Stochastic Techno-economic Evaluation of Cellulosic Biofuel ...
Stochastic Techno-economic Evaluation of Cellulosic Biofuel Pathways Xin Zhao, Purdue University Dr. Tristan R. Brown, SUNY-ESF Dr. Wallace E. Tyner, Purdue University 1 Cellulosic Biofuels Cellulosic biofuels are produced from lignocellulosic biomass or woody crops. Compared with 1st-generation biofuels, cellulosic biofuels have Lower life-cycle greenhouse gas (GHG) emissions Use non-food feedstock Smaller effects on food prices and land use change. Cellulosic biofuels require more expensive technologies which may lead to higher capital costs and lower yields.
2 Cellulosic biofuels production pathways ETG via Catalysis Saccharification Fermentation Bio-Gasoline Ethanol, Butanol, Hydrocarbons Acid or Enzyme Hydrolysis CELLULOSIC BIOMASS
Renewable Diesel, Gasoline, Jet Fuel Bio-Gasoline, Renewable Diesel Source: Advanced biofuels Association 3 Techno-economic analysis Extensive deterministic techno-economic analyses (TEA) have been conducted on cellulosic biofuel productions (NREL, PNNL, etc.). Direct comparisons cannot necessarily be made among the pathways due to important differences in assumptions (e.g., different price
projections, technical and economic assumptions). Uncertainties and risks were not modeled in most studies. 4 Investment decision making Investors are risk-averse and intend to maximize expected utility. Deterministic comparisons may be inadequate. The deterministic breakeven price is the price for which there is a 50 percent probability of earning more or less than the stipulated rate of return. It cannot represent the true investment threshold. Stochastic dominance is a tool providing better information for riskaverse investors. 5 Objective To quantify the breakeven prices of cellulosic biofuel pathways under technical and economic uncertainty in a manner permitting comparisons among different pathways from the point of view of investors.
6 Cellulosic biofuel pathways studied Pathways High temperature gasification & FischerTropsch synthesis Abbreviations Feedstock Inputs HTG & FTS Corn Stover
Electricity Gasoline, Diesel, Electricity Gasoline, Diesel, Electricity Gasoline, Diesel, heavy oil, Electricity 7 Method A financial analysis based TEA. Important uncertain variables. Monte Carlo simulation is employed to account for the uncertainty in techno-economic variables. 10,000 iterations. 8
Pathway modeling Plants were designed in source studies. i.e. Fast pyrolysis and hydroprocessing Plant size of 2,000 dry metric tons per day. Base year: 2011 Production is assumed to begin in 2013 after a two-year construction period, with 20-year production life. 9 Uncertainty Variables Corn stover cost (2011 $/MT)
0.030 Hybrid poplar cost 95.54 (2011 $/MT) Hydrogen cost ($/ Kg) Distribution 0.025 Pert 0.020 0.015
0.010 3.25 Pert 0.005 0.000 Capital investment ($MM) Pert Min, mode and max values are estimated. 10
Conversion technology yield uncertainty A Beta general distribution is (GGE/ Mg of biomass) benchmarked for fuel yield for each of the pathway scenarios based on literature data. Beta general distribution has advantage over Normal, Pert or Triangular in considering Kurtosis and skewness. 11 Output fuel price uncertainty Geometric Brownian Motion
Annual growth rate: 0.27% in real terms (EIA). Historical volatility: $0.318/GGE Diesel and LPG prices are projected using historical price relationship between diesel or LPG and gasoline. 12 Results: deterministic breakeven fuel price Range: 3.11 4.93 $/GGE. FPH is the lowest, 3.11 $/GGE. Capital cost, 0.94 $/GGE, feedstock cost 1.07 $/ GGE, operating cost 1.35 $/GGE, electricity generation credit 0.25 $/GGE. 13 Breakeven fuel price distribution
Breakeven fuel price distribution 15 Stochastic Dominance CDF based on return on investment Thus, risk averse investors prefer to HTG & FTS and FPH are not comparable in terms of FSD or SSD. FPH 0.136-almost first-order stochastically dominate HTG & FTS. Most decision makers would prefer FPH to HTG & FTS.
16 Conclusion With the current level of oil prices, none of the eight cellulosic biofuel pathway scenarios could be profitable at expected values. Most risk-averse decision makers would prefer FPH to other pathways studied. The probability of loss is 59% for FPH. Stochastic analysis provides more information on the measurements and economic feasibility of a project. Risk-averse investors can make better decisions base on these results. 17 Questions 18 Renewable fuel standard
Billions of Gallons (BGY) Ethanol Equivalent 2022 16 BGY 2010 0.1 BGY 2015 3 BGY The targets for cellulosic advanced have never been achieved. EPA revised the 2015 mandate to 106 million gallons.
Source: EISA, 2007; Tyner, 2013. 19 Sensitivity Analysis Future fuel prices play the most important role in determining the economic feasibility of a pathway. Future cellulosic biofuels development research should concentrate on increasing technology conversion yield and lowering capital cost to reduce cost of cellulosic biofuel. 20 Major Contribution 1. Financial stochastic techno-economic assessment (TEA) models were built
in a manner permitting consistent comparisons among eight cellulosic biofuel production pathways. 2. Monte Carlo simulation was used to translate the uncertainty in inputs (conversion yield, capital cost, feedstock cost, and associated inputs) and output fuel prices into distributions of NPV and breakeven price. 3. The stochastic analysis permitted the comparison among pathways from the perspective of a risk-averse investor. According to stochastic dominance based on return on investment, most risk-averse investors would prefer the fast pyrolysis and hydroprocessing pathway. 21 Stochastic TEA and breakeven price distribution 22 Acknowledgement We are pleased to acknowledge funding support from the Indiana Corn Marketing Council and the Federal Aviation Administration for this research.
They are not responsible for the conclusions of the research. 23
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