Danyang Lun
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Danyang Lun has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Bin Xu
Electric Power Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, China
2 shared publications - Chao Li
Tianjin University
2 shared publications - Jingan Shang
State Grid Tianjin Electric Power Company, Tianjin 300010, China
2 shared publications - Peiyu Chen
Electric Power Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, China
2 shared publications - Wenqing Cui
Electric Power Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, China
2 shared publications
Latest Publications
- Open Access
- 30 December 2025
- Published by MDPI AG in Applied Sciences
- Vol. 16 (1) , 381
Correction: Chen et al. Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving. Appl. Sci. 2025, 15, 8656
- 30 December 2025
- Published by MDPI AG in Applied Sciences
- Vol. 16 (1) , 381
Open AccessCorrection: Chen et al. Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving. Appl. Sci. 2025, 15, 8656
- Open Access
- 5 August 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (15) , 8656
Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving
In order to achieve the goals of carbon neutrality, large-scale storage of renewable energy sources has been integrated into the power grid. Under these circumstances, the power grid faces the challenge of peak shaving. Therefore, this paper proposes a coordinated variable-power control strategy for multiple battery energy storage stations (BESSs), improving the performance of peak shaving. Firstly, the strategy involves constructing an optimization model incorporating load forecasting, capacity constraints, and security indices to design a coordination mechanism tracking the target load band with the equivalent power. Secondly, it establishes a quantitative evaluation system using metrics such as peak–valley difference and load standard deviation. Comparison based on typical daily cases shows that, compared with the constant power strategy, the coordinated variable-power control strategy has a more obvious and comprehensive improvement in overall peak-shaving effects. Furthermore, it employs a “dynamic dispatch of multiple BESS” mode, effectively mitigating the risks and flexibility issues associated with single BESSs. This strategy provides a reliable new approach for large-scale energy storage to participate in high-precision peaking.
- 5 August 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (15) , 8656
Open AccessControl Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving
In order to achieve the goals of carbon neutrality, large-scale storage of renewable energy sources has been integrated into the power grid. Under these circumstances, the power grid faces the challenge of peak shaving. Therefore, this paper proposes a coordinated variable-power control strategy for multiple battery energy storage stations (BESSs), improving the performance of peak shaving. Firstly, the strategy involves constructing an optimization model incorporating load forecasting, capacity constraints, and security indices to design a coordination mechanism tracking the target load band with the equivalent power. Secondly, it establishes a quantitative evaluation system using metrics such as peak–valley difference and load standard deviation. Comparison based on typical daily cases shows that, compared with the constant power strategy, the coordinated variable-power control strategy has a more obvious and comprehensive improvement in overall peak-shaving effects. Furthermore, it employs a “dynamic dispatch of multiple BESS” mode, effectively mitigating the risks and flexibility issues associated with single BESSs. This strategy provides a reliable new approach for large-scale energy storage to participate in high-precision peaking.