ABSTRACT 
 
 
 
 
Title of Document: IDENTIFICATION AND ASSESSMENT OF POTENTIAL WIND 
ENERGY PROJECT SITES IN THE STATE OF MARYLAND 
 
  
  
 Erin M. Conroy, David A. Green, Phillip M. Hannam, Matthew R. 
Kosmer, Alexander S. Meiseles, Emma K. Roach, Sumegha K. 
Singhania, Amy D. Zhang 
 
Directed By: Professor Jungho Kim, Mechanical Engineering 
  
 
 
The international scientific community has reached consensus that greenhouse gas 
emissions must be reduced to minimize global climate change impacts on the 
environment, economy, and public health.   In 2007, the University of Maryland's 
Climate Action Plan Workgroup was tasked with charting the University?s path 
toward carbon neutrality.  To aid in this effort, this report identifies the best sites in 
Western Maryland for wind development.  Geographic information systems data on 
wind speed, land protections, and transmission infrastructure were used to assign 
scores for physical, social and environmental characteristics of prospective 
sites.  Attribute scores were entered into a systematic weighting system to determine 
overall site suitability.  A financial analysis was conducted for the highest ranked 
sites using RETScreen software, which generated projections for payback periods and 
return on investment.  The list of suitable sites produced through this research 
provides the University with a starting point for exploration of off-site wind power 
production. 
  
 
 
 
 
 
 
 
 
 
 
IDENTIFICATION AND ASSESSMENT OF POTENTIAL WIND ENERGY 
PROJECT SITES IN THE STATE OF MARYLAND 
    
 
 
 
By 
 
Team Renewables at UMD 
 
Erin M. Conroy, David A. Green, Phillip M. Hannam, Matthew R. Kosmer, Alexander 
S. Meiseles, Emma K. Roach, Sumegha K. Singhania, and Amy D. Zhang 
 
 
 
 
Thesis submitted in partial fulfillment of the requirements of the  
Gemstone Program, University of Maryland, 2009 
 
 
 
 
Advisory Committee: 
Dr. Jungho Kim, Mentor 
Mrs. Joan Kowal 
Mrs. Heather Lair 
Mr. Mark Werner Stewart 
Mrs. Carol Kennedy-Hearle 
Dr. Daniel Kirk-Davidoff 
Dr. Kenneth Conca 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
? Copyright by 
Team Renewables at UMD 
 
Erin M. Conroy 
David A. Green 
Phillip M. Hannam 
Matthew R. Kosmer 
Alexander S. Meiseles 
Emma K. Roach 
Sumegha K. Singhania 
Amy D. Zhang 
 
2009 
 
 ii 
 
 
 
 
 
 
 
Dedication 
We dedicate this research to those who will, and must, continue to strive for 
sustainability on campus long after we have left.  And to their children, whose world 
we hope will be better as a result. 
 
 iii 
 
Acknowledgements 
This project would not have been possible if it were not for the collective efforts of 
the University community in its advancement of the goals of sustainability.  
Particularly, we thank the Climate Action Plan Workgroup, and its stewards in the 
Campus Sustainability Office, for providing motivation and a forum for this research.  
At the beginning of this four-year endeavor, the Campus Energy Manager, Mrs. Joan 
Kowal, provided many of the sources, figures and background to get our team off to a 
good start. Dr. James Wallace and Dr. Rebecca Thomas, the directors of the 
Gemstone Office, kept the team on track by providing deadlines and constructive 
feedback for the numerous iterations of our research methodology.  Most importantly, 
we thank the efforts of our mentor, Dr. Jungho Kim, for providing tough love, in just 
the right dose. 
 
 
 iv 
 
Table of Contents 
 
 
Dedication ..................................................................................................................... ii 
Acknowledgements ...................................................................................................... iii 
Table of Contents ......................................................................................................... iv 
List of Tables .............................................................................................................. vii 
List of Figures .............................................................................................................. ix 
List of Abbreviations ................................................................................................. xiv 
Chapter 1: Introduction ................................................................................................. 1 
Chapter 2: Objectives .................................................................................................... 3 
Chapter 3: Literature Review ........................................................................................ 4 
Climate Action Plan .................................................................................................. 8 
Renewable Energies ................................................................................................ 14 
Introduction to Renewable Energy ..................................................................... 14 
Ways to Acquire Renewable Energy .................................................................. 15 
Solar Power ......................................................................................................... 17 
Geothermal Power .............................................................................................. 18 
Wind Power ........................................................................................................ 19 
Biomass ............................................................................................................... 20 
Future projections and obstacles to Renewable Energy Implementation ........... 26 
Renewable Energy Implementation at the University of Maryland ................... 28 
Wind Power Generation .......................................................................................... 29 
Physical Attributes .............................................................................................. 29 
Social/Infrastructure ............................................................................................ 34 
Environmental Regulations and Legislation ....................................................... 35 
The State of Maryland and Wind Energy ........................................................... 39 
Environmental Benefits of Wind Energy ............................................................ 41 
Environmental Drawbacks of Wind Energy ....................................................... 41 
Offshore Wind Development .................................................................................. 47 
Offshore Wind Projects in the United States ...................................................... 47 
Offshore Wind Projects Outside the United States ............................................. 56 
Indirect Renewable Energy Delivery ...................................................................... 58 
Renewable Energy Credits .................................................................................. 58 
Limitations of Using Renewable Energy Credits ............................................... 59 
Universities and Renewable Energy ....................................................................... 62 
Geographic Information Systems (GIS) ................................................................. 67 
Introduction to Geographic Information Systems (GIS) .................................... 67 
GIS and Wind Energy ......................................................................................... 68 
Chapter 4: Methodology ............................................................................................. 70 
Summary ................................................................................................................. 70 
Generating Wind Energy Site Assessment Maps Using GIS ................................. 71 
Geographically Defining our Focus Area ............................................................... 72 
Element 1:  Wind Speed ..................................................................................... 72 
Element 2:  Classifying Land Protections ........................................................... 74 
 
 v 
 
Element 3:  Proximity to Transmission Lines..................................................... 78 
Creating Focus Sites Based on Areas with Viable Wind Speeds ........................... 78 
Applying GIS Results in an Excel Site Analysis Model......................................... 81 
Weighting Differences ........................................................................................ 81 
Site Suitability ..................................................................................................... 87 
Energy Availability ............................................................................................. 89 
Excluded Factors ................................................................................................. 92 
Data Approximations .......................................................................................... 93 
Flexibility of Weightings .................................................................................... 94 
RETScreen Financial Analysis ............................................................................... 94 
Chapter 5:  Results .................................................................................................... 102 
Detailed Site Analyses .......................................................................................... 104 
Site M ................................................................................................................ 104 
Site N ................................................................................................................ 114 
Site X ................................................................................................................ 115 
Site U ................................................................................................................ 122 
Site V ................................................................................................................ 130 
Site W................................................................................................................ 134 
Sensitivity of RETScreen Financial Analysis ....................................................... 144 
Chapter 6:  Summary and Conclusions ..................................................................... 147 
Appendix ................................................................................................................... 149 
Land Use ............................................................................................................... 149 
Potential Conflict Maps ........................................................................................ 151 
Individual Site Data and Characterization ............................................................ 153 
Site A: Data ....................................................................................................... 153 
Site B: Data ....................................................................................................... 156 
Site C: Data ....................................................................................................... 158 
Site D: Data ....................................................................................................... 162 
Site E: Data ....................................................................................................... 165 
Site F: Data ....................................................................................................... 169 
Site G: Data ....................................................................................................... 171 
Site H: Data ....................................................................................................... 175 
Site I: Data ........................................................................................................ 179 
Site J: Data ........................................................................................................ 181 
Site K: Data ....................................................................................................... 183 
Site L: Data ....................................................................................................... 185 
Site M ................................................................................................................ 189 
Site N: Data ....................................................................................................... 189 
Site O: Data ....................................................................................................... 194 
Site P: Data ....................................................................................................... 198 
Site Q: Data ....................................................................................................... 202 
Site R: Data ....................................................................................................... 206 
Site S: Data ....................................................................................................... 210 
Site T: Data ....................................................................................................... 216 
Site U ................................................................................................................ 217 
Site V: Data ....................................................................................................... 218 
 
 vi 
 
Site V ................................................................................................................ 218 
Site W: Data ...................................................................................................... 221 
Site W................................................................................................................ 221 
Site X ................................................................................................................ 223 
Site Y: Data ....................................................................................................... 223 
Site Z: Data ....................................................................................................... 227 
MATLAB Algorithm ............................................................................................ 232 
Glossary .................................................................................................................... 233 
Bibliography ............................................................................................................. 236 
 
 
 
 
 
 
 
 vii 
 
List of Tables 
 
Table 1: Major emission sources as presented on page 3 of the draft Climate Action 
Plan (CAP, 2009) .......................................................................................................... 9 
Table 2: Greenhouse gas reduction goals for the University of Maryland relative to 
State of Maryland Goals, as presented on page 6 of the draft UMD Climate Action 
Plan (CAP, 2009) ........................................................................................................ 12 
Table 3: Summary of Renewable Energy Technologies............................................. 24 
Table 4: NREL Wind Classifications ......................................................................... 73 
Table 5: AHP Example ............................................................................................... 82 
Table 6: Weighting for AHP Example ........................................................................ 83 
Table 7: Consistency Check for AHP Example .......................................................... 83 
Table 8: Ratio Index Values ....................................................................................... 84 
Table 9: AHP rating matrix......................................................................................... 85 
Table 10: Consistency Matrix for AHP ...................................................................... 85 
Table 11: Weighting Matrix for AHP ......................................................................... 86 
Table 12: Normalized Weighting from AHP .............................................................. 86 
Table 13: Energy Availability Ranking Criteria ......................................................... 92 
Table 14: Site summary ranked by "Overall Site Score" .......................................... 103 
Table 15: Sub-Site summary ranked by "Overall Sub-Site Score" ........................... 104 
Table 16: Overall Site Suitability Scoring Matrix for Site M.1 and M.2 ................. 109 
Table 17: Overall Site Suitability Scoring Matrix for Sub-Site 6 ............................. 113 
Table 18: Overall Site Suitability Scoring Matrix for Site X ................................... 118 
Table 19: Overall Site Suitability Scoring Matrix for Sub-Site 18 ........................... 121 
Table 20: Overall Site Suitability Scoring Matrix for Site U ................................... 126 
Table 21: Overall Site Suitability Scoring Matrix for Sub-Site 21 ........................... 129 
Table 22: Overall Site Suitability Scoring Matrix for Site V ................................... 133 
Table 23:  Overall Site Suitability Scoring Matrix for Site W ................................. 138 
Table 24: Overall Site Suitability Scoring Matrix for Sub-Site 17 ........................... 141 
Table 25: Greenhouse Reduction income parameter assumptions at the upper limit of 
sensitivity analysis .................................................................................................... 145 
Table 26: Overall Site Suitability Scoring Matrix for Site A.1, A.2, and A.3 .......... 154 
Table 27: Overall Site Suitability Scoring Matrix for Site B.1 and B.2 ................... 157 
Table 28: Overall Site Suitability Scoring Matrix for Site C ................................... 159 
Table 29: Overall Site Suitability Scoring Matrix for Sub-Site 1 ............................. 161 
Table 30: Overall Site Suitability Scoring Matrix for Site D.1 and D.2 ................... 163 
Table 31: Overall Site Suitability Scoring Matrix for Site E .................................... 166 
Table 32: Overall Site Suitability Scoring Matrix for Sub-Site 2 ............................. 168 
Table 33: Overall Site Suitability Scoring Matrix for Site F .................................... 170 
Table 34: Overall Site Suitability Scoring Matrix for Site G ................................... 172 
Table 35: Overall Site Suitability Scoring Matrix for Sub-Site 3 ............................. 174 
Table 36: Overall Site Suitability Scoring Matrix for Site H ................................... 176 
Table 37: Overall Site Suitability Scoring Matrix for Sub-Site 4 ............................. 178 
Table 38: Overall Site Suitability Scoring Matrix for Site I ..................................... 180 
Table 39: Overall Site Suitability Scoring Matrix for Site J ..................................... 182 
 
 viii 
 
Table 40: Overall Site Suitability Scoring Matrix for Site K.1 and K.2 ................... 184 
Table 41: Overall Site Suitability Scoring Matrix for Site L .................................... 186 
Table 42: Overall Site Suitability Scoring Matrix for Sub-Site 5 ............................. 188 
Table 43: Overall Site Suitability Scoring Matrix for Site N.1 and N.2 ................... 190 
Table 44: Overall Site Suitability Scoring Matrix for Sub-Site 7 ............................. 193 
Table 45: Overall Site Suitability Scoring Matrix for Site O ................................... 195 
Table 46: Overall Site Suitability Scoring Matrix for Sub-Site 8 ............................. 197 
Table 47: Overall Site Suitability Scoring Matrix for Site P .................................... 199 
Table 48: Overall Site Suitability Scoring Matrix for Sub-Site 13 ........................... 201 
Table 49: Overall Site Suitability Scoring Matrix for Site Q ................................... 203 
Table 50: Overall Site Suitability Scoring Matrix for Sub-Site 12 ........................... 205 
Table 51: Overall Site Suitability Scoring Matrix for Site R ................................... 207 
Table 52: Overall Site Suitability Scoring Matrix for Sub-Site 11 ........................... 209 
Table 53: Overall Site Suitability Scoring Matrix for Site S .................................... 211 
Table 54: Overall Site Suitability Scoring Matrix for Sub-Site 9 ............................. 213 
Table 55: Overall Site Suitability Scoring Matrix for Sub-Site 10 ........................... 215 
Table 56: Overall Site Suitability Scoring Matrix for Site T .................................... 217 
Table 57: Overall Site Suitability Scoring Matrix for Sub-Site 15 ........................... 220 
Table 58: Overall Site Suitability Scoring Matrix for Sub-Site 16 ........................... 222 
Table 59: Overall Site Suitability Scoring Matrix for Site Y ................................... 224 
Table 60: Overall Site Suitability Scoring Matrix for Sub-Site 19 ........................... 226 
Table 61: Overall Site Suitability Scoring Matrix for Site Z .................................... 228 
Table 62: Overall Site Suitability Scoring Matrix for Sub-Site 20 ........................... 230 
 
 ix 
 
List of Figures 
Figure 1: Major sources of campus emissions in 2005 as presented on page 3 of the 
draft UMD Climate Action Plan (CAP, 2009)............................................................ 10 
Figure 2: Grid electricity mix on the state, regional and national levels (PPRP, 2006)
..................................................................................................................................... 11 
Figure 3: Grid electricity mix in the State of Maryland from the Energy Information 
Administration (PPRP, 2006) ..................................................................................... 11 
Figure 4: Impacts of proposed mitigation strategies as presented on page 7 of the draft 
UMD Climate Action Plan (CAP, 2009) .................................................................... 13 
Figure 5: Maryland Wind Resources at 50 meters...................................................... 32 
Figure 6: Garrett County Sites .................................................................................... 80 
Figure 7: Washington County Sites ............................................................................ 80 
Figure 8: Allegany County Sites ................................................................................. 80 
Figure 9: Rectangular vs. Staggered Layouts ............................................................. 90 
Figure 10: GE 1.5MW Power Curve (GE Energy, 2008a) ......................................... 91 
Figure 11: GE 2.5MW Power Curve (GE Energy, 2008b) ......................................... 91 
Figure 12: Dependence on Production Tax Credit for wind development (AWEA, 
2007 in UCS, 2009) .................................................................................................... 98 
Figure 13: Renewable Energy Credit pricing for purchase from generator and sale to 
end user (Evolution Markets, 2009) ............................................................................ 99 
Figure 14: Garrett County Land Use from MD Department of Planning (MDP, 2009), 
showing Site M in red box ........................................................................................ 105 
Figure 15: GIS Image of Site M (Scale is 1:40,000) ................................................ 106 
Figure 16: Aerial image of Site M ............................................................................ 108 
Figure 17: Financial Analysis for Site M.1 ............................................................... 110 
Figure 18: Financial Analysis for Site M.2 ............................................................... 111 
Figure 19: GIS Image of Sub-Site 6 ......................................................................... 112 
Figure 20: Aerial image of Sub-Site 6 ...................................................................... 113 
Figure 21: Financial Analysis for Sub-Site 6 ............................................................ 114 
Figure 22: Allegany County Land Use from MD Department of Planning (MDP, 
2009), showing Site X in red box ............................................................................. 115 
Figure 23: GIS Image of Site X (Scale 1:25,000) ..................................................... 116 
Figure 24: Aerial image of Site X ............................................................................. 117 
Figure 25: Financial Analysis for Site X .................................................................. 119 
Figure 26: Aerial image of Sub-Site 18 .................................................................... 120 
Figure 27: GIS Image of Sub-Site 18 ....................................................................... 120 
Figure 28: Financial Analysis for Sub-Site 18 .......................................................... 121 
Figure 29: Allegany County Land Use from MD Department of Planning (MDP, 
2009), showing Site U in red box ............................................................................. 122 
Figure 30: Map of Allegany County from MD Department of Environment (MDE, 
2009) showing non-coal surface mines..................................................................... 123 
Figure 31: GIS Image of Site U ................................................................................ 124 
Figure 32: Aerial image of Site U ............................................................................. 125 
Figure 33: Financial Analysis for Site U .................................................................. 127 
Figure 34: Aerial image of Sub-Site 21 .................................................................... 128 
 
 x 
 
Figure 35: GIS Image of Sub-Site 21 ....................................................................... 128 
Figure 36: Financial Analysis for Sub-Site 21 .......................................................... 129 
Figure 37: Allegany County Land Use from MD Department of Planning (MDP, 
2009), showing Site V in red box ............................................................................. 130 
Figure 38: GIS data image of Site V ......................................................................... 131 
Figure 39: Aerial image of Site V ............................................................................. 132 
Figure 40: Financial Analysis for Site V .................................................................. 134 
Figure 41: Allegany County Land Use from MD Department of Planning (MDP, 
2009), showing Site W in red box ............................................................................ 135 
Figure 42: GIS Image of Site W (Scale 1:25,000) .................................................... 136 
Figure 43: Aerial image of Site W ............................................................................ 137 
Figure 44: Financial Analysis for Site W ................................................................. 139 
Figure 45: Aerial image of Sub-Site 17 .................................................................... 140 
Figure 46: GIS Image of Sub-Site 17 ....................................................................... 140 
Figure 47: Financial Analysis for Sub-Site 17 .......................................................... 141 
Figure 48: Relationships between Overall Site Score and other site viability 
parameters ................................................................................................................. 142 
Figure 49: Relationship between overall sub-site score and RETScreen Outputs .... 144 
Figure 50: Financial Analysis with upper limit greenhouse reduction income ........ 145 
Figure 51: Maryland land use classifications from MD DNR .................................. 149 
Figure 52:Maryland land use classification from MD DNR, continued ................... 150 
Figure 53: Map of Washington County from MD Department of Environment (MDE, 
2009) showing non-coal surface mines..................................................................... 151 
Figure 54: Map of Garrett County from MD Department of Environment (MDE, 
2009) showing non-coal surface mines..................................................................... 152 
Figure 55: Map of Allegany and Garrett Counties from MD Department of 
Environment (MDE, 2009) showing locations of MD natural gas wells ................. 152 
Figure 56: Aerial image of Site A ............................................................................. 153 
Figure 57: GIS image of Site A ................................................................................ 153 
Figure 58: Financial Analysis for Site A.1 ............................................................... 154 
Figure 59: Financial Analysis for Site A.2 ............................................................... 155 
Figure 60: Financial Analysis for Site A.3 ............................................................... 155 
Figure 61: Aerial image of Site B ............................................................................. 156 
Figure 62: GIS Image for Site B ............................................................................... 156 
Figure 63: Financial Analysis for Site B.1................................................................ 157 
Figure 64: Financial Analysis for Site B.2................................................................ 158 
Figure 65: Aerial image of Site C ............................................................................. 158 
Figure 66: GIS Image for Site C ............................................................................... 159 
Figure 67: Financial Analysis for Site C................................................................... 160 
Figure 68: Aerial image of Sub-Site 1 ...................................................................... 160 
Figure 69: GIS Image of Sub-Site 1 ......................................................................... 161 
Figure 70: Financial Analysis for Sub-Site 1 ............................................................ 162 
Figure 71: Aerial image of Site D ............................................................................. 162 
Figure 72: GIS Image for Site D ............................................................................... 163 
Figure 73: Financial Analysis for Site D.1 ............................................................... 164 
Figure 74: Financial Analysis for Site D.2 ............................................................... 164 
 
 xi 
 
Figure 75: Aerial image of Site E ............................................................................. 165 
Figure 76: GIS image of Site E ................................................................................. 165 
Figure 77: Financial Analysis for Site E ................................................................... 166 
Figure 78: Aerial image of Sub-Site 2 ...................................................................... 167 
Figure 79: GIS Image of Sub-Site 2 ......................................................................... 167 
Figure 80: Financial Analysis for Sub-Site 2 ............................................................ 168 
Figure 81: Aerial image of Site F ............................................................................. 169 
Figure 82: GIS Image of Site F ................................................................................. 169 
Figure 83: Financial Analysis for Site F ................................................................... 170 
Figure 84: Aerial image of Site G ............................................................................. 171 
Figure 85: GIS Image of Site G ................................................................................ 171 
Figure 86: Financial Analysis for Site G .................................................................. 172 
Figure 87: Aerial image of Sub-Site 3 ...................................................................... 173 
Figure 88: GIS Image of Sub-Site 3 ......................................................................... 173 
Figure 89: Financial Analysis for Sub-Site 3 ............................................................ 174 
Figure 90: Aerial image of Site H ............................................................................. 175 
Figure 91: GIS Image of Site H ................................................................................ 175 
Figure 92: Financial Analysis for Site H .................................................................. 176 
Figure 93: Aerial image of Sub-Site 4 ...................................................................... 177 
Figure 94: GIS Image of Sub-Site 4 ......................................................................... 177 
Figure 95: Financial Analysis for Sub-Site 4 ............................................................ 178 
Figure 96: Aerial image of Site I .............................................................................. 179 
Figure 97: GIS Image of Site I .................................................................................. 179 
Figure 98: Financial Analysis for Site I .................................................................... 180 
Figure 99: Aerial image of Site J .............................................................................. 181 
Figure 100: GIS Image of Site J ............................................................................... 181 
Figure 101: Financial Analysis for Site J .................................................................. 182 
Figure 102: Aerial image of Site K ........................................................................... 183 
Figure 103: GIS Image of Site K .............................................................................. 183 
Figure 104: Financial Analysis for Site K.1 ............................................................. 184 
Figure 105: Financial Analysis for Site K.2 ............................................................. 185 
Figure 106: Aerial image of Site L ........................................................................... 185 
Figure 107: GIS Image of Site L............................................................................... 186 
Figure 108: Financial Analysis for Site L ................................................................. 187 
Figure 109: Aerial image of Sub-Site 5 .................................................................... 187 
Figure 110: GIS Image of Sub-Site 5 ....................................................................... 188 
Figure 111: Financial Analysis for Sub-Site 5 .......................................................... 189 
Figure 112: Aerial image of Site N ........................................................................... 189 
Figure 113: GIS Image of Site N .............................................................................. 190 
Figure 114: Financial Analysis for Site N.1 ............................................................. 191 
Figure 115: Financial Analysis for Site N.2 ............................................................. 191 
Figure 116: Aerial image of Sub-Site 7 .................................................................... 192 
Figure 117: GIS Image of Sub-Site 7 ....................................................................... 192 
Figure 118: Financial Analysis for Sub-Site 7 .......................................................... 193 
Figure 119: Aerial image of Site O ........................................................................... 194 
Figure 120: GIS Image of Site O .............................................................................. 194 
 
 xii 
 
Figure 121: Financial Analysis for Site O ................................................................ 195 
Figure 122: Aerial image of Sub-Site 8 .................................................................... 196 
Figure 123: GIS Image of Sub-Site 8 ....................................................................... 196 
Figure 124: Financial Analysis for Sub-Site 8 .......................................................... 197 
Figure 125: Aerial image of Site P ........................................................................... 198 
Figure 126: GIS Image of Site P ............................................................................... 198 
Figure 127: Financial Analysis for Site P ................................................................. 199 
Figure 128: Aerial image of Sub-Site 13 .................................................................. 200 
Figure 129: GIS Image of Sub-Site 13 ..................................................................... 200 
Figure 130: Financial Analysis for Sub-Site 13 ........................................................ 201 
Figure 131: Aerial image of Site Q ........................................................................... 202 
Figure 132: GIS Image of Site Q (Scale 1:50,000) ................................................... 202 
Figure 133: Financial Analysis for Site Q ................................................................ 203 
Figure 134: Aerial image of Sub-Site 12 .................................................................. 204 
Figure 135: GIS Image of Sub-Site 12 ..................................................................... 204 
Figure 136: Financial Analysis for Sub-Site 12 ........................................................ 205 
Figure 137: Aerial image of Site R ........................................................................... 206 
Figure 138: GIS Image of Site R .............................................................................. 206 
Figure 139: Financial Analysis for Site R................................................................. 207 
Figure 140: Aerial image of Sub-Site 11 .................................................................. 208 
Figure 141: GIS Image of Sub-Site 11 ..................................................................... 208 
Figure 142: Financial Analysis for Sub-Site 11 ........................................................ 209 
Figure 143: Aerial image of Site S ........................................................................... 210 
Figure 144: GIS Image of Site S (Scale 1:40,000) ................................................... 210 
Figure 145: Financial Analysis for Site S ................................................................. 211 
Figure 146: Aerial image of Sub-Site 9 .................................................................... 212 
Figure 147: GIS Image of Sub-Site 9 ....................................................................... 212 
Figure 148: Financial Analysis for Sub-Site 9 .......................................................... 213 
Figure 149: Aerial image of Sub-Site 10 .................................................................. 214 
Figure 150: GIS Image of Sub-Site 10 ..................................................................... 214 
Figure 151: Financial Analysis for Sub-Site 10 ........................................................ 215 
Figure 152: Aerial image of Site T ........................................................................... 216 
Figure 153: GIS image of Site T (Scale 1:35,000) ................................................... 216 
Figure 154: Financial Analysis for Site T ................................................................. 217 
Figure 155: Aerial image of Sub-Site 14 .................................................................. 218 
Figure 156: GIS Image of Sub-Site 14 ..................................................................... 218 
Figure 157: Financial Analysis for Sub-Site 14 ........................................................ 219 
Figure 158: Aerial image of Sub-Site 15 .................................................................. 219 
Figure 159: GIS Image of Sub-Site 15 ..................................................................... 220 
Figure 160: Financial Analysis for Sub-Site 15 ........................................................ 221 
Figure 161: Aerial image of Sub-Site 16 .................................................................. 221 
Figure 162: GIS Image of Sub-Site 16 ..................................................................... 222 
Figure 163: Financial Analysis for Sub-Site 16 ........................................................ 223 
Figure 164: Aerial image of Site Y ........................................................................... 223 
Figure 165: GIS Image of Site Y .............................................................................. 224 
Figure 166: Financial Analysis for Site Y ................................................................ 225 
 
 xiii 
 
Figure 167: Aerial image of Sub-Site 19 .................................................................. 225 
Figure 168: GIS Image of Sub-Site 19 ..................................................................... 226 
Figure 169: Financial Analysis for Sub-Site 19 ........................................................ 227 
Figure 170: Aerial image of Site Z ........................................................................... 227 
Figure 171: GIS Image of Site Z (Scale 1:40,000) ................................................... 228 
Figure 172: Financial Analysis for Site Z ................................................................. 229 
Figure 173: Aerial image of Sub-Site 20 .................................................................. 229 
Figure 174: GIS Image of Sub-Site 20 ..................................................................... 230 
Figure 175: Financial Analysis for Sub-Site 20 ........................................................ 231 
Figure 176: MATLAB Algorithm ............................................................................ 232 
  
 
 xiv 
 
List of Abbreviations 
AWEA: American Wind Energy Association 
BLM: Bureau of Land Management  
CAP: Climate Action Plan Workgroup  
CHP: Combined heat and power 
CO2: Carbon dioxide  
CSP: Concentrating Solar Power 
DNR: Department of Natural Resources 
DOD: Department of Defense  
DOE: Department of Energy 
EIA: Energy Information Administration 
EIS: Environmental Impact Statement 
ESA: Endangered Species Act  
FSEC: Florida Solar Energy Center  
g: Grams 
g/kWh: grams per kilowatt hour 
GHG: Greenhouse gas 
GIS: Geographic Information Systems 
GWe: Gigawatt electrical 
HTF: Heat Transfer Fluids 
IEA: International Energy Agency 
IPCC: Intergovernmental Panel on Climate Change 
IRR: Internal rate of return 
 
 xv 
 
kV: kilovolts 
kWh: Kilowatt hours 
LIHI:  Low Impact Hydropower Institute 
MALPF: Maryland Agricultural Land Preservation Foundation 
MATLAB: Matrix laboratory 
MBTA: Migratory Bird Treaty Act  
MDE: Maryland Department of Environment 
MDP: Maryland Department of Planning 
MET: Maryland Environmental Trust  
MTCO2: Metric tons carbon dioxide 
MTCO2e: Metric tons carbon dioxide equivalents 
MWe: Megawatt electrical 
MWh: Megawatt hours 
NEPA: National Environmental Policy Act 
NIMBY: Not In My Backyard 
NOx: Nitrogen oxide 
NREL: National Renewable Energy Laboratory 
O&M: Operation and maintenance 
PJM: Pennsylvania New Jersey Maryland Interconnection LLC 
PPA: Power Purchase Agreement 
ppm: Parts per million 
PPRP: Power Plant Research Program 
REC: Renewable Energy Credit 
 
 xvi 
 
REPI: Renewable Energy Production Incentive  
RETScreen: Renewable Energy Project Analysis Software 
RPS: Renewable Portfolio Standard  
RTO: Regional Transmission Organization  
SO2: Sulfur dioxide 
TWh: Terawatt hour 
UNEP: United Nations Environment Programme 
US DOE: U.S. Department of Energy 
USFWS: U.S. Fish and Wildlife Service 
VERA: Vermont Environmental Research Associates 
WHTP: Wind and Hydropower Technologies Program 
W/m2: Watts per square meter 
 
 1 
 
Chapter 1: Introduction 
The University of Maryland currently uses electricity that is primarily 
generated through the combustion of carbon-based fossil fuels. The University 
generates most of this electricity using a natural gas-fired power plant located next to 
the campus, and supplements it from distributed grid power that relies primarily on a 
mix of coal, natural gas, oil, and nuclear fuels. Mounting scientific evidence suggests 
that the carbon dioxide released through combustion of fossil fuels is enhancing the 
?greenhouse effect,? causing the earth?s surface to warm and accelerating global 
climate change. Even optimistic scientific projections indicate that the potential 
damage caused by global warming is ?unequivocal,? and may cause mass extinctions 
of plant and animal species, while also placing considerable strain on existing human 
environments (Intergovernmental Panel on Climate Change [IPCC], 2007).  
In 2006, the University of Maryland, College Park signed the President?s 
Climate Commitment, an agreement stating that the University intends to reduce its 
net carbon emissions to zero by 2050. As a milestone to reaching this goal, UMD will 
reduce its energy consumption by 10% within five years. Also, at least 15% of the 
UMD?s energy will be purchased or produced from renewable energy. To facilitate 
the development of a plan to reach carbon neutrality, the Climate Action Plan 
Workgroup was formed on campus, on which Team Renewables was represented. 
The workgroup decided that off-site renewable energy purchasing will be part of the 
emission reduction plan. Of the over 350,000 megatons of CO2 equivalent that must 
be reduced to meet carbon neutrality goals, approximately 50,000 megatons will be 
reduced by purchasing off-site renewable energies (CAP, 2009). 
 
 2 
 
Our research study examines the potential for reducing carbon dioxide 
emissions at the University of Maryland by identifying sites in Western Maryland that 
would be prime candidates for the development of renewable wind energy farms. 
Renewable technologies tend to emit very little carbon dioxide, and typically displace 
energy that would have otherwise been produced largely from carbon-intensive fossil 
energy sources. Renewable energy?s high upfront costs relative to fossil fuels can be 
reduced by considering environmental externalities, as well as governmental policies 
to make the cost of renewable energy more competitive. Still, renewable energy is not 
widely implemented. This study intends to examine potential land sites for wind 
energy development based on the site?s wind availability and environmental, 
technological and social suitability.  Through a flexible analysis, the study presents 
sites in Western Maryland that may be best suited for wind energy developments by 
the University of Maryland.  This research contributes to carbon emission reduction 
goals that can have lasting benefits for the environment, economy, and society as a 
whole. 
 
 3 
 
Chapter 2: Objectives 
The research objective of this project is to identify potential wind 
development sites in Western Maryland based on site suitability and wind 
availability. Site suitability was judged specifically on the physical attributes of the 
land, the social and community infrastructure, regulations, and environmental 
concerns. Based on these findings, recommendations of economically feasible sites 
for wind projects are made to the University. 
  The sites examined were all situated in Western Maryland in Garrett, 
Washington and Alleghany counties, which had the highest overall wind speeds. 
Land outside of Maryland was not studied because of the University of Maryland?s 
interests, as a state-funded institution, to meet state renewable energy goals and 
improve the local environment. Off-shore sites were also not considered because of 
the lack of existing financial and regulatory data for U.S. domestic projects on which 
to base our analysis. Though our research speculates where there will be higher 
receptivity to construction based on a site?s proximity to local population, we are 
unable to address the localized social implications of wind developments for each 
site. Our analysis is tailored to prioritize mainly cost efficiency and regulatory land 
use obstacles of the sites when suggesting sites for the University to consider, though 
the analysis framework is flexible to accommodate changing priorities.  In performing 
environmental and economic analyses, we consulted current regulations and market-
based prices, though we recognize that these could change substantially over the 
course of a wind farm?s lifetime.  Whenever possible, we used conservative economic 
projections to accommodate for the impacts of a weak economy on project finance.
 
 4 
 
Chapter 3: Literature Review  
Exigency 
The exigency for action against anthropogenic global warming has warranted 
this research. Most scientists agree that global warming is a severe threat to humanity, 
one justifying immediate and drastic action (IPCC, 2007). Carbon dioxide is a 
naturally occurring greenhouse gas that traps solar radiation close to Earth?s surface, 
allowing the planet to maintain a temperature suitable for sustaining life. However, 
anthropogenic increases in carbon dioxide emissions have intensified this 
?greenhouse effect? causing global warming that far exceeds natural levels. This 
phenomenon is linked to polar and arctic ice melt, sea level rise, redistributed 
precipitation patterns, increased regional flooding and increased drought (Socolow, 
2006). Additionally, massive losses in biodiversity are anticipated if rapid global 
climate change continues (Graziano, 2007).  
Though politicians, the public, and even some scientists were initially 
skeptical of the existence of mankind?s role in this warming process, leading 
scientists across the globe have stated that it is ?very likely? that current warming 
trends are attributed to human activities, especially the burning of fossil fuels for 
energy conversion (IPCC, 2007). Ice core samples demonstrate a rapid increase in the 
concentration of carbon dioxide in the atmosphere since pre-industrial times (IPCC, 
2007). Carbon dioxide concentrations, with which global surface temperature 
increases are correlated, have risen from a historic average of 290 parts per million 
(ppm) to beyond 379 ppm since the industrial revolution.  The rate of increase in the 
atmospheric carbon dioxide concentration is accelerating as the citizens of developing 
 
 5 
 
nations such as China and India adopt increasingly energy intensive lifestyles. Some 
predictions suggest that atmospheric levels of carbon dioxide above 450 ppm will 
result in ?catastrophic? results (Graziano, 2007).  Due to the current global emissions 
trajectory, reaching 450 ppm is a very real possibility. 
There is a clear relationship between the rise in carbon dioxide emissions and 
the increase in the earth?s temperature. Climate sensitivity, as defined by the IPCC, 
refers to this relationship and is specifically the change in global temperature caused 
by a two-fold increase in carbon dioxide emissions from preindustrial levels; from 
280 ppm to 560 ppm (IPCC, 2007). The term, climate sensitivity, can also be applied 
to the doubling of carbon dioxide equivalents. Current climate sensitivity estimates 
range from 2 ? 4.5?C, with 3?C being most likely (IPCC, 2007).  
Modeling climate sensitivity plays an important role in determining Earth?s 
future temperature rise. However, with the capability of oceans to absorb much of the 
heat, it is difficult to determine the precise temperature increase because there is a 
time lapse between when the gases are emitted and when their global warming 
potential stops impacting the global temperature (Dawson, 2009). Scientists have 
estimated that global temperatures could increase by another 1.5-2.0?C by 2050 if 
greenhouse gas emissions continue at current rates (IPCC, 2007). 
To understand how to mitigate the forces contributing to global warming, one 
must understand the factors increasing the atmospheric carbon dioxide concentration. 
A primary reason for the high levels of carbon dioxide emissions by the United States 
is its coal intensive electricity production infrastructure and high per capita energy 
consumption. On a worldwide scale, coal is responsible for 40 percent of all carbon 
 
 6 
 
dioxide emissions, but that is expected to rise to nearly 80 percent as development 
continues and the global population grows (Schrag, 2007). Though some developing 
nations are increasing their energy usage rapidly, the nations that contribute most to 
this atmospheric carbon dioxide are industrialized nations, particularly the United 
States, which is the largest industrialized carbon dioxide emitter (Socolow, 2006).  
Globally, approximately seven billion tons of carbon-based fossil fuels are 
burned every year (Socolow, 2006).  The consumption of these fuels produces 
immense quantities of greenhouse gases, which negatively impact both human health 
and the environment and are not accounted for in the price of the fuel itself.  These 
negative externalities?the consequences of fossil fuel consumption that consumers 
and producers do not pay for?are at the heart of the global climate change problem 
and are evidence that the fossil fuel pricing mechanism is not operating efficiently.   
Economic theory suggests that government intervention becomes necessary 
when society is faced with economic inefficiency.  To address the climate change 
externalities of fossil fuels, which are presently excluded from these fuels? market 
price, the government can take several actions to decrease carbon dioxide emissions.  
One method to alleviate the externalities involves altering energy demand, such as 
through increased product efficiency.  Increased efficiency decreases overall energy 
consumption of a product on a per-use basis.  Additionally, the price of fossil fuel 
consumption can be increased to account for negative externalities.  This can be 
accomplished through one of several policy tools, including a carbon tax or a carbon 
trading (cap-and-trade) scheme.  A carbon tax or cap-and-trade program establish a 
price for each ton of carbon dioxide emitted; by making it more expensive to 
 
 7 
 
consume fossil fuels, these policies effectively cause a decrease in demand for the 
fuels contributing to global warming.  Through efficiency measures and price 
increases, demand for fossil fuels can be decreased, minimizing greenhouse gas 
emissions.   
Government intervention to alter fossil fuel supplies, particularly by lowering 
the price of alternatives, shows promise in efforts to decrease carbon dioxide 
emissions.  Renewable energy requirements, such as Maryland?s Renewable Portfolio 
Standard, require a certain amount of renewable energy to be produced.  This new 
renewable energy capacity can decrease consumption of fossil fuels and resultant 
greenhouse gas emissions, increasing social welfare.  Renewable energy technology 
research, and development of emission abatement technologies such as capture and 
storage of anthropogenic carbon in the atmosphere, affects issues of energy supply 
and emissions.  Any improvement in substitute technologies for fossil fuels, including 
renewable energy, makes investments in alternative energy technologies more cost-
competitive.  As prices for renewable energy become comparable to fossil fuels, a 
shift towards the alternatives will be seen, eventually reducing greenhouse gas 
emissions.  Improvements in abatement and treatment technologies allow fossil fuel 
consumption to continue with decreased externalities, leading to decreased 
greenhouse gas concentrations in the atmosphere.  Though these approaches differ, all 
achieve the goal of increasing social welfare and economic efficiency through 
decreasing negative externalities.  In the case of greenhouse gas emissions, 
government intervention must occur, in any of these ways, to ensure maximum social 
welfare for ours and future generations. 
 
 8 
 
Climate Action Plan 
 
In response to calls from University stakeholders, the University of Maryland 
committed to the following discrete steps toward developing a carbon neutrality plan 
by signing the American College & University Presidents Climate Commitment 
(ACU PCC, 2007): 
square4  The University must perform an emission inventory to gauge the sources of 
carbon and greenhouse gases on the University campus, or that can be 
attributed to University activities. 
square4 Within two years of signing the document, the University must set a target 
date for achieving campus carbon neutrality and establish interim milestones 
for assessing progress. 
square4 The University must implement immediate actions toward reducing carbon 
emissions by choosing from a list of short-term actions published in the 
American College & University Presidents Climate Commitment 
square4 University curriculum must integrate sustainability concepts and make it part 
of the educational experience. 
square4 The University must make publicly available its action plan, inventory and 
interim progress. 
As part of this commitment, the University of Maryland created the Office of 
Sustainability in the summer of 2007.  The Office?s stated mission is to coordinate, 
promote, and track initiatives on campus that contribute to sustainability.  A campus-
wide greenhouse gas inventory tracked University emissions from 2002 to 2007 to 
 
 9 
 
serve as a tool for identifying sources of emissions.  The 2005 emissions were 
established as a baseline to provide a reference for future emission reductions.   
The University formed the Climate Action Plan Workgroup (CAP 
Workgroup) to develop plans to guide the emission reductions for the University in 
its pursuit of carbon neutrality.  The workgroup consists of over forty University 
staff, faculty, and student representatives who were believed to collectively represent 
the campus?s diverse needs (Ruth, 2008).  Gemstone Team Renewables? mission of 
aiding University efforts to acquire off-campus renewable energy led to their 
involvement on the workgroup.  Phillip Hannam, a co-author of this thesis, was 
appointed as a member of the CAP to represent the Gemstone team and to report 
findings of the team?s research to the workgroup for review. 
The campus carbon audit determined that the campus emitted a total of 
351,855 MtCO2 in 2005.  Of this number, 41% was produced during electricity 
generation and another 23% from steam use, as is presented in Table 1 below.  
Table 1: Major emission sources as presented on page 3 of the draft Climate Action Plan (CAP, 2009) 
Major Emission Sources FY 2002 FY 2003 FY 2004 FY 2005 FY 2006 FY 2007
     Electricity Consumption 54% 53% 43% 41% 40% 41%
     Steam Use 13% 14% 22% 23% 24% 24%
     Transportation 30% 29% 30% 31% 32% 31%
     Other Sources* 3% 4% 5% 5% 4% 4%
Total Emissions - MTCO2e 343,430 364,114 341,583 351,855     341,406 337,515        
*Agriculture, Solid waste, Refrigeration, Stationary sources Baseline Year
 
Campus emission sources are disaggregated into broad categories below in 
Figure 1.  Of the total campus emissions, 29% are attributable to purchased 
electricity, or approximately 102,038 MtCO2.  The large contribution of emissions 
 
 10 
 
from grid electricity is a result of the high carbon intensity of the grid electricity mix 
in the state of Maryland.   
Purchased 
Electricity
29%
Co-gen Electric
12%Co-Gen Steam
23%
Student 
Commuters
15%
Faculty/Staff 
Commuters
5%
Air Travel
9%
University Fleet
2%
Solid Waste
2%
Refrigeration
1%
Stationary 
Sources
1% Agriculture1%
 
Figure 1: Major sources of campus emissions in 2005 as presented on page 3 of the draft UMD Climate Action 
Plan (CAP, 2009) 
 
Figure 2, below, indicates that carbon-intensive coal comprises a larger share 
of the electricity grid than the national average.  According to the Maryland Power 
Plant Research Program in Figure 3, 56% of Maryland electricity comes from coal, 
which releases more carbon per MWh of electricity than any other conventional fossil 
fuel source (PPRP, 2006).   
The remainder of the state?s electricity grid is comprised of nuclear energy  at 
26%, natural gas at 2%, hydroelectric at 5%, petroleum at 6% and ?other? at 2%, 
which includes renewable electricity sources like wind and solar.  It is important to 
note that nuclear and hydropower electricity generation is carbon-free.  Natural gas 
has approximately half the carbon intensity of coal, but because the share of grid 
electricity from natural gas and petroleum sources is very small, coal?s contribution to 
the campus?s carbon emissions is significant.   
 
 11 
 
 
Figure 2: Grid electricity mix on the state, regional and national levels (PPRP, 2006)  
 
 
Figure 3: Grid electricity mix in the State of Maryland from the Energy Information Administration (PPRP, 
2006) 
 
Figure 1 shows that 12% of campus carbon emissions are released from the 
campus? natural gas co-generation power plant.  The contribution from this source is 
significant, though the electricity generated per carbon output is lower for the plant 
 
 12 
 
because of the lower carbon intensity of natural gas, high plant efficiency, and low 
transmission losses because of the close proximity to the campus (PPRP, 2006). An 
additional 23% of the carbon emissions are also attributed to the co-generation plant 
to generate steam which is used to heat campus buildings during winter and power 
absorption chillers to provide summer cooling.   
Table 2: Greenhouse gas reduction goals for the University of Maryland relative to State of Maryland Goals, as 
presented on page 6 of the draft UMD Climate Action Plan (CAP, 2009) 
 
From this greenhouse gas inventory, the campus committed to reduce 
emissions to zero by 2050, surpassing a stated goal by the State of Maryland to 
achieve an emission reduction of 90% below 2006 levels by 2050 (CAP, 2009).  A 
phased approach to campus emission reductions is shown in Table 2, indicating an 
emission reduction goal of 15% below 2005 levels by 2012, 50% by 2020, and 100% 
by 2050.  Though the implementation of each of these goals becomes more uncertain 
the further the projection into the future, an effort to map individual strategies for 
emission reduction is made below in Figure 4.  Each bar in the graph indicates a 
possible reduction below the baseline of 365,335 MtCO2, collectively achieving a 
100% reduction by 2050. 
 
 13 
 
365,335
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
MT
CO
2e
MT
CO
2e
Fiscal Year
Impacts of UMD CAP Mitigation Strategies Solid Waste
Virtual Meetings
Air Travel Insets
Hybrid Shuttle UM
Electric Vehicles
E85 Fleet
Faculty/Staff Telecommute
Improved commuter fuel 
efficiency
DOTS Parking Permit reduction
50% Nuclear
15% Large Hydro
Off-site Renewables
PVs on UM flat roofs
AV Williams PVs
Biofuel CHP
Virtual Servers
EPA computer Energy Star Settings
Thin Client
Behavior modification
ESCO retrofits
2005 total GHG emissions
 
Figure 4: Impacts of proposed mitigation strategies as presented on page 7 of the draft UMD Climate Action 
Plan (CAP, 2009) 
 
 
Mitigation strategies proposed by the Climate Action Plan Workgroup that 
achieve the largest emission reduction include ESCO retrofits, which result in greater 
building energy efficiency; behavioral modification that reduces per capita electricity 
consumption on campus; off-site renewable energy; low-carbon electricity purchasing 
that displaces high-carbon intensity fuels; parking permit reductions to reduce the 
number of student commuters; reductions in the generation of solid wastes; 
conversion of the co-gen plant to use bio-fuels instead of natural gas; implementation 
of photovoltaic electricity arrays on campus buildings; and offsets of air travel 
emissions, amongst several other reduction possibilities.   
The mitigation strategy which is implemented most readily with the greatest 
impact is off-site renewable energy purchasing, which accounts for 25,000 MtCO2 
 
 14 
 
reduction by 2010.  The emission reduction from this share steadily increases, 
ultimately to account for nearly a 50,000 MtCO2 annual reduction by 2050.   
Though no clearly defined methodology is laid out in the Climate Action Plan 
for specific off-site renewable energy sources, the purchase of wind energy through a 
University of Maryland commissioned site may be able to meet the ambitious CAP 
goals and serves as the subject of this thesis. 
 
Renewable Energies 
Introduction to Renewable Energy 
Reducing the university?s carbon dioxide emissions to achieve and maintain 
carbon neutrality requires an energy generation shift from non-renewable fossil fuels 
to renewable energy systems. Renewable energy resources occur naturally, 
regenerate, and are mainly derived from solar energy.  However, because of 
regeneration and resupply rates, renewable energy use is limited per unit time. 
Renewable energy resources include biomass, hydropower, geothermal, solar, wind, 
ocean thermal, wave action, and tidal action.  
According the Energy Information Administration, about 90% of the energy 
consumed in the U.S. was from renewable energy resources in 1850. Today, the U.S. 
is heavily reliant on non-renewable fossil fuels for generating energy, namely coal, 
natural gas, and oil. In 2004, about 6% of all energy consumed and about 9% of total 
electricity production was from renewable energy resources (Energy Information 
Administration [EIA], 2007). One primary reason that renewable energy resources 
 
 15 
 
have been used less than non-renewable energy sources is because of costs; 
renewable energy is more expensive per unit of energy supplied. However, with 
escalation in the price of oil and climate change concerns, governments around the 
world are increasingly supporting legislation and incentives for renewable energy 
development and commercialization (United Nations Environment Programme 
[UNEP], 2007).  
Ways to Acquire Renewable Energy 
There are four primary ways in which the University of Maryland can acquire 
renewable energy for its electricity needs.  The first is to acquire renewable energy 
from its local supplier or another local energy supplier owning or operating renewable 
energy facilities.   The University can buy electricity produced by a renewable energy 
facility directly from this supplier.   
The second method is through renewable energy certificates (RECs), 
sometimes referred to as ?green tags.?  RECs do not require the University to have 
access to a local renewable energy supplier, which may make them an attractive 
option.  RECs are sold separately from commodity electricity.  Instead, a premium is 
paid to regional or non-regional companies that own renewable energy production 
facilities or invest money in new renewable energy infrastructure (US Department of 
Energy [USDOE], 2006).  While RECs are not a direct method of purchasing 
electricity from renewable sources, they theoretically will generate the same 
environmental benefits of a direct purchase and the net carbon impact on the 
atmosphere would be the same (USDOE, 2006).  
 
 16 
 
A third method is energy purchase through a contract with an energy utility 
constructing a renewable energy facility.  For example, the Catholic University of 
America purchased the output of a 1.5 MW wind turbine for five years by funding its 
construction (Catholic University of America, 2002).  For many universities and 
companies, this method of renewable energy purchase has been cost effective; it fixes 
the price of the renewable energy for the contract?s period, reducing the institution?s 
vulnerability to price fluctuations in the fossil fuel energy market (Putnam and 
Philips, 2006).  
The fourth option for acquiring renewable energy for use by the University is 
to physically implement a renewable energy system on campus.  Solar photovoltaic 
arrays are one commonly used renewable energy system that might reduce the 
University?s energy demand from the grid.  Globally, relatively few countries have 
installed large-scale photovoltaic capacity bases, instead relying on localized, on-site, 
small-scale generation (Montana Green Power, 2006).  Significant potential may exist 
in constructing photovoltaic solar arrays on the roofs of campus buildings and as 
shading devices for parking lots.  In the United States, thirty-nine states allow owners 
of small-scale photovoltaic systems to supply excess power to the electrical grid, a 
potential source of revenue from a small-scale system.  By the end of 2004, use of 
photovoltaics to generate energy grew by 45% globally in a year (BP Global, 2006). 
Japan, the US, and Germany, all of which are leading manufacturers of photovoltaic 
cells, accounted for 94% of this total growth in photovoltaic system integrations. 
The following sections discuss the common forms of renewable energy and 
technical feasibility for use by the University of Maryland. 
 
 17 
 
Solar Power 
Solar energy uses photovoltaic cells to convert the Sun?s radiation into 
electricity through the photoelectric effect. In this process, light from the sun strikes 
the photovoltaic cell, energizing electrons and releasing them from their crystalline 
lattice.  These freed electrons produce a voltage and current, providing power as they 
are transmitted through wires to an energy load or user. Typical commercially 
available single-crystal silicon photovoltaic cells operate with an efficiency of 
fourteen to sixteen percent (National Renewable Energy Laboratory, 2006). 
Researchers estimate that it would take 60 thousand square kilometers of photovoltaic 
cells to meet the energy requirements of the United States, given current efficiencies.  
A typical photovoltaic module contributes a net increase of 30 to 45 g/kWh of 
carbon dioxide emissions to the atmosphere, primarily through the production of the 
silicon, glass and mounting structure that comprises a photovoltaic cell.  These 
emissions are small compared to the net release of carbon dioxide from conventional 
fossil fuel sources, such as the 1000 g/kWh from coal, not including the carbon 
dioxide required to mine and transport the fuel.  Due to the lower CO2 emissions 
associated with solar energy, photovoltaic technology has the potential to become an 
economically viable and environmentally sustainable source of energy, especially 
when considering new carbon dioxide regulations and taxes the US government is 
contemplating (U.S. Department of Energy, 2006).  However, until such regulations 
become law, it is unlikely that solar cells will be competitive with the low upfront 
costs of fossil fuels. Average costs for solar energy currently range from $0.23 to 
$0.35/ kWh for commercial usage (Energy Information Administration, 2008).  In 
 
 18 
 
order for solar panels to be cost effective, they need to be installed in areas with 
abundant sunlight such as southern California or Arizona.  Maryland lacks levels of 
sunlight that would maximize the efficiency of solar panels. Due to the high costs 
needed for photovoltaic installation and maintenance, solar power generation would 
not be the most cost-effective option at the University of Maryland. 
Geothermal Power 
Another form of renewable energy is geothermal power. Geothermal power 
plants utilize the energy in steam and hot water from underground sources to drive 
electric generators. The water is returned to the ground after use. The first of three 
types of geothermal power plants passes existing steam through power turbines. The 
second type of plant depressurizes hot water into steam, which then powers the 
turbines. The third type of plant uses the hot water to convert a second liquid, 
typically a fluid like isobutane that has a lower boiling point temperature, into steam 
which runs through a closed system to power the turbines. Geothermal energy is most 
easily utilized at plate tectonic fault lines. 
In the United States, geothermal energy potential exists around the Ring of 
Fire, which refers to an area in the Pacific Ocean that experiences a great deal of 
tectonic activity; states such as California, Oregon, and Alaska possess this potential. 
Alaska opened a geothermal plant in 2006, and power plants have been built in 
California, as well. This technology is also utilized in other countries that are located 
along the Ring of Fire, such as Iceland and Indonesia (Fahey). Hundreds of millions 
of dollars have been invested over the past several years to expand this alternative 
source of energy. In order to harvest geothermal energy, a contractor must first drill 
 
 19 
 
for underground steam and water, while also constructing infrastructure in the area. 
There are also a number of geothermal units that are built on the Geysers in northern 
California.  
Geothermal heat pumps use the constant temperature of the Earth below its 
surface to provide heating, cooling, and hot water.  This technology could be utilized 
in the state of Maryland. However, geothermal electricity production does not have 
much potential on the east coast and is not a viable option for the University of 
Maryland. 
Wind Power 
Wind energy is a converted form of solar energy that is more cost effective to 
capture.  The Sun's radiation heats different parts of the earth at different rates, most 
notably during the day and night, but also when different surfaces (i.e. water and 
land) absorb or reflect the Sun?s rays at different rates.  Denser, cooler air from 
higher-pressure areas moves towards lower pressure areas with warmer air, creating 
wind currents. The kinetic energy stored in the air?s movement can be converted to 
mechanical work through the use of windmills (American Wind Energy Association 
[AWEA], 2006).  However, wind energy potential varies across the globe, is 
intermittent, and does not necessarily coincide with areas of the highest electricity 
demand.  
North America contains an estimated 14,000 TWh of land-based wind energy 
resources. According to the Energy Information Agency, the United States consumed 
3,970 TWh of electricity in 2004.  Based on these estimations, it is theoretically 
possible to power current US electrical consumption through only land-based wind 
 
 20 
 
systems (Boyle, 2004).  Land-based wind sites can produce electricity at levelized 
costs of approximately $0.04 to $0.06/kWh, depending on the wind quality of the site.  
These costs are roughly comparable to costs for coal or natural gas fired plants, which 
are widely reported as being about $0.05/kWh (Offshore Wind Collaborative 
Organizing Group, 2005).  Wind pricing is primarily an upfront cost, while coal and 
natural gas plants must pay for fuel and transport.  Because wind is free, wind power 
pricing is not subject to fuel price fluctuations.  These cost estimations suggest that 
although wind and conventional, non-renewable energies are approximately equal in 
price, wind has smaller environmental impact compared to coal or other fossil fuels.   
Ideal locations for wind power generation include high altitudes and wide, 
open lands. Such locations include hilltops and areas near shorelines (AWEA, 2006).  
Offshore wind energy seems to be an even more promising energy resource than 
land-based wind systems since offshore wind travels at higher speeds, has less wind 
shear, experiences lower levels of turbulence due to a lack of air-disrupting 
geographical features, and is typically closer to major coastal urban centers than land-
based wind systems. Wind energy and offshore wind potential are explained in 
greater detail later in the literature review section.  
Biomass 
Electricity generated from biomass is another form of renewable energy. 
Examples of biomass energy include ethanol, biodiesel, waste pyrolysis, and methane 
capture from landfills. Biomass energy was popular in the past when trees were 
burned for power and this form is still used in some parts of the world (Tester, 2005). 
Energy from biomass can be converted and generated into useful energy by direct 
 
 21 
 
combustion. However, it is often necessary to convert the biomass to a liquid or 
gaseous state first through thermo-chemical, physical-chemical or bio-chemical 
processes. (Kaltschmitt, 2007).  
 Biomass power is effective in reducing the pollutants that cause acid rain, 
such as sulfur dioxide (SO2). Since most forms of biomass contain very little amounts 
of SO2, not much SO2 is produced in the process. Coal on the other hand usually 
contains up to 5% sulfur. Biomass can be mixed with other fuels, such as coal, and by 
doing so the amount of SO2 emitted from a biomass co-fired coal power plant can be 
reduced greatly compared to burning just coal (Tester 2005). Also, in tests done at 
coal-fired power plants using biomass, it was observed that NOx emissions could be 
cut in half when using biomass, compared to coal-only operations (US DOE, 2008).  
 Energy from biomass can be a carbon neutral form of power generation, since 
it recycles carbon through the carbon cycle. Growing ?energy crops? can also reduce 
CO2 emissions. These crops create carbon sinks that sequester the carbon 
underground in their roots (US DOE Planet Power, 2008). 
 Electricity produced from methane resulting from the decomposition of 
biomass also has high potential and is already used around the world. Waste from 
landfills and animal manure produce methane, a powerful greenhouse gas, when they 
decompose. This methane can be captured and used to generate heat or electricity (US 
DOE, 2008).  
 Because biomass renewable energy comes from commonly found biological 
materials, it is readily available. It can be used as an energy source in many places 
 
 22 
 
around the world. Biomass energy can be also be generated from many different 
processes, which makes it versatile in its possible applications (Tester 2005).  
Compared to fossil and nuclear-based energies, biomass energy has a higher 
price and lower efficiency (Tester 2005). It has a lower energy density (the amount of 
energy produced per volume or mass of product) than coal or petroleum, though 
proprietary torrefaction processes for increasing energy density of biomass pellets are 
promising. Energy efficiency is lower when biomass has moisture. Drying the 
material requires energy, contributing to the higher price (Tester 2005). Biological 
fuel sources are often grown specifically to be used for energy conversion, which uses 
large amounts of land and resources, such as fertilizer, to ensure sufficient growth 
(Tester, 2005).  Therefore, certain processes that are typically undergone when 
utilizing biomass, such as growing and drying the fuel, result in a substantially higher 
cost to produce biomass electricity when compared to other forms of power. 
Nonetheless, the utilization of biomass to produce electricity has been expanded 
recently in neighboring states, such as Pennsylvania, where Governor Edward 
Rendell ?recently announced the state will fund 49 clean energy and biofuel projects? 
(Voegele, 2009). Of these 49 projects, five that received funding are biomass-to-
energy projects. However, these projects produce substantially less electricity than the 
University of Maryland requires and as a result, may not be well suited for in-depth 
University consideration.   
 
Hydropower 
 
 23 
 
 Hydropower is renewable energy generated by converting potential energy 
contained the water as it flows from a higher elevation to a lower one. About 6% of 
the US?s electricity came from hydropower as of 2007 (Energy Information 
Administration, 2009). 
There are three ways this energy can be converted: impoundment, diversion or 
run-of-river, and pump storage. Impoundment hydropower uses a dam to create a 
reservoir of water from which potential energy is extracted. Diversion, or run-of-
river, hydropower utilizes energy from flowing water. Pump storage hydropower is 
when water is pumped from either a lower basin to a higher plain or vice versa, 
depending on the demand for energy (Tester, 2005).  
 There are many positive aspects of using hydropower. During the electricity 
production process, there are no net CO2, NOx, and SOx emissions. Hydropower has 
high conversion efficiency to electricity, often over 80% (Tester, 2005) high power 
generation capacity. A large installation such as the Three Gorges Dam in China, can 
produce up to 17,000 MWe (Tester, 2005). In addition to high efficiency, hydropower 
plants have low operating and maintenance costs associated with them. Also, the 
construction of these projects can improve the local economy.  The construction of 
the Hoover Dam provided hundreds of jobs in the U.S. during an economic 
depression (Tester, 2005). Dams can be used to control floods and store water to be 
used for agricultural irrigation or consumption (Tester, 2005). 
 However, hydropower is not without negative attributes. One main issue is 
that large volumes of water must be stored for the system to work, which can lead to 
habitat loss, especially of aquatic species. A change in river flow can also affect 
 
 24 
 
surrounding habitats by changing fish migration patterns or depleting oxygen. 
Hydropower plants can displace people native to the area because the structures can 
be very large. Silt can build up and the lifetime of the structure can be shortened if it 
is not constructed properly. Since the technology depends on the amount of water 
present, the amount of energy generated varies?rainfall and snowfall affect how much 
electricity can be produced. Even though there are low operating and maintenance 
cost, there are high initial capital costs associated with constructing a hydropower 
structure. Getting enough money to start a plant can be difficult. Also, even though 
the facility can produce electricity for many decades, it takes many years to construct 
large projects.   
Currently, hydropower is the largest producer of renewable electricity with a 
capacity of over 600 GWe (Tester, 2005). Canada and the US are the leading 
producers of hydropower. The best way to expand to hydropower is to utilize existing 
dams. The initial costs associated with implementing a project are high.   
According to a United States Department of Energy assessment, Maryland 
only has one hydropower facility, which has a capacity of 20 MW.  The undeveloped 
capacity in the state is projected as 0.10 MW (DOE, 1998).  With such limited 
utilization of hydropower in Maryland and minimal potential for future hydropower 
production, hydropower can be deemed an unsuitable renewable energy option for the 
University of Maryland.  Maryland possesses natural resources with greater power 
potential than its hydropower resources, and those options ought to be pursued. 
 
Table 3: Summary of Renewable Energy Technologies 
Technology Advantages Disadvantages 
Solar: Photovoltaic ? Lower net release of CO2  ? High installation and 
 
 25 
 
? Relatively low price of 
energy production 
maintenance costs 
? Not enough sunlight to 
make cost effective 
purchase 
Solar: Solar 
Thermal 
? Decreased utility bill 
? Short payback period 
? Inexpensive 
? Limited range of 
application 
? Does not produce 
electricity as end product 
Solar: 
Concentrating Solar 
Power (CSP) 
? Ability for large scale 
energy production  
? Can construct hybrid 
facilities 
? Can store energy and 
produce electricity without 
sunlight later 
? Possible soil 
contamination from HTF 
leak 
? Large amount of land 
required 
? High price of power 
generation  
Geothermal ? Little CO2 release ? Little potential in 
Maryland 
Wind  ? Low price of power 
generation 
? Reduces greenhouse gas 
emissions 
? High potential for 
development 
? Conserves water 
? Reduces hazardous waste 
? Dependent on wind speed 
? Initial costs of project 
higher than for fossil fuel 
project 
? Social problems, such as 
local resistance 
? Turbines could impact 
wildlife 
Biomass ? Reduces pollutants that 
cause acid rain 
? Can be mixed with other 
fuels 
? Reduces methane and 
odors 
? Readily available 
? High price of production  
? Lower efficiency 
compared to fossil fuels  
? Lower energy efficiency 
? Better for small scale 
production 
Hydropower ? CO2, NOx, SOx and 
hydrocarbons not 
introduced into the 
atmosphere 
? High conversion efficiency 
to electricity 
? High conversion efficiency 
to electricity 
? Low operation and 
maintenance costs 
? Large volumes of water 
must be stored  which can 
lead to habitat loss 
? Energy generation 
dependent on amount of 
water present 
? High initial capital costs 
? Construction time of 
mega-sized project long 
 
Kite Power 
 
 26 
 
Kite power is a developing form of renewable energy, which utilizes kites to 
harvest high-altitude wind. Kites are able to reap the benefits of high-altitude wind, 
which blows at a higher speed than lower-altitude wind, and also blows more 
consistently, thereby making it a more dependable source of energy. Moreover, ?It 
turns out that the power available from wind is tied to the cube of its speed, so the 
higher of these altitudes is a far more attractive option, giving almost four times as 
much power as that available at turbine height? (Brooks, 2008). In November 2007, 
Makani Power, a kite power developer, received a $10 million investment from 
Google (Brooks, 2008). While there seems to be a great deal of interest in this 
developing technology, kite power would not be available for use by the University of 
Maryland in the immediate future. 
Future projections and obstacles to Renewable Energy Implementation 
There is a much greater potential of using renewable energy than is currently 
being utilized. It has been argued that global energy demands can be met using 
existing renewable energy technologies.  Cost and energy distribution remain the 
primary obstacles.  Government policies could make it more feasible to utilize 
renewable energies by subsidizing the technology and creating investment incentives, 
thereby making it more competitive with fossil fuels (Kobos, 2006; Nuehoff, 2005).  
Current state governmental policies in Maryland give incentives for institutions and 
individuals to utilize renewable energy. Some basic legislative goals and strategies for 
increasing the usage of renewable energy were established in 2001. The legislation 
focuses on increasing sustainable energy usage by setting a goal of relying on six 
percent green energy for state-owned building operation (with less than half of the 
 
 27 
 
green energy produced coming from the incineration of municipal solid waste). The 
legislation, however, does not set up a specific timetable for attaining this goal.  Also, 
the legislation outlines the creation of the Maryland Green Buildings Council, which 
will strive to increase green energy usage in Maryland (Maryland State Senate, 2001).   
Maryland also offers loans to non-profit organizations, such as schools or 
other state properties that will help finance the purchase of renewable energy. The 
program started with $3.2 million in funding for such programs, and currently funds 
approximately $1.5 million in renewable energy projects each fiscal year (Maryland 
Energy Administration, 1989).  There are also federal policies that encourage the use 
of renewable energy in Maryland?s state-owned buildings. For example, renewable 
energy investment by a state institution could help the state meet air quality 
requirements established by the Clean Air Act (Putnam and Philips, 2006).  The 
federal government also provides monetary incentives for non-profit organizations 
such as the University of Maryland under the Renewable Energy Production 
Incentive (REPI). Under this legislation, the Federal Government will pay non-profit 
organizations to produce renewable energy at a rate of $0.015/kWh (USDOE, 2006), 
which is further discussed in the Methodology Section. 
 Regardless of whether or not more federal, state and regional governmental 
policies are implemented to make renewable energy more affordable, green energy 
prices will continue to decline as the technology continues to improve and 
competitiveness in the market grows (Kobos, 2006; Nuehoff, 2005; Owen, 2004). 
Kobos developed a method for calculating future costs for renewable energies based 
on a price trajectory model of the declining prices of renewable energy technologies. 
 
 28 
 
This model may be used to determine approximate costs for renewable energy 
systems for an institution (Kobos, 2006).  A study by Owen includes a formula for 
calculating the approximate costs of externalities associated with various renewable 
energy technologies in different environments, which would be useful in assessments 
of potential system usage on a university campus (Owen, 2004). 
Renewable Energy Implementation at the University of Maryland 
A critical step in any renewable energy viability study is determining how to 
finance it.  Studies suggest that technologies that have minimal impact on the 
surrounding landscape and wildlife are more likely to be accepted, and individuals are 
willing to pay extra for those environmentally sound technologies (Bergmann, 2006). 
The Bergmann study includes an economic model to calculate the public?s 
willingness to pay for various green energy technologies (Bergmann, 2006). This 
model could be utilized to calculate how willing students would be to pay to support 
renewable energy. Many universities use student fees to fund renewable energy 
development on their campuses by either using a part of the current fees or by 
increasing fees to offset costs for the renewable energy (Putnam and Philips, 2006).  
According to one viability study, 74% of student voters at the University of North 
Carolina, Chapel Hill agreed to a $4.00 increase in fees per semester to fund 
renewable energy on campus (Putnam and Philips, 2006).  Besides student fees, there 
are grants and loans offered by the state of Maryland as financial incentives for 
renewable energy (Putnam and Philips, 2006) that could make renewable energy a 
feasible option at a public academic institution. 
 
 29 
 
 Because of its current fossil energy use, the University of Maryland has 
significant potential for reducing carbon dioxide emissions, though the metrics for 
execution of these goals, community support, and financial limitations have not yet 
been quantified.  To accomplish this task, this research will need to consider the 
different methods of obtaining renewable energy, determine what the University is 
willing to pay and how to finance the energy adjustment, and determine how feasible 
each method is in terms of cost and benefits. Many options exist for acquiring 
renewable energy for use by the University, and the costs of each of these options will 
likely decrease in the future, though the costs may seem prohibitive now.  Also, there 
is an increasing amount of legislation that will facilitate renewable energy purchase, 
and incentives and grants put forth by the state of Maryland could make renewable 
energy a more viable option.  Other Universities have succeeded in bringing 
renewable energy into their energy mix, financing these ventures through fees, grants, 
and other sources of revenue.  Finally, there are many models available that can be 
used to calculate costs of renewable energies versus their long-term environmental, 
social and economic benefits through consideration of externalities.  These 
advancements in renewable energy technology, policy and marketing will facilitate 
analysis of the University of Maryland?s renewable energy options. 
Wind Power Generation 
Physical Attributes  
After considering the many possible ways that the university could supply 
itself with renewable energy from an off-site location, wind energy stood out as the 
 
 30 
 
preferred method. According to a study done by researchers at Stanford University, if 
only 20% of the world?s Class 3 or greater wind power was captured, it would satisfy 
100% of the world?s energy needs (Archer & Jacobson, 2005). However, climate 
change is affecting wind patterns. The change in temperature can, ?produce focused 
regions for deep cumulonimbus convection,? or different wind patterns. 
Thunderstorm patterns would also be affected since they depend on specific types of 
wind to develop (Cotton, 2007). A change in wind speeds means a change in the 
amount of wind generation. This could be a problem if the wind speed becomes too 
low or too high for the specific turbines to function effectively.  
While there are certainly obstacles to harnessing this power potential, the 
study helps put into perspective the actual amount of wind power that could be 
developed globally. At present, there is a huge potential for wind energy to be 
harvested throughout the United States. According to the National Renewable Energy 
Laboratory (NREL), the US currently has the capacity to generate 25,170 MW of 
electricity from wind, which would be approximately the amount needed to power 7 
million American homes. In 2008, the US became the world?s leader in wind energy, 
having installed 8,358 MW of capacity in 2008, and growing its wind capacity by 
50% ("US Wind Industry Takes Global Lead.", 2009). This rapid growth within the 
industry highlights the ability of wind to provide a clean, renewable source of energy 
for this country, while also creating growth within the economy. According to the 
American Wind Energy Association (AWEA), wind energy contributed over $17 
billion dollars into the US economy and employed about 85,000 workers. 
 
 31 
 
The wind energy now produced eliminates nearly 44 billion tons of carbon 
emissions, the equivalent of removing 7 million cars from the nation?s roads ("US 
Wind Industry Takes Global Lead," 2009). The Department of Energy?s Wind and 
Hydropower Technologies Program (WHTP) states that wind power within the 
United states is ?abundant?, and that the advantages of developing wind power 
include reducing greenhouse gas emissions, conserving water, lowering natural gas 
prices, expanding manufacturing, and generating local revenues ("Advantages and 
Disadvantages of Wind Energy", 2008). At the same time, the WHTP notes that wind 
power is one of the least-expensive renewable technologies available, costing 
between 4 and 6 cents per kilo-watt hour ("Advantages and Disadvantages of Wind 
Energy", 2008). All of these advantages in concert have ensured that wind energy is 
the fastest-growing energy source in the world.  
Within the state of Maryland, DOE?s Wind Program and NREL have found 
that there is wind potential for utility-scale production, generally in areas with above 
either Class 3 or Class 4 winds. In addition, several areas within the state contain 
good to excellent wind speeds ("Maryland Wind Resource Map", 2008).  
 
 32 
 
 
Figure 5: Maryland Wind Resources at 50 meters 
 
One area that is particularly well endowed with wind is the mountainous 
region in the western part of the state, with streaks of wind that are classed as 
outstanding (Figure 5). As of August 2008, several companies have begun to develop 
wind projects throughout Maryland, and Governor Martin O?Malley has pledged his 
support for exploring commercial-scale wind power, both on-shore and off-shore 
(Shay, 2008). Given DOE and NREL?s wind map findings, the state of Maryland has 
many untapped wind energy resources that can be developed.  
However, while there is clearly a huge potential for wind energy development 
around the world, in the United States, and the state of Maryland, there are some 
disadvantages to this technology. Wind energy must compete with established 
 
 33 
 
technologies on a cost basis, which greatly depends on the amount of wind available 
in that geographic location. Additionally, the start-up costs for wind power are higher 
than fossil fuel plants, though the price has been declining over the years ("Wind 
Energy FAQs for Consumers", 2008). 
Perhaps the greatest obstacle to wind energy development is the wind itself. 
Since 
 the wind speed in a given area is intermittent, there will not be a constant flow of 
energy but rather spikes in the production and power may not be available when it is 
needed ("Advantages and Disadvantages of Wind Energy", 2008). Also, the best sites 
for the wind are often in remote areas, which are hard to reach or far from where the 
electricity would be used. Lastly, there are social concerns that must be addressed 
within the community regarding the impact the wind development has on the people 
and the environment in the area.  
While wind energy is clean and can be a profitable investment, there are start-
up, infrastructure and maintenance costs involved in the development of this 
technology. The cost of wind turbines depends on the type and size of turbine that is 
selected, which in turn is dependent on the amount of wind at the site, the size of the 
site, and the efficiency of the turbine at that particular wind speed ("Wind Energy 
FAQs for Consumers", 2008). Next, the turbines need to be connected to the energy 
grid. This can lead to additional costs beyond simply that of connectivity, if the 
turbines are sited in an area that is far from any power lines ("Wind Energy FAQs for 
Consumers", 2008). For example, additional costs will be incurred if the developers 
must site and construct new power lines so that they can reach the grid. Last, an 
 
 34 
 
inverter is needed to convert the turbine power output so that it is compatible with the 
power grid ("Wind Energy FAQs for Consumers", 2008).  
Social/Infrastructure 
While numerical values can fairly easily be assigned to the environmental and 
economic impact of constructing a wind turbine, it is far more difficult to measure the 
social impact. This can pose quite a problem for developers, because despite its 
difficult-to-measure nature, social concerns can often be of the greatest importance to 
the community and stakeholders in the project.  
  These effects can include a wide range of aspects that affect the community, 
but mainly fall into two categories: visual and noise. In the visual category, one major 
problem is referred to as ?NIMBYism?, which is a commonly used tern to reference   
?Not In My Backyard? complaints within the wind farm?s view field ("Advantages 
and Disadvantages of Wind Energy", 2008).  Local residents may be concerned that 
the wind turbines will decrease the natural beauty of their property, effectively 
lowering their quality of life and land values. They do not wish to see large structures 
out of their windows, particularly since the areas that have the best wind  are often 
situated in mountainous areas where land is prized for its scenery.  Other people 
become concerned that their home may lie in the area of land that falls within the 
shadow of the turbines (Asmus, 2008). Overall, the majority of these visual problems 
occur because people feel that the turbines do not belong in the landscape of these 
typically rural areas.  
 There are a few noise-related complaints that wind developers must address. 
First, turbines naturally make a certain amount of noise, which has the potential to 
 
 35 
 
disturb both the people and the wildlife living in the area. Also, when the wind farm 
is being developed, there will be construction in the area. This may be noisy and 
disruptive to residents, particularly if they are located in a rural area that is typically 
very quiet. When choosing a site for wind turbines, it is important to consider these 
factors, among others, because the community opposition can prevent completion of 
the project. 
Environmental Regulations and Legislation  
The environmental regulations and legislation that have already been 
implemented in the United States represent a critical factor that will undoubtedly 
impact wind development, just as social concerns do. Due to the relatively limited 
implementation of wind energy projects in the United States, the federal government 
lacks a regulatory structure tailored to wind development projects. Regulation of wind 
projects is therefore based upon existing environmental statutes and as well as newer 
policies. 
The existing environmental statutes carry the most weight in the legal realm. 
One statute that applies to all projects is the National Environmental Policy Act 
(NEPA). NEPA requires the completion of an Environmental Impact Statement (EIS) 
when a federal agency may impact environmental health. Federal agencies must 
complete an EIS when they are undertaking, permitting, funding or licensing a 
project. An EIS assesses the projected impacts of a proposed action and its 
alternatives, and it often involves comment and analysis by federal agencies beyond 
the acting agency (42 U.S.C. 4371). Any federal entity engaged in a wind 
development project, through funding, permitting, licensing, or taking direct actions, 
 
 36 
 
must complete an EIS by law, which will undoubtedly delay the project and may even 
eliminate the possibility of its existence. 
The Endangered Species Act of 1973 (ESA) must also be considered when 
assessing a site for potential wind development. The ESA is a strict liability statute 
designed to protect species from extinction due to human activities. To discourage 
actions that could potentially harm species, the ESA penalizes violators financially. 
Through the ESA, the U.S. Fish and Wildlife Service manages a list of threatened and 
endangered species. The listing of a species is based solely on science, with no 
consideration given to the costs of species protection. Once a species is listed, it 
becomes illegal for an individual to ?take? an organism of that class. Section 9 of the 
ESA defines ?take? to mean ??to harass, harm, pursue, hunt, shoot, wound, kill, 
trap, capture, or collect or attempt to engage in any such conduct.? ?Harm? was later 
defined as any act altering a species? behavior, breeding habits, or ability to survive in 
general (16 U.S.C. 1531-1544). The effects of this legislation on wind energy could 
be far-reaching. If a listed species is damaged by a wind turbine, that turbine?s owner 
would be held financially liable for a taking. Before wind turbines can be sited, an 
extensive analysis of listed species in the region must be undertaken. 
Two other acts related to wildlife must be considered during the construction 
of wind turbines: the Migratory Bird Treaty Act (MBTA) and the Bald and Golden 
Eagle Protection Act. Both of these are strict liability statutes, as is the case with the 
Endangered Species Act, and have equally noble goals. As is the case with the ESA, 
the MBTA makes the taking or possession of a protected species illegal.  
 
 37 
 
?Unless and except as permitted by regulations ? it shall be unlawful at any 
time, by any means, or in any manner to pursue, hunt, take, capture, kill ? 
possess, offer for sale, sell ? purchase ? ship, export, import ? transport or 
cause to be transported? any migratory bird, any part, nest, or eggs of any 
such bird ? [The Act] prohibits the taking, killing, possession, transportation, 
and importation of migratory birds, their eggs, parts, and nests, except when 
specifically authorized by the Department of the Interior.? (16 U.S.C. 703-
712) 
Individuals and organizations can be found guilty of a misdemeanor or felony under 
the MBTA, which can result in financial penalties and even imprisonment. Under the 
Bald and Golden Eagle Protection Act, any individual found guilty of the above 
offenses is subject to charges for a felony and possible jail time (16 U.S.C. 668-
668d).  
 Under the Endangered Species Act, the Migratory Bird Treaty Act, and the 
Bald and Golden Eagle Protection Act, the element of intent is not needed for 
conviction. An individual or organization may unknowingly jeopardize the safety of a 
species, and if any individual organisms are harmed, legal action may come to pass. 
Again, this emphasizes the importance of pre-construction biological assessments of 
potential wind development sites. 
The policies that have been formulated as the popularity of wind energy has 
grown within the United States span several government agencies, and seek to 
address environmental concerns arising from the construction of wind turbines. The 
Department of Energy (DOE) created a Wind and Hydropower Technologies 
 
 38 
 
Program to address information and research needs related to wind power. In an 
assessment undertaken by DOE, private consulting firms, and the American Wind 
Energy Association, the feasibility of 20 percent of U.S. electricity coming from wind 
power by 2030 was studied. The report found there to be significant challenges to this 
goal, but believed that given significant dedication of resources, it could be done 
(U.S. Department of Energy, 2008).  
The prospect of wind energy development on federally-owned lands falls 
under the authority of the Bureau of Land Management (BLM). The BLM underwent 
a Programmatic Environmental Impact Statement (EIS) in 2005 to determine the 
effects of wind energy development on BLM lands in the western United States. 
Since then, BLM has issued a guidance policy on best management practices to 
mitigate damage to wildlife and habitat through the construction of wind turbines. 
The Lands and Realty Management Program housed in the BLM has permitted 192 
right-of-ways for wind energy production on public lands to date. The total installed 
capacity of 25 authorizations is 327 megawatts (U.S. Department of Interior, 2009). 
To ensure the protection of wildlife, the U.S. Fish and Wildlife Service 
(USFWS) created the Service?s Wind Turbine Siting Working Group. After much 
research and analysis, the Group produced a guidance document titled ?Interim 
Guidelines to Avoid and Minimize Wildlife Impacts from Wind Turbines.? The 
guidelines offer a systematic process for assessing wildlife vulnerability within wind 
resource areas (U.S. Fish and Wildlife Service, 2007). However, these guidelines 
posed by USFWS are not legally binding, and wind developers need not comply.  
 
 39 
 
Agencies less directly involved in the environmental implications of wind 
turbine siting have produced statements on wind energy. The Department of Defense 
(DOD) released a document articulating their stance on wind development. DOD 
stated that it did not oppose any wind development, so long as military readiness is 
not compromised (Pease, 2007). A policy letter produced jointly by DOD and the 
Department of Homeland Security had previously announced this position 
(Kingsmore, 2006). 
The State of Maryland and Wind Energy 
The government of Maryland has proven to be committed to renewable 
energy production and consumption over the last decade.  In 2004, Senate Bill 869, 
entitled ?Electricity Regulation - Renewable Energy Portfolio Standard and Credit 
Trading - Maryland Renewable Energy Fund,? required electricity providers to 
incorporate renewable energy into the mixes they provide (Maryland State Senate, 
2004).  This bill was signed into law by Republican Governor Robert Ehrlich and 
served as the first major step in Maryland creating its renewable energy future.   
In the 2007 legislative session, Maryland Governor Martin O?Malley signed 
into law the EmPower Maryland Initiative. This initiative pledges that Maryland will 
reduce energy consumption 15 percent by 2015. In addition, O?Malley vowed that the 
state?s energy consumption would consist of 20 percent renewable energy by 2022 
(Office of the Governor ?Governor O?Malley Urges??, 2008). Both of these 
legislative moves under the O?Malley-Brown Administration indicate a progressive 
shift in state leadership and intent to increase the prevalence of renewable energy in 
Maryland. 
 
 40 
 
Though Maryland?s leadership has indicated interest in utilizing renewable 
energy, a policy decision made in early 2008 barred commercial wind development 
on state lands. This decision was made following a contentious public review process 
over a proposed wind energy project in the Savage River State Forest.  
U.S. Wind Force, a private company, had plans to build roughly 100 wind 
turbines on the land, which would require the clearing of approximately 500 acres of 
forest. The revenue generated by this wind energy project was projected to be $30 
million in twenty years.  
The Savage River Project was received with hostility by many citizens of 
Garrett County, where the Savage River State Forest is located, exemplifying the 
effect of social concerns on the construction of turbines.  Eighty-three percent of 
public comments received by the Maryland Department of Natural Resources (DNR), 
the agency charged with overseeing state forests and parks, were against the proposed 
commercial wind project. Many of the objections centered around the NIMBYism 
argument, or that the aesthetic value of land would be lost following the construction 
of a wind farm, along with the land?s existence as a public trust (Piotrowski, 2008). 
The minority that was found to support the proposed project cited the fact that state 
forests are currently harvested for timber. These individuals believed that since state 
forests are currently being utilized for commercial purposes, a wind development 
would be minimally different (Tidwell, 2007). Traditional arguments on climate 
change and a need for decreased greenhouse gas emissions were also noted. 
Additionally, Maryland has an increasing demand for energy, which may lead to 
 
 41 
 
rolling blackouts as soon as 2011 unless more capacity can be added, according to 
PJM (Office of the Governor ?Maryland Strategic Energy Investment Fund?, 2008).  
O?Malley?s decision has no implications on wind energy projects on private 
lands, and multiple proposals on private lands are being assessed in western Maryland 
currently. DNR is planning to consider small-scale, non-commercial renewable 
energy projects in the future, where it may be appropriate on state lands.  
Environmental Benefits of Wind Energy 
Wind energy stands to greatly decrease the United States? dependence on 
fossil fuels, thereby significantly decreasing the greenhouse gas emissions that 
contribute to the acceleration of global warming. Aside from the clear benefits 
derived here, wind energy decreases the emissions of other air pollutants and 
hazardous wastes. In general, the utilization of wind turbines for power generation 
can significantly decrease air pollution and hazardous waste problems in the United 
States (U.S. Environmental Protection Agency, 2000). 
Environmental Drawbacks of Wind Energy 
 The major environmental concern that arises with wind power generation is of 
its effect on wildlife. These impacts can be either direct or indirect, but both can pose 
serious threats to the survival and health of various species. However, large holes 
remain in the literature on these topics. Before the implications of wind power on 
organism health can be truly understood, more research must be conducted. 
 
 
 42 
 
 Impacts on Birds and Bats 
Despite being pitched as an environmentally-friendly alternative to fossil 
fuels, wind turbines have been deemed responsible for several negative 
environmental impacts. Opponents of wind energy often highlight the threat that wind 
turbines pose to bats and birds, implicating it with environmental regulations, such as 
the ones previously discussed. Turbines tend to occupy air space through which avian 
species and bats pass. With turbine tip speeds reaching up to 180 mph, birds and bats 
often cannot detect the blade. Some scientists attribute bird deaths to a bird?s inability 
to register the movement of a blade when in close proximity to it. According to 
University of Maryland Professor William Hodos, ?motion smear? may play a 
substantial role in bird deaths at the blades of wind turbines. ?Motion smear? occurs 
when objects close to the eyes pass over the retina at such a speed that only a clear 
blur registers. ?The eye cannot process the information once the bird gets close 
enough to rapidly moving blades,? Hodos said. ?They become transparent? (Cohn, 
2008). 
 The United States? first wind farm, Altamont Pass Wind Farm, raised 
suspicions that wind turbines threatened avian species when a large number of dead 
birds were found around the site (Cohn, 2008). Since then, extensive studies of bird 
deaths in connection with wind energy facilities has been conducted.  
 Experts estimate that three to eleven birds are killed by each wind turbine in a 
given year; when one takes into account the number of turbines in the United States, 
this amounts to about 100,000 birds (Cohn, 2008). Each year, millions of birds die 
from other human causes. Dale Strickland, president of Western Ecosystems 
 
 43 
 
Technology, Inc. claims these deaths to be roughly 50 million from communication 
tower collisions, 80 million from car collisions, and 3.5 million from window 
collisions (Cohn, 2008).  
 The phenomenon behind wind turbine related bat fatalities differs from that of 
bird fatalities, according to some researchers. Research biologist Paul Cryan believes 
bats are drawn to blade rotation and often chase after the blades (Cohn, 2008). Some 
biologists believe that the killed bats? echolocation capabilities failed them and made 
them unable to sense the blades in their path. Others suggest that high-pitched tones 
may be emitted by turbines, which attract nearby bats (Williams, 2004). The cause of 
death of these bats seems to be less simple than the clear collision-related deaths of 
birds. Nearly 70 percent of deceased bats at wind turbine sites seem to have no 
broken bones or signs of collision. Instead, these organisms appear ruptured, 
indicating that they may have been caught in the wind vortex created by the wind 
farm (Cohn, 2008). Still others suggest that the animals may be getting caught in 
wind shear associated with the turning turbines. 
 Obtaining an accurate estimate of the number of bat deaths attributed to wind 
turbines proves to be tougher than estimating bird deaths. Biologist Albert Manville 
from the U.S. Fish and Wildlife Service believes only a small portion of bats killed by 
wind turbines are accounted for due to the small size of bats relative to plants, other 
organisms, and the environment as a whole (Williams, 2004).  
A 2007 report from the Wildlife Society estimated that over 50 bats are killed 
per turbine each year, which is nearly five times the number of birds killed by the 
same technology (Cohn, 2008). A synthesis of 21 studies of 19 wind energy 
 
 44 
 
operations in North America showed that bat fatality estimates were highest in forests 
of the eastern United States and lowest in the Rocky Mountains (Arnett, 2008). Of all 
bat deaths from wind farms in the United States, three-fourths come from either the 
eastern red, hoary, or silver-haired bat variety (Cohn, 2008). Migratory bats seem 
most susceptible to turbine-related death (Arnett, 2008). August and September prove 
to be the deadliest months for bats, which are migrating during this period (Cohn, 
2008). Another analysis suggested that the peak in bat death by turbine surfaced in 
midsummer to fall (Arnett, 2008). Thus, these two independent studies indicate a 
nearly identical timeframe for the peak number of bat fatalities.  
Though a significant number of bat and bird deaths seem connected to wind 
energy facilities, progress in wind farm siting and assessment processes creates a 
better environment for all organisms in the area. Though Altamont Pass Wind Farm, 
built in the 1980s, has been responsible for the death of at least 22,000 birds, minimal 
pre-construction studies were conducted to determine the suitability of the site for 
wind power generation. Had such research been conducted, developers would have 
recognized that the proposed wind farm lay on a key migratory route traveled by 
golden eagles and other raptor species (Whittelsey, 2007).  
As the United States has become more accustomed to wind development, 
greater efforts to protect avian organisms have been taken. The wind industry has 
shifted wind turbine design in a new direction. Modern wind turbines strive for 
minimal bird perching sites, while also trying to deter birds and bats from entering the 
area (Cohn, 2008).  
 
 45 
 
Others groups demand greater efforts by the wind industry to protect birds and 
bats from wind turbines. Some want the federal government, namely the U.S. Fish 
and Wildlife Service, to regulate the wind power facility siting process as well as the 
operation process (Cohn, 2008). Some worry that construction wildlife studies are 
insufficient in length due to industry demands for power production now. Though the 
U.S. Fish and Wildlife Service suggested a minimum study period of two years, the 
suggestion is nonbinding, and the agency lacks enforcement power unless a species 
listed as threatened or endangered per the Endangered Species Act is at risk 
(Williams, 2004).  
Despite growing efforts to study bat and bird fatalities around wind turbines, 
additional information is needed. Post-construction studies of all wind turbines in the 
United States are essential to grasping the relationship between wind power 
production and wildlife fatalities. Biologists must dedicate more time to 
understanding what attracts birds and bats to turbines, so that efforts can be made to 
minimize this attraction. In addition, more needs to be understood about bat and bird 
migration so that future wind power facilities can avoid high-traffic routes.  
Habitat Fragmentation 
An additional environmental impact of wind turbines, which receives 
substantially less attention than the deaths of birds and bats, is habitat fragmentation. 
When a wind farm is constructed, extensive infrastructure is required for the 
construction of the facility, as well as its continued operation and maintenance. This 
infrastructure includes transmission lines and roads, which break apart natural 
 
 46 
 
landscapes and ecological corridors into discontinuous chunks, thereby decreasing 
organisms? abilities to access diverse food, habitat, and breeding opportunities. 
 One study suggests that wind turbines make habitats unusable by bird species. 
In that study, bird densities in areas without turbines were higher than those near 
turbines. The authors believed the turbines or a characteristic of such deterred the 
birds from locating near this project (Kuvlesky, 2007).  
 When transmission lines arise in an area, they prove to be a significant threat 
to birds and bats, because electrical transmission lines possess electrocution potential 
for these species (Kuvlesky, 2007). When near bodies of water, transmission lines 
pose a great threat to swans, geese, ducks, and cranes. In more land-locked regions, 
raptor species and passerines face the greatest threat for electrocution (Kuvlesky, 
2007). 
 Roads pose a considerable threat to wildlife at wind turbine sites as well. Loss 
of biodiversity occurs through habitat fragmentation and is inextricably linked with 
road growth. When a habitat is split into smaller pieces, quality and quantity of the 
habitat diminishes along with biodiversity (Kuvlesky, 2007). Aside from decreasing 
the flow of native organisms, roads tend to encourage an influx of invasive species to 
the wind turbine site. Invasive plants have been known to flourish around roads, 
allowing them to eventually overtake native species (Kuvlesky, 2007).  
 To understand the full effects of wind farm induced habitat fragmentation, 
more studies are necessary. Wind power facilities should be studied over the course 
of their lifetime, including both pre- and post-construction phases. By examining the 
 
 47 
 
evolution of a site over time, the consequences of habitat fragmentation can be more 
accurately gauged. 
 
Promising Practices in Wind Power:  Wind Power and Cellular Phone Towers 
As renewable energies become increasingly prevalent across the globe, it is 
probable that they will become more incorporated into the everyday lives of people. 
This phenomenon has already occurred to an extent, in that some cellular phone 
towers have recently begun to harness wind energy. In October 2008, Ericsson 
released a ?wind-powered Tower Tube,? which is designed to ?improve energy 
efficiency, reduce environmental impacts and lower the costs of mobile networks for 
operators.? This Tower Tube currently utilizes small turbine blades attached to a 
cellular phone tower, which produce electricity and reduce the outside electrical 
consumption of the phone tower. Thus, it is clear that there could be technologies in 
the future that may condense the needs of a wind farm with those of cellular phone 
towers.  
Offshore Wind Development 
Offshore Wind Projects in the United States 
Ideal locations for wind power generation include those with high altitudes 
and wide, open lands. Such locations include tops of hills and areas near shorelines 
(AWEA, 2006). The Great Plain states of the United States Midwest contain most of 
the U.S.?s inland wind resources (AWEA, 2006). Unfortunately, much of Maryland 
lacks the geographical features and terrain favorable for on-shore wind power 
 
 48 
 
generation. However, offshore wind energy could be a promising energy resource.  
Offshore wind travels at higher speeds, have less wind shear, experience lower levels 
of turbulence due to a lack of air-disrupting geographical features, and tend to be 
closer to major coastal urban centers than land-based wind programs.  
There have been a number of proposals to construct offshore wind turbines 
along the Atlantic Coast of the United States. In 2003, the New York-based company 
Winergy LLC applied to for federal permits to build thousands of steel and fiberglass 
wind turbines at several sites off the stretch of land along the Atlantic shoreline 
known as the Mid-Atlantic Bight, which extends from the Massachusetts to North 
Carolina. The company identified possible sites to build offshore wind farms based 
on criteria including water depth, nearby fishing activity, and wind resources. 
Winergy planned to build 2,000 turbines off the coasts Delaware, Maryland, and 
Virginia, and began to complete pre-application notices for federal permits from the 
Army Corps of Engineers (Alliance for the Chesapeake Bay, 2003). However, their 
progress for obtaining permission to begin the Maryland and Virginia projects was 
slowed by the federal government?s lack of solid rules and regulations for issuing 
zoning permits. In the meantime, the company has diverted its energy to obtaining a 
permit to construct a wind farm off the coast of Long Island, New York, and along 
the offshore regions of either Rehoboth Beach or Bethany Beach in Delaware 
(Fahrenthold, 2007).  
Winergy is not the only company that has been interested in developing a 
wind farm off the coast of Delaware?s popular beach attractions. An offshore wind 
project is being considered by the Delaware state government and community as an 
 
 49 
 
alternative to constructing additional power plants that would burn coal or natural gas 
to produce more power in the state, after electricity prices there spiked in 2006. The 
idea was welcomed in Delaware after legislators called for more power production to 
deal with the state?s recent electricity price surge. Concerned about the rising 
electricity rates, the Delaware General Assembly passed House Bill 6 in 2006. The 
bill called for the local utility company Delmarva Power & Light to direct and 
contract with new power resources that will guarantee stable prices for electricity. In 
addition, the state legislature passed Senate Bill 74, a Renewable Portfolio Standard 
(RPS) requiring that 20 percent of the state's electricity to come from renewable 
sources by the year 2019 (Bluewater Wind, 2009). 
Bluewater Wind, a New Jersey-based company, proposed to build the offshore 
wind project approximately 11.5 nautical miles off the Rehoboth Beach shore. Their 
proposal called for a 150 turbine offshore wind farm that would generate a 450 MW 
nameplate capacity. Each turbine would extend 263 feet high and contain three 150-
foot blades. Bluewater Wind estimated that such a project could be completed by 
2012 (Bluewater Wind, 2009). Bluewater Wind has built a land-based wind farm in 
Montana, but has never built an offshore farm (Fahrenthold, 2007). In their 2006 bid 
submission, Bluewater Wind proposed three possible sites with specifications and 
pricing for each. Two of the proposed facilities were in the Atlantic Ocean and would 
produce a nameplate capacity of 600 MW using 200 Vestas V90-3.0 turbines. 
Calculations by University of Delaware scientists showed that the wind resource in 
this area would have a capacity factor of about 40 percent, yielding an average of 240 
MW for a 600 MW nameplate capacity facility (Dhanju, Whitaker, & Kempton, 
 
 50 
 
2007). The third site proposed by Bluewater Wind would have been located in the 
Delaware Bay. The facility would have been slightly smaller than the ones proposed 
for the Atlantic Ocean along with a slightly lower capacity factor because its location 
(Dhanju, Whitaker, & Kempton, 2007). 
Bluewater Wind?s proposal was faced with expected opposition by 
representatives of the coal and gas plants, who also are competing for the bid from the 
Delaware state government. They claim that the wind farm would be an unreliable 
source of energy, since the wind is intermittent and does not blow all the time, 
potentially forcing the state to buy power from elsewhere. On the other hand, they 
claimed that there would be no such problems with coal, because the U.S. has an 
abundant supply of it. Some of the Delaware officials and staff members 
recommended a combination of a smaller wind farm and a gas plant that could supply 
power when the wind does not blow (Fahrenthold, 2007).  
Other obstacles remain for the proposed Delaware offshore wind project. The 
biggest concern is that Delmarva Power, the utility company that operates in the 
Rehoboth Beach and Bethany Beach vicinities, would need to agree to buy electricity 
from the wind farm. The company publicly announced that it was against all of the 
proposals, and that none were cost-effective. Also, some local business owners were 
concerned that the windmills along the shoreline view might drive tourists away. 
However, it seems that the wind farm has generated significant public support, 
including that from the Delaware Audubon Society, which stated that the windmills 
could probably be built in a way that will not pose a serious threat to migrating birds 
(Fahrenthold, 2007).  
 
 51 
 
A 2007 study by Dhanju, Whitaker, and Kempton at the University of 
Delaware College of Marine and Earth Studies research group affirmed that Delaware 
has immense wind energy resource. In their study, the researchers estimated an 
average annual output of over 7,000 MW in the waters off the Delaware coast out to a 
depth of 50 meters (Dhanju, Whitaker, & Kempton, 2007). This is over five times the 
current electrical consumption of the entire state of Delaware. At current local 
electricity market prices, this would produce just over $2 billion per year in revenue 
(Dhanju, Whitaker, & Kempton, 2007). In addition to an analysis on the state?s 
offshore wind resource potential, the study also included a statewide survey which 
indicated that Delaware residents are strongly in favor of offshore wind power as a 
future energy source for the state. In the survey, 949 Delaware residents were asked 
to select from a variety of sources to help the state increase its energy supply. Over 90 
percent of respondents supported an offshore wind option in which wind turbines as 
tall as 40-story buildings would be erected off the coast to generate electricity, even if 
the option were to add between $1 and $30 per month to their electric bills. Fewer 
than 10 percent of those surveyed opted for an expansion of coal or natural gas power 
at current prices (Dhanju, Whitaker, & Kempton, 2007). Marine policy scientists 
Jeremy Firestone and Willett Kempton, both on the faculty of the University of 
Delaware?s College of Marine and Earth Studies, and doctoral student Andrew 
Krueger, suggested that the overwhelming majority of Delaware residents they 
surveyed would rather pay more to support offshore wind turbines than support coal 
or natural gas sources at lower prices. 
 
 52 
 
The policy scientists who conducted the study were themselves surprised at 
their results. In 2004 and 2005, Kempton and Firestone had conducted two surveys of 
residents of Cape Cod, Massachusetts about the controversial Cape Wind project, in 
which Energy Management Incorporated proposed to establish a wind farm of 130 
wind turbines in the Nantucket Sound. Those results showed that ?a plurality of Cape 
Cod residents was opposed to that project" (Bryant, 2007). However, they found that 
nearly 78 percent of Delawareans statewide would ?give a project identical to Cape 
Wind a thumbs-up if it were located in Delaware,? and only 4 percent would oppose 
such an idea, with the remainder unsure (Bryant, 2007). With such great support, 
Firestone said that, ?based on our results, Delaware could become the Denmark of the 
United States when it comes to relying on offshore wind power as a major energy 
source? (Bryant, 2007).  
The researchers proposed a number of factors that could be contributing to the 
Delawareans? positive outlook toward offshore wind power. One reason may be 
because the offshore wind proposal includes a ?well-financed opposition,? namely the 
local utility company Delmarva Power & Light. Local residents may believe that 
Delmarva is primarily responsible for the recent electricity rate spikes, making them 
eager to welcome a proposal from an energy company with a new concept and 
fundamentally different approach to solving the problem. Additionally, Delaware 
residents may generally be more aware of and concerned about the rate of climate 
change, global warming, and its health effects. While the study did not do any actual 
research on the reason for Delawareans? support of wind energy, they plan to 
 
 53 
 
determine these factors through additional research in the near future (Dhanju, 
Whitaker, & Kempton, 2007; Bryant, 2007).  
The University of Delaware survey also included questions to determine any 
potential effects on beach visitation by in-state residents. The survey asked 
participants how their vacation plans would be affected if a large, 500-turbine wind 
farm were installed six miles off the state's coast. While ?88.6 percent would continue 
to go to the same beach they last went to in Delaware even if a large wind farm were 
constructed offshore there,? 5.6 percent said they would switch to another beach in 
Delaware, another 3.5 percent said they would go to a beach outside Delaware, and 
2.4 percent said they would visit no beach at all. Another question asked if 
respondents would be inclined at least once to visit a Delaware beach that they did 
not typically frequent if a wind farm was visible offshore. A high response of 84 
percent said ?yes,? suggesting that the wind turbines would actually draw visitors 
instead of driving them away (Dhanju, Whitaker, & Kempton, 2007). 
In June 2008, Bluewater Wind and Delmarva Power and Light signed a Power 
Purchase Agreement (PPA) under the order of the Delaware Public Service 
Commission. The PPA states that Bluewater Wind agrees to build a 150 turbine, 450 
MW wind facility in the Atlantic Ocean approximately 12.5 miles off the Rehoboth 
Beach shore. Delmarva Power agrees to buy up to 300 MW at any one time. 
Delmarva ratepayers will pay a cost of 10.56 cents per kWh for energy and capacity 
in 2007 dollars (Delaware Public Service Commission, 2008). Delmarva has also 
stated that it will be purchasing RECs associated with its energy purchases (Firestone, 
2008). According to the state of Delaware?s independent consultant, the ?typical 1000 
 
 54 
 
kWh/month Delmarva household? would see an estimated rate impact on its energy 
bill of $6.46 per month (New Energy Opportunities, La Capra Associates, Merrimack 
Energy Group, & McCauley Lyman, 2007). The rate impact is defined as a 
comparison of the known Bluewater price for wind power and the estimated future 
market price of other power, which is subject to uncertainties in the cost of natural 
gas and carbon allowances (Firestone, 2008).  
Bluewater Wind has also had its sights set on building an offshore wind park 
in waters off the Maryland state coastline. The company has issued a proposal to 
build an offshore wind farm near Ocean City, Maryland?s most popular beach 
vacation destination. In the fall of 2007, Bluewater Wind began discussing its 
proposal to put 150 turbines approximately 11.5 miles off the coast of Ocean City 
with Governor Martin O?Malley and other Maryland state officials. If approved, the 
proposed wind farm could provide enough electrical power for 110,000 local homes 
(Associated Press, 2007).  
From the Ocean City beach, the turbines would look like toothpicks far out at 
sea. However, some local residents and business owners have voiced apprehension 
about how their visibility could negatively affect business and tourism to the area. In 
contrast, supporters of the project emphasize the importance of using alternative 
forms of energy to retard the accelerating rate of global warming, which could 
directly affect coastal areas like Ocean City because of predicted rising sea levels 
over the next few decades. Mike Tidwell of the Chesapeake Climate Action Network 
replied to the locals? worries over the potential aesthetic damage the wind turbines 
might cause by saying, "if you say you don't want a dime-size windmill in the 
 
 55 
 
distance, we'll have to abandon all the hotels and all the houses and all the restaurants 
because the ocean?s coming to swallow them? (Associated Press, 2007). 
The project has yet to formally contact other agencies, including the Maryland 
Public Service Commission and the Department of Natural Resources, which are 
critical deciding bodies for the approval of the project. Governor O?Malley?s 
spokesman Rick Abbruzzese said that after preliminary talks with Bluewater about 
the proposal, he found the project to be ?an intriguing idea,? but wanted to obtain 
additional information, admitting that Maryland ?needs to find ways to produce 
alternative forms of energy? (Associated Press, 2007). 
These proposals by wind development companies, to construct offshore wind 
farms of up to several hundred turbines along the coastal lines of the mid-Atlantic 
states, suggest that there is great promise in this region for producing significant 
amounts of energy. As the study by Dhanju, Whitaker, and Kempton shows, the small 
coastal state of Delaware has the potential to generate an annual output of over 5,000 
MW, which would offset more than four times the state?s total average electrical 
consumption. With similar terrain and geographical characteristics along its coastal 
regions, the neighboring state of Maryland could undoubtedly produce a considerable 
amount of wind energy, which would help offset the state?s current electricity use and 
carbon footprint. Maryland is not only a bigger state by measure of its total 
geographical area, but also contains a larger proportion of the Atlantic continental 
shelf, naturally giving it greater potential to host more wind turbines for the proposed 
projects along its eastern shore.  
 
 56 
 
Offshore Wind Projects Outside the United States  
Several countries in Europe have had notable success in the development of 
offshore wind power in the last decade. Europe currently is the global leader in 
offshore wind power development, owing largely to rich wind resources and the 
shallow waters of the North Sea and Baltic Sea. With eight operational wind farms, 
and 590 MW of nameplate capacity installed, the United Kingdom is the world?s 
leading generator of offshore wind power. Denmark comes in at a close second with 
seven currently operational farms, and 410 MW of nameplate capacity. In 1991, the 
first offshore wind farm, Vindeby, was constructed in Denmark with a capacity of 
just 5 MW. As of 2009, the Lynn and Inner Dowsing Wind Farm off the coast of 
Lincolnshire, in the United Kingdom, is the world?s largest offshore wind farm. It 
consists of 54 turbines and generates a capacity of 194 MW, enough to power 
130,000 homes (Byrne & Houlsby, 2003). There are five more wind farms currently 
under construction in the United Kingdom, which are all planned to be completed by 
2011. One of them, called The Greater Gabbard, will consist of up to 140 turbines to 
be built 23 km off the coast of Suffolk, and will generate a planned capacity of 500 
MW (Scottish and Southern Energy, 2008). Another offshore wind project called the 
Thanet is scheduled to be completed in 2009, will be composed of 100 turbines, and 
will generate a planned capacity of 300 MW, enough to supply approximately 
240,000 homes per year (Warwick Energy, 2008). 
In Denmark, the Nysted Wind Farm is the largest in the country, and was 
previously the largest offshore wind farm in the world before the Lynn and Inner 
Dowsing Wind Farm was completed in 2008. The wind farm is a joint Danish-
 
 57 
 
Swedish venture built in 2003, with 72 turbines and a total capacity of 166 MW. Its 
annual production is approximately 595 million kWh, which is equivalent to the 
electricity consumption of 145,000 Danish homes, and is projected to save up to 
500,000 tons of carbon dioxide emissions (Dong Energy, Vattenfall, Danish Energy 
Authority, & Danish Forest and Nature Agency, 2006). Another large wind farm built 
in Denmark is the Horns Rev offshore wind farm in the North Sea, which was 
constructed by DONG Energy, formerly known as Elsam. The wind park consists of 
80 turbine units, and generates a total capacity of 160 MW. In 2005, the wind farm 
was bought by Vattenfall, which continues to operate it today (Dong Energy, 
Vattenfall, Danish Energy Authority, & Danish Forest and Nature Agency, 2006). A 
2006 report from Danish energy and environmental government agencies said that 
based on an eight-year study of Horns Rev and Nysted, both wind farms are expected 
to double in size in the coming years. In an evaluation of the two wind farms? impact 
on the aquatic ecosystem, including birds, fish, and seals found on the surrounding 
seabed, the International Advisory Panel of Experts on Marine Ecology had a positive 
reaction to the projects, and concluded that they ?operate in harmony with the 
surrounding environment? (Hansen, 2006). 
 
 
 
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Indirect Renewable Energy Delivery 
Renewable Energy Credits 
Renewable Energy Credits (RECs) constitute one of the simpler ways that the 
University of Maryland can works towards fulfilling its Presidents? Climate 
Commitment promise of inevitably reaching complete carbon neutrality. The Center 
for Resource Solutions? Green-e Certification Program represents the ??nation?s 
leading independent renewable energy certification and verification program,? 
according to the program?s website. In fact, as of 2005 Green-e certified more than 53 
percent of the renewable energy sold in the United States. RECs, by the Green-e 
standards, are created when electricity is generated by facilities utilizing renewable 
energy. A REC ??represents all of the environmental attributes or benefits of a 
specific quantity of renewable generation, namely the benefits that everyone receives 
when conventional fuels?.are displaced.? Essentially, when a REC is purchased, the 
buyer is paying to offset the environmental impact of the non-renewable energy 
source that is otherwise being paid for and utilized.  
It is clear that RECs can be used to offset the University?s carbon emissions 
that result from purchasing other types of power, such as coal, oil, or gas, thereby 
working towards the University?s stated goal of carbon neutrality. While there are 
currently no RECs produced in the state of Maryland, if the University were to 
generate renewable energy through the construction of wind turbines, the Green-e 
Certification and Verification Process could be followed in order to get this energy 
certified as renewable. These RECs that the University?s wind turbines would 
generate would offset the carbon emissions produced at the actual College Park 
 
 59 
 
campus. Indeed, the purchase of RECs has been utilized by a number of large 
universities throughout the United States in an effort to offset these institutions? 
carbon emissions. 
Limitations of Using Renewable Energy Credits 
There is debate about how to regulate RECs effectively and efficiently within 
the environmental community. Of the two primary viewpoints taken, ?the more 
conservative camp believes that regulatory regimes should never allow RECs to be 
sold separately from the energy that generated them? (Castro, 2008). This group 
reasons that if the benefits of RECs are separated from their energy source, large 
consumers of electricity who are forced to adhere to environmental regulations would 
be paying this premium, essentially, for the right to pollute. The opposing viewpoint 
is that ?renewable energy development will benefit more if RECs and their associated 
energy are allowed to be sold separately? (Castro, 2008). This group suggests that this 
market-based exchange of the benefits that RECs represent will encourage greater 
investment in renewable energy generation, which will lead to an increase in overall 
renewable energy development. However, there has been some controversy regarding 
the success and effectiveness of RECs in the United States, as some firms have 
recently purchased and traded them for profit (Aston, 2007). While RECs do 
represent the offset of carbon emissions, it is less clear if their trade has any negative 
impacts upon their purchaser. Moreover, in 2006 ?Clean Air-Cool Planet, a nonprofit 
global warming group, released ?A Consumer?s Guide to Retail Offset Providers,? a 
report that questioned the use of RECs as carbon offsets? (Barcott, 2007). This report 
suggested that RECs, by definition, were unable to represent the offset of carbon 
 
 60 
 
emissions, and should not be traded as a commodity of this nature. Thus, although it 
is still generally accepted that RECs can serve to offset carbon emissions at 
institutions like the University of Maryland, more direct options would be preferred 
over RECs. 
In general, there are a number of issues that threaten the expansion of the 
generation of renewable energy and RECs. Federal renewable energy tax credits will 
expire this year, meaning that companies that supply renewable energy will no longer 
be able to take advantage of these benefits. This is problematic since such tax credits 
help to make renewable energy more affordable (it is currently more expensive to 
generate wind energy, in particular, compared to energy generated by a coal or oil 
fueled power plant). According to Vic Abate, General Electric vice president, 
?Without [tax credits]? new wind power installations could drop by 90 percent.? It is 
therefore clear that these tax credits provide major incentive to build wind turbines, 
which supply renewable energy. In addition, it is projected that if tax credits no 
longer exist, this ?would lead to the loss of $19 billion in investments and 116,000 
job opportunities through 2009.? Therefore, the financial return on investing in wind 
energy is significantly reduced through the elimination of tax credits, and few 
investors would be willing to offer capital for the reduced returns that would exist 
without the tax credits. Moreover, investment in wind energy seems to have positive 
externalities other than the environmentally-friendly nature of energy production, 
especially the generation of jobs. Thus, it appears that if the United States intends to 
transition towards more sustainable energy practices, it must provide tax credits to 
these developing energy industries, particularly the wind power industry. In any case, 
 
 61 
 
the University of Maryland could work to operate its own wind turbines, which could 
generate RECs and offset the University?s carbon output. If the University?s goal is 
not necessarily to profit, but instead to offset carbon emissions in a financially viable 
way, then the construction of wind turbines could be quite practical.  
In response to these concerns of wind energy producers, Congressional Global 
Warming committee members have suggested that the primary goal should be to 
create permanent tax credits, which would quell any uncertainty in the marketplace 
and encourage greater investment of capital in renewable energy. Then-ranking 
committee member James Sensenbrenner (Wisconsin ? Republican) has ?most 
favored the research and development and production tax credits.? If the United 
States government commits to promoting the development of renewable energy, then 
the nation would certainly benefit from this aid, transitioning quicker towards the 
sustainable generation of electricity. Accordingly, those companies that develop 
renewable energy would be able to offer RECs at more competitive prices, thereby 
benefitting institutions such as the University of Maryland. Furthermore, if the 
University did, indeed, elect to construct its own turbines, the University could take 
advantage of any applicable incentives. Hence, if greater investment in renewable 
energies is encouraged through legislation, it should only become easier for the 
University to work towards its goal of carbon neutrality. 
Recent legislation passed in October 2008 will likely impact the REC market 
in the aforementioned manner. Passed as part of the United States financial bail-out 
plan, ?this legislation provides an extension of the existing renewable tax credits, but 
also includes provisions for new tax incentives as well as bonds and the relaxation of 
 
 62 
 
regulations for industry and changes for major utilities (Conlin, 2008).? Lawmakers 
have elected to extend the current tax credits for generating renewable energies, 
which should encourage renewable energy expansion and may make it more 
advantageous for the University to look towards generating its own renewable energy. 
This recent legislation suggests that the United States government is committed to 
encouraging renewable energy development, and the University of Maryland may be 
able to benefit as a result. 
Universities and Renewable Energy 
Many other institutions have already started consuming renewable energy on 
their campuses. There are two main ways that universities use renewable energy. 
There are institutions that are buying Renewable Energy Credits (RECs), and there 
are institutions that have projects to construct renewable energy infrastructure. 
Several universities throughout the United States, both public and private, 
have already begun to utilize RECs to offset their carbon emissions. Northwestern 
University, which is a private university of 14,000 undergraduate students in Illinois, 
currently purchases 40 million kWh of RECs annually, according to the U.S. 
Department of Energy. This purchase represents 20 percent of Northwestern?s annual 
electrical consumption, and dramatically reduces their carbon footprint. Similarly, the 
University of Colorado at Boulder purchases 8.8 million kWh of RECs each year, 
which provides electricity for three student-run buildings on campus. Finally, Duke 
University purchases 54 million kWh of RECs annually, and the Fuqua School of 
Business at Duke offsets 100 percent of its electrical consumption through the 
purchase of RECs. Thus, a number of institutions similar to the University of 
 
 63 
 
Maryland in size, as well as a geographically local school in Duke, have begun to 
utilize RECs to offset their electrical purchases. It would not be unreasonable to think 
that the University can begin to do the same, which would work towards the 
University?s stated goal of carbon neutrality. 
 
RECs in Maryland 
It is clear that RECs can be used to offset the University?s carbon emissions 
that result from purchasing other types of power, such as coal, oil, or gas, thereby 
working towards the University?s stated goal of carbon neutrality. While there are 
currently no Green-e certified RECs produced in the state of Maryland, if the 
University were to generate renewable energy through the construction of wind 
turbines, the Green-e Certification and Verification Process could be followed in 
order to get this energy certified as renewable. There are currently RECs being 
produced in Maryland at the Conowingo Hydroelectric Project that have been 
certified by the Low Impact Hydropower Institute (LIHI), which focuses on certifying 
the electricity produced by low-impact hydropower dams. However, while it is 
important to note that there are RECs currently being produced in the state of 
Maryland, the LIHI certification would not be applicable if the University attempted 
to produce RECs through the use of wind turbines. The RECs that the University?s 
wind turbines would generate through the use of wind turbines would offset the 
carbon emissions produced at the actual College Park campus, and could be certified 
by Green-e. Indeed, the purchase of RECs has been utilized by a number of large 
universities throughout the United States in an effort to offset these institutions? 
carbon emissions. 
 
 64 
 
Additionally, a 2008 study by the National Wildlife Federation cites a number 
of ?Exemplary Schools for Environmental or Sustainable Goal-Setting.? Schools in 
this category that are large, state institutions similar to the University of Maryland 
include Michigan State University, University of Arizona, University of Minnesota, 
University of South Carolina, and the University of Virginia. According to the report, 
?these schools have taken the lead in setting and reviewing goals for conservation and 
environmental or sustainability issues? (National wildlife Federation, 2008). George 
Washington University, which is close to the University of Maryland, also falls into 
this category. As leaders in this area, these institutions have demonstrated the ability 
to establish goals for campus sustainability that are both feasible and environmentally 
friendly. The University of Maryland should undoubtedly make an effort to establish 
a clear, effective plan of this nature, because it will signal an even stronger 
commitment to its stated goals of sustainability. Moreover, this study identifies a 
number of schools similar to the University that ?are noteworthy for having both 
recruiting programs for students and for offering interdisciplinary degree programs in 
environmental or sustainability studies? (National Wildlife Federation, 2008). In 
particular, Arizona State University, University of California at Los Angeles, 
University of Illinois, and the University of Wisconsin are identified, in addition to 
other institutions that were mentioned previously. It is obvious that programs of this 
nature would contribute to creating an environmentally-conscious student body at an 
institution, since students already possessing an advanced interest in the environment 
or sustainability would be attracted to these programs. The University of Maryland, 
through programs of this nature, would be more likely to have students support 
 
 65 
 
environmental or sustainable initiatives, and would also have more students on 
campus obtain education about these subjects. This would certainly be beneficial for 
the University as it works towards the fulfillment of the Presidents? Climate 
Commitment.  
The University of Maryland is identified by this 2008 study as an ?Exemplary 
School for On-Campus Clean Energy Sources and CoGeneration? (National Wildlife 
Federation, 2008). The University meets these criteria due to its on-campus combined 
heat and power (CHP) plant, which generates heat and electricity. Thus, the study 
does suggest that the University utilizes some technologies that are environmentally 
beneficial, although there is clearly the opportunity for expansion into other 
sustainable methods for energy generation. Unfortunately, this is the only program 
that the University currently has in place, or has made plans to put in place, that the 
study identifies as ?Exemplary? (National Wildlife Federation, 2008). It is therefore 
evident that the University of Maryland still has a great deal of work to do if it wishes 
to demonstrate a genuine commitment to sustainability. 
Constructing renewable energy infrastructure is a method that is commonly 
being used to offset a large portion of the carbon footprint of small colleges. For these 
small schools building the infrastructure represents a onetime investment that can 
replace a large portion of the institution?s energy usage. For larger institutions, such 
as the University of Maryland, a much larger one time investment would be necessary 
to offset the same percentage of the carbon footprint. For example, Carleton College 
is a small institution in Northfield, Minnesota that currently enrolls about 2,000 
students. In 2004, the college spent $1.8 million to build a 1.65 MW wind turbine 
 
 66 
 
near campus. This 1.65 MW turbine produced 40% of the college?s total energy usage 
(Heinz, 2006).  
Some smaller colleges are funding renewable energy projects that completely 
offset the institution?s carbon footprint. For example, Middlebury College is a small 
college in Vermont which enrolls about 2,300 students. In 2006, the college pledged 
to attain complete carbon neutrality by 2016. Since then, the college has built a 10 
kW wind turbine, roughly 10 kW worth of solar panels, and a $12 million biomass 
gasification boiler. The university is funding these projects through their 
endowments, as well as donations from students and local residents (Green Report 
Card, 2009).  
Some larger universities have grass roots programs that fund small energy 
projects. For example, the University of Vermont authorized an organization that was 
formed in May 2008 called the ?The Clean Energy Fund.? This organization created a 
$10 per semester charge for all students, totaling about $20,000 per year. This money 
will be used to fund small renewable energy projects starting as early as fall 2009 
(Vermont, 2009).  
There are a few large universities that have constructed large renewable 
energy projects. These are large projects that are built off campus, depending on 
where conditions are optimal. For example, Colorado State University announced 
plans in March 2007 to build a wind farm that will produce more energy than the 
university consumes. The 11-acre wind farm is still under construction, and will have 
25 turbines rated at 4 MW. When complete, the wind farm will produce about 65 
MW, which is significantly larger than the university?s average electrical 
 
 67 
 
consumption of roughly 16 MW (Green Report Card, 2009). Unfortunately, large 
projects like this are unusual for large universities. In general, larger institutions such 
as the University of Maryland buy RECs in order to diminish their carbon footprint. 
Geographic Information Systems (GIS) 
Introduction to Geographic Information Systems (GIS) 
Geographic Information Systems (GIS) is an increasingly popular spatial 
analysis tool being utilized across disciplines ranging from epidemiology, to crime 
control, to resource management. GIS software installed upon a user?s computer 
allows him or her to analyze relationships between geographic characteristics and 
other attributes through the generation of a digital map (Kures, 2009). 
In order to conduct spatial analysis, the GIS user combines two or more 
groups of data, known as layers, into a single digital map document (Kures, 2009). 
Each of these layers contains data linking geographic areas to other attributes. The 
linked attributes can communicate any data the user wishes to link to that geographic 
entity. In planning and policymaking, these attributes most often highlight 
demographic factors, environmental health, human health, or land use.  
Once these layers are combined into a single digital map, many analytical 
tools are available to system users. Functions allow users to hone in on specific 
geographic areas, overlay data from multiple layers into a single one, and create 
buffers around geographic shapes.  
GIS layer data can be obtained through many sources. Basic boundary files 
are often available with the purchase of GIS software. Shapefiles, the file type of a 
 
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GIS data layer, can be downloaded from many federal entities, including the U.S. 
Census Bureau, the National Renewable Energy Laboratory, and the Environmental 
Protection Agency. For the state of Maryland, the Department of Natural Resources 
and Department of Planning have extensive GIS resources available.  
GIS and Wind Energy 
GIS has been utilized to analyze potential wind power potential in several 
projects to date. In 2001, a report delved into the development and application of GIS 
to wind farm location in the United Kingdom. This approach designated rankings of 
importance to the layers included in the map. The resulting areas were ranked on a 
scale of 0 to 10, with 0 representing ideal wind farm sites and 10 indicating unsuitable 
locations (Baban, 2001).  
In a 2003 report to the Vermont Department of Public Works, Vermont 
Environmental Research Associates, Inc. (VERA) analyzed wind power potential on 
Vermont?s public lands. As part of their methodology, VERA used GIS technology. 
First, analysts mapped areas of Vermont having wind speeds of category 3 or above, 
levels at which wind power generation is at least marginally productive. Once these 
areas were pinpointed, public lands data was imported to the map with level of 
development protection indicated. By overlaying data on suitable wind strength with 
land protection categories, analysts were able to hone in on possible sites for wind 
farm development in Vermont (Vermont Environmental Research Associates, Inc., 
2003). 
A 2006 study of Northern California depended upon GIS analysis to select 
potential wind power sites in nine counties. This project incorporated socioeconomic 
 
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elements into its analysis. The result was a model indicating which areas of the region 
may be suitable for wind power development. The model?s predictions have been 
largely accurate when compared to proposed and installed large-scale wind power 
development sites (Rodman, 2006). Hence, it is clear that GIS can be utilized as a tool 
to analyze potential sites for wind power development, and that it has been used 
effectively in this area in several studies already. 
 
 70 
 
Chapter 4: Methodology 
Summary 
A total of twenty-six sites in Garrett, Allegany and Washington Counties were 
identified as having adequate wind energy potential to warrant a cursory wind energy 
review.  Heterogeneity within several of these sites necessitated further revision into 
sub-sites for more detailed analysis.   Attributes were characterized for each site and 
generated measurements of land use protection, suitability for construction on the 
site, and approximate proximity of the windy regions to roads, transmission 
infrastructure, and nearby populations.  Attributes were assigned scores between 1 
and 5, where a score of 5 characterized the best suited condition of that attribute to 
warrant wind farm development.   
Wind speed measurements at 50m height along with the length and width of 
individual sites characterized as rectangles, were input into a MATLAB algorithm 
that calculated the number of turbines and capacity of turbines on the site, based on 
the most efficient packing structure for the turbines and the best suited turbine size.  
The capacity factor of the site and the energy generation potential were then assigned 
scores on a scale of 1 to 5 based on how they compared to each of the other sites in 
our analysis.   
Weights assigned to each attribute were determined through a systematic 
subjective process based on the perceived priorities of the University and the 
conditions that would make the site best suited for University implementation.  The 
weights for each factor were added to produce a final score between 1 and 5. This 
 
 71 
 
score was used to rank the sites relative to the others to establish the best suited sites 
for University development. 
Additional data on financial viability were generated based on the capacity 
factor, number of turbines on the site, and each turbine?s generation potential.  
Upfront capital costs of the wind farm included turbine expenses, as well as an 
?Other? category that included transmission, connectivity, and site and labor costs.  
Operations and Maintenance costs were also incorporated.  The financial feasibility 
assumed a regionally appropriate rate for ?Electricity Export Rate?, transmission 
losses, and financial parameters including debt ratio, debt term, and inflation rate.  
The resulting analysis output a ?Cumulative Cash Flow? graph showing cash flows 
for the life of the wind farm, and approximations for the Equity payback time, and 
Pre-tax IRR for equity. 
The above methodology is explained in more detail in the following sections. 
Generating Wind Energy Site Assessment Maps Using GIS 
 To conduct an analysis of potential wind power generation sites in Maryland, 
we used Geographical Information Systems software (GIS).  Through GIS, maps are 
created using combinations of data layers which link geographic entities to 
nongeographic attributes.   
For our analysis, we chose to focus on three specific qualities linked to wind 
energy siting feasibility.  First, we examined the wind speed as categorized in 
National Renewable Energy Laboratory(NREL) data.  Secondly, we looked at the 
level of legal and environmental protection afforded to geographic areas.  Finally, we 
analyzed proximity to transmission lines of suitable voltages.  This information was 
 
 72 
 
constrained by the political boundaries of the three westernmost Maryland counties?
Alleghany, Garrett, and Washington.  By investigating these three components in GIS 
software within these counties, potential sites could be labeled and analyzed using our 
suitability model. 
Geographically Defining our Focus Area 
To evaluate locations for wind turbine construction suitability, we examined 
potential onshore lands in Maryland on a county-by-county basis.  This approach 
allowed us to focus geographically only on regions in Maryland with high wind 
power potential, most notably counties with mountainous terrain in western Maryland 
(refer to Figure 5). Consequently, our research was focused on collecting land use and 
wind power data from Allegany, Garrett, and Washington counties in Maryland. Data 
on land use protections and orthotopography were provided primarily through the 
commissioner?s office of each county. 
Element 1:  Wind Speed 
Data Source:  Our assessment of potential sites for wind turbine construction 
through the state of Maryland depended on the NREL?s national wind resource 
assessment of the continental United States. The national wind resource assessment 
was created for the U.S. Department of Energy (DOE) in 1986 by the Pacific 
Northwest Laboratory and is documented in the Wind Energy Resource Atlas of the 
United States (US DOE, 1986). It provides an estimate of the annual average wind 
resource for the contiguous U.S. and high-resolution wind data.  
 
 73 
 
Our data estimates are consistent with NREL?s classifications for wind power 
from Wind Energy Resource Atlas of the United States (US DOE, 1986). The wind 
resource assessment divided the continuous U.S. into grid cells measuring ? degree 
of latitude by 1/3 degree of longitude. Each grid cell was assigned a wind power class 
based on a classification system ranging from class 1 (least windy) to class 7 (most 
windy). Each assigned wind power class represents a range of mean wind power 
densities (in units of W/m2) or equivalent mean wind speed at the specified height(s) 
above ground that are likely to occur at exposed sites within a grid cell (Table 4).  
Table 4: NREL Wind Classifications 
Classes of wind power density at 10 m and 50 m(a) 
Wind 
Power 
Class 
10 m (33 ft) 50 m (164 ft) 
Wind Power 
Density (W/m 2) 
Speed (b) 
m/s (mph) 
Wind Power 
Density (W/m 2) 
Speed (b) m/s 
(mph) 
1 0 0 0   
100 4.4 (9.8) 200 5.6 (12.5) 
2 
150 5.1 (11.5) 300 6.4 (14.3) 
3 
200 5.6 (12.5) 400 7.0 (15.7) 
4 
250 6.0 (13.4) 500 7.5 (16.8) 
5 
300 6.4 (14.3) 600 8.0 (17.9) 
6 
400 7.0 (15.7) 800 8.8 (19.7) 
7 1000 9.4 (21.1) 2000 11.9 (26.6) 
 
 
 74 
 
The wind resource assessment was based on surface wind data, coastal marine 
area data, and upper-air data.  The wind power estimates apply to areas free of local 
obstructions to the wind and to terrain features that are well-exposed to the wind such 
as open plains, tablelands, and hilltops. Within areas containing mountainous terrain, 
the wind resource estimates apply to exposed ridge crests and mountain summits. 
Our Analysis:  When the Maryland wind resource shapefile was imported 
into ArcGIS for our analysis, we limited the data expressed to wind classes greater 
than or equal to three.  NREL considers areas designated Class 3 or greater to be 
suitable for most wind turbine applications, whereas Class 2 areas are marginal and 
Class 1 areas are generally not suitable for wind turbine applications. For the 
purposes of our research, we regarded areas of Class 3 and above as those with 
potential for wind turbine construction and focused our efforts on these areas.   
Element 2:  Classifying Land Protections 
Data Source:     In order to identify sites that had potential for wind turbine 
construction based on availability and inhibition by state and federal land protections, 
we relied on land protection classifications within our counties of interest as 
represented by the Maryland Department of Natural Resources (DNR). As the 
overseer and manager of over 400,000 acres of public land in Maryland, DNR 
classifies the state?s land units according to their significance, resource management 
practices and recreational focus, or by a program created by the Maryland General 
Assembly. DNR?s GIS resources divided land into ten different designations related 
to protection level:  
square4 Maryland Agricultural Land Preservation Foundation (MALPF) Easements 
 
 75 
 
square4 County Parks 
square4 Maryland Department of Natural Resources (DNR) Lands 
square4 Maryland Environmental Trust (MET) Easements 
square4 Federal Lands 
square4 Forest Legacy Easements 
square4 Private Conservation Properties 
square4 Rural Legacy Areas 
square4 Targeted Ecological Areas 
Our Analysis: A very significant part of wind power siting is assessing the level 
of legal protection afforded to potential sites.  To gain a grasp on the spatial 
relationship between different land protection levels and wind speeds, DNR?s data on 
land protection was added to our maps.  The classes of protection included in the GIS 
resources of DNR?and therefore included in this study?are mentioned in the ?Data 
Source? section above (page 74).  Lands not encompassed in one of these categories 
will be referred to as ?all other lands.?   
 For the purposes of our research, we used these DNR land protection 
designations and re-classified them into three land protection categories to 
communicate the implications of land use protections on wind energy development. 
These three categories classify lands as being under ?Full Protection,? ?Potential 
Conflict,? or ?No Conflict.?  A detailed description of each of these categories and 
their components follows:  
square4 Full Protection:  Sites labeled as such outlaw development or limit it to 
agricultural or similar low disturbance activities.  Most of these categories rely 
 
 76 
 
on an easement, which is a legal contact restricting activity upon the property.  
A majority of such easements limit development on that property in 
perpetuity.  Any areas under this categorization were excluded from the list of 
possible wind power generation sites and were represented by black polygons 
in the GIS map, and include: 
o MALPF Easements 
o MET Easements 
o Forest Legacy Easements 
o Rural Legacy Lands 
square4 Potential Conflict:  Geographic areas falling in this category featured some 
limitations on development, but due to uncertainties in policy or discrepancies 
in level of protection afforded, they were not excluded from the analysis.  
Though hurdles may exist to wind energy development on these sites, the 
uncertain strength of their protections makes their incorporation in our 
analysis a possibility. 
o County Parks:  The idea of using county park-land for wind power 
production seems absent in the literature and news.  Because no 
specific statute or policy exists on this matter, potential exists for wind 
turbines to be constructed in county parks.   
o DNR Lands:  As discussed in the literature review, the state of 
Maryland has outlawed commercial wind development on state lands, 
which are held in the public trust.  However, the University of 
Maryland could potentially legally challenge this policy in respect to 
 
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University wind power development on state lands; this could be 
disputed from the angle that such development would not be 
commercial or that development would be an exercise of holding the 
land in the public trust.   
o Federal Lands:  The federal government lacks clear policy on wind 
power development on public lands and seems to be evaluating 
proposals on a case-by-case basis.  This indicates the University has an 
opportunity to pursue this option. 
o Private Conservation Properties:  The stipulations of private 
conservation lands differ and would need to be examined on a case-by-
case basis.  One cannot assume wind power production would be an 
outlawed activity upon them because of variances in the covenants. 
o Targeted Ecological Areas:  Though these areas are viewed as 
environmentally significant, no legal doctrine explicitly protects these 
properties from development.  However, it is likely that highly 
sensitive areas under this category would be aggressively protected by 
conservationists and environmentalists from both the private and 
public sectors. 
square4 No Conflict:  All locations without any of the above land protections were 
assumed to have no direct conflict with wind power production.  We 
recognize, however, that land protection plans unbeknownst to DNR may 
exist on these properties, so further research will be required to analyze them 
if the site selection process were to proceed.   
 
 78 
 
Once the wind speed and land protection layers were overlaid, potential wind 
production sites were selected.  This process is described in more detail in the section 
titled ?Creating Focus Sites Based on Areas with Viable Wind Speeds? (page 77). 
Element 3:  Proximity to Transmission Lines 
Data Source:  PJM Interconnection is the name of a regional transmission 
organization (RTO) that coordinates the movement of wholesale electricity across 
parts of the mid-Atlantic states of central and eastern Pennsylvania, and virtually all 
of New Jersey, Delaware, western Maryland, Washington, D.C, along with other 
states. PJM operates this region?s wholesale electricity market and manages its high-
voltage electricity grid, which serves over 51 million people.  
Our Analysis:  In order to maximize the amount of power reaching 
consumers, it is essential that wind power facilities be sited near adequate 
transmission lines.  This was the third element of our GIS analysis.  A layer 
indicating power lines of four classes was added to our digital map.  These classes 
were indicative of the following voltages: 115 kV, 116-138 kV, 139-230 kV, and 
231-500 kV. 
For each of the 26 sites targeted through the overlay of element 1 and element 
2, proximity to transmission lines needed to be measured.  Through a GIS measuring 
tool, the center of each site was found.  From there, a measurement from the site?s 
center straight to the nearest transmission line was taken. 
Creating Focus Sites Based on Areas with Viable Wind Speeds 
 
 
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Once all of the data layers were placed onto the GIS map, the data was ready 
to be analyzed. In order to focus the analysis, the maps were divided into small 
sections which represent separate sites for wind farm development. Each section is 
approximately 16 square miles, and contains an area of at least Class 3 wind speeds. 
The map was divided into these areas in two steps. First, the maps were divided into 
windy (Class 3 or higher) and no-wind areas. Then, the windy areas were broken 
down into small squares. 
Examination of the map indicated there were small areas with sufficient wind 
for energy production surrounded by large areas devoid of wind. Since the other 
layers in the GIS maps were not homogenous, an analysis of large stretches of wind 
would not be valid. A mountain ridge has high wind speeds, but land use protections 
could potentially apply in some areas but not others. For example, Sites H and M are 
both along the same mountain ridge and have similar wind speeds.  However, they 
have very different protection regulations as well as grid connectivity. Another 
division was necessary in order to properly analyze the GIS maps. Sixteen square 
miles was judged to be large enough to conduct a feasibility study, but small enough 
to be roughly homogenous. The three counties were divided into 26 sites (A-Z) and 
each was separately analyzed (Figure 6, Figure 7, and Figure 8). 
 
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Figure 6: Garrett County Sites 
 
Figure 7: Washington County Sites 
 
Figure 8: Allegany County Sites 
 
 
 81 
 
Applying GIS Results in an Excel Site Analysis Model 
In an effort to consistently and systematically rate sites based on their viability 
for purchase by the University of Maryland, with the intention of constructing wind 
turbines, a Microsoft Excel spreadsheet was utilized. In this model, a value for 
Overall Suitability was determined by assigning rankings to multiple criteria 
comprising Site Suitability and Energy Availability. Rankings were derived through 
evaluation of GIS maps and a power production model.   
Weighting Differences 
The influence that each criterion would have on the site score?the 
weighting?was determined by a subjective analysis using an Analytic Hierarchy 
Process (AHP) to evaluate the relative importance of the criteria.  Selected criteria 
and their assigned weights reflect the collective sentiments of the research group 
based on reviewed literature and similar analyses conducted. 
AHP is a method of ranking criteria that is commonly employed by 
engineering design firms. This system entails comparing the criteria in pairs, then 
using the scores from the individual comparisons to create a weighting scheme. The 
advantage of AHP is the fact that it allows for human judgment while simultaneously 
running a mathematical analysis that checks for fallacies in the subjective ratings. 
First, each criterion is all paired with all of the other criteria for the 
comparisons. Then, from each pair, one of the two criteria is judged to be more 
important than the other. The criterion from each pair that is judged to be more 
important receives a score from 1 to 9, 1 being equally important, and 9 being vastly 
more important. The criterion that is judged to be less important receives a score that 
 
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is the reciprocal of the other score (on a scale of 1 to .111). Generally, every score is 
an odd number, or the reciprocal of an odd number. This is done for two reasons. 
First of all, this system minimizes ambiguity. For example, a score of 5 is decidedly 
greater than a score of 3. However, a score of 5 is not so clearly greater than a score 
of 4. This helps keep the data consistent. Also, the even numbered scores are reserved 
for tie breakers. Next, the eigenvectors of the matrix are calculated. The weights of 
the criteria are the normalized values of the first column of ? (Dieter, 2009). 
For example, say that four criteria were to be compared through AHP (Criteria 
A-D). First, the criteria need to be paired. The pairs would be AA, AB, AC, AD, BB, 
BC, BD, and so on, in that fashion. Next, the criteria need to be subjectively 
compared. For the pair AB, if A was judged to be slightly more important than B, 
then A would receive 3 points. B, at the same time would receive .333 points. For the 
pair AC, if A was judged to be significantly more important than C, then A would 
receive 5 points while C would receive 0.2 points. This is done for all of the pairs, 
and placed into the matrix as shown in Table 5. In the matrix, the criteria in the 
column on the left are the dominant criteria. For example, since A was judged to be 
slightly more important than B, the 3 points are placed in the ?A? row, not the ?A? 
column. Similarly, the .333 points are placed in the ?B? row. The scores are placed 
into a matrix as shown below (Dieter, 2009).  
Table 5: AHP Example 
 
 
 83 
 
 Next, the eigenvectors of matrix shown in Table 5 are calculated. For this 
analysis, MATLAB was used. The following code was entered into the program to 
find the eigenvectors for the matrix, which is named ?A?: [W,?] = eig (A). The 
weights are the values in the far left column on the matrix shown in Table 6. 
Table 6: Weighting for AHP Example 
 
 As seen in Table 6, the sum of the weights is more than 1. In order to find the 
weights that will go into the decision matrices, it is necessary to normalize each value 
by the sum of the values. The sum of the weights in Table 6 is 1.573. Dividing 
through, it is found that the weight of criterion A is 54.374%, the weight of criterion 
B is 31.093%, the weight of criterion C is 9.746%, and the weight of criterion D is 
4.787%.  
An important aspect of the AHP tool to note is the fact that there is a 
consistency calculation that is performed in order to find out whether or not the 
subjective ratings contradict each other. The principle eigenvalue of ? is used to 
calculate the Consistency Index (CI), and subsequently the Consistency Ratio (CR). If 
this ratio is less than 0.10, then the data is considered to be sufficiently consistent. 
The ? matrix of the aforementioned example is shown below in Table 7. 
 
Table 7: Consistency Check for AHP Example 
 
 
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 In Table 7, the principle eigenvalue that will be used for a consistency check 
is 4.2281. The CI is calculated by the equation , where n is the number of criteria 
that are being compared. In the case of the example, n is equal to 4. Plugging values 
into this equation, the Consistency Index of this example is .076. Next, the 
Consistency Ratio is calculated by the equation   , where RI is the Ratio Index. 
Shown below in Table 8 are the values of the Ratio Index based on the number of 
criteria being compared. 
Table 8: Ratio Index Values 
 
 In this example, there are 4 criteria being compared, so the Ratio Index, found 
in Table 8, is 0.9. Plugging this value into the Consistency Ratio equation, it is found 
that the Consistency Ratio in this example is 0.844. Since this value is below 0.10, the 
subjective ratings are consistent with each other throughout the matrix. If the CR is 
above 0.10, then it is necessary to change the input of Table 5 in order to eliminate 
any inconsistency in the subjective ratings. 
  For the purpose of this study, there were eight different criteria considered in 
the Pairwise-Decision Matrix. The criteria are grid connectivity (voltage suitability), 
grid connectivity (distance to center), road connectivity, land protection, suitability 
for construction, nearby populations, capacity factor, and overall power production. 
Shown below in Table 9 is the rating matrix that was used for this analysis.  
 
 85 
 
Table 9: AHP rating matrix 
  
Next, it was necessary to assure that these ratings are consistent throughout 
the matrix. The ? matrix is shown below in Table 10. 
 
Table 10: Consistency Matrix for AHP 
 
 
 As shown in Table 10, the principle eigenvalue of ? is 8.9259. Plugging this 
value into the CI equation, using a value of n=8, the CI of this analysis is .1323. In 
order to find the CR, it is necessary to use Table 8 to find the correct value of the 
Ratio Index. When comparing eight criteria, the Ratio Index is 1.4. Plugging this 
value into the CR equation, it is found that the CR is 0.0945. Since the CR value is 
 
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less than 0.10, the rating analysis shown in Table 8 is considered to be sufficiently 
consistent. 
 Next it is necessary to use MATLAB in order to find the weightings for each 
criterion. The weighting matrix is shown below in Table 11. 
Table 11: Weighting Matrix for AHP 
  
 The sum of the weightings in Table 10 is -1.9984. Normalizing the weightings 
with regard to this value gives a weighting scheme as shown below in Table 12. 
 
Table 12: Normalized Weighting from AHP 
  
 As shown in the weighting table, the most important factor in site selection is 
capacity factor, followed by land protection. Also, Site Suitability is weighted at 
47.72% of the total score whereas Energy Availability is weighted at 52.28%. 
 
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 It is pertinent to note that the low weighting of the power production at 
13.07% helps to justify arbitrary selection of the boundaries of sites and sub-sites.   
The size of each site matters little, relative to the capacity factor dictated by wind 
speeds, which is weighted much higher.  The financial analysis discussed later further 
supports this assertion. 
Site Suitability 
Within the Site Suitability category, Infrastructure, Environmental, and Social 
considerations were all considered. Specifically, Grid Connectivity was assessed 
within the Infrastructure, by assigning weight of 0.016 to voltage suitability and 
weight of 0.0366 to the distance from the center of the land site to the nearest power 
line. The capacity of the nearby power lines were quantified in this system by using a 
value of 2 to represent low voltage lines, or those of 115 kV; 3 to represent lines with 
voltages of 116-138 kV; 4 to represent lines of 139-230 kV; and 5 to represent lines 
with voltages of 231-500 kV.  If the nearby power line has higher voltage capabilities, 
it is better equipped to efficiently receive the power generated by constructed wind 
turbines, since high voltage transmission systems lose less power than their low 
voltage counterparts over the same transmission distance (Tester, 2005). It is 
therefore evident that power lines with higher voltage capacities are advantageous 
and thus receive a higher value as a result. 
To incorporate the importance of the transmission distance from the wind 
turbines into the nearby power lines, approximate measurements were taken from the 
center of the potential sites to the nearest power line, with a ranking of 5 being given 
to a distance of 1.9 km or less; 4 representing a distance of 2-4.9 km; 3 representing a 
 
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distance of 5-10.9 km; 2 representing a distance of 11-22.9 km; 1 representing a 
distance of 23-46.9 km; and 0 representing a distance of greater than 47 km. Since 
longer transmission distances result in greater losses of energy and larger costs of 
installation, shorter distances are given a greater value than longer distances. 
The final aspect of infrastructure suitability that was considered in the analysis 
is road connectivity. The rankings given to the road connectivity of each site were 
identical to those given to the distance from the site to the power line rankings.   
Within the Environmental category, assessment of the site?s land use 
protection was given a weight of 0.2597, while the suitability of the site for 
immediate construction was given a weight of 0.0704. The land use protection ratings 
of each site were given ratings of 0, 2.5, or 5. These ratings represent ?Full 
Protection? of the site and little to no chance that a wind power facility could be 
constructed; ?Potential Conflict? with a project, indicating a moderate possibility of 
wind power production; and ?No Conflict? with wind power, indicating no perceived 
sign of conflict, respectively. The suitability of the land for construction was also 
ranked on a 0-5 scale, with 0 representing land conditions that suggest that it would 
be nearly impossible to build on the land, and 5 representing an extremely stable site 
that should be ready for immediate construction of wind turbines.  These rankings 
were determined by inspecting Google Earth imagery of each site?s terrain conditions. 
Within the Social category, the presence of nearby populations was used to 
assess if there would be any likely resistance to the construction of wind turbines, and 
was given a weighting of 0.0697. Rankings ranging from 1-5 were used in this 
category, with 1 representing a population distribution that would be likely to prohibit 
 
 89 
 
development, and 5 representing a population distribution that would be unlikely to 
have any negative impact upon the construction of wind turbines at the site.  Again, 
imagery from Google Earth was used to determine the rankings for this category. 
The total Site Suitability value was calculated using the values assigned 
within the Infrastructure, Environmental, and Social categories, as well as their 
aforementioned weights. As previously stated, the total Site Suitability was given a 
weight of 0.4772 towards the determination of overall site suitability. Thus, for each 
of the 26 sites analyzed in Western Maryland, the Site Suitability as defined through 
Infrastructure, Environmental, and Social considerations represents 47.72% of the 
weight towards the final value given to each site. 
Energy Availability 
In order to quantify the amount of wind energy that can be extracted from 
each site, two values must be calculated: the maximum number of turbines that can be 
placed within the confines of the site and the capacity factor at which the turbines 
operate (a value which represents the percentage of a turbine?s maximum rated power 
production that it actually achieves).  For the purposes of this analysis, each site was 
approximated as a rectangle based on the average length and width of the strip of 
Class 3 or higher NREL wind data. 
 Turbine spacing is an important design choice and represents a tradeoff 
between the number of turbines and efficiency. As wind passes through a turbine, it 
slows down and becomes more turbulent such that when it reaches the next turbine in 
line, there is less power to be extracted.  Placing turbines closer together allows more 
turbines to fit onto one site, but in turn, each turbine becomes less efficient.  In our 
 
 90 
 
analysis, we separated turbines by a distance of ten rotor diameters, which keeps the 
inter-turbine wake losses below 10% (Dhanju A, Et al., 2007).  A MATLAB 
algorithm was written to determine the optimal method of placing turbines for each 
site, choosing between rectangular and staggered layouts, as shown in Figure 9.  
Another consideration included in the algorithm is whether larger, more powerful 
turbines or smaller, less powerful turbines can produce more power in a given area.  
In each site, we compared the two most common wind turbines used in the United 
States: General Electric?s 1.5 MW and 2.5 MW turbines (US DOE, 2008).  The type 
and number of turbines for a site were then determined based on the maximum 
possible power production (for full algorithm, see appendix on page 232). 
Figure 9: Rectangular vs. Staggered Layouts 
In order to calculate the capacity factor of a site, wind speed data was 
translated into turbine power output based on the turbine manufacturer?s power 
curves (Figure 10 and Figure 11).  This was then divided by the rated power capacity 
to obtain the capacity factor.  For example, at an average wind speed of 8 meters per 
second, a 1.5 MW turbine manufactured by GE produces 0.6 MW of power; since 0.6 
MW is 40% of the rated 1.5 MW capacity, this turbine would operate at a 40% 
capacity factor (GE Energy, 2008a)  
 
 91 
 
 
Figure 10: GE 1.5MW Power Curve (GE Energy, 2008a) 
 
 
Figure 11: GE 2.5MW Power Curve (GE Energy, 2008b) 
 
  Once values for the capacity factor and expected power production were 
determined for each site, we gave each criterion a normalized ranking between zero 
and five based on where it fell in the overall range of values for the 26 sites (Table 
13).  Capacity factor received a weight of .3923 while power production was 
weighted at .1307. Capacity factor was weighted more heavily because it is 
 
 92 
 
paramount in making a wind farm economically feasible.  As capacity factor 
increases, the ratio of money generated to operational costs also increases, increasing 
the wind farm?s annual profits (more information on the financial analysis is given in 
the section ?RETScreen Financial Analysis? on page 94). 
Table 13: Energy Availability Ranking Criteria 
Capacity Factor 
(%) 
Normalized Rank Expected Power 
Production (MW) 
Normalized Rank 
< 20 0 < 2.9 0 
20-22.9 1 3-4.9 1 
23-24.9 2 5-6.9 2 
25-26.9 3 7-8.9 3 
27-28.9 4 9-10.9 4 
> 29 5 > 11 5 
Excluded Factors 
While the majority of factors impacting overall site suitability were 
considered in this analysis, there were several factors excluded for various reasons. 
Bird flyways?migratory routes integral to the lifecycle of some bird species?are an 
environmental factor that would undoubtedly impact the viability of constructing a 
large number of wind turbines on a given land site, yet we excluded this particular 
factor from our analysis.  Information on the locations of these flyways was 
unavailable at high enough resolutions to make a meaningful contribution to our site-
by-site analysis. 
In addition, certain lands in Western Maryland have historical value because 
of their roles in the Civil War, which could be perceived as being compromised 
through the construction of wind turbines. Due to a lack of GIS compatible data, this 
factor could not be incorporated into the analytical framework, but could be assessed 
on a site-by-site basis in the future. 
 
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 Thus, while there are some factors that could affect the overall suitability of a 
site which were not explored, this analysis is intended to be prefatory. It was deemed 
acceptable to exclude less quantifiable factors, which can be analyzed by the 
University of Maryland in greater depth should it elect to pursue the construction of 
wind turbines on one of these sites. 
Data Approximations 
When the data were collected, certain approximations of the metrics were 
needed. In order to determine the number of turbines that could be placed, the site 
areas had to be measured. These sites were estimated as rectangles, with defined 
measurements based on their average length and width. By doing so, the calculation 
of the total available area was simplified in order to fit with the algorithm calculating 
turbine count and power production. If a site had an irregular shape, it was broken up 
into a few smaller rectangles to get the best approximation of the area. 
The distance from a transmission line to each site was also measured to 
determine land suitability. This is important because the further energy has to travel, 
the more energy that will be lost and also the higher the cost of constructing new 
transmission lines. When making this measurement, the closest power line was found 
and a linear path from it to the center of the site was measured. This was the simplest, 
most consistent way of measuring this distance among all of the sites.  In some 
instances, there were two power lines of different voltages that were nearby. In this 
case, the measurement to the closer power line, not to the power line with the higher 
voltage, was used. Distance was more important than voltage because of the cost 
involved in constructing new transmission lines. 
 
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Flexibility of Weightings 
An important feature of this analysis is the ability to alter the decision 
matrices created and therefore change the site rankings. The University may have 
different priorities than anticipated in this study, so the weights of different aspects 
for the site rankings can easily be changed to reflect them. For example, suppose the 
University?s top priority is to achieve carbon neutrality as soon as possible. In order 
to achieve this, they want the site that can produce the most energy, regardless of the 
number of turbines it would take or the efficiency of these turbines. To do so, the 
weight for energy produced could be increased to reflect this new target.  Similarly, if 
the University felt that land use protections could be easily overcome in the 
construction of a wind farm, the weighting for this category could be reduced, thereby 
increasing the rating of sites which were previously ranked poorly due to heavy land 
use protections.  By having dynamic decision matrices, the University can pick sites 
based on its own agendas and changing regulations and trends, making this data even 
more useful and applicable. 
RETScreen Financial Analysis 
The energy production data were also used to produce a financial feasibility 
analysis based on RETScreen software, which is used internationally to gauge wind 
farm site viabilities. RETScreen is software that was released by the Canadian 
government with the purpose of facilitating the development of renewable energy 
products with the idea that, with the use of RETScreen, less investment would be 
necessary for the pre-feasibility study. The software has been used for 20 wind farm 
projects that are in construction or online that total 100 MW (RETScreen, 2004a).   
 
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Financial analyses created using RETScreen accompany each site?s final site 
suitability score for consideration by the University. 
 Using data from NREL?s annual report on U.S. wind power installation, cost, 
and performance trends from 2007 as our inputs to software, we produced a financial 
summary of each site?s wind farm over the next 30 years (US DOE, 2008).  NREL 
found the initial investment in wind turbine projects to be linearly related to the 
installed capacity of the project and that no evidence of economies of scale exists for 
wind farms on a project-by-project basis.  In other words, a 20 MW farm can be 
predicted to cost the same amount per MW as a 200 MW farm. 
 The majority of the costs for a wind farm come from the purchase of wind 
turbines.  In 2007 for example, the average cost of turbines for a wind farm was 
$1,240 per KW.  The remainder of the costs come from the infrastructure that must be 
built around the wind farm such as operational facilities, transmission lines, access 
roads, grid connections, etc.  Over the period spanning 2004-2007, total project costs 
averaged $1,540 per KW (US DOE, 2008).  In our financial analyses, we used a value 
of $1,600 per KW for the total installation costs, which included $1,300 per KW for 
turbines and $300 per KW for infrastructure costs. 
 The remainder of the costs of a wind farm are found in the annual costs of 
operation and maintenance, or O&M.  These costs are more difficult to estimate as 
they vary with the age of the turbines.  For example, the average yearly O&M cost 
over the period from 2000 through 2007 (expressed in dollars per MWh of annual 
energy production) were $30/MWh for projects built in the 1980s, $20/MWh for 
projects built in the 1990s, and $9/MWh for projects built after 2000.  This decline in 
 
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the cost of O&M can be attributed to two possible factors.  First, as turbines get older, 
the cost of maintaining them increases such that a 20-year-old wind project will cost 
more to maintain than a 10-year-old project.  Second, as turbines have become larger 
and more sophisticated, the initial costs of O&M have decreased (US DOE, 2008).  
As such, we had to estimate the average annual cost of O&M over the course of a 
project?s approximately 30-year lifespan.  For the purposes of our financial analysis, 
we used a value of $15/MWh annual O&M costs. 
 The last monetary value that needed to be input into the RETScreen analysis 
was the price of electricity sold to the grid, called the electricity export rate, which is 
a measure of how much money a wind farm will receive for the energy it produces.  
This value varies by region across the United States ranging from $30/MWh to over 
$60/MWh.  In 2007, the average value of electricity export rate for wind farms in the 
eastern United States was approximately $50/MWh, which is the value we use in our 
financial analysis (US DOE, 2008). 
 Finally, RETScreen requires the user to input a series of financial parameters 
that describe the flow of money over the course of the project life; suggested values 
for most of these are found in the RETScreen user manual (RETScreen, 2004b).  The 
first parameter is the rate of inflation that the model will simulate.  In North America, 
inflation over the next 25 years is predicted to be approximately 2-3%- a value of 
2.0% was used.  The project life, which typically ranges from 20 to 30 years for a 
wind farm, was selected to be 30 years, in order to see the performance of each 
potential wind farm over its maximum expected lifetime.  The debt ratio represents 
the percentage of the total initial costs which will be paid via loans.  The RETScreen 
 
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manual suggests that values of 50%-90% are most common for the debt ratio, so a 
debt ratio of 50% was selected, i.e., half of the project costs would be paid with loans.  
The debt interest rate, the yearly interest assessed on the loans, was set to be 5% and 
the debt term, the period over which the loans will be paid back, was set to be 8 years, 
approximately one fourth of the project lifespan. 
In addition to the capital and maintenance costs of the wind development, 
government grants and incentives are available to stimulate wind energy 
development.  The State of Maryland offers the ?Maryland Renewable Electricity 
Production Tax Credit? which awards $0.085 per kWh of electricity that is exported 
by the farm to the grid.  For the purposes of our study, this credit was maintained over 
the entirety of the wind farm life, though policy change may alter the amount or 
duration of credit.  Local and regional governments are signaling an increased 
emphasis on renewable energy, which argue for retaining the credit.  
A Federal Production Tax Credit (FPTC) is also offered for wind developers 
at a lower rate of $0.019/ kWh, for the first ten years of wind farm operation.  
Extension of the FPTC beyond the first ten years is possible, though this cannot be 
assured to wind developers.  Figure 12, below, shows the amount of added capacity 
of wind developments each year since 1999.  In years when the PTC is active, 
development is significantly more active than in years when the PTC has expired, 
even if temporarily.   
 
 98 
 
 
 
Figure 12: Dependence on Production Tax Credit for wind development (AWEA, 2007 in UCS, 2009) 
Since the RETScreen tool is unable to accommodate removal of the FPTC 
after 10 years, the annual benefit is normalized over the 25 year lifetime.  At $0.019/ 
kWh for ten years, the equivalent credit received by the electricity producer would be 
$0.0076/ kWh over the 25 year lifespan.  This approximation negatively impacts the 
payback period, though pre-tax IRR-equity value should be equivalent.  The value of 
the wind farm at the end of its lifetime is approximately the same as if the FPTC were 
phased properly.  Regardless, the FPTC is so small that the impact is minor, and the 
approximation is reasonable. 
For use in RETScreen, the added economic incentives of the FPTC and 
Maryland Renewable Electricity Production Tax Credit are aggregated and inserted as 
?Incentives and grants? as a function of the kWh produced by the wind farm. 
Additional value may be added to the wind site through the sale of Renewable Energy 
Credits (RECs) to third party vendors.  These vendors buy RECs from wind 
generators, and then sell them back to the market for a higher price. 
 
 99 
 
One reputable third party vendor recommended by the Maryland Department 
of Natural Resources (Rice, 2009) is Evolution Markets.  In figure 13, below, the bid 
price is the amount paid by Evolution Markets to the electricity producer for a single 
REC.  One REC represents 1 MWh of electricity.  The offer price is the price offered 
by Evolution Markets for purchase. 
 
Figure 13: Renewable Energy Credit pricing for purchase from generator and sale to end user (Evolution 
Markets, 2009) 
The value of Renewable Energy Credit sales is manually added to the ?Annual 
Savings and Income? tab of RETScreen.  The 2009average price per REC received by 
the electricity producer is approximated to be $1.25 in Maryland, though this value 
has increased steadily since 2007.  RECs are an interstate trading commodity and 
limitations for REC trading are specified by some states? Renewable Portfolio 
Standards (RPS).  A more mature RPS, in Massachusetts for instance, causes a much 
higher market price for RECs.  Given the low cost of RECs in Maryland, there is 
currently little incentive for generators to produce them.  However, as interstate 
markets are integrated, supply and demand forces are expected to normalize interstate 
pricing.  Maryland?s low-cost RECs, for instance, will cause higher demand and will 
 
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drive the price up over time.  The approach of using the 2009 MD bid price, 
therefore, is conservative. 
 One option of the RETScreen software that was not included in this analysis 
was the environmental and financial benefits detailed in the program?s emission 
analysis.  Because wind turbines do not produce any emissions, there is a 
considerable reduction in the production of greenhouse gases (GHG) compared to a 
conventional fossil fuel plant.  Recently proposed cap and trade systems governing 
the release of GHG are poised to increase the cost of energy produced by fossil fuel 
plants without changing the cost of energy produced by wind farms (Obama, 2009).  
As a result, the profitability of wind farms compared to that of fossil fuel plants 
would increase, theoretically stimulating growth in the industry.  However, because a 
cap and trade system on GHG emissions is not yet in place, we excluded this portion 
of the RETScreen analysis from our analysis.  The potential impact of these policies 
is discussed further through a sensitivity analysis in the results. 
  From this data, RETScreen calculates a number of important financial 
statistics including the Internal Rate of Return on equity (IRR), the equity payback 
period, and a graph of the year-to-date cumulative cash flow over the project?s 
lifespan.  A site?s IRR is an indication of how profitable it will be over the course of 
its operation.  For example, the amount of money made from a site with an IRR of 
6% is comparable to the money made by taking the entire cost of building the site and 
placing it instead into a savings account with a 6% interest rate.  The equity payback 
period is the length of time it will take for the site to pay back its initial investment 
and begin to earn a profit.  This value, as well as others such as maximum levels of 
 
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debt and profit for the project, can be seen on the cash flow graph.  This financial 
analysis is presented alongside the rankings and power production for each site in the 
following section. 
 
 
 
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Chapter 5:  Results 
 The first result of this analysis is a ranked list of 26 sites in Maryland for the 
construction of a wind energy project.  The cumulative effect of each site?s wind 
power potential, capacity factor, proximity to infrastructure, land use protection, 
suitability of land for development, and proximity to population centers is factored 
into their overall rankings.  Presented alongside these rankings is a financial analysis 
of each site?s anticipated financial characteristics.  While financial outputs do not 
influence the site scores directly or their ranking, their importance is internalized in 
the weighted decision analysis mainly because capacity factor is an important input to 
both the weighted decision matrix and financial analysis models.  In addition, site 
images were taken from Google Earth for each site, and the approximate location of 
the wind farm on the site is sketched to show the affected land area.   
The site scores are summarized below in Table 14, ordered by their site scores 
to suggest a prioritization of future investigation by the University.  The table also 
summarizes the Pre-Tax IRR-equity and Equity payback for each site.  The five most 
suitable sites according to the weighted decision matrix are Site M1, Site N1, Site X, 
Site V, Site U and Site W.  Each site is discussed in the following section. 
To further clarify the value of each site, an additional weighted decision and 
RETScreen analysis was performed within sites where there were concentrations of 
the highest wind speeds.   The results of this ?sub-site? analysis are reported in Table 
15, and the best sub-sites that correspond to the best sites are discussed.
 
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Table 14: Site summary ranked by "Overall Site Score" 
 
 
 104 
 
Table 15: Sub-Site summary ranked by "Overall Sub-Site Score" 
 
 
Detailed Site Analyses 
Site M 
Site M is located on a mountain chain in eastern Garrett County that was 
identified as having high wind speeds from a cursory view of the Maryland NREL 
wind data maps. A more detailed review of the site investigated the land use of the 
entire county as shown in figure 14.  According to the MD Department of Planning 
Legend in the appendix, Site M is located in a forested region classified as ?evergreen 
forest? (MD DNR, 2009).  The dense forest cover presents a moderate obstacle for 
construction, so the site received a ranking of 3 for ?Suitability of Land for 
Construction.?   There were some visible agricultural areas in the immediate 
 
 105 
 
proximity of the wind farm, and the site is very close to Big Savage Mountain state 
park (MD DNR, 2009), which is common venue for outdoor recreation.  There is 
little likelihood of population impacts in this rural site, though tourist industries may 
object to obstructed or altered views from the wind farm.  The site received a ranking 
of 4 for ?nearby populations?.   
 
Figure 14: Garrett County Land Use from MD Department of Planning (MDP, 2009), showing Site M in red box 
Big Savage Mountain state park is the largest park facility in the State of 
Maryland forest system.  Though wind projects are not legally prohibited on this site, 
there are direct environmental and tourism impacts that must be assessed before site 
development.   Figure 15, below, shows the available wind potential on the site in 
yellow, orange and red.  The pink coloration outlines the extent of the Big Savage 
Mountain state park, and adjacent protected lands.  Because there is clear overlap of 
the potential development area for the wind farm and the state park, the ?land use 
 
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protection? attribute is given a ranking of 2.5, to represent a potential development 
conflict.   
 
Figure 15: GIS Image of Site M (Scale is 1:40,000) 
  
The wind potential on Site M is constituted of two strong parallel bands of 
high wind speeds, with measurements at 50m indicating speeds ranging between 6.4 
and 8 meters per second.  Because of potentially different attributes of the two bands, 
and simplicity of inputting rectangular approximations of site dimensions into the 
MATLAB algorithm, Site M assessment was disaggregated into two sub-sites, Site 
M1 and Site M2.  Site M1 is the stronger of the two bands, located south of Site M2. 
For Site M1, a strong central strip of ?good? wind is surrounded by ?fair? 
wind at the strip?s borders.  A contiguous thin string of ?excellent? wind is also on the 
strip.  The wind speed ranking was determined be assigning a weight to each wind 
strength in the string.  The averaged rank was assigned as 2.65.  For Site M2, the 
majority of the wind was ?fair? with a thin, discontinuous strip of ?good? wind that 
 
 107 
 
constituted only a small fraction of the strip.  The site ranking was assigned as 1.2.  
The dimensions of land with available wind were approximated as a rectangle for 
both sites.  The length for Site M1 was 8.38 km x 0.82 km.  The length for Site M2 
was 5.68 km x 0.52 km.  The land dimensions and wind speed ranking were input 
into MATLAB algorithm to determine optimized energy characteristics for each sub-
site.  The program determined that for Site M1, the GE Wind 1.5 MW turbine is most 
suitable for the site, and that 22 turbines can be placed on the site with an optimal 
packing structure, producing 33 MW at maximum capacity.  Expected power output 
was determined to be 10.2 MW based on the wind characteristics of the site relative 
to the power curve of the selected wind turbine.  The expected power output, divided 
by the maximum turbine power, yielded a 31% capacity factor, which was the highest 
of any other site in this study.  Site M2 had more modest potential corresponding to 
the weaker wind, planning for the use of GE Wind 2.5MW turbines with an optimized 
packing structure allowing six turbines.  The maximum power output was 15 MW, 
with an anticipated output of 3.2 MW.  The associated capacity factor was 21.2%. 
It is important to note that the surface characteristics of the land impact the 
wind speed gradient with increasing height above the ground.  This feature is 
represented through the shear exponent of the site, with a higher shear exponent for 
forested, mountainous regions, and low shear exponents for open water and fields.  
The effect of shear exponent was not considered in this analysis because the heights 
of both types of considered turbines are much taller than the wind measurement at 
50m.  Considering the wind speeds at the hub height of the turbine as the same as the 
 
 108 
 
wind speeds at 50m is a conservative estimate, as greater speeds are observed at 
higher heights. 
The GIS image in Figure 15 also revealed that the center of the windy region 
of the site M1 was 1.8 km away from a 116-138 kV transmission line, corresponding 
to rankings of 3 and 5 for ?voltage suitability? and ?transmission distance?, 
respectively.  The center of Site M2 is measured as 0.77 km from a 231-500 kV 
transmission line, corresponding to rankings of 5 for both ?voltage suitability? and 
?transmission distance.?  The Google Earth image in Figure 16 shows a road running 
through the site M1 at approximately 0.4 km from the windy region?s center, 
corresponding with a ranking of 5 for ?road connectivity.? Site M2 also received a 
ranking of 5 for ?road connectivity? because the site has a different road running less 
than 0.1 km from the site center. 
 
Figure 16: Aerial image of Site M 
Each of these site and energy rankings were put into the weighted scoring 
matrix in Table 16, below, yielding a site value of 3.38 and 3.29 for sites M1 and M2, 
 
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respectively.  These values constitute 47.7% weight of the final overall score in the 
weighted decision matrix.  Site M1 received a high ranking of 5 for capacity factor 
with 39.2% weight, and a rank of 4 for energy generated.  The overall Site M1 score 
accounting for 100% of these inputs is 4.10.  Site M2 received a very low ranking of 
1 for capacity factor, and energy generated rank of 1.  Corresponding to these poor 
rankings, the overall suitability of Site M2 is 2.09.  It is important to notice that the 
?Value of site? based on site suitability characteristics is higher for Site M2 than for 
M1, even though M1 is distinguished as a far superior site based on its energy 
characteristics. 
Table 16: Overall Site Suitability Scoring Matrix for Site M.1 and M.2 
 
RETScreen analyses revealed financial indicators of feasibility for Sites M1 
and M2, though discussion of M2 is for completeness and comparison purposes only, 
 
 110 
 
and does not constitute an endorsement of that site.  RETScreen utilized the capacity 
factor and number and size of turbines to estimate an output of 89,600 MWh of 
annual generation from the Site M1 wind farm.  Using the financial parameters listed 
in the ?RETScreen Financial Analysis? section on page 94, it was determined that the 
initial upfront plant costs would be $52,800,000.  This figure includes plant power 
system costs and ?other? costs at $9,900,000, which internalizes site purchase costs, 
transmission interconnect, substation costs, and road connections. Operations and 
maintenance costs were expected to be ~$1,340,000 annually.  The cumulative cash 
flows graph and worksheet are presented below in Figure 17Figure 17. 
 
Figure 17: Financial Analysis for Site M.1 
RETScreen utilized the capacity factor, and number and size of turbines, to 
estimate an output of 27,900 MWh of annual generation from the Site M2 wind farm.  
Using the financial parameters listed in the ?RETScreen Financial Analysis? section, 
it was determined that the initial upfront plant costs would be $24,000,000.  This 
figure includes plant power system costs and ?other? costs at $4,500,000.  Operations 
 
 111 
 
and maintenance costs were expected to be $418,000 annually.  The cumulative cash 
flows graph and worksheet are presented below in Figure 18. 
 
Figure 18: Financial Analysis for Site M.2 
It was determined that the Site M1 wind farm would begin making profit after 
13.5 years of operation, which is equivalent to an investing the upfront costs with 
8.1% interest.   A cursory RETScreen emission analysis determined that the site 
would annually displace 53,800 tCO2 that would otherwise be generated by an 
average electricity fuel mix in the United States.   This is the equivalent of 45,700 
acres of forested land absorbing CO2 from the atmosphere over the course of the year.   
A wind farm on Site M2 would begin making profit after a much longer 
period of 19.5 years of operation, which is equivalent to an investing the upfront costs 
with 2.9% interest.   An emission analysis determined that the site would annually 
displace 16,700 tCO2 that would otherwise be generated by an average electricity fuel 
mix in the United States.   This is the equivalent of 14,200 acres of forested land 
absorbing CO2 from the atmosphere over the course of the year. 
 
 112 
 
It is apparent that the Site M1 wind farm is a better investment for the 
University given its shorter payback time and high energy production.  The 
economies of scale do not significantly affect the financial viability of Site M2, 
though the low capacity factor effectively ensures that the site will not have a 
preferable payback period relative to a project with a higher capacity factor. 
Site M1 ? Sub-Site 6: 
 
The contiguous strong band of wind categorized as ?5? and ?6? (between 7.5 
and 8.8 meters per second) by NREL was isolated for detailed sub-site analysis.  The 
region is significantly smaller, though by focusing on the best wind, our analysis will 
reflect the best possible use of the site by the University with the fastest payback 
period possible.  The strong winds of sub-site 6 are shown below in Figure 19, and 
the relative location of the site and aerial photograph are shown in Figure 20.  
 
 
Figure 19: GIS Image of Sub-Site 6 
 
 
 
 
 113 
 
 
Figure 20: Aerial image of Sub-Site 6 
Sharing many of the same physical characteristics as Site M1, the weighted decision 
matrix is provided below in Table 17.  The value of the site is 1.54 and the capacity 
factor is significantly higher than any of the site analyses at 35%, though that 
criterion cannot be awarded a score higher than 5.  The energy generation at 5.25 
MW receives a score of 2.  Overall suitability is 2.96, which is the fourth highest 
amongst sub-sites.   
Table 17: Overall Site Suitability Scoring Matrix for Sub-Site 6 
 
 
 114 
 
 
The most important feature of sub-site 6 is its strong financial attributes.  With 
its high capacity factor, the site is expected to have an equity payback of 11.9 years, 
over two years sooner than the main Site M1.  The Pre-tax IRR-equity is also higher 
at 10.3%.   
 
Figure 21: Financial Analysis for Sub-Site 6 
The difference between a capacity factor of 35% and 31% is significant, and it 
is a weakness in the weighted decision matrix and the assigned ranking ranges which 
prevents the higher capacity factor to be rated above 5. 
Site N 
Despite Site N1 having the second highest overall site suitability, Site N has 
been excluded from in depth discussion in this section because the corresponding sub-
site within the region, sub-site 7, did not rank highly and is not considered a priority 
site for in depth review.  The findings for Site N and sub-site 7 are located in the 
appendix. 
 
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Site X 
Site X is located in the Dans Mountain range in south Allegany County, and 
was identified as having high wind speeds.  Review of the site?s land use in Figure 
22, below, indicates that the land is largely undeveloped.  According to the MD 
Department of Planning Legend in the appendix, the windy regions of Site X are 
located in ?evergreen forest? (MD DNR, 2009).  The dense forest cover presents a 
moderate obstacle to build, though there is also some cleared land used currently in 
agriculture that would simplify development, as is shown in the Figure 24 Google 
Earth image.  Because some of the site is conducive to development, it receives a 
favorable ranking of 4 for ?Suitability of Land for Construction.?    
 
Figure 22: Allegany County Land Use from MD Department of Planning (MDP, 2009), showing Site X in red box 
The site is mainly wooded, though the GIS image below in Figure 23 indicates 
that there are no land use protections in proximity to the regions of high wind speed 
that would prohibit development.   A thorough analysis of the site would still require 
 
 116 
 
an investigation of impacts on local ecology, though the site?s ?land use protection? is 
given a ranking of 5, to represent no anticipated conflict.  
 
Figure 23: GIS Image of Site X (Scale 1:25,000) 
Figure 23, above, shows the available wind potential on the site in yellow, 
orange and red.  The best wind on Site X is available in a contiguous band, with 
measurements at 50m indicating speeds ranging between 6.4 and 8 meters per second.  
A strong central strip of ?good? wind runs through the wind pattern, and is 
surrounded by ?fair? wind at the strip?s borders.  Some ?excellent? wind is 
centralized through the middle of the strip.   The site?s wind characteristics are ranked 
as 1.9, which is between ?fair? and ?good,? based on the approximate percentage of 
land occupied by each classification of wind.  The dimensions of land with available 
wind were approximated as a rectangle with length 6.0 km and width of 1.0 km.  The 
land dimensions and wind speed ranking were input into MATLAB algorithm to 
determine that the GE Wind 2.5 MW turbine is most suitable for the site, and that 12 
turbines can be placed on the site, producing 30 MW at maximum capacity.  Expected 
 
 117 
 
power generation was determined to be 7.62 MW based on the wind characteristics of 
the site relative to the power curve of the selected wind turbine.  Expected power 
output divided by the maximum turbine power yielded a 25.4% capacity factor.   
The GIS image also revealed that the center of the windy region of the site 
was 1.6 km away from a 116-138 kV transmission line, corresponding to a ranking of 
3 and 5 for ?voltage suitability? and ?transmission distance,? respectively.  The 
Google Earth image in Figure 24 shows a road running through the site, with a 
perpendicular distance of 0.3 km from the center.  This corresponds with a ranking of 
5 for ?road connectivity.?   
 
Figure 24: Aerial image of Site X 
Each site and energy attribute rankings were put into the weighted scoring 
matrix in Table 18 below, yielding a site value of 2.21 with 47.7% weight, a ranking 
of 3 for capacity factor, and energy generated rank of 3.  The overall site score was 
2.62. 
 
 
 
 
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Table 18: Overall Site Suitability Scoring Matrix for Site X 
 
 
The financial analysis using RETScreen utilized the capacity factor, and 
number and size of turbines, to estimate an output of 66,800 MWh of annual 
generation from the Site X wind farm.  The initial upfront plant costs would be 
$48,000,000.  This figure includes plant power system costs and ?other? costs at 
$9,000,000.  Operations and maintenance costs were expected to be $1,001,000 
annually.  The cumulative cash flows graph and worksheet are presented below in 
Figure 25. 
 
 119 
 
 
Figure 25: Financial Analysis for Site X 
It was determined that the wind farm would begin making profit after 16.4 
years of operation, which is equivalent to an investing the upfront costs with 5.2% 
interest.   A cursory RETScreen emission analysis determined that the site would 
annually displace 40,000 tCO2 that would otherwise be generated by an average 
electricity fuel mix in the United States.   This is the equivalent of 34,000 acres of 
forested land absorbing CO2 from the atmosphere over the course of the year.   
Site X ? Sub-site 18: 
 
The analysis of site X was then focused on the areas that had a wind speed of 
category 5 and higher. Figure 27 shows the GIS image of the site with wind speeds of 
category 5 and 6. Figure 26 shows the aerial view of the sub-site. 
 
 120 
 
 
Figure 26: Aerial image of Sub-Site 18 
 
 
Figure 27: GIS Image of Sub-Site 18 
 
 The weighted decision matrix for Sub-Site 18 is shown below in Table 19. 
Since Sub-Site 18 is located within Site X, many of the properties are the same. The 
value of the site is 3.1, which makes it the highest scoring Sub-Site. This high score 
features a capacity factor of 33.2% and a total power output of 3.32 MW. 
 
 
 
 
 
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Table 19: Overall Site Suitability Scoring Matrix for Sub-Site 18 
  
 One major strength of Sub-Site 18 is its financial attributes, which are detailed 
in Figure 28. This Sub-site has payback period of 12.6 years. This value is 
significantly lower than the payback period of Site X, which had a payback period of 
16.4 years. Also, the IRR of Sub-Site 18 is 9.3%, which higher than that of the larger 
Site. 
 
Figure 28: Financial Analysis for Sub-Site 18 
 
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Site U 
Site U is located on a mountain chain on the north-west corner of Allegany 
County north of Frostburg on the border with Pennsylvania.  The site was identified 
as having high wind speeds from a cursory view of the Maryland NREL wind data. A 
more detailed review of the site investigated the land use of the entire county as 
shown in Figure 29, below.  According to the MD Department of Planning Legend in 
the appendix, Site U is located in a forested region classified as ?evergreen forest? 
(MD DNR, 2009), though inspection of the aerial image from Google Earth in Figure 
28 reveals that the site is mixed use.   
 
Figure 29: Allegany County Land Use from MD Department of Planning (MDP, 2009), showing Site U in red box 
There appears to also be non-coal surface mining in the proximity of the site, 
as shown by the yellow indicator number ?2? in Figure 30, below.  Proximity to a 
mining site may be well suited for development because of the need for 
environmental reclamation at mining sites to prevent soil erosion and environmental 
damage. 
 
 123 
 
 
Figure 30: Map of Allegany County from MD Department of Environment (MDE, 2009) showing non-coal 
surface mines 
The dense forest cover and site preparation from a mine would likely present a 
moderate obstacle to build, warranting a ranking of 3 for ?Suitability of Land for 
Construction.?   The site does not appear to have inhabited lands nearby and is not 
used for tourism, though agricultural and mining activities nearby may prevent some 
objection from ?nearby populations.?  The site receives a 4 for this attribute.  
The GIS image below in Figure 31 indicates that there is a full-protection 
region on the site that overlaps with some of the windy areas of the site.   The reason 
for classification of these areas as ?full protection? must be further investigated 
before a site can be developed on Site U, though since the majority of windy area is 
located on ?no protection? areas, the region is given a ranking of 5 for ?land use 
protection,? to represent no anticipated conflict. 
 
 124 
 
 
Figure 31: GIS Image of Site U 
Figure 31, above, shows the available wind potential on the site in yellow, 
orange and red.  Wind on Site U is available in an irregular pattern, with 
measurements at 50m indicating speeds ranging between 6.4 and 8 meters per second.  
Some ?excellent? wind is centralized through the middle of the strip, though only 
?good? and ?fair? wind is available at the perimeter.   The site wind characteristics 
are approximated with a ranking of 1.8 because of the prevalence of ?fair? wind.  The 
dimensions of land with available wind were approximated as a rectangle with length 
6.4 km and width of 1.4 km.  The output of the MATLAB algorithm indicated that 
the GE Wind 2.5 MW turbine is most suitable for the site, and that 14 turbines can be 
placed on the site, producing 35 MW at maximum capacity.  Expected power 
generation was determined to be 8.68 MW based on the wind characteristics of the 
site relative to the power curve of the selected wind turbine.  Expected power output 
divided by the maximum turbine power yielded a 25% capacity factor. 
The GIS image in Figure 31 also revealed that the center of the windy region 
of the site was 1.8 km away from a 116-138 kV transmission line, corresponding to a 
ranking of 3 and 5 for ?voltage suitability? and ?transmission distance,? respectively.  
The Google Earth image in Figure 32 shows a road running through the site, with a 
 
 125 
 
perpendicular distance of 0.3 km from the center.  This corresponds with a ranking of 
5 for ?road connectivity.? 
 
Figure 32: Aerial image of Site U 
Each site and energy attribute rankings were put into the weighted scoring 
matrix in Table 20 below, yielding a site value of 2.143 with 47.7% weight, a ranking 
of 3 for capacity factor with 39.2% weight, and energy generated rank of 3.  The 
overall site score is 2.59. 
 
 
 
 
 
 
 
 
 
 
 
 
 126 
 
Table 20: Overall Site Suitability Scoring Matrix for Site U 
 
The financial analysis using RETScreen utilized the capacity factor, and the 
number and size of turbines, to estimate an output of 76,700 MWh of annual 
generation from the Site U wind farm.  The initial upfront plant costs would be 
$56,000,000.  This figure includes plant power system costs and ?other? costs at 
$10,500,000.  Operations and maintenance costs were expected to be $1,150,000 
annually.  The cumulative cash flows graph and worksheet are presented below in 
Figure 33. 
 
 
 127 
 
Figure 33: Financial Analysis for Site U 
It was determined that the wind farm would begin making profit after 16.7 
years of operation, which is equivalent to an investing the upfront costs with 5.0% 
interest.   A cursory RETScreen emission analysis determined that the site would 
annually displace 46,000 tCO2 that would otherwise be generated by an average 
electricity fuel mix in the United States.  This is the equivalent of 39,000 acres of 
forested land absorbing CO2 from the atmosphere over the course of the year. 
Site U ? Sub-site 21: 
 
 Site U was then focused into Sub-site 21, which only had wind of category 5 
and 6. The high wind speeds are shown in the GIS form in Figure 35, and the Google 
Earth image is shown in Figure 34. 
 
 
 128 
 
 
Figure 34: Aerial image of Sub-Site 21 
 
 
Figure 35: GIS Image of Sub-Site 21 
 
 The site suitability and energy availability factors were analyzed using the 
weighted decision matrix. Sub-Site 21 received an Overall Suitability score of 2.9, 
which is the fifth highest score for a Sub-Site. This Sub-Site has a capacity factor of 
32.6%, which received a score of 5. Also, the Sub-Site produces a total of 1.63 MW, 
which receives a score of 0. 
 
 129 
 
Table 21: Overall Site Suitability Scoring Matrix for Sub-Site 21 
  
 The financial attributes of Sub-Site 21 were measured using RETScreen. The 
results from the analysis are shown in Figure 36. The payback period for Sub-Site 21 
is 12.8 years, which is less than the payback period for Site U. Sub-Site 21 also has an 
IRR of 9.0%, which is higher than that of the larger Site U. 
 
Figure 36: Financial Analysis for Sub-Site 21 
 
 130 
 
Site V 
Site V is located on a mountain chain in south-west Allegany County that was 
identified as having high wind speeds from a cursory view of the Maryland NREL 
wind data maps. A more detailed review of the site investigated the land use of the 
entire county as shown in Figure 37, below.  Site V is located in a forested region 
classified as ?evergreen forest? (MD DNR, 2009).  The dense forest cover presents a 
moderate obstacle to build, so the site receives a ranking of 3 for ?Suitability of Land 
for Construction.?   There are agricultural or developed areas in the immediate 
proximity, corresponding to a ranking of 5 for ?nearby populations?.  Local 
population impacts are unlikely to play a significant role in commissioning this wind 
site. 
 
Figure 37: Allegany County Land Use from MD Department of Planning (MDP, 2009), showing Site V in red box 
The site includes Dans Mountain State Wildlife Management Area, which is a 
9,200 acre contiguous tract of forest famous for its numerous species of songbirds and 
wildlife (DNR, 2009).  Though wind projects are not legally prohibited on this site, 
 
 131 
 
there are direct environmental impacts that must be assessed before site development.   
Figure 38, below, shows the available wind potential on the site in yellow, orange and 
red.  The pink coloration outlines the extent of the Dans Mountain State Wildlife 
Management Area.  Because there is clear overlap, the ?land use protection? is given 
a ranking of 2.5, to represent a potential development conflict.     
 
Figure 38: GIS data image of Site V 
The wind pattern on Site V is very strong, with measurements at 50m 
indicating speeds ranging between 6.4 and 8 meters per second.  A strong central strip 
of ?good? wind runs through the wind pattern, and is surrounded by ?fair? wind at the 
strip?s borders.  A few patches of ?excellent? wind are on the strip also.   The site?s 
wind characteristics are approximated as ?good? between 7 and 7.5 meters per 
second, corresponding to a ranking of 2.  The dimensions of land with available wind 
were approximated as a rectangle with length 10.9 km and width of 1.3 km.  The land 
dimensions and wind speed ranking were input into MATLAB algorithm to 
determine that the GE Wind 2.5 MW turbine is most suitable for the site, and that 22 
 
 132 
 
turbines can be placed on the site with an optimal packing structure.  Expected power 
output was determined to be 14.3 MW based on the wind characteristics of the site 
relative to the power curve of the selected wind turbine.  The expected power output, 
divided by the turbine power of 55 MW (assuming 100% output), yielded the 
capacity factor of 26%.  
The GIS image in Figure 38 also revealed that the center of the windy region 
of the site was 3.5 km away from a 351-500 kV transmission line, corresponding to a 
ranking of 5 and 4 for ?voltage suitability? and ?transmission distance,? respectively.  
The Google Earth image in Figure 39 shows a road running through the site at 
approximately 0.7 km from the windy region?s center, corresponding with a ranking 
of 5 for ?road connectivity.?   
 
Figure 39: Aerial image of Site V 
Each of these site and energy rankings were put into the weighted scoring matrix in 
Table 22, below, yielding a site value of 1.56 with 47.7% weight, a ranking of 3 for 
capacity factor , and energy generated rank of 5.  The overall site score accounting for 
100% of these inputs is 2.5735.  
 
 133 
 
Table 22: Overall Site Suitability Scoring Matrix for Site V 
 
The financial analysis using RETScreen estimated an output of 125,000 MWh 
of annual generation from the Site V wind farm.  It was determined that the initial 
upfront plant costs would be $88,000,000.  This figure includes plant power system 
costs and ?other? costs, which internalizes site purchase costs, transmission 
interconnect, substation costs, and road connections. Operations and maintenance 
costs were expected to be $1,880,000 annually.  The cumulative cash flows graph and 
worksheet are presented below in Figure 40. 
 
 134 
 
 
Figure 40: Financial Analysis for Site V 
It was determined that the wind farm would begin making profit after 16.1 
years of operation, which is equivalent to an investing the upfront costs with 5.5% 
interest.   A cursory RETScreen emission analysis determined that the site would 
annually displace 75,200 tCO2 that would otherwise be generated by an average 
electricity fuel mix in the United States.   This is the equivalent of 63,900 acres of 
forested land absorbing CO2 from the atmosphere over the course of the year.   
Site V also contains sub-sites 14 and 15, though those sub-sites are not 
included here for discussion.  Sub-site 15 did not receive an overall site score that 
placed it amongst the strongest sub-sites.  An error occurred in the weighted decision 
matrix analysis for sub-site 14, so it has been excluded altogether from the rankings, 
though the incomplete data may be viewed in the appendix. 
Site W 
Site W is located on a mountain chain in south Allegany County overlapping 
with Dans Mountain State Wildlife Management Area that was discussed in the Site 
 
 135 
 
V analysis.  The site was identified as having high wind speeds from a cursory view 
of the Maryland NREL wind data maps. A more detailed review of the site 
investigated the land use of the entire county as shown in Figure 41, below.  
According to the MD Department of Planning Legend in the appendix, Site W is 
located in a forested region classified as ?evergreen forest? (MDP, 2009), and the 
windy regions as shown in Figure 42 show that there is little human infrastructure on 
the potential development site.   
 
Figure 41: Allegany County Land Use from MD Department of Planning (MDP, 2009), showing Site W in red box 
The dense forest cover warrants a ranking of 3 for ?Suitability of Land for 
Construction.?   The site does not appear to have inhabited lands nearby but may be 
used for tourism because of its proximity to the wildlife area.  The site receives a 4 
for ?nearby populations.? 
The GIS image indicates that half of the site is no protection (5), while the 
other half is partial protection (2.5).  The average reveals our ranking of 3.75 for 
?land use protection.? 
 
 136 
 
 
Figure 42: GIS Image of Site W (Scale 1:25,000) 
Figure 42, above, shows the available wind potential on the site in yellow, 
orange and red.  Wind on Site W is available in a contiguous strip.  Some ?excellent? 
wind is centralized through the middle of the strip, though only ?good? and ?fair? 
wind is available at the perimeter.   The site wind characteristics are approximated 
with a ranking of 1.8 because of the prevalence of ?fair? wind.1  The dimensions of 
land with available wind were approximated as a rectangle with length 12 km and 
width of 2.2 km.  The output of the MATLAB algorithm indicated that the GE Wind 
2.5 MW turbine is most suitable for the site, and that 36 turbines can be placed on the 
site, producing 90 MW at maximum capacity.  Expected power generation was 
determined to be 22.32 MW based on the wind characteristics of the site relative to 
the power curve of the selected wind turbine.  Expected power output divided by the 
maximum turbine power yielded a 24.8% capacity factor. 
                                                 
1 Though intended as an objective process for assessing wind speed rankings, uncertainty oftentimes exists in the 
measure of relative proportions of each wind classification.  Resultantly, the Site W wind characteristics may be 
judged to be as high as 2.8, which would significantly increase the site?s value. 
 
 137 
 
The site?s GIS image also revealed that the center of the windy region was 4.0 
km away from a 116-138 kV transmission line, corresponding to a ranking of 3 and 4 
for ?voltage suitability? and ?transmission distance,? respectively.  There is a road in 
close proximity to the site, with a perpendicular distance of 0.2 km from the center, 
corresponding to a ranking of 5 for ?road connectivity.? 
Figure 43: Aerial image of Site W 
Each site and energy attribute rankings were put into the weighted scoring 
matrix in Table 23, below, yielding a site value of 1.78 with 47.7% weight, a ranking 
of 2 for capacity factor with 39.2% weight, and energy generated rank of 5.  The 
overall site score accounting for 100% of these inputs is 2.29. 
 
 
 
 
 
 
 
 
 138 
 
Table 23:  Overall Site Suitability Scoring Matrix for Site W 
 
The financial analysis using RETScreen utilized the capacity factor, and the 
number and size of turbines, to estimate an output of 196,000 MWh of annual 
generation from the Site W wind farm.  The initial upfront plant costs would be 
$144,000,000.  This figure includes plant power system costs and ?other? costs at 
$27,000,000.  Operations and maintenance costs were expected to be $2,933,000 
annually.  The cumulative cash flows graph and worksheet are presented below in 
Figure 44. 
 
 139 
 
 
Figure 44: Financial Analysis for Site W 
It was determined that the wind farm would begin making profit after 16.8 
years of operation, which is equivalent to investing the upfront costs with 4.8% 
interest.   A cursory RETScreen emission analysis determined that the site would 
annually displace 117,000 tCO2 that would otherwise be generated by an average 
electricity fuel mix in the United States.  This is the equivalent of 100,000 acres of 
forested land absorbing CO2 from the atmosphere over the course of the year. 
Site W ? Sub-site 17: 
 
Site W was then focused into Sub-site 17, which only had wind of category 4 
and 5. The high wind speeds are shown in the GIS form in Figure 46, and the Google 
Earth image is shown in Figure 45. 
 
 
 140 
 
 
Figure 45: Aerial image of Sub-Site 17 
 
 
Figure 46: GIS Image of Sub-Site 17 
 
The site suitability and energy availability factors were then analyzed using 
the weighted decision matrix shown in Table 24. Sub-Site 17 received an Overall 
Suitability score of 3.1, which is the second highest score for a Sub-Site. This Sub-
Site has a capacity factor of 33.5%, which received a score of 5. This is significantly 
higher than the capacity factor of the larger site. Also, the Sub-Site produces a total of 
4.19 MW, which receives a score of 1. This is significantly lower than the total power 
output of the larger site. 
 
 141 
 
Table 24: Overall Site Suitability Scoring Matrix for Sub-Site 17 
  
The financial attributes of Sub-Site 17 were measured using RETScreen. The 
results from the analysis are shown in Figure 47. The payback period for Sub-Site is 
12.5 years, which is less than the payback period for Site U. Sub-Site 21 also has an 
IRR of 9.5%, which is higher than that of the larger Site U. 
 
 
Figure 47: Financial Analysis for Sub-Site 17 
 
 142 
 
Site W also contains sub-site 16, which is included in the appendix but not in 
this discussion.  The site?s characteristics did not qualify it amongst the best sub-sites. 
Performance of the Weighted Decision Matrix: 
It is important to consider the validity of each series of results, particularly 
regarding the efficacy of the weighted decision matrix in predicting financial 
feasibility of the site.  Financial feasibility is an important consideration for any off-
site renewable project.  A low equity payback period is one indicator of a high 
potential site.  In Figure 48, the overall site scores for each of the sites were plotted in 
descending order, and a trendline was added.  Each site?s corresponding equity 
payback period was also plotted.  There is a strong increasing trend of the equity 
payback as the overall site score decreases.  This behavior indicates that poor site 
scores correlate with poor equity payback periods.  This attests to the strength of the 
Weighted Decision Matrix analysis.   
 
Figure 48: Relationships between Overall Site Score and other site viability parameters 
Additionally, the performance of the weighted decision matrix for the innately 
stronger sub-sites can also be assessed.  Relationships between the overall sub-site 
 
 143 
 
scores from the weighted decision matrix and the RETScreen financial outputs are 
much less apparent than those in the site plot in Figure 48, above.  Figure 49, below, 
shows the financial parameters specified for each sub-site ordered by the overall sub-
site scores from highest to lowest.  Very weak correlations exist between the best sub-
site score and a high Pre-tax IRR ? equity and low equity payback period.   The 
trendlines for the pre-tax IRR ? equity data and equity payback indicate that pre-tax 
IRR ? equity decreases slightly with decreasing site score, as would be anticipated, 
though the correlation is weak and poorly fit.  With similar utility, a weak correlation 
for equity payback shows increasing payback periods as the sub-site scores get worse.   
The weak correlation results from several factors.  Firstly, each of these sites have 
very high capacity factors that were awarded a score of ?5? for capacity factor in the 
weighted decision matrix.  The equity payback period and pre-tax IRR ? equity is 
strongly correlated with capacity factor, and as a result strong correlations between 
sub-site scores and financial parameters would exist.  However, because attribute 
scores cannot surpass ?5?, differences within these best sub-sites were not reflected in 
the weighted decision matrix.  This result effectively ?washed out? the contribution of 
capacity factor from the weighted decision matrix, causing significant distortion of 
the sub-site values.  Resultantly, the relative value of the weighted decision output for 
each sub-site indicates the value of the physical site attributes, particularly land 
protection.  These inputs have no impact on the RETScreen financial analysis, 
causing the weak correlations discussed above.   
In future analyses, this issue could be addressed by adjusting capacity factor 
attribute score ranges to reward the highest capacity factors relative to others.  
 
 144 
 
However, this would skew the value of the sub-site scores relative to the site scores.  
Alternatively, the range of attribute scores could be extended, for example, by 
assigning a value of ?6? for the highest capacity factors.  This approach would 
remove some of the intentionally included margin of error which is assured by using 
only round measures of attribute value. 
 
Figure 49: Relationship between overall sub-site score and RETScreen Outputs 
 
Sensitivity of RETScreen Financial Analysis 
Policy change can significantly impact the Equity Payback and Pre-tax IRR-
equity of any wind development.  According to the IPCC report (IPCC, 2007c), 
economic instruments that could increase renewable energy production include 
capital grants, feed-in tariffs, quote obligation and permit trading, greenhouse gas 
taxes, and tradable emissions permits.  As discussed in the methodology, MD state 
and federal government policies have already stimulated wind development since the 
 
 145 
 
end of the 1990?s.   Further policies could enhance demand for wind, primarily by 
increasing the price of carbon intensive energy development.   
RETScreen simulations were performed to predict the impact of a carbon 
price on the Equity Payback and Pre-tax IRR ? equity.    The size of the tax, the 
number of carbon sources the tax encompasses, and the complimentary government 
policies to alleviate high electricity prices all influence the impact of the carbon tax 
on the wind farm site.  Here, an upper end estimate of the tax?s value is intended to 
suggest a reasonable maximum benefit.   
Table 25: Greenhouse Reduction income parameter assumptions at the upper limit of sensitivity analysis 
  
 
 
Figure 50: Financial Analysis with upper limit greenhouse reduction income 
The RETScreen software intends for the emission reduction credit fields to be 
used in conjunction with a policy like the Kyoto Protocol?s Clean Development 
Mechanism, or for Joint Implementation Projects.  These projects reward renewable 
energy projects that can demonstrate an emission reduction above a baseline scenario 
 
 146 
 
where the project would not have otherwise been implemented.  Though the United 
States has not ratified the Kyoto Protocol, and a post-Kyoto international climate 
treaty to govern carbon emission reductions is unlikely to resemble the Kyoto 
Accords, the level of this crude analysis allows for a broad estimate of the impact of a 
policy that awards emission reductions of a ton-by-ton CO2 basis.  Prices for emission 
reduction credits range on the present market from $1 to $35, depending on whether 
the market is voluntary or mandatory, traded publicly or privately, traded regionally, 
nationally or internationally, and other factors.  The upper value of $35 was selected 
for this emission analysis to understand the current extent of impact possible. Clean 
Development Mechanism projects use a fixed crediting period of 10 years, which is 
maintained for this crude analysis. 
For simplicity, the benefit of the Renewable Energy Credits has been 
removed, assuming that the carbon reduction credit will be larger and would take the 
place of any RECs in future markets.  The other federal and state incentives and 
grants are also removed, assuming that the emission reduction credit supplements 
them as the primary means for policy-driven emission reductions.  The escalation rate 
accounts for inflation in the emission reduction credit rate over the duration of the 
credit period. 
The result of the analysis indicates that a pricing of carbon at $35 per ton of 
CO2 reduced results in an equity payback period reduction from 11.9 years to 9.4 
years, which is a significant reduction.  Future carbon policy will increase the 
profitability of any of the proposed sites and sub-sites.   
 
 147 
 
Chapter 6:  Summary and Conclusions 
The purpose of this study was to assess one possible means for the University 
of Maryland to purchase renewable electricity produced in the state of Maryland.  
This University objective is laid out in the campus?s Climate Action Plan. Though 
significant potential exists in other states, as well as along the Atlantic coast of 
Maryland and in the Chesapeake Bay, this study aimed only to assess wind resources 
in Western Maryland.  Maryland regions that had commercially exploitable wind 
energy potential were broken into sites for analysis.  Each site was judged based on 
eight criteria: 
- The suitability of nearby transmission based on voltage 
- The distance to nearby transmission lines 
- The distance to roadways 
- The suitability of the site land for construction 
- Proximity to nearby populations 
- Land use protection 
- Energy Generated 
- Capacity factor 
 
An analysis using Analytical Hierarchy Process (AHP) determined weightings for 
each criterion, concluding that capacity factor and land use protection were the most 
important criteria for assessing overall site suitability.  Criterion rankings were 
assigned to each site, and based on each criterion?s weight, a relative score was 
produced ? indicating the best suited sites for development.  Further site investigation 
using RETScreen financial analysis software further supported the value of our 
criteria in determining site value, particularly the influence of capacity factor on site 
score.  More extensive analysis of selected ?sub-sites? indicated the value of the sites 
if the University were to build on only the lands with the highest winds. 
 
 148 
 
Should the University of Maryland choose to pursue its goals of buying 
renewable electricity from off-campus sources as outlined in the Climate Action Plan 
via the construction of wind turbines, the sites analyzed in this report provide a firm 
starting point for more thorough feasibility studies.  Additionally, the flexibility of 
this analysis provides a framework for the determination of other potential areas for 
wind development.   
 Through our cursory analysis of 26 potential wind-farm sites in western 
Maryland, we have shown that the top 5 sites are capable of producing between 8 and 
22 MW of power.  Additionally, these sites show the potential of having equity 
payback periods as low as 13.5 years, and pre-tax internal rates of return as high as 
8.1%.  Sub-sites identified for their high wind speeds may have equity payback 
periods as low as 11.9 years and Pre-tax IRR-equity as high as 10.3%.  Not only do 
they represent a sound way of fulfilling the University?s Climate Action Plan, these 
sites may also represent a sound investment.  Also, with our flexible weighting 
system, which can be tailored to reflect current priorities, other sites can be identified 
which may show more potential as circumstances change in the future than they 
currently do in our analysis.  The influence of emerging policies on renewable energy 
development cannot be overstated, and the RETScreen tool is capable of 
accommodating greenhouse gas emission regulations. 
 As the need for renewable energy increases, it becomes increasingly important 
to identify available resources.  Wind power represents an important source of 
renewable energy, one that is currently underutilized and rapidly developing in the 
state of Maryland.  
 
 149 
 
Appendix 
Land Use 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MARYLAND LAND USE\LAND COVER CLASSIFICATION SCHEME 
 
The land use\land cover classification scheme described below has been used to identify the 
predominant usage of land that could be interpreted from high altitude aerial photography and satellite 
imagery. The LU_CODE field, in each county land use shape file, contains the 2 or 3 digit integer 
numbers identified below. 
In general, only land uses greater than 10 acres in size have been identified. Transportation features such 
as roads, highways, rail lines and utility lines have not been included in this GIS database. 
Transportation features are better represented by the point and line files available from the Maryland 
State Highway Administration. 
Urban Built-Up 
11 Low-density residential - Detached single-family/duplex dwelling units, yards and associated 
areas. Areas of more than 90 percent single-family/duplex dwelling units, with lot sizes of less than five 
acres but at least one-half acre (.2 dwelling units/acre to 2 dwelling units/acre). 
12 Medium-density residential - Detached single-family/duplex, attached single-unit row housing, 
yards, and associated areas. Areas of more than 90 percent single-family/duplex units and attached 
single-unit row housing, with lot sizes of less than one-half acre but at least one-eighth acre (2 dwelling 
units/acre to 8 dwelling units/acre). 
13 High-density residential - Attached single-unit row housing, garden apartments, high-rise 
apartments/condominiums, mobile home and trailer parks. Areas of more than 90 percent high-density 
residential units, with more than 8 dwelling units per acre. 
14 Commercial - Retail and wholesale services. Areas used primarily for the sale of products and 
services, including associated yards and parking areas. 
15 Industrial - Manufacturing and industrial parks, including associated warehouses, storage yards, 
research laboratories, and parking areas. 
16 Institutional - Elementary and secondary schools, middle schools, junior and senior high schools, 
public and private colleges and universities, military installations (built-up areas only, including 
buildings and storage, training, and similar areas), churches, medical and health facilities, correctional 
facilities, and government offices and facilities that are clearly separable from the surrounding land 
cover. 
17 Extractive - Surface mining operations, including sand and gravel pits, quarries, coal surface 
mines, and deep coal mines. Status of activity (active vs. abandoned) is not distinguished. 
18 Open urban land - Urban areas whose use does not require structures, or urban areas where non-
conforming uses characterized by open land have become isolated. Included are golf courses, parks, 
recreation areas (except areas associated with schools or other institutions), cemeteries, and entrapped 
agricultural and undeveloped land within urban areas. 
 
           Continued? 
Figure 51: Maryland land use classifications from MD DNR 
 
 150 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Agriculture 
21 Cropland - Field crops and forage crops. 
22 Pasture - Land used for pasture, both permanent and rotated; grass. 
23 Orchards/vineyards/horticulture - Areas of intensively managed commercial bush and tree crops, 
including areas used for fruit production, vineyards, sod and seed farms, nurseries, and green houses. 
241 Feeding operations - Cattle feed lots, holding lots for animals, hog feeding lots, poultry houses. 
242 Agricultural building breeding and training facilities, storage facilities, built-up areas associated 
with a farmstead, small farm ponds, commercial fishing areas. 
25 Row and garden crops - Intensively managed truck and vegetable farms and associated areas. 
 
40 Forest 
41 Deciduous forest - Forested areas in which the trees characteristically lose their leaves at the end 
of the growing season. Included are such species as oak, hickory, aspen, sycamore, birch, yellow poplar, 
elm, maple, and cypress. 
42 Evergreen forest - Forested areas in which the trees are characterized by persistent foliage 
throughout the year. Included are such species as white pine, pond pine, hemlock, southern white cedar, 
and red pine. 
43 Mixed forest - Forested areas in which neither deciduous nor evergreen species dominate, but in 
which there is a combination of both types. 
44 Brush - Areas which do not produce timber or other wood products but may have cut-over timber 
stands, abandoned agriculture fields, or pasture. These areas are characterized by vegetation types such 
as sumac, vines, rose, brambles, and tree seedlings. 
Water - Rivers, waterways, reservoirs, ponds, bays, estuaries, and ocean. 
Wetlands - Forested or non-forested wetlands, including tidal flats, tidal and non-tidal marshes, and 
upland swamps and wet areas. 
 
Barren land 
71 Beaches - Extensive shoreline areas of sand and gravel accumulation, with no vegetative cover or 
other land use. 
72 Bare exposed rock - Areas of bedrock exposure, scarps, and other natural accumulations of rock 
without vegetative cover. 
73 Bare ground - Areas of exposed ground caused naturally, by construction, or by other cultural 
processes. 
Transportation - Miscellaneous Transportation features not elsewhere classified. 
 
 Figure 52:Maryland land use classification from MD DNR, continued 
 
 151 
 
Potential Conflict Maps 
 
The maps below indicate reference lands that may conflict with wind development 
sites.  Use of the wind farm as environmental reclamation for mining sites, however, 
may increase their economic value. 
 
 
Figure 53: Map of Washington County from MD Department of Environment (MDE, 2009) showing non-coal 
surface mines 
 
 152 
 
 
 
Figure 54: Map of Garrett County from MD Department of Environment (MDE, 2009) showing non-coal surface 
mines 
 
Figure 55: Map of Allegany and Garrett Counties from MD Department of Environment (MDE, 2009) showing 
locations of MD natural gas wells 
 
 153 
 
Individual Site Data and Characterization 
Site A: Data 
 
Figure 56: Aerial image of Site A 
 
Figure 57: GIS image of Site A 
 
 
 
 
 
 
 
 154 
 
Table 26: Overall Site Suitability Scoring Matrix for Site A.1, A.2, and A.3 
  
 
Figure 58: Financial Analysis for Site A.1 
 
 155 
 
 
Figure 59: Financial Analysis for Site A.2 
 
Figure 60: Financial Analysis for Site A.3 
 
 
 
 156 
 
Site B: Data 
 
Figure 61: Aerial image of Site B 
 
Figure 62: GIS Image for Site B 
 
 157 
 
 
Table 27: Overall Site Suitability Scoring Matrix for Site B.1 and B.2 
  
 
Figure 63: Financial Analysis for Site B.1 
 
 158 
 
 
Figure 64: Financial Analysis for Site B.2 
 
Site C: Data 
 
 
Figure 65: Aerial image of Site C 
 
 159 
 
 
Figure 66: GIS Image for Site C 
 
Table 28: Overall Site Suitability Scoring Matrix for Site C 
  
 
 160 
 
 
Figure 67: Financial Analysis for Site C 
Site C ? Sub-site 1: 
 
Figure 68: Aerial image of Sub-Site 1 
 
 
 161 
 
 
Figure 69: GIS Image of Sub-Site 1 
 
Table 29: Overall Site Suitability Scoring Matrix for Sub-Site 1 
  
 
 162 
 
 
Figure 70: Financial Analysis for Sub-Site 1 
 
Site D: Data 
 
Figure 71: Aerial image of Site D 
 
 
 163 
 
 
Figure 72: GIS Image for Site D 
 
Table 30: Overall Site Suitability Scoring Matrix for Site D.1 and D.2 
  
 
 164 
 
 
Figure 73: Financial Analysis for Site D.1 
 
Figure 74: Financial Analysis for Site D.2 
 
 165 
 
Site E: Data 
 
Figure 75: Aerial image of Site E 
 
Figure 76: GIS image of Site E 
 
 166 
 
Table 31: Overall Site Suitability Scoring Matrix for Site E 
  
 
Figure 77: Financial Analysis for Site E 
 
 167 
 
Site E ? Sub-Site 2: 
 
Figure 78: Aerial image of Sub-Site 2 
 
 
Figure 79: GIS Image of Sub-Site 2 
 
 
 168 
 
Table 32: Overall Site Suitability Scoring Matrix for Sub-Site 2 
  
 
Figure 80: Financial Analysis for Sub-Site 2 
 
 169 
 
Site F: Data 
 
Figure 81: Aerial image of Site F 
 
Figure 82: GIS Image of Site F 
 
 170 
 
Table 33: Overall Site Suitability Scoring Matrix for Site F 
  
 
Figure 83: Financial Analysis for Site F 
 
 
 
 171 
 
Site G: Data 
 
Figure 84: Aerial image of Site G 
 
Figure 85: GIS Image of Site G 
 
 172 
 
Table 34: Overall Site Suitability Scoring Matrix for Site G 
  
 
Figure 86: Financial Analysis for Site G 
 
 173 
 
Site G ? Sub-Site 3: 
 
Figure 87: Aerial image of Sub-Site 3 
 
 
Figure 88: GIS Image of Sub-Site 3 
 
 
 174 
 
Table 35: Overall Site Suitability Scoring Matrix for Sub-Site 3 
  
 
Figure 89: Financial Analysis for Sub-Site 3 
 
 
 175 
 
Site H: Data 
 
Figure 90: Aerial image of Site H 
 
Figure 91: GIS Image of Site H 
 
 176 
 
Table 36: Overall Site Suitability Scoring Matrix for Site H 
  
 
Figure 92: Financial Analysis for Site H 
 
 177 
 
Site H ? Sub-site 4: 
 
Figure 93: Aerial image of Sub-Site 4 
 
 
Figure 94: GIS Image of Sub-Site 4 
 
 
 178 
 
Table 37: Overall Site Suitability Scoring Matrix for Sub-Site 4 
  
 
Figure 95: Financial Analysis for Sub-Site 4 
 
 
 179 
 
Site I: Data 
 
Figure 96: Aerial image of Site I 
 
Figure 97: GIS Image of Site I 
 
 180 
 
Table 38: Overall Site Suitability Scoring Matrix for Site I 
  
 
Figure 98: Financial Analysis for Site I 
 
 
 
 181 
 
Site J: Data 
 
Figure 99: Aerial image of Site J 
 
Figure 100: GIS Image of Site J 
 
 182 
 
Table 39: Overall Site Suitability Scoring Matrix for Site J 
  
 
Figure 101: Financial Analysis for Site J 
 
 
 183 
 
Site K: Data 
 
Figure 102: Aerial image of Site K 
 
Figure 103: GIS Image of Site K 
 
 
 
 
 
 
 
 
 184 
 
Table 40: Overall Site Suitability Scoring Matrix for Site K.1 and K.2 
  
 
Figure 104: Financial Analysis for Site K.1 
 
 185 
 
 
Figure 105: Financial Analysis for Site K.2 
 
Site L: Data 
 
Figure 106: Aerial image of Site L 
 
 186 
 
 
Figure 107: GIS Image of Site L 
Table 41: Overall Site Suitability Scoring Matrix for Site L 
  
 
 187 
 
 
Figure 108: Financial Analysis for Site L 
 
Site L ? Sub-site 5: 
 
Figure 109: Aerial image of Sub-Site 5 
 
 
 188 
 
 
Figure 110: GIS Image of Sub-Site 5 
 
Table 42: Overall Site Suitability Scoring Matrix for Sub-Site 5 
  
 
 189 
 
 
Figure 111: Financial Analysis for Sub-Site 5 
 
 
Site M (and sub-site 6) is discussed in the Results section on page 96. 
 
Site N: Data 
 
Figure 112: Aerial image of Site N 
 
 
 190 
 
 
Figure 113: GIS Image of Site N 
Table 43: Overall Site Suitability Scoring Matrix for Site N.1 and N.2 
  
 
 191 
 
 
Figure 114: Financial Analysis for Site N.1 
 
Figure 115: Financial Analysis for Site N.2 
 
 
 
 192 
 
Site N1 ? Sub-site 7: 
 
Figure 116: Aerial image of Sub-Site 7 
 
 
Figure 117: GIS Image of Sub-Site 7 
 
 
 193 
 
Table 44: Overall Site Suitability Scoring Matrix for Sub-Site 7 
  
 
Figure 118: Financial Analysis for Sub-Site 7 
 
 
 194 
 
Site O: Data 
 
Figure 119: Aerial image of Site O 
 
 
Figure 120: GIS Image of Site O 
 
 195 
 
Table 45: Overall Site Suitability Scoring Matrix for Site O 
  
 
Figure 121: Financial Analysis for Site O 
 
 
 196 
 
Site O ? Sub-Site 8: 
 
Figure 122: Aerial image of Sub-Site 8 
 
 
Figure 123: GIS Image of Sub-Site 8 
 
 
 197 
 
Table 46: Overall Site Suitability Scoring Matrix for Sub-Site 8 
  
 
Figure 124: Financial Analysis for Sub-Site 8 
 
 
 198 
 
Site P: Data 
 
Figure 125: Aerial image of Site P 
 
 
Figure 126: GIS Image of Site P 
 
 199 
 
Table 47: Overall Site Suitability Scoring Matrix for Site P 
  
 
Figure 127: Financial Analysis for Site P 
 
 
 200 
 
Site P ? Sub-site 13: 
 
Figure 128: Aerial image of Sub-Site 13 
 
 
Figure 129: GIS Image of Sub-Site 13 
 
 
 201 
 
Table 48: Overall Site Suitability Scoring Matrix for Sub-Site 13 
  
 
Figure 130: Financial Analysis for Sub-Site 13 
 
 202 
 
Site Q: Data 
 
Figure 131: Aerial image of Site Q 
 
 
Figure 132: GIS Image of Site Q (Scale 1:50,000) 
 
 203 
 
Table 49: Overall Site Suitability Scoring Matrix for Site Q 
  
 
Figure 133: Financial Analysis for Site Q 
 
 
 
 204 
 
Site Q ? Sub-site 12: 
 
Figure 134: Aerial image of Sub-Site 12 
 
 
Figure 135: GIS Image of Sub-Site 12 
 
 
 205 
 
Table 50: Overall Site Suitability Scoring Matrix for Sub-Site 12 
  
 
Figure 136: Financial Analysis for Sub-Site 12 
 
 206 
 
Site R: Data 
 
Figure 137: Aerial image of Site R 
 
 
Figure 138: GIS Image of Site R 
 
 207 
 
Table 51: Overall Site Suitability Scoring Matrix for Site R 
  
 
Figure 139: Financial Analysis for Site R 
 
 
 208 
 
Site R ? Sub-site 11: 
 
Figure 140: Aerial image of Sub-Site 11 
 
 
Figure 141: GIS Image of Sub-Site 11 
 
 
 209 
 
Table 52: Overall Site Suitability Scoring Matrix for Sub-Site 11 
  
 
Figure 142: Financial Analysis for Sub-Site 11 
 
 210 
 
Site S: Data 
 
Figure 143: Aerial image of Site S 
 
Figure 144: GIS Image of Site S (Scale 1:40,000) 
 
 211 
 
Table 53: Overall Site Suitability Scoring Matrix for Site S 
  
 
Figure 145: Financial Analysis for Site S 
 
 212 
 
Site S ? Sub-Site 9:  
 
Figure 146: Aerial image of Sub-Site 9 
 
 
Figure 147: GIS Image of Sub-Site 9 
 
 213 
 
Table 54: Overall Site Suitability Scoring Matrix for Sub-Site 9 
  
 
Figure 148: Financial Analysis for Sub-Site 9 
 
 
 214 
 
Site S ? Sub-site 10: 
 
Figure 149: Aerial image of Sub-Site 10 
 
 
Figure 150: GIS Image of Sub-Site 10 
 
 
 215 
 
Table 55: Overall Site Suitability Scoring Matrix for Sub-Site 10 
  
 
Figure 151: Financial Analysis for Sub-Site 10 
 
 
 
 216 
 
Site T: Data 
 
Figure 152: Aerial image of Site T 
 
Figure 153: GIS image of Site T (Scale 1:35,000) 
 
 217 
 
Table 56: Overall Site Suitability Scoring Matrix for Site T 
  
 
Figure 154: Financial Analysis for Site T 
 
 
 
 
Site U is discussed in the Results section, on Page 119. 
 
 
 218 
 
 
Site V: Data 
Site V is discussed in the Results section, on Page 112. 
 
Site V ? Sub-site 14: 
 
Figure 155: Aerial image of Sub-Site 14 
 
 
Figure 156: GIS Image of Sub-Site 14 
 
Data error with Sub-site 14 weighted decision matrix.  Data excluded. 
 
 219 
 
 
Figure 157: Financial Analysis for Sub-Site 14 
Site V ? Sub-site 15: 
 
Figure 158: Aerial image of Sub-Site 15 
 
 
 220 
 
 
Figure 159: GIS Image of Sub-Site 15 
 
Table 57: Overall Site Suitability Scoring Matrix for Sub-Site 15 
 
 
 221 
 
 
Figure 160: Financial Analysis for Sub-Site 15 
 
Site W: Data 
 
Site W (and sub-site 17) is discussed in the Results section, on Page 125. 
 
Site W ? Sub-site 16: 
 
Figure 161: Aerial image of Sub-Site 16 
 
 
 222 
 
 
Figure 162: GIS Image of Sub-Site 16 
 
Table 58: Overall Site Suitability Scoring Matrix for Sub-Site 16 
 
 
 223 
 
 
Figure 163: Financial Analysis for Sub-Site 16 
 
 
Site X (and sub-site 18) is discussed in the Results Section, on page 105. 
 
Site Y: Data 
 
Figure 164: Aerial image of Site Y 
 
 
 224 
 
 
Figure 165: GIS Image of Site Y 
 
Table 59: Overall Site Suitability Scoring Matrix for Site Y 
 
 
 
 225 
 
 
Figure 166: Financial Analysis for Site Y 
 
Site Y ? Sub-site 19: 
 
Figure 167: Aerial image of Sub-Site 19 
 
 
 226 
 
 
Figure 168: GIS Image of Sub-Site 19 
 
Table 60: Overall Site Suitability Scoring Matrix for Sub-Site 19 
  
 
 227 
 
 
Figure 169: Financial Analysis for Sub-Site 19 
 
Site Z: Data 
 
Figure 170: Aerial image of Site Z 
 
 
 228 
 
 
Figure 171: GIS Image of Site Z (Scale 1:40,000) 
 
Table 61: Overall Site Suitability Scoring Matrix for Site Z 
  
 
 229 
 
 
Figure 172: Financial Analysis for Site Z 
 
Site Z ? Sub-Site 20: 
 
Figure 173: Aerial image of Sub-Site 20 
 
 
 230 
 
 
Figure 174: GIS Image of Sub-Site 20 
 
Table 62: Overall Site Suitability Scoring Matrix for Sub-Site 20 
 
 
 
 231 
 
 
Figure 175: Financial Analysis for Sub-Site 20 
 
 
 232 
 
MATLAB Algorithm 
 
Below is commented code which can be utilized in MATLAB to calculate turbine 
count, layout, power production, and capacity factor for GE 1.5 and 2.5 MW turbines. 
 
 
 
L0=input('Enter length of land strip in kilometers\n'); L=L0*1000; 
W0=input('Enter width of land strip in kilometers\n');  W=W0*1000; 
Wrank=input('Enter Wind Ranking Value\n'); 
Wspd = 6+.5*Wrank; 
Turbines = [1,1.5,77/2;2,2.5,50]; 
%Matrix rows: turbine type number, rated power (MW), blade length (m) 
%Calculate wind power based on power curves 
P(1) = (200*Wspd - 1000)/1000; 
P(2) = (300*Wspd - 1450)/1000; 
TSize = size(Turbines); 
for i=1:TSize(1) 
    r=Turbines(i,3); 
%Interturbine spacing based on blade length 
R=10*r; 
%Determine stacking method M = 0 for square, 1 for staggered 
Nw0=floor((W-2*r)/(2*R))+1; 
Nw1=floor((W-2*r)/(R*sqrt(3)))+1; 
if Nw0>=Nw1 
    M=0; 
    Nw=Nw0; 
    Nl=floor((L-2*r)/(2*R))+1; 
    N=Nl*Nw; 
else 
    M=1; 
    Nw=Nw1; 
    Nl=(floor((L-2*r)/R)+2)/2-0.5; %ends in .5 if different numbers per row 
    if (Nl-floor(Nl))==0 
        N=Nl*Nw; 
    else 
        if ((-1)^(abs(Nw)))==-1     %odd 
            N=Nl*Nw+0.5; 
        elseif ((-1)^(abs(Nw)))==1  %even 
            N=Nl*Nw; 
        end 
    end 
end 
fprintf('\n'); 
fprintf('Number of GE %g MW Turbines = %g \n',Turbines(i,2),N); 
fprintf('Area = %g km^2 \n',L0*W0); 
if M==0 
    disp('Rectangle Format') 
    fprintf('%g turbines wide by %g turbines long \n',Nw,Nl); 
elseif M==1 
    disp('Staggered Format') 
    fprintf('%g turbines wide by %g turbines long \n',Nw,Nl); 
end 
fprintf('Rated Power Capacity = %g MW\n',N*Turbines(i,2)); 
fprintf('Average Power Production = %g MW\n',N*P(i)); 
fprintf('Capacity Factor = %g %%\n',100*N*P(i)/(N*Turbines(i,2))); 
fprintf('\n'); 
end
Figure 176: MATLAB Algorithm 
 
 233 
 
Glossary 
ArcGIS:  Software allows users to work with GIS data as a means of spatial analysis. 
 
Bald and Golden Eagle Protection Act:  A strict liability statute making it illegal to 
take or possess a protected species. The hunting, pursuit, taking, capturing, killing, 
possession, sale, purchase, import, export of either species, its nest, or its eggs is 
grounds for financial penalty and incarceration.   
 
Carbon Neutral: Term commonly used in reference to a process or entity that is 
responsible for zero net carbon emissions.  Though a process or entity may produce 
directly, or be responsible for, carbon emissions, a net emission of zero may be 
achieved through other activities which extract or prevent an equivalent amount of 
carbon from the atmosphere. 
 
Easement:  A legal contract restricting activity upon a property, typically enduring in 
perpetuity.  Easements are typically made between a private landholder and a private 
or public conservation organization.  Individual easements may have varying 
covenants regarding activity upon the property, but typically, land use is restricted to 
agriculture, forest, or some other ?natural? landscape. 
 
EmPower Maryland Initiative:  Maryland legislation signed into law in 2007 by 
Governor Martin O?Malley.  It pledges that Maryland will reduce energy 
consumption 15% by 2015. 
 
Endangered Species Act (ESA):  a strict liability statute designed to protect species 
from extinction.  It requires the biological assessment of species to determine if they 
are threatened or extinct; if they are deemed to be such, they are listed.  If a person or 
organization engages in a ?taking? of a listed species, that party faces financial 
sanctions and possible incarceration. 
 
Environmental impact statement (EIS):  a document that federal agencies must 
complete, as described in the National Environmental Policy Act, before undertaking 
an action that may affect environmental quality.  It outlines the purpose and need of 
the action, the details of the proposed action, possible alternative actions, and the 
projected environmental impacts of all actions (proposed and alternative).   
 
Forest Legacy Easements:  Easements designed to protect forests from conversion 
to other uses.  The United States Department of Agriculture (USDA) works with state 
agencies to operate this program.  Private forests are targeted for protection.  In 
Maryland, the Maryland Department of Natural Resources is in charge of 
administering the Forest Legacy Program, and the USDA matches bids for Forest 
Legacy easements.   
 
Full Protection: Defined in this research as areas that outlaw development or limit it 
to agricultural or similarly low disturbance activities. 
 
 234 
 
 
Geographic information systems (GIS):  spatial analysis tool that allows users to 
analyze relationships between geographic characteristics and other attributes through 
the generation of digital maps. 
 
Habitat Fragmentation:  The division of habitat by human development.  
Construction of infrastructure, commercial establishments, and residential areas 
breaks apart natural landscapes into discontinuous chunks, which decrease organisms? 
abilities to access diverse food, habitat, and breeding opportunities.  This leads to 
decreased genetic diversity within populations and can hinder the success of species. 
 
Maryland Agricultural Land Preservation Foundation (MALPF) Easements:  
Easements ensuring the protection of forests and farmlands from development 
pressures.  MALPF was created in 1977 to protect farms and forests to ensure the 
continued provision of food and wood for Maryland citizens.  The procedure for 
enrolling one?s property in MALPF is a lengthy, competitive process.  Once land is 
enrolled in this easement program, however, it is protected in perpetuity from the 
pressures of suburban sprawl and development. 
 
Maryland Department of Natural Resource Lands:  Lands managed by DNR, 
which include state forests and state parks.  These lands are meant to be held in the 
public trust for the benefit of Maryland citizens.   
 
Maryland Environmental Trust (MET) Easements:  Easements managed by the 
Maryland Environmental Trust.  MET was created to ?conserve, improve, stimulate, 
and perpetuate the aesthetic, natural, health and welfare, scenic, and cultural qualities 
of the environment, including but not limited to land, water, air, wildlife, scenic 
qualities, open spaces, buildings or any interests therein.?  To serve this purpose, 
MET manages easements donated by private landowners; these donations are tax 
deductible for landowners. 
 
Maryland Rural Legacy Areas:  Large tracts of land in rural uses protected from 
development. The Rural Legacy Program was founded in 1997.  Parties interested in 
conservation of Maryland lands?land trusts, county governments, and interest 
groups?work with landowners to target sites as potential Rural Legacy Areas 
(RLAs).   Those parties serve as sponsors for RLAs that they nominate, crafting a 
long-term conservation and funding plan.  Plans are presented to the Rural Legacy 
Board, which then recommends to the Board of Public Works what sites ought to be 
designated as RLAs and funded.  The Board of Public Works makes the final decision 
on whether a site is designated as a Rural Legacy Area. 
 
Migratory Bird Treaty Act:  A strict liability statute making it illegal to take or 
possess a protected species.  Specifically, it states that it is illegal ?to pursue, hunt, 
take, capture, kill ? possess, offer for sale, sell ? purchase ? ship, export, import 
? transport or cause to be transported? any migratory bird, any part, nest, or eggs of 
any such bird ? (The Act) prohibits the taking, killing, possession, transportation, 
 
 235 
 
and importation of migratory birds, their eggs, parts, and nests, except when 
specifically authorized by the Department of the Interior.? 
 
National Environmental Policy Act (NEPA): A statute signed into law by Richard 
Nixon in 1970.  NEPA holds federal agencies responsible for environmental health by 
requiring the completion of an environmental impact statement by the acting agency 
in any case where environmental quality may be affected.  
 
No Conflict:  Defined in this research as areas not under the jurisdiction of land 
protections listed in the methodology section.  For the purpose of this analysis, they 
were assumed to be compatible with development. 
 
Potential Conflict:  Defined in this research as areas that feature some limitations on 
development, but due to uncertainties in policy or discrepancies in level of protection 
afforded, some development may be acceptable. 
 
Shapefile:  The file type of a GIS data layer. 
 
Taking:  Defined in Section 9 of the Endangered Species Act as ?to harass, harm, 
pursue, hunt, shoot, wound, kill, trap, capture, or collect or attempt to engage in any 
such conduct.?  ?Harm? was later defined as any act altering a species? behavior, 
breeding habits, or ability to survive in general.  A ?taking? is illegal under the ESA.   
 
Targeted ecological area:  Areas DNR defines as meeting a particular ecological 
baseline. 
 
 
 
 
 
 
 236 
 
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