improved arithmetic method for determining the optimum placement and size of EV charging stations

An improved arithmetic method for determining the optimum placement and size of EV charging stations

Multi-Stage Hybrid Planning Method for Charging Stations Based on Graph Auto-Encoder

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