Residential electric vehicle charging datasets from apartment buildings
| Autor: | Inger Andresen, Åse Lekang Sørensen, Karen Byskov Lindberg, Igor Sartori |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
business.product_category
Science (General) End-user flexibility Smart meter Energy management Computer science Computer applications to medicine. Medical informatics R858-859.7 Residential electricity demand Load profiles Discount points Work related Session (web analytics) Teknologi: 500 [VDP] Transport engineering 03 medical and health sciences Idle Q1-390 0302 clinical medicine Hardware_GENERAL Electric vehicle 030304 developmental biology Data Article Flexibility (engineering) 0303 health sciences Multidisciplinary Electric vehicle (EV) charging business 030217 neurology & neurosurgery |
| Zdroj: | Data in Brief Data in Brief, Vol 36, Iss, Pp 107105-(2021) |
| Popis: | This data article refers to the paper "Analysis of residential EV energy flexibility potential based on real-world charging reports and smart meter data" [1] . The reported datasets deal with residential electric vehicle (EV) charging in apartment buildings. Several datasets are provided, with different levels of detail, aiming to serve various needs. The paper provides real-world EV charging reports describing 6,878 charging sessions registered by 97 user IDs, from December 2018 to January 2020. The charging reports include identifiers, plug-in time, plug-out time and charged energy for the sessions. Synthetic charging loads are provided with hourly resolution, assuming charging power 3.6 kW or 7.2 kW and immediate charging after plug-in. The non-charging idle time reflects the flexibility potential for the charging session, with synthetic idle capacity as the energy which could potentially have been charged during the idle times. Synthetic hourly charging loads and idle capacity are provided both for individual users, and aggregated for users with private or shared charge points. For a main garage with 33% of the charging sessions, smart meter data and synthetic charging loads are available, with aggregated values each hour. Finally, local hourly traffic density in 5 nearby traffic locations is provided, for further work related to the correlation with plug-in/plug-out times. Researchers, energy analysts, charge point operators, building owners and policy makers can benefit from the datasets and analyses, serving to increase the knowledge of residential EV charging. The data provides valuable insight into residential charging, useful for e.g. forecasting energy loads and flexibility, planning and modelling activities. |
| Databáze: | OpenAIRE |
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