Strategic Development of Bioenergy in the Western States

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Strategic Development of Bioenergy in the Western
States

• Task 3 Spatial Analysis and Supply Curve
  Development

Bryan Jenkins, Nathan Parker, Peter Tittmann,
Quinn Hart, Joshua Cunningham, Mui LayUniversity
of California, Davis
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Objectives

• Assess the potential biofuel supply from biomass
  resources in the Western United States
• Develop facility cost and spatially-explicit
  feedstock and product supply models to optimize
  biofuel facility siting and scale
• Quantify optimal biofuel amounts by feedstock and
  conversion technology types and feedstock and
  fuel prices
• Examine sensitivity of supply to policy and
  development alternatives

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Approach
Mixed Integer Linear

• Geographic Information System (GIS) model for
  spatial analysis
• Mixed-integer linear optimization model to solve
  optimal biofuel system design using input from GIS

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Participants

• UC Davis
• Task lead
• GIS/Optimization modeling
• Input identification and analysis, quality
  control
• Antares Group, Inc
• Conversion technology costs
• Kansas State University
• Agricultural resource/energy crops
• US Forest Service
• Forest resource
• California Biomass Collaborative
• Municipal resource
• National Renewable Energy Laboratory
• Infrastructure and database support
• WGA, USDA, USDOE, California Energy Commission
  PIER Program, UC Davis STEPS Program, CSTARS
• Project support

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Task Organization
Bio-refinery Site Selection
Mapping of Feedstock
Network Analysis of Transportation Costs
Optimization
Supply Curves
Mapping of Fuel Supply
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GIS Spatial Analysis

• Feedstock mapping
• Biorefinery location analysis
• Network Analysis

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Biomass Resource Procurement Cost
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Mapping Biomass Resources
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Siting Criteria for Potential Biorefineries
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GIS Network Analysis

• Methodology
• Develop road, rail and marine transportation
  networks.
• Calculate transportation cost matrix from
  feedstock locations to potential biorefinery
  locations
• Calculate fuel transportation cost from
  biorefinery to closest distribution terminal.

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GIS Network Analysis

• Network Nodes
• Supply
• County centroid
• Municipal/other facility point source
• Supply accumulation points
• Inter-Modal facilities equipped to transfer
  biomass from road to rail and rail/road to
  marine.
• Potential biorefinery locations
• Product accumulation points
• Inter-Modal facilities equipped to transfer
  liquid fuel from rail, road or pipeline to
  marine.
• Terminals
• Product endpoint for mixing with petroleum fuels

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GIS Network Analysis

• Network Connectivity
• Road
• Transport feedstock from source to inter-modal or
  refinery.
• Marine
• Transport feedstock from inter-modal facility to
  refinery
• Transport product from refinery to terminal
• Rail
• Transport feedstock from inter-modal facility to
  refinery
• Transport product from refinery to terminal

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Geographic Network
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GIS Network Analysis
Cities / Population from National Atlas
Inter-modal Facilities from BTS
Roads/Marine/Rail from BTS
Facilities from EPA EnviroFacts
Terminals from OPIS/STALSBY
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Matching Feedstocks and Technologies
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Matching Feedstocks and Technologies
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Optimization Model

• Methodology
• Mixed integer-linear programming model of biofuel
  industry.
• Objective minimize annual cost to produce a
  given quantity of biofuels.
• Single technology models and a separate
  integrated model with all technologies
• Reference assumptions
• Conversion costs are linearized functions
• All other costs are constant with scale

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Simple Model Schematic
Fuel Distribution Terminals
Potential Biorefinery Sites
Biomass Supply Points
Biomass Types
Price Levels
Xjt Yfjt Ybjt
Fijfp
Sifp
Pifp
DCjk
TCij
k
i
j
f
p
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Model Formulation

• Minimize annual cost of production
• Sum of feedstock procurement, transportation,
  conversion, and fuel distribution costs for
  annual production.
• Subject to
• Biomass leaving a supply point at a price level
  must be less than the maximum supply.
• Biofuel produced at a biorefinery must be less
  than the biofuel potential of the biomass
  entering the biorefinery.
• Biofuel quantity produced at a biorefinery must
  be less than the maximum biorefinery size for
  that technology.
• Biofuel cannot be produced at a location unless
  the fixed cost has been paid.
• The total biofuel produced must equal the biofuel
  demanded for a given model run.

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Model Equations
Maximize
(1)
(2)
Subject to
(3)
(4)
(5)
(6)
(7)
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Linearized Conversion Costs
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Integrated Model Architecture
Modified routes, sources, destinations
Cities
ESRI Network Analyst
Facilities
Summaries
Simplified Costs
Maps
GAMS Modeling
Supply Curves
Supply Curves
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Results

• Individual Technology Supply Curves
• Combined Model Supply Curve
• Type of Biomass
• Maps

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Supply Curves for Individual Technologies
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Supply Curve for All Biofuels
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Type of Biomass Consumed
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Contributions to Cost
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1.30 per GGE
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1.50 per GGE
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1.75 per GGE
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2.00 per GGE
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2.30 per GGE
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2.50 per GGE
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2.75 per GGE
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3.05 per GGE
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4.05 per GGE
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Sensitivity

• To be completed
• Capital Cost /- 25
• Coproduct Value
• Tax Incentives
• Required Fuel Mix

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Conclusions

• 4 billion gallons of gasoline equivalent
  biofuels could be produced at approximately
  2/GGE
• 12 billion gallons of gasoline equivalent
  biofuels could be produced at approximately
  3/GGE
• The base model costs favor LCE for cellulosic
  biomass resources and FAHC for oil/grease
  resources.
• Costs used need further inspection and validation
• Sensitivity analysis needed to investigate range
  of potential outcomes

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Future Analysis

• Completion of analysis for Western US
• Model extension to entire US
• Proposal in review by USDA
• Model extension to include Canada
• In discussion
• Incorporate analysis of potential changes to land
  use based upon increased demand for feedstock
• Incorporate seasonal and probabilistic effects,
  add other sustainability and LCA aspects, explore
  alternative economy of scale formulations

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Extra Slides
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Target Price Analysis
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Transportation Costs
Trucking
Rail
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Transportation Costs (2)
Marine
Biomass Properties
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Conversion Costs