Within the BENOPT family, BENOPT-DE focuses on modeling Germany as a whole. It is a classic energy system optimization model developed to analyze the optimal use of Germany’s limited biomass resources in the context of the energy transition. However, biomass can contribute to transformation not only in the energy sector; it is also expected to play a greater role in material use in the future. Furthermore, it is assumed that biomass will play a decisive role in generating negative emissions. For this reason, BENOPT-DE has been expanded to include additional material use sectors (chemicals, wood construction, peat substitutes) and a comprehensive portfolio of negative emission technologies. Figure 1 provides an overview of potential material and energy flows in the model, including the implemented demand sectors, technologies, and the biogenic raw material and product levels. Where technically feasible, fossil and alternative renewable options are also depicted. Negative emission technologies are implemented at various points along the value chain. Infrastructure developments, such as the expansion of the electricity grid, district heating networks, and the conversion of hydrogen and gas networks, are not modeled. The objective function is to optimize the total costs of the system while simultaneously meeting climate targets and fossil fuel decarbonization goals. The accounting of greenhouse gas emissions in the model is based on Germany’s official reporting. BENOPT-DE is a linear optimization model. It optimizes the period from 2020 to 2050 at an annual resolution. In the electricity sector, an aggregated hourly resolution is considered. The aggregation is performed using tsam (time series aggregation module) from Forschungszentrum Jülich to represent the annual residual load across 12 typical days, each consisting of 24 hours.

Contact person:

Sectors:

In the model, the energy sector is divided into three subsectors: transportation, heat, and electricity. The transportation sector is further subdivided into six subsectors: passenger road transport, heavy-duty transport, air transport, rail transport, inland waterway transport, and maritime transport. The heating sector is divided into 19 subsectors. In the electricity sector, BENOPT-DE considers only options for meeting the residual load. This is because biomass will most likely not be competitive with fluctuating renewable energy sources in the future and will compete with options for flexible electricity supply.

Recent developments:

The chemical sector has been integrated into the latest version of BENOPT-DE, which posed a particular challenge. For one thing, in many conversion pathways, the raw material is not directly processed into the desired product; instead, there is an intermediate step involving an intermediate compound. For this reason, a pool for intermediates (naphtha, methanol, ethanol, and biomethane) was implemented in the model. A wide variety of renewable technologies from various sectors can feed into this pool; by-products—such as naphtha from PtL fuel production—can also be fed into it. Similarly, any technology for which it makes technical sense can draw from the pool and use the intermediates as raw materials. In the chemical sector, this applies to most technologies. This approach provides greater freedom and flexibility to optimally utilize raw materials and byproducts within the overall system.

Another challenge is that, in the chemical industry, a conversion technology does not produce just one product from a raw material—specifically, an intermediate. In some cases, up to five products are produced simultaneously, e.g., in naphtha cracking. This fact was taken into account in the modeling, and the various products were allocated to the respective demands. The goal in the chemical sector—which drives the transformation within the modeling—is either to completely phase out fossil raw materials by 2050 or to retain only a small proportion of fossil raw materials in the system.

Furthermore, the system boundary in BENOPT-DE was expanded to include the wood construction sector. The research question regarding this expansion aims to investigate the cost-optimal contribution the wood construction sector can make toward achieving the climate goals by 2050. The construction sector is currently dominated by solid construction methods (steel, concrete, brick, etc.). An alternative is timber construction, which, however, is currently used only to a limited extent, depending on the building type. In the context of modeling, a multitude of factors play a role in determining the cost-optimal use of wood to achieve climate targets. Qualitatively, however, the following advantages can be described with regard to the use of wood in timber construction or for energy purposes:

• Wood offers a more cost-effective way to transform areas that are difficult to electrify than PtX alternatives (energy + chemicals).
• Wood construction and solid construction cost about the same.
• Generating negative emissions in the energy and chemical sectors requires additional costs (BECCS).
• Negative emissions can be generated in wood construction (wood storage) at no additional cost.

Consequently, the added value of wood construction in terms of its contribution to climate protection may lie in wood storage in the future. According to the current German Climate Protection Act, negative emissions from technical sinks as well as from the LULUCF sector (including wood construction) may be counted toward climate targets starting in 2041. This provision has been incorporated into the model.
Wood utilization in timber construction was implemented in BENOPT-DE across three levels, as shown in Figure 2. First, the existing raw material level was expanded to include coniferous roundwood and hardwood roundwood. At the biomass products level, semi-finished wood products and wood-based materials were added. Equivalent product categories on the energy side of this level include, for example, firewood or wood chips. The final level consists of engineered wood products, which remain in timber construction and also quantify carbon storage. It was assumed that the ratio of the individual finished products to one another always remains the same, although the absolute total quantity may increase or decrease over the years. For semi-finished products and raw materials, changes in usage ratios are possible within the limits of technical feasibility. The cost-optimized, future use of wood to meet demand or to generate negative emissions is determined endogenously by the model and thus represents a model result.

The options for generating biogenic negative emissions (BioCCS) were implemented in BENOPT-DE as an add-on to existing technologies. This allows individual BioCCS model concepts to be applied to a broader range of technologies and helps identify the sectors in which CO₂ capture is most beneficial to the system. Negative emissions are needed to offset unavoidable future emissions from agriculture, waste management, and industrial processes. These annual quantities are specified externally to the model and are not determined endogenously by the model. In addition to the BioCCS options, the model includes the option to generate negative emissions through wood storage in the wood construction sector or to purchase allowances totaling up to 30 Mt CO2/a via intra-European trading at a price of at least 250 €/tCO2. DACCS is currently not available in the model. Fossil CO₂ can be captured using natural gas technologies in non-ETS sectors. However, this does not result in negative emissions; on the contrary, these options still result in residual emissions that must be offset, for example, through BioCCS options.

In terms of volume, peat substitutes play a minor role compared to the demands in other sectors; however, this is the only sector in which there are no alternatives to biogenic raw materials to meet future demands. In BENOPT-DE, a total of 9 peat substitutes compete with one another to meet demand and facilitate the transition to a peat-free horticulture sector. The key factors driving this competition are the efficiency of the conversion processes, market prices, the possible blending ratios of the options, and the availability of raw materials.

Alexander Cyfka (2025), Modeling and Analysis of Agroforestry and Agri-PV Options in the BENOPT Energy System Model.

Data and expertise from external institutions:

German Biomass Research Center (DBFZ): Technical and economic data from various sources, such as the BET.db database: https://zenodo.org/records/7586039#.Y-y6W4SZNaQ

German Biomass Research Center (DBFZ): Biomass potential of residual and waste materials, including data from the DBFZ Resource Database.

Thünen-Institut: Data on roundwood potential based on GFPM (Global Forest Product Model) modeling, Bepaso Scenarios.

Öko-Institut e.V.: Heat demand data for the building sector through 2050 from the B-Star model (Großbaustelle Gebäudesektor, Minimum Energy Performance Standards for Non-Residential Buildings) 

Jülich Research Center: Nutzung von tsam - Time Series Aggregation Module zur Aggregation hoch aufgelöster zeitlicher Datensätze.

Using tsam - Time Series Aggregation Module to aggregate high-resolution time-series datasets.

Bio-based CO2 removal methods: BioNET Factsheets

Publications (Peer reviewed):

Cyfka, A., Jordan, M., Vollmers, J., Thrän, D. (2026): The future role of agroforestry and Agri-PV in the German energy system - An analysis with the BENOPTex model. Energy Conv. Manag.-X 10.1016/j.ecmx.2026.101614

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

Meisel, K., Jordan, M., Dotzauer, M., Schröder, J., Lenz, V., Naumann, K., Cyffka, K.-F., Dögnitz, N., Schindler, H., Daniel-Gromke, J., Costa de Paiva, G., Schmid, C., Szarka, N., Majer, S., Müller-Langer, F., Thrän, D. (2024):
Quo vadis, biomass? Long-term scenarios of an optimal energetic use of biomass for the German energy transition
Int. J. Energy Res. 2024 , art. 6687376
10.1155/2024/6687376

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: https://doi.org/10.1016/j.egyr.2023.10.055

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: https://doi.org/10.1002/ceat.202200217

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

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