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Abstract：
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. However, the existing dataset at NERC that covers the whole of Great Britain (GB) does not provide information about demand and capacity, which is insufficient for evaluating the connection feasibility. Although each distribution network operator (DNO) has detailed network information for their supply area, the information is scattered in separate files and different formats even within the same DNO, which limits usability. On the other hand, studying the coupling between energy systems and societal attributes such as household heating is important in promoting social welfare, which calls for more comprehensive datasets that integrate the social data and the energy network data. However, social datasets are usually provided on a regional basis, and the link to energy networks is not straightforward, which explains the lack of the comprehensive datasets. To fill these gaps, this paper introduces two datasets. The first is the main dataset for the GB distribution networks, collecting information on firm capacity, peak demands, locations, and parent transmission nodes (grid supply points, namely GSPs) for all primary substations (PSs). PSs are a crucial part of UK distribution networks and are at the lowest voltage level (11 kV) with publicly available data. Substation firm capacity and peak demand facilitate an understanding of the remaining room in the existing network. The parent GSP information helps link the released datasets to transmission networks. These datasets are collected, standardised, and merged from various files with different formats published by the six DNOs in GB, using a Python script and manual validation. The second dataset extends the main network dataset, linking each PS to the number of households that use different types of central heating recorded in census data (Census in year 2021 for England and Wales, and Census 2011 for Scotland as the up-to-date Census 2022 data is not fully released). The derivation of the second dataset is based on the locations of PSs collected in the main dataset with appropriate assumptions. The derivation process may be replicated to integrate other social datasets. The datasets have the following reuse potentials: 1) Given the PS demand, capacity, and locations in our datasets, users can estimate the connection feasibility and evaluate the optimal deployment locations for different energy technologies, including electric vehicles, heat pumps, and the growing data centres, under different scenarios and at a national scale. These evaluations are beneficial not only for academic research, but also for industrial planning and policy making. 2) Our extended dataset links household information to distribution networks. The integrated information facilitates cross-disciplinary research and analysis across social science, energy policy, and power systems. 3) The network demand and capacity information provided by the datasets can also help with realistic parameter settings to improve the accuracy of case studies in broader power system research.
摘要：
对电气化供暖的需求不断增长，电气化运输,和电力密集型数据中心挑战分配网络。如果在不考虑配电基础设施的情况下进行电气化项目，可能会有意外的停电和经济损失。需要包含真实世界分发网络信息的数据集来解决此问题。然而,NERC覆盖整个英国（GB）的现有数据集不提供有关需求和容量的信息，这不足以评估连接的可行性。尽管每个分销网络运营商（DNO）都有其供应区域的详细网络信息，即使在同一个DNO中，信息也分散在单独的文件和不同的格式中，这限制了可用性。另一方面,研究能源系统与家庭供暖等社会属性之间的耦合对促进社会福利非常重要，这需要整合社交数据和能源网络数据的更全面的数据集。然而,社交数据集通常是在区域基础上提供的，与能源网络的联系并不简单，这解释了缺乏全面的数据集。为了填补这些空白，本文介绍了两个数据集。第一个是GB分配网络的主要数据集，收集关于公司能力的信息，高峰需求，地点,和父传输节点(网格供应点，即GSP)适用于所有一次变电站(PS)。PS是英国配电网络的重要组成部分，并且在公开数据下处于最低电压水平（11kV）。变电站的固定容量和高峰需求有助于了解现有网络中的剩余空间。父GSP信息有助于将发布的数据集链接到传输网络。收集这些数据集，标准化,并从6个DNOs在GB中发布的具有不同格式的各种文件中合并，使用Python脚本和手动验证。第二个数据集扩展了主网络数据集，将每个PS与人口普查数据中记录的使用不同类型中央供暖的家庭数量联系起来(2021年英格兰和威尔士的人口普查，以及苏格兰的2011年人口普查，因为2022年最新人口普查数据尚未完全发布)。第二数据集的推导是基于在具有适当假设的主数据集中收集的PS的位置。可以复制推导过程以整合其他社交数据集。数据集具有以下重用潜力：1)鉴于PS需求，容量,以及我们数据集中的位置，用户可以估计连接可行性，并评估不同能源技术的最佳部署位置，包括电动汽车，热泵,和不断增长的数据中心，在不同的情况下，在全国范围内。这些评估不仅有利于学术研究，也是为了产业规划和政策制定。2)我们的扩展数据集将家庭信息链接到分销网络。整合的信息促进了社会科学的跨学科研究和分析，能源政策,和电力系统。3）数据集提供的网络需求和容量信息也可以帮助进行实际的参数设置，以提高更广泛的电力系统研究中案例研究的准确性。
