Zhang, Bo, Zhang, Lu, Liang, Jun  ORCID: https://orcid.org/0000-0001-7511-449X, Wu, Mingzhe, Li, Gen, Tang, Wei, Li, Yun Wei, Zhang, Yujing and Yu, James
2026.
Hybrid AC/DC distribution systems enabling more renewable energy: A review.
Renewable and Sustainable Energy Reviews
240
, 117173.
10.1016/j.rser.2026.117173
|
Preview |
PDF
- Accepted Post-Print Version
Available under License Creative Commons Attribution. Download (804kB) | Preview |
Abstract
The rapid growth of distributed renewable generation, electric vehicles, energy storage systems, and DC loads is transforming distribution networks and creating challenges related to voltage regulation, renewable accommodation, fault restoration, and islanded operation. Hybrid AC/DC distribution systems can address these challenges by enabling flexible power transfer, efficient DC load integration, and converter-based controllability. However, existing reviews mainly focus on specific topologies, control methods, or individual voltage levels, while the differentiated functions and cross-level coupling of hybrid AC/DC technologies remain insufficiently synthesized. This paper presents a hierarchical review of hybrid AC/DC distribution systems, covering low-voltage distribution networks, medium-voltage distribution networks, and off-grid hybrid AC/DC microgrids. A structured literature classification framework is adopted to organize existing studies according to network level, technical objective, enabling device, method category, and performance metric. Key technologies are reviewed for power quality and reliability enhancement, power transfer and restoration capability improvement, and stable and resilient microgrid operation. A cross-level performance comparison further compares representative indicators, quantitative evidence, and implementation constraints. Finally, future research directions are identified, including cross-level coordination, standardized benchmarking, protection- and stability-constrained optimization, fixed–mobile flexible interconnection, and data-driven decision support.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Engineering |
| Additional Information: | RRS applied. |
| Publisher: | Elsevier BV |
| ISSN: | 1364-0321 |
| Date of First Compliant Deposit: | 14 July 2026 |
| Date of Acceptance: | 6 June 2026 |
| Last Modified: | 14 Jul 2026 15:30 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/187998 |
Actions (repository staff only)
 |
Edit Item |
Dimensions
Dimensions