Representative Residential LV Feeders: A Case Study for the North West of England
Open accessAbstract
The adoption of residential-scale low carbon technologies, such as photovoltaic panels or electric vehicles, is expected to significantly increase in the near future. Therefore, it is important for distribution network operators (DNOs) to understand the impacts that these technologies may have, particularly, on low voltage (LV) networks. The challenge, however, is that these LV networks are large in number and diverse in characteristics. In this work, four clustering algorithms (hierarchical clustering, k- medoids++, improved k- means++, and Gaussian Mixture Model-GMM), are applied to a set of 232 residential LV feeders from the North West of England to obtain representative feeders. Moreover, time-series monitoring data, presence of residential-scale generation, and detailed customer classification are considered in the analysis. Multiple validity indices are used to identify the most suitable algorithm. The improved k- means++ and GMM showed the best performances resulting in eleven representative feeders with prominent characteristics such as number and type of customers, total cable length, neutral current, and presence of generation. Crucially, the results from studies performed on these feeders can then be extrapolated to those they represent, simplifying the analyses to be carried out by DNOs. This is demonstrated with a hosting capacity assessment of photovoltaic panels in LV feeders.
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In the UK, there are more than 1.3 million LV feeders.
“For example, in the UK, there are more than 1.3 million LV feeders [38].”
From Chance constrained day-ahead robust flexibility needs assessment for low voltage distribution network (2022) · cites this paper as [38]
The available passages labeled Abstract contain bibliographic information, not a UK-wide feeder count. Searches for “1.3 million LV feeders” and “number of low voltage feeders in the UK” found no passage addressing the claimed figure. (LLM, conf=0.76)
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Monte Carlo-based assessment of PV impacts on real UK low voltage networks
Navarro, A., Ochoa, L. F., & Randles, D. (2013) · 2013 IEEE Power & Energy Society General Meeting · doi:10.1109/pesmg.2013.6672620 · Cited by 93 · see citing works
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Rodríguez-Pajarón, P., Hernández, A., & Milanović, J. V. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.106807 · Cited by 89 · see citing works
“When running one scenario considering odd harmonic orders up to 7th and 160 iterations (each one simulating a week with 1008 10-min intervals) the computation time is around 180 min for the IEEE European LV test feeder [20] and around 12 hrs for the Representative European Network #7 [21].”
MethodInfluentialHashmi, M. U., Koirala, A., Ergun, H., & Van Hertem, D. (2022) · arXiv · doi:10.48550/arxiv.2207.10234 · Cited by 1 · see citing works
- Authors
- Rigoni, V., Ochoa, L. F., Chicco, G., Navarro-Espinosa, A., & Gozel, T.
- Affiliations
- University of Manchester (GB)
- Politecnico di Torino (IT)
- Venue
- IEEE Transactions on Power Systems
- Published
- 2016-01
- Volume
- 31
- Issue
- 1
- Pages
- 348-360
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W2169101722
- Cited by
- 81
- References
- 1
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 3 · 2023: 7 · 2022: 16 · 2021: 9 · 2020: 15 · 2019: 6 · 2018: 7 · 2017: 6 · 2016: 2 · 2015: 2
- FWCI
- 2.73
- Type
- journal article
- Language
- en
- Open access
- Locations
- IEEE Transactions on Power Systems (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion, other-oa)
- PORTO@Iris (submittedVersion)
- PORTO Publications Open Repository TOrino (Politecnico di Torino) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
- Keywords
- low-voltage feeders, clustering algorithms, K-means clustering, Gaussian mixture model, hierarchical clustering, K-medoids clustering, clustering validity indices, photovoltaic hosting capacity, electric vehicles, distribution network operators
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