Yu, Hao, Rong, Yapeng, Ji, Haoran, Li, Peng, Yu, Jiancheng, Song, Guanyu, Zhao, Jinli and Wu, Jianzhong  ORCID: https://orcid.org/0000-0001-7928-3602
2025.
Data-driven adaptive control of distributed PV clusters in active distribution networks based on primal-dual transformation.
IEEE Transactions on Sustainable Energy
10.1109/tste.2025.3648118
|
Preview |
PDF
- Accepted Post-Print Version
Available under License Creative Commons Attribution. Download (12MB) | Preview |
Abstract
The high penetration of photovoltaic (PV) systems brings significant operational challenges in active distribution networks (ADNs), such as overloads and voltage violations. To address these issues, this paper proposes a data-driven adaptive control method for distributed PV clusters based on primal-dual transformation. The complex physical constraints for PV control are relaxed and decoupled to reduce the problem complexity. A hierarchical coordinated control framework is developed to facilitate data-driven optimization of PV control strategies under fluctuating conditions without requiring accurate ADN parameters. Furthermore, a dynamic coordination approach based on the Gompertz curve model is introduced to manage the active and reactive power of PVs in real time, eliminating the need for prior knowledge of converter reactive power capacity. The proposed method is validated on modified IEEE 33-node and IEEE 123-node test systems. Simulation results demonstrate its effectiveness in mitigating voltage and power violations, confirming its practicality and adaptability in uncertain network conditions.
| Item Type: | Article |
|---|---|
| Date Type: | Published Online |
| Status: | In Press |
| Schools: | Schools > Engineering |
| Additional Information: | License information from Publisher: LICENSE 1: URL: https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html, Start Date: 2025-01-01 |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 1949-3029 |
| Date of First Compliant Deposit: | 6 January 2026 |
| Date of Acceptance: | 21 December 2025 |
| Last Modified: | 06 Jan 2026 10:15 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/183486 |
Actions (repository staff only)
 |
Edit Item |
Dimensions
Dimensions