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Robust bioenergy technologies for the German heat transition: A novel approach combining optimization modeling with Sobol’ sensitivity analysis

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  • Jordan, Matthias
  • Millinger, Markus
  • Thrän, Daniela

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.

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  • Jordan, Matthias & Millinger, Markus & Thrän, Daniela, 2020. "Robust bioenergy technologies for the German heat transition: A novel approach combining optimization modeling with Sobol’ sensitivity analysis," Applied Energy, Elsevier, vol. 262(C).
    • Handle: RePEc:eee:appene:v:262:y:2020:i:c:s0306261920300465
    DOI: 10.1016/j.apenergy.2020.114534
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    1. Sebastian Lubjuhn & Sandra Venghaus, 2024. "Unlocking the potential of the bioeconomy for climate change reduction: The optimal use of lignocellulosic biomass in Germany," Journal of Industrial Ecology, Yale University, vol. 28(1), pages 144-159, February.
    2. Wang, Jing & Kang, Lixia & Huang, Xiankun & Liu, Yongzhong, 2021. "An analysis framework for quantitative evaluation of parametric uncertainty in a cooperated energy storage system with multiple energy carriers," Energy, Elsevier, vol. 226(C).
    3. Lauma Balode & Kristiāna Dolge & Dagnija Blumberga, 2023. "Sector-Specific Pathways to Sustainability: Unravelling the Most Promising Renewable Energy Options," Sustainability, MDPI, vol. 15(16), pages 1-24, August.
    4. Millinger, M. & Reichenberg, L. & Hedenus, F. & Berndes, G. & Zeyen, E. & Brown, T., 2022. "Are biofuel mandates cost-effective? - An analysis of transport fuels and biomass usage to achieve emissions targets in the European energy system," Applied Energy, Elsevier, vol. 326(C).
    5. Ruichen Xu & Yong Pang & Zhibing Hu & Xiaoyan Hu, 2022. "The Spatiotemporal Characteristics of Water Quality and Main Controlling Factors of Algal Blooms in Tai Lake, China," Sustainability, MDPI, vol. 14(9), pages 1-17, May.
    6. Carta, José A. & Díaz, Santiago & Castañeda, Alberto, 2020. "A global sensitivity analysis method applied to wind farm power output estimation models," Applied Energy, Elsevier, vol. 280(C).

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