School of Public Policy, Georgia Institute of Technology, Atlanta, GA, USA.
Institute for Data Engineering & Science, Georgia Institute of Technology, Atlanta, GA, USA.
Sci Data. 2021 Jul 7;8(1):168. doi: 10.1038/s41597-021-00956-1.
Problems of poor network interoperability in electric vehicle (EV) infrastructure, where data about real-time usage or consumption is not easily shared across service providers, has plagued the widespread analysis of energy used for transportation. In this article, we present a high-resolution dataset of real-time EV charging transactions resolved to the nearest second over a one-year period at a multi-site corporate campus. This includes 105 charging stations across 25 different facilities operated by a single firm in the U.S. Department of Energy Workplace Charging Challenge. The high-resolution data has 3,395 real-time transactions and 85 users with both paid and free sessions. The data has been expanded for re-use such as identifying charging behaviour and segmenting user groups by frequency of usage, stage of adoption, and employee type. Potential applications include but are not limited to simulating and parameterizing energy demand models; investigating flexible charge scheduling and optimal power flow problems; characterizing transportation emissions and electric mobility patterns at high temporal resolution; and evaluating characteristics of early adopters and lead user innovation.
电动汽车 (EV) 基础设施的网络互操作性差的问题，即实时使用或消耗的数据不易在服务提供商之间共享，一直困扰着对交通能源的广泛分析。在本文中，我们提出了一个高分辨率的数据集，该数据集记录了在美国能源部工作场所充电挑战赛中由一家公司运营的 25 个不同设施中的 105 个充电站在一年时间内的实时充电交易，时间分辨率精确到秒。这包括 85 名用户的 3395 笔实时交易，其中包括付费和免费时段。这些数据已经经过扩展，可以重复使用，例如识别充电行为和根据使用频率、采用阶段和员工类型对用户组进行细分。潜在的应用包括但不限于模拟和参数化能源需求模型；研究灵活的充电调度和最优潮流问题；以高时间分辨率刻画交通排放和电动交通模式；以及评估早期采用者和领先用户创新的特征。
