A profitability assessment of fast-charging stations under vehicle-to-grid smart charging operation

Abstract

Vehicle-to-grid (V2G) technology, a key driver for reducing carbon emissions and promoting sustainability, promises significant economic benefits through efficient energy exchanges between electric vehicles (EVs) and power distribution grids. However, the inherent uncertainty and variability in EV charging behavior pose challenges in accurately estimating potential economic gains from coordinated smart charging, presenting difficulties for charging service providers. In response to this, our study introduces a data-driven framework for assessing the profitability of fast-charging stations based on real-world operational data. This framework integrates data analytics, mixed-integer optimization, and behavioral theory. To address the computational challenge posed by optimizing charging schedules for numerous sessions, a tailored sub-gradient method is proposed. Additionally, a logit-based choice model is incorporated to account for the participation decisions of users. The flexibility of 0.7 million charging session profiles is thoroughly analyzed under peak-shaving incentives from the grid and quantifies the resultant monetary benefits. The analysis further extended to multiple influencing factors such as climate and the Covid-19 pandemic. Results suggest that fast charging stations under a coordinated charging scheme could experience a monthly bill curtailment of 20–30% compared to uncoordinated circumstances. These findings could pave the way for more efficient and profitable V2G operations, thereby accelerating the transition toward a more sustainable transportation infrastructure.