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Research Papers: Alternative Energy Sources

Wind Farm Layout Sensitivity Analysis and Probabilistic Model of Landowner Decisions

[+] Author and Article Information
Le Chen

Independent Researcher,
Mountain View, CA 94040
e-mail: chenle86@gmail.com

Erin MacDonald

Department of Mechanical Engineering,
Stanford University,
Stanford, CA 94305
e-mail: erinmacd@stanford.edu

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received August 28, 2015; final manuscript received December 1, 2016; published online February 24, 2017. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 139(3), 031202 (Feb 24, 2017) (13 pages) Paper No: JERT-15-1324; doi: 10.1115/1.4035423 History: Received August 28, 2015; Revised December 01, 2016

This paper offers tools and insights regarding wind farm layout to developers in determining the conditions under which it makes sense to invest resources into more accurately predicting of the cost-of-energy (COE), a metric to assess farm viability. Using wind farm layout uncertainty analysis research, we first test a farm design optimization model's sensitivity to surface roughness, economies-of-scale costing, and wind shear. Next, we offer a method for determining the role of land acquisition in predicting uncertainty. This parameter—the willingness of landowners to accept lease compensation offered to them by a developer—models a landowner's participation decision as a probabilistic interval utility function. The optimization-under-uncertainty formulation uses probability theory to model the uncertain parameters, Latin hypercube sampling to propagate the uncertainty throughout the system, and compromise programming to search for the nondominated solution that best satisfies the two objectives: minimize the mean value and standard deviation of COE. The results show that uncertain parameters of economies-of-scale cost-reduction and wind shear have large influence over results in the sensitivity analysis, while surface roughness does not. The results also demonstrate that modeling landowners' participation in the project as uncertain allows the optimization to identify land that may be risky or costly to secure, but worth the investment. In an uncertain environment, developers can predict the viability of the project with an estimated COE and give landowners an idea of where turbines are likely to be placed on their land.

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Figures

Grahic Jump Location
Fig. 1

Propagation of uncertainty through the system model to calculate the COE

Grahic Jump Location
Fig. 2

Tornado diagram, represented for the three uncertain parameters

Grahic Jump Location
Fig. 3

Optimal layout for cases 1–7

Grahic Jump Location
Fig. 4

Wind rose from 2011 generated from IEM website [58]

Grahic Jump Location
Fig. 5

Optimal layouts for scenarios 1–3 avoid placing turbines on plots owned by type-D landowners

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