(348c) Quantifying the Economic Feasibility of Cellulosic Biofuel Pathways Under Uncertainty | AIChE

(348c) Quantifying the Economic Feasibility of Cellulosic Biofuel Pathways Under Uncertainty


Brown, T. R. - Presenter, Iowa State University
Wright, M. M., Iowa State University

In 2012 a U.S. appellate court ruled that the methodology on which the U.S. Environmental Protection Agency (EPA) based its annual projections of cellulosic biofuel production was weighted toward overproduction and therefore flawed. This ruling came after three consecutive years (2010-2012) in which actual cellulosic biofuel production fell far short of the revised cellulosic biofuel volumetric mandates for each year under the revised Renewable Fuel Standard (RFS2), which in turn were much lower than the original mandates. The EPA’s projections are based on similar projections by the U.S. Energy Information Administration (EIA). The EPA and EIA have both been criticized as being too optimistic and overestimating production of cellulosic biofuels. As a consequence, EPA’s annual mandates have exceeded cellulosic biofuel supply. In 2011, the EPA revised the original 250 million gallons cellulosic biofuel mandate to 6.0 million gallons. The 2012 mandate called for 500 million gallons but was later lowered to 10.45 million. The EPA expected 1 billion gallons of cellulosic biofuel production in 2013 and later mandated 14 million. The EPA’s stance has been that skewing its projections toward overproduction improves the economic feasibility of cellulosic biofuel pathways, since the value of the mandate’s flexible subsidy component (Renewable Identification Numbers, or RINs) increases when actual production falls short of the volumetric mandate. This approach has prompted multiple lawsuits from the petroleum industry against the EPA, which bears the cost of the flexible subsidy.

The debate over the EPA and EIA projections is driven in part by the fact that the cellulosic biofuels mandate has not incentivized as much cellulosic biofuels capacity investment as originally expected. The RFS2 mandates the purchase of annual volumes of cellulosic biofuel by “obligated parties”, generally large refiners. It encourages production of the corresponding biofuel via RINs, which are a tradable compliance commodity that takes the form of a flexible subsidy payment from obligated parties to cellulosic biofuel producers for every gallon of cellulosic biofuel traded (in addition to the biofuel’s market value). In order to prevent the subsidization of windfall profits, the core RIN value is a function of the cellulosic biofuel’s production cost and the market price of petroleum; the RIN value increases when the biofuel’s production cost increases and decreases when petroleum’s market value increases, so long as total production does not exceed the volumetric mandate. This flexible subsidy has worked well for the other biofuel categories under the RFS2, for which significant capacity existed at the time of the program’s implementation. It has had less success encouraging cellulosic biofuel production and capacity investment since the core RIN value can only be calculated when production exists and production costs are well-known. Since no cellulosic biofuel production occurred in 2010 and 2011 and only 22,000 gallons was produced in 2012, neither production costs nor cellulosic biofuel RIN values are known. The lack of an established RIN value has discouraged cellulosic biofuel capacity construction, as investors are reluctant to invest in the sector without knowing the value of the flexible subsidy. Efforts to estimate future cellulosic biofuel RIN values have been greatly hampered by the diversity of pathways qualifying for the cellulosic biofuel mandate, each with a unique set of technical and economic specifications.

Techno-economic analysis (TEA) is a methodology employed to estimate the production costs of energy pathways. It is frequently used to estimate the production costs of biorenewable pathways that have yet to achieve commercialization. By calculating the production costs of various cellulosic biofuel pathways, TEA can be used to estimate RIN core values for years in which petroleum price projections are also available. This use of TEA requires the accurate modeling of the specific technical and economic operating conditions of each pathway. A shortcoming of the current TEA methodology is that it often focuses on the technical conditions and employs simple assumptions for the economic conditions: while the exact technical specifications of each pathway are carefully modeled, generic economic and financial assumptions are made across all pathways. For example, economic feasibility is commonly defined according to a financial standard, such as a minimum fuel selling price (MSFP) that is lower than the biofuel’s market value, or a 20-year internal rate of return (IRR) that exceeds a predetermined benchmark. Furthermore, the results of cellulosic biofuel pathway TEAs are highly uncertain due to the lack of commercialization in the sector. While some TEAs attempt to quantify this uncertainty by computing probabilities using stochastic simulations, these analyses commonly employ statistical fit distributions that do not accurately reflect the range of potential values.

The purpose of this study is to investigate the role of uncertainty in evaluating the economic feasibility of cellulosic biofuel pathways. Specifically, this study addresses how uncertainty in the market price of energy commodities impacts uncertainty on profitability projections of emerging cellulosic biorefineries. This project employs comprehensive uncertainty analysis to expand upon the existing TEA methodology for cellulosic biofuel pathways. By accounting for variability in energy market prices, it expands the definition of economic feasibility to more closely represent that encountered by cellulosic biofuel pathways as they pursue commercialization. Furthermore, by expanding the TEA methodology to account for pathway-specific differences in these factors, this analysis provides additional insight into the probability that cellulosic biorefineries achieve profitability (defined here as a positive NPV with an average 10% IRR over a 20-year operation period).

The approach of this project is to employ uncertainty analysis to determine the NPV probability distributions of five cellulosic biofuel pathways that qualify under the RFS2: fast pyrolysis and upgrading; gasification and acetic acid synthesis; gasification and Fischer-Tropsch synthesis; gasification and methanol synthesis; and enzymatic hydrolysis and fermentation. The NPV distributions are a function of fitted distributions around energy market price projections from the EIA, and pathway specific production costs gathered from the public literature. The cumulative probability of NPV distributions serves as an indicator for comparing process profitability. The results from this study allow for comparing the relative profitability of different pathways and can inform decisions on how to determine core RIN values.

The findings from this study show that the probability that selected advanced cellulosic biofuel technologies will be profitable depends significantly on the macroeconomic operating environment. The most probably scenario was determined to be biomass gasification and methanol-to-gasoline synthesis with a 78% chance of achieving a 10% IRR based on projected prices. Low temperature gasification with Fischer-Tropsch synthesis was estimated to have a 1.2% chance of being profitable. This wide range of probabilities underscores the need to consider the wide range of potential outcomes due to changes in market energy prices. However, there are several additional difficult-to-quantify factors that would impact confidence in these pathways. For example, Fischer-Tropsch and methanol-to-gasoline synthesis have been employed for decades by the fossil fuels industry, whereas there is relatively little experience with recently developed pathways. Nonetheless, there should be additional effort invested in properly quantifying the uncertainty surrounding cost estimates for emerging biorefinery technologies.