Open Access

ARTICLE

Two-Stage Optimization Strategy of EHDT-BSS Participating in Grid Frequency Regulation

Xin Li, Shuang Shi^*, Qiyi Liu, Yan Zhang

State Grid Sichuan Electric Power Company Marketing Service Center (Metrology Center), Chengdu, China

* Corresponding Author: Shuang Shi. Email:

(This article belongs to the Special Issue: Operation and Control of Grid-connected New Energy and Emerging Loads)

Energy Engineering 2026, 123(7), 8 https://doi.org/10.32604/ee.2026.079956

Received 31 January 2026; Accepted 01 April 2026; Issue published 18 June 2026

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.

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Keywords

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