Data-driven demand response aggregation for public EV charging stations: Overcoming decoupled governance challenges

Yongjun Cho, Donghoon Kim and Jinho Kim

Applied Energy, 2026, vol. 402, issue PB, No S0306261925017167

Abstract: Electrification of the transportation sector will expand the public charging infrastructure globally to 600 GW by 2030, enabling stations to mitigate renewable energy curtailment through an upward demand response. With prohibitive smart charging and bidirectional power flow costs, greedy charging-based demand response participation remains the only viable pathway for integrating electric vehicles (EVs) as flexible resources until advanced technologies mature. However, this integration is hindered by decoupled governance challenges unique to public charging stations. Our proposed data-driven aggregation framework addresses these challenges through machine learning techniques, providing practical solutions for demand response aggregators (DRAs) to effectively integrate distributed resources despite their inherent operational uncertainties. The framework utilizes readily available station-level data to support critical decision-making problems in market participation through three interconnected processes: comprehensive flexibility assessment, dispatchable demand response amount prediction for resources without market records, and efficient classification of distributed small-scale charging stations. Validation using data from 700 public charging stations on Jeju Island, South Korea, demonstrated the framework’s effectiveness, revealing unique characteristics of EV charging stations compared to conventional demand resources. Economic analysis confirmed that our tier-based selective aggregation significantly improved cost-effectiveness over that of conventional practices in the actual market. This promotes reduction in the gap between DRAs’ cleared volume and actual dispatch amount, thereby contributing to cost-effective system operation.

Keywords: Demand response; Public EV charging station; Demand flexibility; Machine learning; Aggregation (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1016/j.apenergy.2025.126986

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