Publication Date
11-6-2025
Document Type
Article
Organizational Units
Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering
Keywords
Frequency response, Battery energy storage system, Learning-based control, Genetic algorithm
Abstract
This paper proposes an advanced strategy for managing multiple battery energy storage systems (BESS) to enhance frequency support during contingencies. A novel deep reinforcement learning (DRL) framework based on a guided surrogate-gradient-based evolutionary strategy (GSES) was developed to dynamically regulate BESS outputs for rapid power injection or absorption. This approach effectively mitigates the rate of change of frequency (ROCOF) and stabilizes the system frequency under varying operating conditions. Parallel computing techniques are employed to accelerate training and ensure robust performance. In addition, a genetic algorithm is implemented to determine the placement of BESS within the grid network, strategically minimizing ROCOF during disturbances by accounting for active power injections and inertia contributions from multiple BESS units. The proposed GSES-DRL methodology is rigorously validated using extensive simulations on a modified IEEE 39-bus system, demonstrating effective performance in frequency response compared with existing strategies. In severe scenarios, the proposed method successfully prevents unnecessary load-shedding relay operations, thereby enhancing system stability.
Copyright Date
11-6-2025
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 International License.
Rights Holder
Abu Shouaib Hasan, Rui Fan, Wei Gao, and Di Wu
Provenance
Received from Elsevier
File Format
application/pdf
Language
English (eng)
Extent
9 pgs
File Size
15.2 MB
Publication Statement
Copyright is held by the Authors. User is responsible for all copyright compliance. This article was originally published as
Hasan, A. S., Fan, R., Gao, W., & Wu, D. (2025). Deep Reinforcement Learning Based Control for Enhanced Frequency Response with Multi-Energy Storage Systems. Electric Power Systems Research, 252.https://doi.org/10.1016/j.epsr.2025.112432
Publication Title
Electric Power Systems Research
Volume
252
First Page
112432
ISSN
0378-7796
Recommended Citation
Hasan, Abu Shouaib; Fan, Rui; Gao, Wei; and Wu, Di, "Deep Reinforcement Learning Based Control for Enhanced Frequency Response with Multi-Energy Storage Systems" (2025). Electrical and Computer Engineering: Faculty Scholarship. 58.
https://digitalcommons.du.edu/electrical_engineering_faculty/58
https://doi.org/10.1016/j.epsr.2025.112432