Scalable Data-Driven EV Charging Optimization Using HDBSCAN-LP for Real-Time Pricing Load Management

Highlights: What are the main findings? Advanced Behavioral Segmentation: HDBSCAN segmented 72,856 EV charging sessions into nine clusters (Davies-Bouldin score: 0.355, noise: 1.62%), capturing temporal and seasonal patterns. Enhanced Load Optimization: HDBSCAN-LP integration with RTP achieved 23.10–25.41% peak load reductions (321.87–555.15 kWh) and 2.87–5.31% cost savings ($27.35–$50.71), improving load factors by up to 17.14%. What is the implication of the main finding? Provides a scalable, data-driven approach for precise EV load management adaptable to seasonal and behavioral dynamics, enhancing grid stability and economic efficiency. Enables utility planners and policymakers to implement targeted and effective demand-response strategies, supporting sustainable urban energy transitions. The fast-changing scenario of the transportation industry due to the rapid adoption of electric vehicles (EVs) imposes significant challenges on power distribution networks. Challenges such as dynamic and concentrated charging loads necessitate intelligent demand-side management (DSM) strategies to ensure grid stability and cost efficiency. This study proposes a novel two-stage framework integrating Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and linear programming (LP) to optimize EV charging loads across four operational scenarios: Summer Weekday, Summer Weekend, Winter Weekday, and Winter Weekend. Utilizing a dataset of 72,856 real-world charging sessions, the first stage employs HDBSCAN to segment charging behaviors into nine distinct clusters (Davies-Bouldin score: 0.355, noise fraction: 1.62%), capturing temporal, seasonal, and behavioral variability. The second stage applies linear programming optimization to redistribute loads under real-time pricing (RTP), minimizing operational costs and peak demand while adhering to grid constraints. Results demonstrate the load optimization by total peak reductions of 321.87–555.15 kWh (23.10–25.41%) and cost savings of $27.35–$50.71 (2.87–5.31%), with load factors improving by 14.29–17.14%. The framework’s scalability and adaptability make it a robust solution for smart grid integration, offering precise load management and economic benefits.