Biomass Electricity Plant Allocation through Non-Linear Modeling and Mixed Integer

186 Pages·2012·7.07 MB·English

BIOMASS ELECTRICITY PLANT ALLOCATION THROUGH NON-LINEAR MODELING AND
MIXED INTEGER OPTIMIZATION
by
Robert Kennedy Smith
A dissertation submitted to Johns Hopkins University in conformity with
the requirements for the degree of Doctor of Philosophy
Baltimore, Maryland
June, 2012
©2012 Robert Kennedy Smith
All Rights Reserved
UMI Number: 3532713
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UMI 3532713
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Abstract
Electricity generation from the combustion of biomass feedstocks provides low-carbon
energy that is not as geographically constricted as other renewable technologies. This
dissertation uses non-linear programming to provide policymakers with scenarios of
possible sources of biomass for power generation as well as locations and types of
electricity generation facilities utilizing biomass, consistent with a set of policy, technology
and economic assumptions. The scenarios are obtained by combining the output from
existing agricultural optimization models with a non-linear mathematical program that
calculates the least-cost ways of meeting an assumed biomass electricity standard. The
non-linear program considers region-specific cultivation and transportation costs of
biomass fuels as well as the costs of building and operating both coal plants capable of co-
firing biomass and new dedicated biomass combustion power plants. The results of the
model provide geographically-detailed power plant allocation patterns that minimize the
total cost of meeting the generation requirements, which are varying proportions of total
U.S. electric power generation, under the assumptions made. The amount of each cost
component comprising the objective functions of the various requirements are discussed in
the results chapter for all cases, and the results show that approximately two-thirds of the
total cost of meeting a biomass electricity standard occurs on the farms and forests that
produce the biomass. Plant capital costs and biomass transportation costs comprise the
largest share of the remaining costs. A conclusion section that places the findings in a
policy-setting context follows the results. The most important policy conclusion is that
biomass use in power plants will require significant subsidies, perhaps as much as half of
their cost, if they are to achieve significant penetrations in U.S. electricity markets.
ii
Advisor: Professor Benjamin Hobbs, Johns Hopkins University, Department of Geography
and Environmental Engineering.
Readers: Professor Cila Herman, Johns Hopkins University, Department of Mechanical
Engineering and Professor Maqbool Dada, Johns Hopkins University, Carey Business School
iii
Acknowledgments
Ad Maiorem Dei Gloriam
I would like to acknowledge my parents Rick and Joan, in appreciation of their constant love
and support.
1 would also like to acknowledge my colleagues at E1A for their professional advice and
assistance with the NEMS model. 1 want to thank Dr. Robert Perlack of Oak Ridge National
Laboratory and Dr. Daniel De la Torre Ugarte of the University of Tennessee for sharing data
with me, without which this research would not be possible. 1 appreciate the financial
support offered by the U.S. Department of Energy and Johns Hopkins University. In
addition, special appreciation goes to Benjamin Hobbs my advisor at Johns Hopkins
University and to Candice Ekstrom and Kyle Belott of R.A. Smith National who helped with
GIS mapping and formatting.
iv
Table of Contents
1. Introduction 1
1.1 Research Motivation 1
1.2 The Role of Biomass Within the Energy System 3
1.3 Research Approach 5
1.4 Cost and Value Tradeoffs in Location and Technology Choice for Biomass Electricity ..7
1.5 Scope of the Dissertation 11
2. Literature Review 13
2.1 Overview 13
2.2 Current U.S. Renewable Energy Policies 13
2.3 Sources of Biomass Supply 16
2.4 Biomass Carbon Emissions and Environmental Impact 19
2.5 A Comparison of POLYSYS to Other Models 24
2.5.1 Bottom-Up Agricultural and Forestry Models 25
2.5.2 POLYSYS Structural Assumptions 26
2.6 Transmission 30
2.7 Land Competition Issues 32
3. Biomass Modeling Assumptions 37
3.1 Overview 37
3.2 Exclusion of Plant-Specific Transmission, Inclusion of Regional Transmission 37
3.3 Power Plant Characterizations 40
v
3.3.1 Dedicated Plant Technology and Size 40
3.3.2 Carbon Sequestration 42
3.3.3 Co-firing 42
3.3.4 Cost Data 43
3.3.5 Co-firing Sites 45
3.4 Avoided Coal Use Savings and Dedicated Plant Capacity Credits 46
3.4.1 Coal Prices 46
3.4.2 Electricity Prices 50
3.5 Co-firing in Nonattainment Areas 54
3.5.1 Nonattainment Areas 54
3.5.2 Coal Plant Retirements 56
3.6 Biomass Transportation and Interregional Utilization 57
3.7 Feedstock Energy Content, Usage and Competition with Transportation Demands....60
3.7.1 Energy Content 60
3.7.2 Competition in the Electric Power Sector with Cellulosic Ethanol 61
4. Modeling Methodology 64
4.1 Overview 64
4.2 Biomass Fuel Supply Curve Derivation Using POLYSYS 64
4.3 Power Plant Allocation Model Structural Considerations 68
4.3.1 Discounting 68
4.3.2 Decision Variables of the Generation Model 70
vi
4.3.3 Conversion of Multiyear Formulation into Recursive Formulation 71
4.4 Power Plant Allocation Model Structure 74
4.4.1 Decision Variables 74
4.4.2 Objective Function 77
4.4.3 Constraints 79
4.5 Linearization of the Power Plant Allocation Model 83
4.6 Model Software Implementation Procedure 88
5. Results 90
5.1 Overview 90
5.2 Biomass Generation Target Policies Considered 91
5.3 Biomass Co-firing and Dedicated Plant Allocation Patterns 94
5.3.1 Core Case 94
5.3.2 Alternative Cases 100
5.3.3 Capital Costs 106
5.4 Biomass Cultivation Costs 106
5.5 Biomass Transportation Costs 109
5.6 Avoided Costs Ill
5.7 Objective Function Values 119
5.7.1 Objective Function Summary Tables 119
5.7.2 Levelized Costs of Biomass Generation 123
5.8 Plant Allocation Patterns 125
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6. Conclusions and Future Research 138
6.1 Conclusions 138
6.2 Future Research 141
6.2.1 Additional Policy and Technology Advances 141
6.2.2 Additional Sensitivity Analyses 142
6.2.3 Validation 143
6.2.4 Model Enhancements 144
7. Appendix 148
8. Bibliography 161
viii
List of Tables
Table 3.1: Power Plant Cost and Performance Assumptions 45
Table 3.2: 2012,2020,2025,2030, and 2035 NEMS Regional Delivered Coal Prices 49
Table 4.1: Targets and Assumed 5-Percent Target Escalation in the 100-Percent Scenario.74
Table 4.2: Effective Biomass Heat Rate 85
Table 4.3: AIMMS Solution Times for the Power Plant Allocation MILP Model 89
Table 5.1: Biomass Generation Requirements in MWhs and Percent of Total Projected
Electricity Generation for the Core Case 93
Table 5.2: Co-fired and Dedicated Capacity Expansion and Generation Decisions for the 25-
percent Scenario of the Core Case 96
Table 5.3: Co-fired and Dedicated Capacity Expansion and Generation Decisions for the 50-
percent Scenario of the Core Case 97
Table 5.4: Co-fired and Dedicated Capacity Expansion and Generation Decisions for the 75-
percent Scenario of the Core Case 98
Table 5.5: Co-fired and Dedicated Capacity Expansion and Generation Decisions for the 100-
percent Scenario of the Core Case 99
Table 5.6: Co-fired and Dedicated Capacity Expansion and Generation Decisions for the Max
Scenario of the Core Case 99
Table 5.7: Co-fired and Dedicated Capacity in the 75-percent Scenario in which all Plants
have the Same Heat Rate 101
Table 5.8: Co-fired and Dedicated Capacity in the 75-percent Scenario of the Cellulosic
Ethanol Case 102
Table 5.9: Co-fired and Dedicated Capacity in the 75-percent Scenario of the Non-
Attainment Case 103
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