Dotzauer, Martin; Oehmichen, Katja; Thrän, Daniela; Weber, Christoph:
Empirical greenhouse gas assessment for flexible bioenergy in interaction with the German power sector
In: Renewable Energy, Vol. 181 (2022), pp. 1100 - 1109
2022article/chapter in journalOA Hybrid
EconomicsFaculty of Business Administration and Economics » Business Administration » Energy Economics
Related: 1 publication(s)
Title in English:
Empirical greenhouse gas assessment for flexible bioenergy in interaction with the German power sector
Author:
Dotzauer, Martin
;
Oehmichen, Katja
;
Thrän, Daniela
;
Weber, ChristophUDE
GND
171222180
LSF ID
12106
ORCID
0000-0003-0197-7991ORCID iD
Other
connected with university
Year of publication:
2022
Open Access?:
OA Hybrid
Web of Science ID
Language of text:
English
Keyword, Topic:
Biogas plants ; Empirical assessment ; Energy system ; Flexible bioenergy generation ; German power market ; Greenhouse gas emissions

Abstract in English:

Wind and solar power are already the major pillars of renewable power generation in Germany and will become even more dominant in the future. At the same time, dispatchable power plants phasing out. The expected increase of fluctuations in the residual load could be partial balanced by flexible bioenergy. However, there is currently no assessment approach for quantifying the systemic GHG impacts for flexible bioenergy generation. Based on the merit order concept, we develop an empirical approach to systemically assess the GHG emissions impact for electricity generated by flexible bioenergy plants. We estimate price response functions using the historical data of market prices and feed-in time series for the different forms of dispatchable non-renewable power generation (NRPG). By calculating the expected NRPG based on these functions, and using specific emission factors, we are able to estimate the net impact for a switch from invariable to flexible bioenergy generation. The calculated net impact ranges from −20 to −36 g CO₂ₑq per kWh, which is equivalent to a benefit of −10% to −18% respectively for an average carbon footprint of 200 g CO₂ₑq per kWh. The calculation tools are written in Python and freely accessible on ZENODO and GitLab.