Real-time Feedback Based Online Aggregate EV Power Flexibility Characterization

TL;DR

This paper presents a real-time feedback-based online method for characterizing and utilizing the aggregate power flexibility of electric vehicles to improve power grid management amidst uncertainties.

Contribution

It introduces an online algorithm with real-time feedback for EV flexibility characterization, addressing uncertainties in real-time electricity prices and EV availability.

Findings

The offline model accurately bounds the aggregate EV flexibility region.

The online algorithm guarantees bounded charging delays.

Performance comparisons show the proposed method's advantages.

Abstract

As an essential measure to combat global warming, electric vehicles (EVs) have witnessed rapid growth. Flexible EVs can enhance power systems' ability to handle renewable generation uncertainties. How EV flexibility can be utilized in power grid operation has captured great attention. However, the direct control of individual EVs is challenging due to their small capacity and large number. Hence, it is the aggregator that interacts with the grid on behalf of the EVs by characterizing their aggregate flexibility. In this paper, we focus on the aggregate EV power flexibility characterization problem. First, an offline model is built to obtain the lower and upper bounds of the aggregate EV power flexibility region. It ensures that any trajectory within the region is feasible. Then, considering that parameters such as real-time electricity prices and EV arrival/departure times are not known in advance, an online algorithm is developed based on Lyapunov optimization techniques. We provide a theoretical bound for the maximum charging delay under the proposed online algorithm. Furthermore, real-time feedback is designed and integrated into the proposed online algorithm to better unlock EV power flexibility. Comprehensive performance comparisons are carried out to demonstrate the advantages of the proposed method.