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CSLens: Towards Better Deploying Charging Stations via Visual Analytics - a Coupled Networks Perspective
IEEE Transactions on Visualization and Computer Graphics
|September 10, 2024
Summary
This study introduces CSLens, a visual analytics system to optimize electric vehicle (EV) charging station placement. CSLens holistically assesses impacts on transportation and power grids, improving infrastructure planning.
Area of Science:
- Transportation Engineering
- Computer Science
- Energy Systems
Background:
- Global electric vehicle (EV) adoption is rapidly increasing, necessitating efficient charging station deployment.
- Existing charging station location problem (CSLP) algorithms face limitations in spatiotemporal evaluation and computational efficiency.
- Charging station placement significantly impacts interconnected road and power networks, requiring holistic assessment.
Purpose of the Study:
- To develop a visual analytics system, CSLens, for informed electric vehicle charging station deployment decisions.
- To enable users to analyze existing layouts, explore alternatives, and evaluate impacts on coupled networks.
- To address the limitations of current CSLP algorithms by providing comprehensive spatiotemporal and network impact evaluations.
Main Methods:
- Development of CSLens, a visual analytics system integrating transportation and power network data.
- Implementation of interactive visualizations and analytical features for deployment scenario exploration.
- Validation through case studies and domain expert interviews.
Main Results:
- CSLens provides a holistic view of charging station deployment impacts on transportation and power grids.
- The system facilitates the exploration of alternative deployment strategies and their consequences.
- Case studies and expert feedback confirmed the usability and practical utility of CSLens.
Conclusions:
- CSLens enhances decision-making for electric vehicle charging infrastructure planning.
- The system offers a valuable tool for navigating the complexities of deploying charging stations.
- CSLens demonstrates the potential for visual analytics in optimizing sustainable transportation systems.
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