BENSIM/BENOPT Biofuel pathways
Example of modeled pathway options for the transport sectors


Model family for investigating future transformation scenarios for energetic biomass usage in the transport, power and heat sectors. The latest published versions are available online with and open source license: BENSIM / BENOPT.

BENSIM (BioENergy SImulation Model) and BENOPT (BioENergy OPTimisation model) have been developed in order to model the competition between different bioenergy technology options. BENSIM/BENOPT exists in two main variants: (1) a myopic, recursive simulation model that looks for the most cost-effective technology mix under certain conditions (BENSIM), and (2) a perfect foresight optimization model for optimal allocation of biomass across different sectors and goal functions.

Through the recursive elements of learning effects and previously built capacities, path dependencies are captured by the model BENSIM. Investment costs, operation and maintenance costs, as well as black box input and output variables for the processes (feedstock, power, byproducts, GHG‐emissions etc.) coupled with costs and greenhouse gas emissions serve as a data basis for the modelling. The model includes a module to estimate the future development of biomass costs.

The main drivers of the model are bioenergy provision costs, mainly influenced by costs for biomass, technical learning (a reduction of investment costs with increased capacity and/or through R&D) and efficiency improvements. Biomass potentials also serve as a limit for some pathways. Main output from the model are production shares of the different options, resulting from competition based on the internally generated cost developments to satisfy a bioenergy target or until biomass limits are reached.

The model can also be used to investigate the sensitivity of the developments by means of various methods (Monte Carlo, SOBOL), on which a large number of parameters have an influence, especially in the complex area of biomass use.

BENSIM/BENOPT was/is used in the following projects and in each case adapted and further developed:

Meilensteine 2030 (Simulation: Power, Heat, Fuels)
BEPASO (Optimisation: Power, Heat, Fuels, Chemicals)
BioPlanW (Optimisation: Heat)
BKSQuote (Optimisation: Fuels)
TATBio (Optimisation: Power, Heat, Fuels)
BEniVer (Optimisation: Biofuels and Electrofuels)


Millinger, M., Meisel, K., Thrän, D., (2019): Greenhouse gas abatement optimal deployment of biofuels from crops in Germany. Transport. Res. Part D-Transport. Environ. 69 , 265 - 275: Open source model available here:

Millinger, M., (2018): Systems assessment of biofuels : modelling of future cost and greenhouse gas abatement competitiveness between biofuels for transport on the case of Germany. Dissertation, Universität Leipzig, Wirtschaftswissenschaftliche Fakultät. PhD Dissertation 3/2018. Helmholtz-Zentrum für Umweltforschung - UFZ, Leipzig, XVII, 92 pp.

Millinger, M., Meisel, K., Budzinski, M., Thrän, D., (2018): Relative greenhouse gas abatement cost competitiveness of biofuels in Germany. Energies 11 (3), art. 615: Open source model available here:

Millinger, M., Thrän, D., (2018): Biomass price developments inhibit biofuel investments and research in Germany: The crucial future role of high yields. J. Clean Prod. 172 , 1654 - 1663

Millinger, M., Ponitka, J., Arendt, O., Thrän, D., (2017): Competitiveness of advanced and conventional biofuels: Results from least-cost modelling of biofuel competition in Germany. Energy Policy 107 , 394 - 402

Thrän, D., Arendt, O., Banse, M., Braun, J., Fritsche, U., Gärtner, S., Hennenberg, K.J., Hünneke, K., Millinger, M., Ponitka, J., Rettenmaier, N., Schaldach, R., Schüngel, J., Wern, B., Wolf, V., (2017): Strategy elements for a sustainable bioenergy policy based on scenarios and systems modeling: Germany as example. Chem. Eng. Technol. 40 (2), 211 - 226

Thrän, D., Schaldach, R., Millinger, M., Wolf, V., Arendt, O., Ponitka, J., Gärtner, S., Rettenmaier, N., Hennenberg, K., Schüngel, J., (2016): The MILESTONES modeling framework: An integrated analysis of national bioenergy strategies and their global environmental impacts. Environ. Modell. Softw. 86 , 14 - 29