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 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.
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: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)
AGRI-TRANSFORM (optimisation: all sectors, including food)
SoBio (optimisation: power, heat, fuels)
News and Updates
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: http://dx.doi.org/10.1016/j.jclepro.2021.127465
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: http://dx.doi.org/10.1016/j.jclepro.2021.126319
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: http://dx.doi.org/10.1039/D0SE01067
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: http://dx.doi.org/10.3390/en13071712
Musonda, F., Millinger, M., Thrän, D., (2020): Greenhouse gas abatement potentials and economics of selected biochemicals in Germany. Sustainability 12 (6), art. 2230: http://dx.doi.org/10.3390/su12062230
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: http://dx.doi.org/10.1016/j.apenergy.2020.114534
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: https://doi.org/10.1016/j.energy.2019.116194
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: https://doi.org/10.1016/j.trd.2019.02.005. Open source model available here: https://doi.org/10.5281/zenodo.2812986
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: https://doi.org/10.3390/en11030615. Open source model available here: https://doi.org/10.5281/zenodo.2810903
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