BENOPT renewable fuel 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.

Model documentation for BENOPT: Millinger et. al., 2022; Jordan et. al., 2022;

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 including technological learning (BENSIM), and (2) a perfect foresight optimization model for optimal allocation of dispatchable renewable energy carriers across different sectors and goal functions (BENOPT).

BENOPT is a model for optimizing the use of dispatchable renewable energy carriers. Two goal functions can be used or combined: greenhouse gas (GHG) abatement or cost minimisation for fulfilling set energetic or GHG targets. In combination, pareto analyses can be performed.

BENOPT contains sectors for transport (road passenger, road goods, shipping and aviation), power and heat (industry, household and commercial). Work is planned on integrating important chemical products. The model functions on a yearly resolution (with the exception of surplus power usage, which can be broken down to an hourly resolution) and is not spatially explicit. Detailed input-output, capex and opex data are integrated for feedstocks, conversion and supply (vehicle data is planned), which allows detailed cost analyses and combined with relevant emission factors also GHG analyses.

Stand-alone versions focusing on chemical products (Frazer Musonda) as well as on the heat sector (Matthias Jordan) have been developed.

The models are also 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:

BioNET (optimisation: negative emission technologies)
Milestones 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)
AGRI-TRANSFORM (optimisation: all sectors, including food)
SmartWirbelschicht, KonditorGas (optimisation: incl. advanced technologies in heat)

SoBio, BIOSTRAT, Symobio 2.0 (optimisation: power, heat, fuels)
SoBio ll (optimisation: bioeconomy)
Man0EUvRE (optimisation, integrated view, EU)

News and Updates 
  • 2023.07 - The first paper regarding the new graphical user interface of the BENOPTex model has been published.


Jordan, M., Meisel, K., Dotzauer, M., Schindler, H., Schröder, J., Cyffka, K.-F., Dögnitz, N., Naumann, K., Schmid, C., Lenz, V., Daniel-Gromke, J., Costa de Paiva, G., Esmaeili Aliabadi, D., Szarka, N., Thrän, D. (2024): Do current energy policies in Germany promote the use of biomass in areas where it is particularly beneficial to the system? Analysing short- and long-term energy scenarios. Energy Sustain. Soc. 14 , art. 32 10.1186/s13705-024-00464-1

Sadr, M., Esmaeili Aliabadi, D., Avşar, B., Thrän, D. (2024): Assessing the impact of seasonality on bioenergy production from energy crops in Germany, considering just-in-time philosophy. Biofuels Bioprod. Biorefining

Musonda, F., Millinger, M., & Thrän, D. (2024): Modelling assessment of resource competition for renewable basic chemicals and the effect of recycling. GCB Bioenergy, 16, e13133.

Esmaeili Aliabadi, D., Chan, K., Wulff, N., Meisel, K., Jordan, M., Österle, I., Pregger, T., Thrän, D. (2023): Future renewable energy targets in the EU: Impacts on the German transport, Transportation Research Part D: Transport and Environment, 124, 103963:

Jordan, M., Meisel, K., Dotzauer, M., Schröder, J., Cyffka, K.-F., Dögnitz, N., Schmid, C., Lenz, V., Naumann, K., Daniel-Gromke, J., Costa de Paiva, G., Schindler, H., Esmaeili Aliabadi, D., Szarka, N., Thrän, D. (2023): The controversial role of energy crops in the future German energy system: The trade offs of a phase-out and allocation priorities of the remaining biomass residues. Energy Reports, 10, 3848 - 3858:

Esmaeili Aliabadi, D., Manske, D., Seeger, L., Lehneis, R., Thrän, D. (2023). Integrating knowledge acquisition, visualization, and dissemination in energy system models: BENOPTex study. Energies 16 (13), art. 5113:

Millinger, M., Tafarte, P., Jordan, M., Musonda, F., Chan, K., Meisel, K., & Aliabadi, D. E. (2022). A model for cost-and greenhouse gas optimal material and energy allocation of biomass and hydrogen. SoftwareX, 20, 101264:

Jordan, M., Millinger, M., & Thrän, D. (2022). Benopt-Heat: An economic optimization model to identify robust bioenergy technologies for the German heat transition. SoftwareX, 18, 101032:

Lauer, M., Dotzauer, M., Millinger, M., Oehmichen, K., Jordan, M., Kalcher, J., ... & Thraen, D. (2022). The Crucial Role of Bioenergy in a Climate‐Neutral Energy System in Germany. Chemical Engineering & Technology:

Mutlu, Ö., Jordan, M., Zeng, T., & Lenz, V. (2022). Competitive Options for Bio‐Syngas in High‐Temperature Heat Demand Sectors: Projections until 2050. Chemical Engineering & Technology:

Aliabadi, D. E., Chan, K., Jordan, M., Millinger, M., & Thrän, D. (2022, April). Abandoning the Residual Load Duration Curve and Overcoming the Computational Challenge. In 2022 Open Source Modelling and Simulation of Energy Systems (OSMSES) (pp. 1-6). IEEE:

Musonda, F., Millinger, M., Thrän, D., (2021): Optimal biomass allocation to the German bioeconomy based on conflicting economic and environmental objectives. J. Clean Prod. 309, art. 127465:

Jordan, M., Hopfe, C., Millinger, M., Rode, J., Thrän, D., (2021) Incorporating consumer choice into an optimization model for the German heat sector: Effects on projected bioenergy use. J. Clean Prod. 295, art. 126319:

Millinger, M., Tafarte, P., Jordan, M., Hahn, A., Meisel, K., Thrän, D. (2021): Electrofuels from excess renewable electricity at high variable renewable shares: cost, greenhouse gas abatement, carbon use and competition. Sustainable Energy & Fuels 5 (3): 828-843:

Meisel, K., Millinger, M., Naumann, K., Müller-Langer, F., Majer, S., Thrän, D., (2020): Future renewable fuel mixes in transport in Germany under RED II and climate protection targets. Energies 13 (7), art. 1712:

Musonda, F., Millinger, M., Thrän, D., (2020): Greenhouse gas abatement potentials and economics of selected biochemicals in Germany. Sustainability 12 (6), art. 2230:

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:

Jordan, M., Lenz, V., Millinger, M., Oehmichen, K., Thrän, D., (2019): Future competitive bioenergy technologies in the German heat sector: Findings from an economic optimization approach. Energy 189 , art. 116194:

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