Offshore wind farm layout optimization

2007 2007

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Abstract (summary)

Offshore wind energy technology is maturing in Europe and is poised to make a significant contribution to the U.S. energy production portfolio. Building on the knowledge the wind industry has gained to date, this dissertation investigates the influences of different site conditions on offshore wind farm micrositing—the layout of individual turbines within the boundaries of a wind farm. For offshore wind farms, these conditions include, among others, the wind and wave climates, water depths, and soil conditions at the site.

An analysis tool has been developed that is capable of estimating the cost of energy (COE) from offshore wind farms. For this analysis, the COE has been divided into several modeled components: major costs (e.g. turbines, electrical interconnection, maintenance, etc.), energy production, and energy losses. By treating these component models as functions of site-dependent parameters, the analysis tool can investigate the influence of these parameters on the COE. Some parameters result in simultaneous increases of both energy and cost. In these cases, the analysis tool was used to determine the value of the parameter that yielded the lowest COE and, thus, the best balance of cost and energy. The models have been validated and generally compare favorably with existing offshore wind farm data.

The analysis technique was then paired with optimization algorithms to form a tool with which to design offshore wind farm layouts for which the COE was minimized. Greedy heuristic and genetic optimization algorithms have been tuned and implemented. The use of these two algorithms in series has been shown to produce the best, most consistent solutions.

The influences of site conditions on the COE have been studied further by applying the analysis and optimization tools to the initial design of a small offshore wind farm near the town of Hull, Massachusetts. The results of an initial full-site analysis and optimization were used to constrain the boundaries of the farm. A more thorough optimization highlighted the features of the area that would result in a minimized COE. The results showed reasonable layout designs and COE estimates that are consistent with existing offshore wind farms.

Indexing (details)

Mechanical engineering;
0548: Mechanical engineering
0791: Energy
Identifier / keyword
Applied sciences; Cost of energy modeling; Offshore wind energy; Optimization; Wind farms
Offshore wind farm layout optimization
Elkinton, Christopher Neil
Number of pages
Publication year
Degree date
School code
DAI-B 68/11, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
Manwell, James; McGowan, Jon
Committee member
Barthelmie, Rebecca; DeJong, Jason
University of Massachusetts Amherst
Mechanical Engineering
University location
United States -- Massachusetts
Source type
Dissertations & Theses
Document type
Dissertation/thesis number
ProQuest document ID
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
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