Carbon-Aware Quantification of Real-Time Aggregate Power Flexibility of Electric Vehicles

Abstract

Electric vehicles (EVs) can be aggregated to offer flexibility services to the power system. However, the rapid growth in EV adoption leads to increased grid-level carbon emissions due to higher EV charging demand, complicating grid decarbonization efforts. Quantifying and managing EV flexibility while controlling carbon emissions is crucial. This paper introduces a methodology for carbon-aware quantification of real-time aggregate EV power flexibility. An offline model is first developed to determine the upper and lower bounds of the EV flexibility region. To address uncertainties in EV charging behaviors and grid carbon intensity, we propose a carbon-aware online optimization algorithm based on Lyapunov optimization, incorporating a queue model to capture system dynamics. To enhance EV flexibility, we integrate dispatch signals from the system operator into the queue update through a two-stage disaggregation process. The proposed approach is prediction-free and adaptable to various uncertainties. Additionally, the maximum charging delay for EV charging tasks is theoretically bounded by a constant, and carbon emissions are effectively controlled. Numerical results demonstrate the effectiveness of the proposed online method and highlight its advantages over several benchmarks through comparisons.