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

TL;DR

This study proposes CSLens, a visual analytics system designed to inform charging station deployment decisions through the lens of coupled transportation and power networks, and substantiates the usability and practical utility of CSLens in enhancing the decision-making process surrounding charging station deployment.

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.

Figures (5)

• Figure 1: The system pipeline of CSLens includes both backend and frontend components. In the back-end engine, we formulate a model for the coupled transportation and power networks using three types of collected data. Users can generate candidate solutions for CSLP and analyze the post-deployment impacts. On the front-end visualization, we provide six coordinated views with interactive features. These views facilitate the exploration of current charging station layouts, the generation of deployment solutions, and the comprehensive evaluation of various alternatives.
• Figure 2: Design alternatives for the Temporal Overview. (A) Use a stacked area chart to show the charging demand across various traffic hotspots. (B) Present the basic layout of the current Temporal Overview. (C) Showcase a glyph design depicting demand and traffic hotspots. (D) Demonstrate a glyph design featuring the road network, traffic hotspots, charging demand, and grid load.
• Figure 3: Design alternatives of the Impact View. (A) An adjacency matrix method showcasing traffic volume, charging demand and voltage. (B) A line chart integrated with road maps illustrating the impact after deployment. (C) A combined line chart and road map visualization illustrating both the impact after deployment and charging demand simultaneously.
• Figure 4: Experts’ operations in case I. (A) Experts used the "Link" layout and the "Rank" layout to illustrate the connection among different traffic hotspots. (B) The surrounding traffic condition of a charging station with low charging demand.
• Figure 5: Experts' operations in case II: (A) The overall information of each candidate solution for charging station deployment. (B) Compare and evaluate the impact after the deployment of new charging stations. (C) An example to explore the location of a new charging station.