Abstract

This paper presents an agent-based model to investigate interactions between wind farm developers and landowners. Wind farms require hundreds of square miles of land for development and developers typically interact with landowners to lease land for construction and operations. Landowners sign land lease contracts without knowing the turbine layout, which affects aesthetics of property as well as value of the lease contract. Having a turbine placed on one's land is much more lucrative than alternative land uses, but landowners must sign over the use of their land without knowing whether they will receive this financial benefit or not. This process, typically referred to as “Landowner Acquisition,” is highly uncertain for both stakeholders—a source stated up to 50% of wind projects fail due to landowner acquisition issues. We present an agent-based model to study the landowner acquisition period with unique decision-making characteristics for nine landowners and a developer. Citizen participation is crucial to the acceptance of wind farms; thus, we use past studies to quantify three actions a developer can take to influence landowners: (1) community engagement meetings, (2) preliminary environmental studies, and (3) sharing the wind turbine layout with the landowner. Results show how landowner acceptance rates can change over time based on what actions the developer takes. While still in the “proof of concept” stage, this model provides a framework for quantifying wind stakeholder interactions and potential developer actions. Suggestions for how to validate the framework in the future are included in the discussion.

References

References
1.
Energy Information Administration
,
2018
, “
Electricity Generation from Wind
,” https://www.eia.gov/energyexplained/index.php?page=wind_electricity_generation, Accessed February 1, 2019.
2.
Tegen
,
S.
,
Hand
,
M.
,
Maples
,
B.
,
Lantz
,
E.
,
Schwabe
,
P.
, and
Smith
,
A.
,
2012
,
2010 Cost of Wind Energy Review
,
National Renewable Energy Laboratory
,
Golden, CO
.
3.
Stehly
,
T.
,
Heimiller
,
D.
, and
Scott
,
G.
,
2016
,
2016 Cost of Wind Energy Review
,
National Renewable Energy Laboratory
,
Golden, CO
.
4.
Chen
,
L.
, and
MacDonald
,
E.
,
2011
, “
A New Model for Wind Farm Layout Optimization With Landowner Decisions
,”
ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Washington, DC
,
Aug, 28–31
, pp.
303
314
,
ASME
Paper No. DETC2011-47772
.
5.
Windustry
,
2009
, “
Wind Energy Easements and Leases: Compensation Packages
,” https://d3n8a8pro7vhmx.cloudfront.net/windustry/legacy_url/944/Compensation-2009-07-06.pdf?1421782808, Accessed January 25, 2019.
6.
Aakre
,
D.
,
2009
, “
Wind Turbine Lease Considerations for Landowners
,” https://library.ndsu.edu/ir/bitstream/handle/10365/4898/ec1394.pdf?sequence=1, Accessed February 1, 2019.
7.
Shoemaker
,
J. A.
,
2007
, “
Negotiating Wind Energy Property Agreements
,” http://www.flaginc.org/wp-content/uploads/2013/03/WindPropertyAgrmnts20071.pdf, Accessed on January 15, 2019.
8.
Mosetti
,
G.
,
Poloni
,
C.
, and
Diviacco
,
B.
,
1994
, “
Optimization of Wind Turbine Positioning in Large Windfarms by Means of a Genetic Algorithm
,”
J. Wind Eng. Ind. Aerodyn.
,
51
(
1
), pp.
105
116
. 10.1016/0167-6105(94)90080-9
9.
Grady
,
S. A.
,
Hussaini
,
M. Y.
, and
Abdullah
,
M. M.
,
2005
, “
Placement of Wind Turbines Using Genetic Algorithms
,”
Renewable Energy
,
30
(
2
), pp.
259
270
. 10.1016/j.renene.2004.05.007
10.
Du Pont
,
B. L.
, and
Cagan
,
J.
,
2012
, “
An Extended Pattern Search Approach to Wind Farm Layout Optimization
,”
ASME J. Mech. Des.
,
134
(
8
), p.
081002
. 10.1115/1.4006997
11.
Chen
,
L.
, and
MacDonald
,
E.
,
2012
, “
Considering Landowner Participation in Wind Farm Layout Optimization
,”
ASME J. Mech. Des.
,
134
(
8
), p.
084506
. 10.1115/1.4006999
12.
Chen
,
L.
, and
Macdonald
,
E.
,
2014
, “
A System-Level Cost-of-Energy Wind Farm Layout Optimization With Landowner Modeling
,”
Energy Convers. Manage.
,
77
, pp.
484
494
. 10.1016/j.enconman.2013.10.003
13.
Chen
,
L.
,
Harding
,
C.
,
Sharma
,
A.
, and
MacDonald
,
E.
,
2016
, “
Modeling Noise and Lease Soft Costs Improves Wind Farm Design and Cost-of-Energy Predictions
,”
Renewable Energy
,
97
, pp.
849
859
. 10.1016/j.renene.2016.05.045
14.
Chen
,
L.
, and
MacDonald
,
E.
,
2017
, “
Wind Farm Layout Sensitivity Analysis and Probabilistic Model of Landowner Decisions
,”
ASME J. Energy Resour. Technol.
,
139
(
3
), p.
031202
. 10.1115/1.4035423
15.
Bonabeau
,
E.
,
2002
, “
Agent-Based Modeling: Methods and Techniques for Simulating Human Systems
,”
Proc. Natl. Acad. Sci. U. S. A.
,
99
(
3
), pp.
7280
7287
. 10.1073/pnas.082080899
16.
Fernandes
,
J. V.
,
Henriques
,
E.
,
Silva
,
A.
, and
Pimentel
,
C.
,
2017
, “
Modelling the Dynamics of Complex Early Design Processes: An Agent-Based Approach
,”
Des. Sci.
,
3
, p.
e19
. 10.1017/dsj.2017.17
17.
Le
,
Q.
, and
Panchal
,
J. H.
,
2009
, “
Modeling the Effect of Product Architecture on Mass Collaborative Processes: An Agent-Based Approach
,”
ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
San Diego, CA
,
Aug. 30–Sept. 2
, pp.
1163
1172
,
ASME
Paper No. C2009/CIE-86798 DETC2009-86798
. 10.1115/detc2009-86798
18.
Mashhadi
,
A. R.
,
Esmaeilian
,
B.
, and
Behdad
,
S.
,
2016
, “
Simulation Modeling of Consumers’ Participation in Product Take-Back Systems
,”
ASME J. Mech. Des.
,
138
(
5
), p.
051403
. 10.1115/1.4032773
19.
Wang
,
Z.
,
Azarm
,
S.
, and
Kannan
,
P. K.
,
2011
, “
Strategic Design Decisions for Uncertain Market Systems Using an Agent Based Approach
,”
ASME J. Mech. Des.
,
133
(
4
), p.
041003
. 10.1115/1.4003843
20.
Zadbood
,
A.
, and
Hoffenson
,
S.
,
2017
, “
Agent-Based Modeling of Automobile Producer and Consumer Behavior to Support Design for Market Systems Analysis
,”
ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Cleveland, OH
,
Aug. 6–9
, p.
V02AT03A041
,
ASME
Paper No. DETC2017-68351
. 10.1115/detc2017-68351
21.
Inchiosa
,
M. E.
, and
Chadha
,
B.
,
2008
, “
Role of Agent Based Financial Market Models in Global Product Development
,”
ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Brooklyn, NY
,
Aug. 3–6
, pp.
65
73
,
ASME
Paper No. DETC2008-49670
. 10.1115/detc2008-49670
22.
Meluso
,
J.
, and
Austin-Breneman
,
J.
,
2018
, “
Gaming the System: An Agent-Based Model of Estimation Strategies and Their Effects on System Performance
,”
ASME J. Mech. Des.
,
140
(
12
), p.
121101
. 10.1115/1.4039494
23.
Fay
,
B. E.
, and
Hoffenson
,
S.
,
2017
, “
An Agent-Based Market System Simulation for Design Education
,”
ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Cleveland, OH
,
Aug. 26–29
, p.
V003T04A002
,
ASME
Paper No. DETC2017- 67358
. 10.1115/detc2017-67358
24.
Sinitskaya
,
E.
,
Gomez
,
K. J.
,
Bao
,
Q.
,
Yang
,
M. C.
, and
MacDonald
,
E. F.
,
2019
, “
Examining the Influence of Solar Panel Installers on Design Innovation and Market Penetration
,”
ASME J. Mech. Des.
,
141
(
4
), p.
041702
. 10.1115/1.4042343
25.
Hoffenson
,
S.
, and
Wisniowski
,
M.
,
2018
, “
An Electricity Grid as an Agent-Based Market System: Exploring the Effects of Policy on Sustainability
,”
ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Quebec City, Canada
,
Aug. 26–29
, p.
V02AT03A027
,
ASME
Paper No. DETC2018-86031
. 10.1115/detc2018-86031
26.
Zeiler
,
W.
,
Boxem
,
G.
,
van Houten
,
R.
,
van der Velden
,
J.
,
Wortel
,
W.
, and
Kamphuis
,
R.
,
2008
, “
Agent Based Modelling to Improve Comfort and Save Energy in the Built Environment
,”
ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Brooklyn, NY
,
Aug. 3–6
, pp.
3
12
,
ASME
Paper No. DETC2008-49202
. 10.1115/detc2008-49202
27.
Wüstenhagen
,
R.
,
Wolsink
,
M.
, and
Bürer
,
M. J.
,
2007
, “
Social Acceptance of Renewable Energy Innovation: An Introduction to the Concept
,”
Energy Policy
,
35
(
5
), pp.
2683
2691
. 10.1016/j.enpol.2006.12.001
28.
Dwyer
,
J.
, and
Bidwell
,
D.
,
2019
, “
Chains of Trust: Energy Justice, Public Engagement, and the First Offshore Wind Farm in the United States
,”
Energy Res. Soc. Sci.
,
47
, pp.
166
176
. 10.1016/j.erss.2018.08.019
29.
Langer
,
K.
,
Decker
,
T.
, and
Menrad
,
K.
,
2017
, “
Public Participation in Wind Energy Projects Located in Germany: Which Form of Participation Is the Key to Acceptance?
,”
Renewable Energy
,
112
, pp.
63
73
. 10.1016/j.renene.2017.05.021
30.
Swofford
,
J.
, and
Slattery
,
M.
,
2010
, “
Public Attitudes of Wind Energy in Texas: Local Communities in Close Proximity to Wind Farms and Their Effect on Decision-Making
,”
Energy Policy
,
38
(
5
), pp.
2508
2519
. 10.1016/j.enpol.2009.12.046
31.
Devine-Wright
,
P.
,
2005
, “
Beyond NIMBYism: Towards an Integrated Framework for Understanding Public Perceptions of Wind Energy
,”
Wind Energy
,
8
(
2
), pp.
125
139
. 10.1002/we.124
32.
Geißler
,
G.
,
Köppel
,
J.
, and
Gunther
,
P.
,
2013
, “
Wind Energy and Environmental Assessments—A Hard Look at Two Forerunners’ Approaches: Germany and the United States
,”
Renewable Energy
,
51
, pp.
71
78
. 10.1016/j.renene.2012.08.083
33.
Mulvaney
,
K. K.
,
Woodson
,
P.
, and
Prokopy
,
L. S.
,
2013
, “
A Tale of Three Counties: Understanding Wind Development in the Rural Midwestern United States
,”
Energy Policy
,
56
, pp.
322
330
. 10.1016/j.enpol.2012.12.064
34.
Álvarez-Farizo
,
B.
, and
Hanley
,
N.
,
2002
, “
Using Conjoint Analysis to Quantify Public Preferences Over the Environmental Impacts of Wind Farms. An Example From Spain
,”
Energy Policy
,
30
(
2
), pp.
107
116
. 10.1016/S0301-4215(01)00063-5
35.
National Research Council
,
2007
,
Environmental Impacts of Wind-Energy Projects
,
The National Academies Press
,
Washington, D.C.
36.
Gross
,
C.
,
2007
, “
Community Perspectives of Wind Energy in Australia: The Application of a Justice and Community Fairness Framework to Increase Social Acceptance
,”
Energy Policy
,
35
(
5
), pp.
2727
2736
. 10.1016/j.enpol.2006.12.013
37.
Howard
,
R. A.
, and
Abbas
,
A. E.
,
2016
,
Foundations of Decision Analysis
,
Pearson
,
Boston, MA
, p.
44
.
38.
WINDExchange
. “
U.S. Average Annual Wind Speed at 80 Meters
,”
U.S. Department of Energy
, https://windexchange.energy.gov/maps-data?height=80m, Accessed February 15, 2019.
39.
United States Government Accountability Office
,
2014
, “
National Environmental Policy Act: Little Information Exists on NEPA Analyses Report to Congressional Requesters
,”
Washington DC
. https://www.gao.gov/assets/670/662543.pdf, Accessed February 9, 2020.
40.
Stehly
,
T.
,
Beiter
,
P.
,
Heimiller
,
D.
, and
Scott
,
G.
,
2017
,
2017 Cost of Wind Energy Review
,
National Renewable Energy Laboratory
,
Golden, CO
.
You do not currently have access to this content.