Datasets of Great Britain Primary Substations Integrated with Household Heating Information
<|reference_start|>Datasets of Great Britain Primary Substations Integrated with Household Heating Information: The growing demand for electrified heating, electrified transportation, and power-intensive data centres challenge distribution networks. If electrification projects are carried out without considering electrical distribution infrastructure, there could be unexpected blackouts and financial losses. Datasets containing real-world distribution network information are required to address this. On the other hand, social data, such as household heating composition, are closely coupled with people's lives. Studying the coupling between the energy system and society is important in promoting social welfare. To fill these gaps, this paper introduces two datasets. The first is the main dataset for the distribution networks in Great Britain (GB), collecting information on firm capacity, peak demands, locations, and parent transmission nodes (the Grid Supply Point, namely GSP) for all primary substations (PSs). PSs are a crucial part of the UK distribution network and are at the lowest voltage level (11 kV) with publicly available data for most UK Distribution Network Operators (DNOs). Substation firm capacity and peak demand facilitate an understanding of the remaining room of the existing network. The parent GSP information helps link the dataset of distribution networks to datasets of transmission networks. The second dataset extends the main network dataset, linking each PS to information about the number of households that use different types of central heating recorded in census data. The derivation of the second dataset is based on locations of PSs collected in the main dataset with appropriate assumptions. The derivation process may also be replicated to integrate other social datasets.<|reference_end|>
@article{zhou2024datasets,
    title={Datasets of Great Britain Primary Substations Integrated with Household
  Heating Information},
    author={Yihong Zhou, Chaimaa Essayeh, Thomas Morstyn},
    journal={arXiv preprint arXiv:2403.16313},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16313},
    primaryClass={eess.SY cs.SY}
}