Optimising peak energy reduction in networks of buildings Article Swipe
Anush Poghosyan
,
Nick McCullen
,
Sukumar Natarajan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-52676-2
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-52676-2
Buildings are amongst the world’s largest energy consumers and simultaneous peaks in demand from networks of buildings can decrease electricity system stability. Current mitigation measures either entail wasteful supply-side over-specification or complex centralised demand-side control. Hence, a simple schema is developed for decentralised, self-organising building-to-building load coordination that requires very little information exchange and no top-down management—analogous to other complex systems with short range interactions, such as coordination between flocks of birds or synchronisation in fireflies. Numerical and experimental results reveal that a high degree of peak flattening can be achieved using surprisingly small load-coordination networks. The optimum reductions achieved by the simple schema can outperform existing techniques, giving substantial peak-reductions as well as being remarkably robust to changes in other system parameters such as the interaction network topology. This not only demonstrates that significant reductions in network peaks are achievable using remarkably simple control systems but also reveals interesting theoretical results and new insights which will be of great interest to the complexity and network science communities.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-024-52676-2
- https://www.nature.com/articles/s41598-024-52676-2.pdf
- OA Status
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- References
- 84
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4391885233Canonical identifier for this work in OpenAlex
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https://doi.org/10.1038/s41598-024-52676-2Digital Object Identifier
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Optimising peak energy reduction in networks of buildingsWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-16Full publication date if available
- Authors
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Anush Poghosyan, Nick McCullen, Sukumar NatarajanList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-024-52676-2Publisher landing page
- PDF URL
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https://www.nature.com/articles/s41598-024-52676-2.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-024-52676-2.pdfDirect OA link when available
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Computer science, Schema (genetic algorithms), Distributed computing, Electricity, Peak demand, Topology (electrical circuits), Operations research, Mathematics, Engineering, Combinatorics, Machine learning, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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