A Scalable Geospatial Data-Driven Localization Approach for Modeling of Low Voltage Distribution Networks and Low Carbon Technology Impact Assessment Article Swipe
Connor McGarry
,
Amy Anderson
,
Ian Elders
,
Stuart Galloway
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3288811
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3288811
The electrification of heat and transport through the uptake of low carbon technologies (LCTs) is expected to pose significant planning and management challenges for distribution network operators (DNOs) in the coming decades. Therefore, to support investment decision making there is a requirement to understand the impact LCTs will have on low voltage (LV) distribution network infrastructure across diverse geographical areas. However, LV networks are not only radically different in terms of topology and physical asset characteristics, but also in terms of the demand they serve which is sensitive to the diversity of local conditions such as climate, consumer demographic and building stock. As such, there is an increasing requirement to capture elements of this diversity in the development of LV network and LCT modeling approaches to better quantify place-based LCT impact and to inform the quantification of local area flexibility. In turn, using Python and OpenDSS, this work presents a novel scalable approach to localized LV network and LCT impact modeling by coupling two methodologies; a LV network model development methodology and a LCT impact assessment methodology which accounts for both the electrification of heat and transport with consideration for the diversity of residential heat demand. The methodology is demonstrated on LV networks in Scotland through quantification of LCT network impact against key network assessment metrics. The findings demonstrate the value in spatial and temporal high-resolution modeling at scale, emphasizing a need to consider the combined impact of electrified heat and transport in future network investment planning.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2023.3288811
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10159367.pdf
- OA Status
- gold
- Cited By
- 13
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381802278
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4381802278Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2023.3288811Digital Object Identifier
- Title
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A Scalable Geospatial Data-Driven Localization Approach for Modeling of Low Voltage Distribution Networks and Low Carbon Technology Impact AssessmentWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Connor McGarry, Amy Anderson, Ian Elders, Stuart GallowayList of authors in order
- Landing page
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https://doi.org/10.1109/access.2023.3288811Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/10159367.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10159367.pdfDirect OA link when available
- Concepts
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Geospatial analysis, Computer science, Scalability, Electrification, Transport network, Environmental economics, Database, Electricity, Engineering, Economics, Cartography, Geography, Computer network, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 5Per-year citation counts (last 5 years)
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32Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2023-01-01 |
| publication_year | 2023 |
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