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Journal of Cleaner Production
Article . 2020 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Comparing empirical and model-based approaches for calculating dynamic grid emission factors: An application to CO2-minimizing storage dispatch in Germany

Authors: Braeuer, F.; Finck, R.; McKenna, R.;

Comparing empirical and model-based approaches for calculating dynamic grid emission factors: An application to CO2-minimizing storage dispatch in Germany

Abstract

As one possibility to increase flexibility, battery storage systems (BSS) will play a key role in the decarbonization of the energy system. The emissions-intensity of grid electricity becomes more important as these BSSs are more widely employed. In this paper, we introduce a novel data basis for the determination of the energy system’s CO$_{2}$ emissions, which is a match between the ENTSO-E database and the EUTL databases. We further postulate four different dynamic emission factors (EF) to determine the hourly CO$_{2}$ emissions caused through a change in electricity demand: the average emission factor (AEF), the marginal power mix (MPM), the marginal system response (MSR) and an energy-model-derived marginal power plant (MPP). For generic and battery storage systems, a linear optimization on two levels optimizes the economic and environmental storage dispatch for a set of 50 small and medium enterprises in Germany. The four different emission factors have different signaling effects. The AEF leads to the lowest CO$_{2}$ reduction and allows for roughly two daily cycles. The other EFs show a higher volatility, which leads to a higher utilization of the storage system from 3.4 to 5.4 daily cycles. The minimum mean value for CO$_{2}$ abatement costs over all 50 companies is 14.13 €/t$_{CO}$$_{2}$.

Countries
Denmark, Germany
Keywords

CO$_{2}$-emissions, info:eu-repo/classification/ddc/330, 330, ddc:330, Economics, Empirical emission factors, Energy storage system, Dynamic emission factors, https://dbkit.bibliothek.kit.edu/start.php#, CO2 emissions, Dynamic emission factor, 620, German industry, CO$_{2}$-minimizing dispatch, CO2-minimizing dispatch

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
31
Top 10%
Top 10%
Top 10%