CSLens: Towards Better Deploying Charging Stations via Visual Analytics -- A Coupled Networks Perspective

TL;DR

This paper introduces CSLens, a visual analytics system that integrates transportation and power network data to improve electric vehicle charging station deployment decisions, addressing limitations of existing algorithms and considering network impacts.

Contribution

The paper presents CSLens, a novel visual analytics tool that holistically evaluates charging station deployment by coupling transportation and power network analyses.

Findings

CSLens enhances decision-making in charging station deployment.

Case studies demonstrate CSLens's usability and practical utility.

Expert interviews confirm its effectiveness in real-world planning.

Abstract

In recent years, the global adoption of electric vehicles (EVs) has surged, prompting a corresponding rise in the installation of charging stations. This proliferation has underscored the importance of expediting the deployment of charging infrastructure. Both academia and industry have thus devoted to addressing the charging station location problem (CSLP) to streamline this process. However, prevailing algorithms addressing CSLP are hampered by restrictive assumptions and computational overhead, leading to a dearth of comprehensive evaluations in the spatiotemporal dimensions. Consequently, their practical viability is restricted. Moreover, the placement of charging stations exerts a significant impact on both the road network and the power grid, which necessitates the evaluation of the potential post-deployment impacts on these interconnected networks holistically. In this study, we propose CSLens, a visual analytics system designed to inform charging station deployment decisions through the lens of coupled transportation and power networks. CSLens offers multiple visualizations and interactive features, empowering users to delve into the existing charging station layout, explore alternative deployment solutions, and assess the ensuring impact. To validate the efficacy of CSLens, we conducted two case studies and engaged in interviews with domain experts. Through these efforts, we substantiated the usability and practical utility of CSLens in enhancing the decision-making process surrounding charging station deployment. Our findings underscore CSLens's potential to serve as a valuable asset in navigating the complexities of charging infrastructure planning.