@Article{ee.2026.079956,
AUTHOR = {Xin Li, Shuang Shi, Qiyi Liu, Yan Zhang},
TITLE = {Two-Stage Optimization Strategy of EHDT-BSS Participating in Grid Frequency Regulation},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/26925},
ISSN = {1546-0118},
ABSTRACT = {Electric heavy-duty truck battery swapping stations (EHDT-BSS) are emerging as flexible resources for power systems due to their high controllability and significant power capacity. However, the participation of EHDT-BSSs in grid frequency regulation is severely constrained by the limited battery quantity and the high stochasticity of swapping demand, where forecasting errors can affect system reliability. To address these challenges, this paper proposes a two-stage optimization strategy for EHDT-BSSs participating in frequency regulation considering demand uncertainty. First, the basic operation mode of BSS is designed, and a deep learning-based method is utilized to forecast swapping demand. A state-of-charge (SOC)-based battery classification mechanism is then established to ensure inter-temporal battery availability. The proposed framework includes a day-ahead scheduling stage to maximize the total revenue from frequency regulation and swapping services, followed by an intraday rolling optimization stage designed to compensate for real-time forecasting deviations. The results demonstrate that the proposed method effectively balances economic efficiency and operational reliability, enabling EHDT-BSSs to provide stable ancillary services while meeting heavy-duty truck swapping needs.},
DOI = {10.32604/ee.2026.079956}
}