气候变化领域集成服务门户(CCPortal): Carbon efficient smart charging using forecasts of marginal emission factors

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DOI	10.1016/j.jclepro.2020.124766
	Carbon efficient smart charging using forecasts of marginal emission factors
	Huber J.; Lohmann K.; Schmidt M.; Weinhardt C.
发表日期	2021
ISSN	9596526
卷号	284
英文摘要	Battery electric vehicles do not emit CO2 from an internal combustion engine but can cause emissions while charging electricity generated by remote fossil power plants. Smart charging offers the possibility to reduce carbon dioxide emissions (CO2) by shifting the charging sessions to moments with lower emissions in power generation. However, this requires the battery electric vehicle user to accept postponed charging and a system to shift charging towards times with lower CO2 emissions. This requires a forecast of the marginal emission factors of the energy system. While marginal emission factors are often used to evaluate CO2 saving potentials of smart charging in long-term scenarios, there is little insight on the saving potentials of individual charging sessions in the short run. We derive marginal emission factors for Germany in 2017 using an established regression model and generate short-term predictions of marginal emission factors using a multilayer perception (MLP). The forecasts can provide feedback to drivers and allow scheduling for CO2 efficient smart charging. Comparison between this approach and immediate charging throughout a year in the German power system allows calculating the CO2 saving potentials of the system. The results show that marginal emission factors in Germany depend on the system's load with peak load levels having the smallest marginal emission factors. Using average instead of marginal emission factors, can result in misinformation and even increase emissions. We propose forecasts of marginal emission factors that rely on short-term load forecasts and are accurate enough to obtain substantial saving in CO2 emissions from 1% to 10% depending on the charging parameters. While using this approach as a smart charging algorithm would result in increasing peak loads, the methodology might be a way to provide BEV users with real-time feedback of their charging behaviour and increase their willingness to postpone charging. © 2020 Elsevier Ltd
英文关键词	Battery electric vehicles; Carbon footprint; Forecasting; Smart charging
scopus关键词	Automotive batteries; Battery electric vehicles; Carbon dioxide; Electric power plants; Forecasting; Fossil fuel power plants; Global warming; Regression analysis; Carbon dioxide emissions; Fossil power plants; Multi-layer perception; Real-time feedback; Regression model; Saving potentials; Short term load forecast; Short term prediction; Charging (batteries)
来源期刊	Journal of Cleaner Production
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/177243
作者单位	FZI Research Center for Information Technology, Haid-und-Neu-Str. 10-14, Karlsruhe, 76131, Germany; Karlsruhe Institute for Technology (KIT), Kaiserstraße 89-93, Karlsruhe, 76133, Germany
推荐引用方式 GB/T 7714	Huber J.,Lohmann K.,Schmidt M.,et al. Carbon efficient smart charging using forecasts of marginal emission factors[J],2021,284.
APA	Huber J.,Lohmann K.,Schmidt M.,&Weinhardt C..(2021).Carbon efficient smart charging using forecasts of marginal emission factors.Journal of Cleaner Production,284.
MLA	Huber J.,et al."Carbon efficient smart charging using forecasts of marginal emission factors".Journal of Cleaner Production 284(2021).