Project Optimise Prime - Commercial electric vehicles trial

IntroductionOptimise Prime has gathered data fromover 8,000 electric vehicles (EVs) driven for commercial purposes through three trials. The project also implemented a range of technical and commercial solutions aiming to accelerate the transition to EVs for commercial fleet operators, while helping GB’s distribution networks plan and prepare for the mass adoption of EVs. The project aimed to reduce the impact of EVs ondistribution networks and ensure security of electricity supply while saving money for electricity customers, helping the UK meet its clean air and climate change objectives.OtherDownload dataset information: Metadata (JSON)Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary:https://ukpowernetworks.opendatasoft.com/pages/glossary/Methodological ApproachThe project consisted of three trialworkstreams (WS): WS1, investigating the impact of commercial vehicles charging at homes WS2, monitoring and optimising commercial vehicles charging in depots WS3, which uses private hire vehicle (PHV) journey data to model the impact of these vehicles on the distribution network.The trial period for WS3 began in August2020, with WS1 and WS2 trials commencing on 1 July 2021. All trials concludedat the end of June 2022.Optimise Prime is sharing the projectdatasets by making them publicly available to the wider electricity, fleet and PHV industry to optimise their vehicle electrification plans. Distribution Network Operators (DNOs), academicsand interested parties will be able to utilise this anonymised data created bythe project for further research, analysis and forecasting. Along with thedatasets, an introductory document is available below. It provides informationon the content of those datasets, guidance on how to interpret each data fieldand instructions on how to access the data.Over the course of the trials, theproject has collected and analysed data from a wide range of sources in orderto carry out a wide range of experiments. The key datasets collected and usedin analysis are available here. These includes: WS1 – Return-to-Home Trials ·      Charge Point (CP) session data ·      Vehicle telematics journey data WS2 – Depot Trials ·      Details of the assets and asset types at each depot ·      Charging data gathered from the CPs at each depot ·      Building load measurement data from each depot ·      Details of the profiled connections, flexibility trials and smart charging profiles trialled at each depot WS3 – Mixed Trials ·      PHV charging demand overlaid onto substation available capacity data aggregated by borough The project is not able to release alldata collected and used by the project, as some are commercially confidentialto the project partners (such as precise volumes of Uber trips or substationcapacities), contains potentially personally identifiable information (such asprecise start and end locations of home charging journeys or Uber trips) orwere purchased by the project under license (e.g. charge point (CP) locations,weather data and off-street parking data).The figure below shows how thedatasets are built:To access the datasets, use the below Account name and Shared Access Signature (SAS) token (used due to the large size of these data sets - if you are not familiar with this method, please refer to the guide at the bottom of this page):Account Name or URLhttps://ukpnoppublicdata001.blob.core.windows.net/optimiseprimeShared Access Signature (SAS)?sv=2022-11-02&ss=bfqt&srt=sco&sp=rlp&se=2025-10-16T17:48:02Z&st=2024-10-16T09:49:02Z&spr=https,http&sig=sJUKAh698V2nQ8dBAmXBw6FIZooyhXGmWgAXkyfbadI%3DShared Access Signature URL (SAS)https://ukpnoppublicdata001.blob.core.windows.net/optimiseprime?sv=2022-11-02&ss=bfqt&srt=sco&sp=rlp&se=2025-10-16T17:48:02Z&st=2024-10-16T09:49:02Z&spr=https,http&sig=sJUKAh698V2nQ8dBAmXBw6FIZooyhXGmWgAXkyfbadI%3D Refer to Section 5 in the Optimise Prime Dataset Guidance Document, in the Attachments section below, for more information on how to access the datasets. We'd love to hear how you have used our datasets. Please submit a use case below (click on Submit a reuse).Any questions, please refer to the FAQ document in the attachments section first, if your questions is not answered, please contact [e-mail] further information about Optimise Prime and access the project reports, please visit: UK Power Networks Innovation - Optimise Prime

优化先锋项目汇聚了超过8000辆商用电动汽车（EV）的行驶数据，这些数据来源于三次试验。该项目还实施了一系列技术及商业解决方案，旨在加速商用车队运营商向电动汽车的转型，同时协助英国配电网络规划并准备电动汽车的大规模普及。项目旨在减轻电动汽车对配电网络的影响，确保电力供应的安全性，并为电力用户节省费用，助力英国实现清洁空气和气候变化的目标。其他下载数据集信息：元数据（JSON）。与该数据集相关的关键术语定义可在开放数据门户术语表中找到：https://ukpowernetworks.opendatasoft.com/pages/glossary/。方法论方法：该项目包括三个试验工作流（WS）：WS1，研究商用车辆在家充电的影响；WS2，监控和优化车辆在停车场的充电；WS3，利用私人出租车车辆（PHV）行程数据来模拟这些车辆对配电网络的影响。WS3的试验期始于2020年8月，WS1和WS2的试验于2021年7月1日开始，所有试验于2022年6月底结束。优化先锋项目通过使数据集对更广泛的电力、车队和PHV行业公开，以优化其车辆电气化计划，共享项目数据集。配电网络运营商（DNO）、学者和有兴趣的各方将能够利用该项目创建的匿名数据，进行进一步的研究、分析和预测。除了数据集外，以下提供了简介文档，其中包含有关数据集内容的信息，如何解释每个数据字段以及如何访问数据的说明。在试验期间，项目收集并分析了来自各种来源的数据，以进行广泛的实验。收集并用于分析的关键数据集包括：WS1 – 返回家中试验 · 充电点（CP）会话数据 · 车辆遥测行程数据 WS2 – 停车场试验 · 每个停车场的资产和资产类型详情 · 来自每个停车场的充电点充电数据 · 来自每个停车场的建筑负荷测量数据 · 每个停车场试验的配置连接、灵活性试验和智能充电配置文件详情 WS3 – 混合试验 · 私人出租车车辆（PHV）充电需求叠加在由区划汇总的变电站可用容量数据上。由于部分数据对项目合作伙伴具有商业机密性（例如，Uber行程的确切数量或变电站容量），包含可能识别个人身份的信息（例如，家庭充电行程或Uber行程的确切起始和结束位置）或项目许可购买（例如，充电点（CP）位置、天气数据和路边停车数据），因此项目无法发布所有收集和使用的数据。下图中展示了数据集的构建方式：要访问数据集，请使用以下账户名称和共享访问签名（SAS）令牌（由于数据集较大，使用此方法 - 如果您不熟悉此方法，请参阅页面底部的指南）：账户名称或URL https://ukpnoppublicdata001.blob.core.windows.net/optimiseprime 共享访问签名（SAS）?sv=2022-11-02&ss=bfqt&srt=sco&sp=rlp&se=2025-10-16T17:48:02Z&st=2024-10-16T09:49:02Z&spr=https,http&sig=sJUKAh698V2nQ8dBAmXBw6FIZooyhXGmWgAXkyfbadI= 共享访问签名URL（SAS）https://ukpnoppublicdata001.blob.core.windows.net/optimiseprime?sv=2022-11-02&ss=bfqt&srt=sco&sp=rlp&se=2025-10-16T17:48:02Z&st=2024-10-16T09:49:02Z&spr=https,http&sig=sJUKAh698V2nQ8dBAmXBw6FIZooyhXGmWgAXkyfbadI= 请参考附件中的优化先锋数据集指南文档的第5节，以获取有关如何访问数据集的更多信息。我们很乐意了解您如何使用我们的数据集。请在以下处提交用例（点击提交重用）。如有任何疑问，请首先参阅附件中的常见问题解答（FAQ）文档，如果问题未得到解答，请联系 [e-mail]。有关优化先锋的更多信息以及访问项目报告，请访问：英国电力网络创新 - 优化先锋。