Journal article
Optimal placement of smart hybrid transformers in distribution networks
- Abstract:
- Hybrid transformers are a relatively new technology that combine conventional power transformers with power electronics to provide voltage and reactive power control capabilities in distribution networks. This paper proposes a novel method of determining the optimal location and utilisation of hybrid transformers in 3-phase distribution networks to maximise the net present value of hybrid transformers based on their ability to increase the export of power produced by distributed generators over their operational lifespan. This has been accomplished through sequential linear programming, a key feature of which is the consideration of nonlinear characteristics and constraints relating to hybrid transformer power electronics and control capabilities. Test cases were carried out in a modified version of the Cigre European Low Voltage Distribution Network Benchmark, which has been extended by connecting it with two additional low voltage distribution test networks. All test case results demonstrate that the installation and utilisation of hybrid transformers can improve the income earned from exporting excess active power, justifying their installation cost (with the highest net present value being £6.56 million, resulting from a 45.53% increase in estimated annual profits due to coordinated HT compensation).
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 582.1KB, Terms of use)
-
- Publisher copy:
- 10.1109/tsg.2025.3628489
Authors
+ Scottish Government
More from this funder
- Funder identifier:
- https://ror.org/04v2xmd71
- Grant:
- 180
- Programme:
- ETP Energy Industry Doctorate Programme
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/X027384/1
- Publisher:
- IEEE
- Journal:
- IEEE Transactions on Smart Grid More from this journal
- Volume:
- 17
- Issue:
- 1
- Pages:
- 161-177
- Publication date:
- 2025-11-04
- Acceptance date:
- 2025-10-30
- DOI:
- EISSN:
-
1949-3061
- ISSN:
-
1949-3053
- Language:
-
English
- Keywords:
- Pubs id:
-
2326196
- UUID:
-
uuid_af046e1c-ff3d-4fa6-90a5-81addb2a8ae1
- Local pid:
-
pubs:2326196
- Deposit date:
-
2025-12-19
- ARK identifier:
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2025
- Rights statement:
- © 2025 IEEE. All rights reserved, including rights for text and data mining, and training of artificial intelligence and similar technologies. Personal use is permitted, but republication/redistribution requires IEEE permission.
- Notes:
- The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford's Open Access Publications Policy, and a CC BY public copyright licence has been applied.
- Licence:
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record