Feedstock Supply & Logistics in the Biomass Scenario Model

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About The Biomass Scenario Model

Supply Chain in the Biomass Scenario Model

  • Feedstock Supply and Logistics

The Biomass Scenario Model (BSM) supports construction of bioenergy scenarios based on detailed representations of feedstock availability, land use, and markets.


How Feedstock Supply and Logistics are Modeled in the Biomass Scenario Model

The BSM models feedstock supply and logistics, in part, by classifying and allocating land. It classifies land by product and allocates land according to a pricing logic determined by time and geography. The prices are responsive to competition between energy crops and conventional crops and to the demand for biofuel feedstocks.

For example, the BSM includes the 10 U.S. Department of Agriculture (USDA) Farm Production Regions. The model divides each region into three categories: cropland used for crops, Conservation Reserve Program (CRP) land, and cropland used for pasture.

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USDA Economic Research Service Crop Production Regions, Source: National Renewable Energy Laboratory

Within each land base, land is allocated among different uses based on expected relative per-acre grower payment accruing to producers from the various products. Land allocation within the module is region specific, reflecting the production economics of different crops in different regions.

The BSM typically treats these land bases as static quantities over the course of a simulation. However, the BSM structure supports scenarios that cause land to move from one category to another.

The logic surrounding pricing for the various commodity crops, cellulosic products, and hay in the BSM is shown in the Causal Loop Diagram. The logic is central to a feedback mechanism that uses land allocation to equilibrate production and consumption across all product categories in the BSM.


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Causal Loop Diagram, Source: National Renewable Energy Laboratory


Questions the Feedstock Modules Can Answer

The feedstock supply and logistics modules of the BSM answers a variety of questions. Among them:


• What are the potential effects of different feedstock system conditions, such as production, technologies, and incentives, on the biofuels system as a whole?

• What impact do potential feedstock-conversion coupling options have on the biofuel industry?

• What are the potential impacts of extreme weather, climate change, and feedstock exports on feedstock supply and the biofuel industry?

• What are the effects of different land bases and subsidy schemes on the development of the biofuels industry, especially conservation reserve program land?

• What is the impact of feedstock prices on the cellulosic ethanol industry supply chain?


A Sampling of Biomass Scenario Model Feedstock Results

Sample feedstock models run on the BSM have concluded:


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Cellulosic biofuel production in response to market prices or to a price floor, Source: National Renewable Energy Laboratory


Sustained, moderate feedstock prices are sufficient to support high levels of cellulosic biofuel production. BSM developers conducted a sensitivity study in which they artificially held feedstock prices at a constant throughout the simulation. Under such conditions, a feedstock plant-gate price that is approximately 50% greater than the initial conditions maximally stimulates total biofuel production.


The market conditions created under the blended feedstock (“feedstock flexible”) concept could increase industry growth relative to feedstock specific cases in which processes must use certain feedstocks. In a simulation with blended feedstock, diverse feedstock supply develops, primarily composed of dedicated biomass crops. The blended feedstock case simulates conditions under which a robust market for feedstocks develops, starting with the least-cost and most readily available feedstock: forest residues. Taking advantage of this low-cost feedstock allows the simulated industry to begin the learning process early. As the industry develops, feedstock prices increase in response to demand to levels that become attractive to potential producers in the model.

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Examples of Feedback Production Levels, Source: National Renewable Energy Laboratory