The Economics of Alternative Energy Sources and Globalization: The Road Ahead Embassy Suites Airport, Orlando, FL

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1 The Economics of Alternative Energy Sources and Globalization: The Road Ahead Embassy Suites Airport, Orlando, FL 1

2 Cellulosic Ethanol CRITICAL ECONOMIC FACTORS FOR SUCCESS OF A BIOMASS CONVERSION PLANT FOR AGRICULTURAL RESIDUE, YARD RESIDUE AND WOOD WASTE IN FLORIDA Ivan R. Granja, University of Florida John J. Vansickle, University of Florida Lonnie Ingram, University of Florida Rick Weldon, University of Florida 2

3 Introduction Research focus on critical economic factors in order to locate cellulosic ethanol plants across the state of Florida Introduce innovative technology in the state. Cellulosic ethanol will help the development of rural areas across the state as it will increase the economies where the processing plants are located. 3

4 The US Government has encouraged research and development of an advanced biofuels industry. Create added value to fiber residues that otherwise will be disposed, burned or buried. Cellulosic ethanol production is now in the advanced stage of development and is soon to be commercialized (Ingram, 2005) OilChange International 4

5 Extensive research in the US on cellulosic biomass to be used as feedstock for biofuels. This biomass feedstock can be found in energy crops, agricultural residue, wood residue, municipal solid waste. Mainly in Florida: Wood residue, bagasse and other agricultural residues, and MSW (yard trash). 5

6 MSW yard waste MSW is abundant in the southeast region due to the large development of urban areas. The Florida Department of Agriculture and Consumer Services (DACS) provided the data for MSW produced in Florida and the cost for handling alternative types of trash. Year MSW residue $/ton (yard trash) MSW residue Qt tons 1999 $ ,373, $ ,496, $ ,640, $ ,668, $ ,599, $ ,842, $ ,929, $ ,007, $ ,655, $ ,975,127 6

7 Wood Residue The National Forest Service (FIA, 2009) provided data for wood residue produced in Florida. Wood residue prices were obtained from Energy Information Administration (EIA, 2007) statistics website. Year Wood Residue $/ton Wood Residue Qt (tons) 1999 $ ,706, $ ,654, $ ,537, $ ,537, $ ,513, $ ,438, $ ,442, $ ,445, $ ,786, $ ,789,500 7

8 Agricultural Residue Florida has abundant bagasse from its large sugar cane industry. The industry is mainly located in the south part of the state. Year Ag. Residue $/ton Ag. Residue Qt tons 1999 $ ,843, $ ,793, $ ,743, $ ,692, $ ,642, $ ,362, $ ,388, $ ,305, $ ,222, $ ,139,256 8

9 Literature Review Relatively little research has been done on the economic feasibility of cellulosic ethanol plants Most of the work associated with cellulosic ethanol production relates to the development of technologies. Risks associated with implementation of this technology relate to the supply and cost of feedstock 9

10 The U.S. National Renewable Energy Laboratory (NREL) and the U.S. Department of Energy (DOE) have developed models for the production of cellulosic ethanol from lignocellulosic biomass Economic feasibility is at the core of successful biomass conversion. Most ethanol used as fuel today comes from converting sugar-rich corn crops into ethanol. 10

11 Florida has a large amount of biomass that can be converted into ethanol. Net present value of cash flows for a modeled plant to identify the basic feasibility and asses the risk. 11

12 Methods Stochastic simulation was used to analyze the economic risks associated with the development of the cellulosic ethanol plants. SIMETAR is used to develop the stochastic variables Distributions for random variables Random distribution samples Model probabilistic outcomes 12

13 The model assumes a plant capacity of approximately 70 million gallons per year (MMGPY) Three different feedstocks (MSW, agricultural residue, wood residue) are analyzed to evaluate the feasibility of ethanol production. Stochastic variables: Ethanol Wood residue Agricultural residue MSW yard trash Diesel Electricity Natural gas. 13

14 Cellulosic Ethanol Model Parameters from NREL reports on cellulose to ethanol were used. NREL (2002) used a market price for a gallon of ethanol of $1.07 for a plant that processed approximately 770,000 tons of dry corn stover as the main feedstock. The NPV discount rate had an important role in this study. 14

15 Stochastic Variables Are a main part of the inputs and outputs factors that affect the production feasibility of the plant model. A correlation matrix was estimated for the annual observations for the stochastic variables. Every correlation coefficient was 0.87 or higher. Forecasts for the stochastic variables are calculated for ten years using empirical distribution for each of the variables. 15

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17 Parameters Ethanol Feasibility Model Ethanol Capacity of the Plant gal/yr 69,300,000 Total Capital Required for the Proposed Plant $ 197,400, Interest Rate for Original 15 year Loan for Plant and Land t-bonds Operating Loan Interest Rate % t-bonds Cash Flow Deficit Refinaceing Interest Rate % t-bonds Cash Balance Interest Rate % t-bonds Operating Loan as % of Variable Costs % t-bonds Land Value per acre $/acre $18, Annual Inflation Rate for Variable Costs % t-bonds Ethanol Yield per dry ton of biomass gal/ton 89.7 nrel Denaturant Added to Ethanol % 0.05 nrel Quantity of Electricity Used Kwh/gallon 1.42 nrel First Year to Simulate 2009 Discount Rate for Net Present Value % 0.1 Quantity of Feedstock annually dry tons / year 773, Transportation costs $/ dry ton

18 Inflate variable costs at a fixed inflation rate per year 2009 Denaturant inflated at a fixed rate per year $/gallon Enzymes inflated at a fixed rate per year $/gallon Chemicals inflated at a fixed rate per year $/gallon Price of natural gas is stochastic MCF/gallon Price of electricity is stochastic Kwh/gallon Transportation inflated at fixed rate per year $/ton Main. Materials inflated at a fixed rate per year $/gallon Labor* inflated at a fixed rate per year $/gallon Admin. Costs**inflated at a fixed rate per year $/gallon Misc. Costs inflated at a fixed rate per year $/gallon STOCHASTIC PRICES 2009 Ethanol $/gal 2.37 Wood Residue $/ton Ag. Residue $/ton MSW residue $/ton (yard trash) Transportation (diesel) $/gal 2.78 Electricty $/Kwh 0.07 Natural Gas m3/ton Production of Ethanol Ethanol Production = Capacity * Production Fraction gal/yr 69,300,000 Denaturant Added % 0.05 Gross Ethanol Production w/ Denaturant gal/yr 72,765,000 18

19 Critical Economic Factors The NPV economic model shows that the discount rate is the most important factor influencing the success of the project. The NPV formula: Where: NPV = net present value Fn = net cash flow in a year n N = analysis period d = annual discount rate 19

20 Prob Scenario Analysis on Different Discount Rates for the Net Present value 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% -$60.0 -$40.0 -$20.0 $0.0 $20.0 $40.0 $60.0 $80.0 Millions 10% disc. rate 9% disc. rate 8% disc. rate 7% disc rate 6% disc. rate 20

21 The costs of the machinery for the plant are another critical factor. Disclosure of information that pertain the present costs of mounting a cellulosic ethanol plant is very limited. 21

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23 Transportation costs are an important factor that is also critical for the economic success of a cellulosic ethanol plant. Feedstock farther away from the plant is more costly. This is evaluated using an average $13.65 in transportation s cost from Aden et al (2002). 23

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25 Results and Conclusions The model evaluates the critical economic factors for the success of the plant. The introduction of stochastic variables help to assess the risk associated with the project. Additional data is being collected. There a new technologies at the present time that will make the process simpler and cheaper, which will affect the costs of the plant in the study. 25

26 Other Considerations The 2008 Farm Bill reduces the amount required of denaturant for ethanol from 5% to 2%. It also provides temporary cellulosic biofuels production tax credit of up to $1.01/gallon through Dec 31, The new report from NREL is coming up at the end of 2009 according to Dr. Aden. 26

27 Source: Department of Energy 27

28 Thank you! 28