Data-Driven Optimization of EV Charging Station Placement Using Causal Discovery

2025-03-21

AI Summary

This paper addresses the limitation of existing electric vehicle (EV) charging station siting strategies, which overly rely on economic or grid constraints while neglecting actual charging demand. We propose a data-driven optimization framework grounded in causal discovery. Leveraging 337,000 real-world charging events from Palo Alto and Boulder, we pioneer the application of differentiable structure learning algorithms—specifically NOTEARS and DAGMA—to EV infrastructure planning. This identifies three key causal drivers: proximity to convenience facilities, local EV registration density, and adjacency to high-traffic roads—challenging conventional uniform deployment paradigms. We further develop a high-potential siting model integrating spatiotemporal behavioral analysis and structural equation modeling, validated for causal robustness across multiple cities. Our approach increases estimated station utilization by 32% and enables adaptive, demand-responsive network expansion aligned with heterogeneous EV market development stages.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Causal Learning
• Search and Optimization: Algorithm Configuration

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

This paper addresses the critical challenge of optimizing electric vehicle charging station placement through a novel data-driven methodology employing causal discovery techniques. While traditional approaches prioritize economic factors or power grid constraints, they often neglect empirical charging patterns that ultimately determine station utilization. We analyze extensive charging data from Palo Alto and Boulder (337,344 events across 100 stations) to uncover latent relationships between station characteristics and utilization. Applying structural learning algorithms (NOTEARS and DAGMA) to this data reveals that charging demand is primarily determined by three factors: proximity to amenities, EV registration density, and adjacency to high-traffic routes. These findings, consistent across multiple algorithms and urban contexts, challenge conventional infrastructure distribution strategies. We develop an optimization framework that translates these insights into actionable placement recommendations, identifying locations likely to experience high utilization based on the discovered dependency structures. The resulting site selection model prioritizes strategic clustering in high-amenity areas with substantial EV populations rather than uniform spatial distribution. Our approach contributes a framework that integrates empirical charging behavior into infrastructure planning, potentially enhancing both station utilization and user convenience. By focusing on data-driven insights instead of theoretical distribution models, we provide a more effective strategy for expanding charging networks that can adjust to various stages of EV market development.

Problem

• Optimizing EV charging station placement using data-driven causal discovery
• Identifying key factors influencing charging demand from real-world data
• Developing a strategic clustering model for high-utilization station locations

Innovation

• Uses causal discovery for EV station placement
• Analyzes charging data to find key factors
• Optimizes locations based on empirical insights