Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1016/j.apenergy.2020.114534
Document author version
Title (Primary) Robust bioenergy technologies for the German heat transition: A novel approach combining optimization modeling with Sobol’ sensitivity analysis
Author Jordan, M. ORCID logo ; Millinger, M.; Thrän, D.
Source Titel Applied Energy
Year 2020
Department BIOENERGIE
Volume 262
Page From art. 114534
Language englisch
Keywords Heat sector; Bioenergy; Optimization; Sensitivity analysis; Sobol’
Abstract Uncertainties are one of the major challenges of energy system optimization models (ESOM), yet little use is made of systematic uncertainty assessments in ESOM-based analyses. In this paper, an ESOM is combined with the global sensitivity analysis of Sobol’ to identify robust, competitive bioenergy technologies to fulfill the climate targets in the German heat sector under uncertain developments. Through the outlined method, only three out of 32 investigated parameters were identified to have uncertainties with significant impacts on the future competitiveness of bioenergy technologies: the power price, gas price and the defined climate target. Based on these findings, a solution space is quantified showing which bioenergy technologies are robust, competitive options under the uncertainty of the three influencing parameters. The use of biomass in the form of wood chips in (high temperature) industry applications is found to be the most robust choice in all cases, while hybrid combined heat and power wood pellet systems are an additional robust option when future power prices are increasing. Both technologies have the potential to close gaps in a sustainable energy system and should be considered for the future use of biomass in the German heat sector, when designing policies.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=22741
Jordan, M., Millinger, M., Thrän, D. (2020):
Robust bioenergy technologies for the German heat transition: A novel approach combining optimization modeling with Sobol’ sensitivity analysis
Appl. Energy 262 , art. 114534 10.1016/j.apenergy.2020.114534