Biomass Feasibility Project Final Report - Xcel Energy

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$0.350 295 kW CHP $0.300 $0.250 $0.200 $/kWh $0.150 $0.100 $0.050 50 MW 70 MW IGCC 13 MW Local Biomass 5.4 MW Gasification w/ CHP 600 MW Coal w/ 5% Co-Fire 3.1 MW Gasification w/ CHP $0.000 Agricultural Residues Woody Residues Coal Simple Sensitivity Analysis Figure VIII-4: Example LAC Runs The sensitivity analysis performed on each proposal evaluation illustrates the change in constant levelized cost of energy that one could expect from varying the rate of one of the included variables, while holding all other variables constant. The sensitivity analysis is presented in terms of the relative (%) change of each variable, as opposed to an absolute change in value. Sensitivity Analysis 0.18 Feedstock Costs 0.16 0.14 Cost of Energy ($/kWh, Constant) 0.12 0.10 0.08 0.06 0.04 0.02 0.00 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 100 Relative Change (%) Figure VIII-5: BioPET Sensitivity Analysis Example Identifying Effective Biomass Strategies: Page 127 Quantifying Minnesota’s Resources and Evaluating Future Opportunities
MULTIPLE VARIABLE SENSITIVITY ANALYSIS (MONTE CARLO) BioPet performs one type of sensitivity analysis, allowing the user to see how variations in a single selected parameter affect outcomes. We also used Monte Carlo techniques to analyze the effects of several variable assumptions jointly. The single value assigned to each assumption/parameter may not always reflect the true condition of the system we are examining. We know all variables with imprecision. In some cases such as crop yields, the variability is due to stochastic events like climate. In other cases, such as local production participation rates, we simply are ignorant of the “true” value of the factor. But in every case, we can assign a probability density function which describes our prior expectation of each factor’s variability. The key feature of the Monte Carlo analysis is repetition. Because we don’t know the single “true” value of certain factors, but only the distribution of the probability for that value (assigned by us), we can’t simply run the model once and then report the result. Instead, we run the model over and over, each time sampling from the parameter distributions, according to the stated form of the distribution. In a given run we might draw from the upper end of one parameter’s distribution and the mid-point of another’s. In the next run, the draw might be opposite. And so on, for hundreds or thousands of runs. For each run, we record the outcomes of the model for that run’s parameter sample selections. Each new set of parameter values yields a different outcome. After hundreds or thousands of runs, we obtain a distribution of outcome estimates. This can be interpreted as the spread of possible outcomes given the stated distributions of the variable assumption. A wide range suggests that we should be very careful about too quickly adopting the single-value outcome of the initial parameters. Too, we can compare among scenarios to see if some have narrower outcome ranges than to others, or if some clearly “outperform” others despite a wide range of parameter uncertainty. The Monte Carlo simulations were performed using feedstock and power plant templates generated with the BioPET software tool. Most of the variables were generated using the default values within BioPET. For those variables which would be varied within the Monte Carlo Simulation the mean value of the variable’s range was used as a default. Some of the Monte Carlo variables are market or processing costs which have considerable variability. In those cases the mean value is somewhat different than the default value due to fact that the default values were chosen to be representative of current prices as opposed to average long term prices. These templates include all costs incurred to deliver fuel to the power plant. Any processing costs at the power plant are included in the operating costs of the plant. Four power plant templates were generated. The templates are derived from publicly available documents. Two of the templates are derived from engineering and financial studies of specific projects. The others are derived from more general documents that have contained information on power plant costs. Power Plant Assumptions The source documents for each of the power plant templates use somewhat different financial and operating assumptions. For the purposes of the Monte Carlo simulations a number of uniform assumptions were utilized to provide a degree of standardization across power plants. A uniform mean capacity factor of 85% was utilized for each power plant template. All other Page 128 Identifying Effective Biomass Strategies: Quantifying Minnesota’s Resources and Evaluating Future Opportunities
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FINAL REPORT Identifying Effective
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OVERVIEW The profile of bio-power (
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This net-net amount usable for fuel
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Chapter VII: Government Policies, I
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Chapter XI: Project Development Han
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Fuel Selection Considerations......
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Carbon Emissions Policy ...........
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APPENDIX D : DATA SOURCES .........
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Figure VII-1: A Timeline of Minneso
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CHAPTER I : INTRODUCTION Bio-power
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into renewable technologies that su
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BioPET Figure I-2: BioPET Main Scre
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CHAPTER II : BIOMASS FUELS BIOMASS
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when evaluating individual projects
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450,000 Corn Grain Corn Grain 51% 4
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For purposes of this analysis, all
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Billion Btu 70,000 60,000 50,000 40
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Billion Btu 300,000 250,000 200,000
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30,000 Dairy Manure Hog Manure 19%
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Identifying Effective Biomass Strat
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Dedicated Energy Crops Crops raised
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primary purpose, like food, animal
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Manures Variability. Depending on t
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equipment, logistics, soil health,
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Even a limited contribution to Minn
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Since NASS doesn’t provide a simi
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Billion Btu 160,000 140,000 120,000
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Capacity Factor = The amount of tim
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$9.00 $0.06 $8.00 $7.00 $3.16 $ per
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PHYSICAL FACTORS AFFECTING BIOMASS
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Thermal treatment of SSO also invol
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Wood Chipping Trees and logging res
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Baled Agricultural Biomass Preparin
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fuels exceeding the moisture conten
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• Rail. For most biomass plants,
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CHAPTER V : BIO-POWER CONVERSION TE
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Pile Burners Pile burners are a ver
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suspends them as they burn. Spreade
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Technology Type Table V-1: Emission
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Fuel (Gas) COMBUSTOR COMPRESSOR TUR
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• increased thermal to electrical
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Biodiesel has somewhat different ma
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Table V-4: Bio-Power Generation Res
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fuel plant to co-fire biomass than
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Co-Firing Gasified or Liquefied Bio
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esidues leave energy on the table b
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The industry’s most strenuous cha
Page 98 and 99: pulp from logs. Minnesota’s two c
Page 100 and 101: The result has been satisfactory, i
Page 102 and 103: City (plant name) Table VI-6: Ethan
Page 104 and 105: cut energy consumption 10%. But his
Page 106 and 107: Laurentian Energy in Hibbing and Vi
Page 108 and 109: achieve a 50 reduction in overall e
Page 110 and 111: (Nelson and Lamb, 2002) Figure VI-4
Page 112 and 113: e a 75 MW plant using alfalfa stems
Page 114 and 115: District Energy’s intervention re
Page 116 and 117: Fibrominn Concerns about the enviro
Page 118 and 119: 1994 Legislature - As part of the P
Page 120 and 121: Minnesota Jobs Opportunities Buildi
Page 122 and 123: Real Estate Tax Exemptions Since re
Page 124 and 125: For more information, contact Paul
Page 126 and 127: Fort Snelling, MN 55111-4007 612-71
Page 128 and 129: USDA Rural Development Because biom
Page 130 and 131: egion encompassing approximately 13
Page 132 and 133: the boundaries of the Taconite Assi
Page 134 and 135: certain communities located in inel
Page 136 and 137: Suitable borrowers. For-profit SBCs
Page 138 and 139: http://www.state.mn.us/portal/mn/js
Page 140 and 141: NEW POLICY INITIATIVES Community-Ba
Page 142 and 143: The Energy Act also established the
Page 144 and 145: Overall Structure Fuel Delivery Pat
Page 146 and 147: expenses, rate of return, inflation
Page 150 and 151: plant characteristics and plant exp
Page 152 and 153: Table VIII-4: Power Plant Financial
Page 154 and 155: Table VIII-7: Scenario Definitions
Page 156 and 157: Disregarding the manure digester, t
Page 158 and 159: CHAPTER IX : OVERCOMING BARRIERS Mi
Page 160 and 161: Biomass also tends to have a low en
Page 162 and 163: Learning more about the energy pote
Page 164 and 165: Recent projects in Minnesota and ne
Page 166 and 167: e calculated. Plants above 10 MW do
Page 168 and 169: To realize its goal, a mandate must
Page 170 and 171: alone. A decade or two from now, ev
Page 172 and 173: project in South Dakota because it
Page 174 and 175: surrounding air permits for biomass
Page 176 and 177: CHAPTER X : SEEKING OPPORTUNITIES W
Page 178 and 179: OPPORTUNITY 4: RETROFITTING EXISTIN
Page 180 and 181: for a full-time permit is more comp
Page 182 and 183: While this study did not focus spec
Page 184 and 185: Waste Landfill: a contract with a l
Page 186 and 187: pipelines, city water and sewer con
Page 188 and 189: ENVIRONMENTAL PERMITS A bio-power p
Page 190 and 191: AIR PERMITS The Minnesota Pollution
Page 192 and 193: Rate Agreements Facilities of 10 M
Page 194 and 195: BIBLIOGRAPHY AND RESOURCES The foll
Page 196 and 197: Cinergy Business Solutions - Combin
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Energy Efficiency and Renewable Ene
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Gordon, G. (2005, December 26). Wat
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McNeil Technologies, Inc. (2003). B
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Pollution Control Agency, ed. [PCA]
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Stephens, W.R. (2006, March). A Bio
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Xenergy and Energetic Management As
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OVERALL STRUCTURE The evaluation to
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Field Characteristics Energy Conten
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Feedstock Site Processing Yard (opt
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Storage Facility Figure A-6: Feedst
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“Logs” in the Navigation Menu.
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2001 legislation ordered Minnesota
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Figure A-10: Financial Assumption S
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Navigation Menu. Note that every ti
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Project Comparisons $0.140 $0.135 $
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APPENDIX B : INDUSTRY CONTACT LIST
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Clean Energy Resource Teams (CERTs)
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Home Farms Technologies Brandon, Ma
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RCM Digesters Berkeley, CA 94704 (5
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Table B-2: Categorized List CATEGOR
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CONSULTING RW Beck St. Paul, MN 551
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NON- PROFIT/ADVOCACY The Minnesota
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TECHNOLOGY VENDOR Recovered Energy
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INTRODUCTION APPENDIX D : DATA SOUR
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Sweet Corn Stalks • Inventory/Ava
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Sugar Beets • Material Characteri
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Aspen 8 • Material Characteristic
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FOSSIL FUELS Minnesota Average Coal
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