Data-Driven Privacy-Preserving Modeling and Frequency Regulation with Aggregated Electric Vehicles via Bilinear Hidden Markov Model

TL;DR

This paper introduces a privacy-preserving, data-driven framework for frequency regulation using aggregated electric vehicles, avoiding individual data collection while maintaining high accuracy and robustness.

Contribution

It proposes a novel bilinear hidden Markov model-based approach that estimates EV flexibility and power output without individual EV data, enhancing privacy and data robustness.

Findings

Accurately estimates power output and flexibility of aggregated EVs.

Outperforms existing methods under SOC data inaccuracies.

Effective for real-time frequency regulation without individual EV info.

Abstract

Vehicle-to-Grid (V2G) technology allows bidirectional power flow for real-time grid support, making electric vehicles (EVs) well-suited for ancillary services such as frequency regulation. However, existing methods for flexibility estimation and coordinating aggregated EVs often rely on individual EV traveling information (e.g., arrival/departure time) and/or characteristic parameters (e.g., charging efficiency, battery capacity) as well as real-time state-of-charge (SOC), which raises privacy concerns and faces data quality issues. To address these challenges, this paper proposes a data-driven, privacy-preserving modeling and control framework for frequency regulation using aggregated EVs. The proposed method can provide accurate estimation for power outputs and flexibility of aggregated EVs and carry out effective frequency regulation without any individual EV information. Simulation results validate the accuracy and effectiveness of the proposed method, which also outperforms the model-based and federated learning-based method under SOC data inaccuracies.