Multi-Timescale Battery-Charging Optimization for Electric Heavy-Duty Truck Battery-Swapping Stations, Considering Source–Load–Storage Uncertainty

Peijun Shi, Guojian Ni, Rifeng Jin, Haibo Wang, Jinsong Wang, Zhongwei Sun and Guizhi Qiu ()
Additional contact information
Peijun Shi: Datang Beijing Tianjin Hebei Energy Marketing Co., Ltd., Beijing 100031, China
Guojian Ni: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Rifeng Jin: Datang Henan Power Generation Co., Ltd., Zhengzhou 450000, China
Haibo Wang: Datang Beijing Tianjin Hebei Energy Marketing Co., Ltd., Beijing 100031, China
Jinsong Wang: China Datang Corporation Science and Technology General Research Institute North China Electric Power Test and Research Institute, Beijing 100043, China
Zhongwei Sun: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Guizhi Qiu: China Datang Corporation Science and Technology General Research Institute North China Electric Power Test and Research Institute, Beijing 100043, China

Energies, 2025, vol. 18, issue 2, 1-21

Abstract: With the widespread adoption of renewable energy sources like wind power and photovoltaic (PV) power, uncertainties in the renewable energy output and the battery-swapping demand for electric heavy-duty trucks make it challenging for battery-swapping stations to optimize battery-charging management centrally. Uncoordinated large-scale charging behavior can increase operation costs for battery-swapping stations and even affect the stability of the power grid. To mitigate this, this paper proposes a multi-timescale battery-charging optimization for electric heavy-duty truck battery-swapping stations, taking into account the source–load–storage uncertainty. First, a model incorporating uncertainties in renewable energy output, time-of-use pricing, and grid load fluctuations is developed for the battery-swapping station. Second, based on day-ahead and intra-day timescales, the optimization problem for battery-charging strategies at battery-swapping stations is decomposed into day-ahead and intra-day optimization problems. We propose a day-ahead charging strategy optimization algorithm based on intra-day optimization feedback information-gap decision theory (IGDT) and an improved grasshopper algorithm for intra-day charging strategy optimization. The key contributions include the following: (1) the development of a battery-charging model for electric heavy-duty truck battery-swapping stations that accounts for the uncertainty in the power output of energy sources, loads, and storage; (2) the proposal of a day-ahead battery-charging optimization algorithm based on intra-day-optimization feedback information-gap decision theory (IGDT), which allows for dynamic adjustment of risk preferences; (3) the proposal of an intra-day battery-charging optimization algorithm based on an improved grasshopper optimization algorithm, which enhances algorithm convergence speed and stability, avoiding local optima. Finally, simulation comparisons confirm the success of the proposed approach. The simulation results demonstrate that the proposed method reduces charging costs by 4.26% and 6.03% compared with the two baseline algorithms, respectively, and improves grid stability, highlighting its effectiveness for managing battery-swapping stations under uncertainty.

Keywords: electric heavy-duty truck battery-swapping station; source–load–storage uncertainty; multi-timescale optimization; battery-charging optimization; improved IGDT (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/2/241/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/2/241/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:2:p:241-:d:1562161

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().