
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
Comparing empirical and model-based approaches for calculating dynamic grid emission factors: An application to CO2-minimizing storage dispatch in Germany

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}$.
- Technical University of Denmark Denmark
- Karlsruhe Institute of Technology Germany
CO$_{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}$-minimizing dispatch, CO2-minimizing dispatch
CO$_{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
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).31 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 This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
