Document Type

Open Access Thesis

Embargo Period

5-7-2017

Degree Program

Mechanical Engineering

Degree Type

Master of Science in Mechanical Engineering (M.S.M.E.)

Year Degree Awarded

2017

Month Degree Awarded

May

Advisor Name

Matthew

Advisor Middle Initial

A

Advisor Last Name

Lackner

Co-advisor Name

Erin

Co-advisor Last Name

Baker

Third Advisor Name

James

Third Advisor Last Name

Manwell

Abstract

Currently, most micrositing techniques aim to maximize annual energy production (AEP) or minimize cost of energy (COE) with no direct regard to revenue. This research study developed a method that utilizes the seasonal electricity price and wind data to microsite wind farms in terms of profitability. To accomplish this, six candidate wind farms with differing layouts and spacing were selected at a given location. They were then simulated using a wake modeling software to produce expected power outputs at different wind speeds, wind directions, and turbulence intensities. By interpolating the power output tables with wind data, a power time-series was created for each wind farm over a multi-year period. Electrical price was then incorporated with the power time-series to produce a revenue time-series of the revenue produced at each hour over the same time-period. Each relative wind farm was then rotated in increments to evaluate new candidate wind farms and revenue totals. This method is site specific and results may differ dependent on location and seasonal correlation between wind and electrical data. Overall, the method looks to exploit a different approach to the micrositing problem.

First Advisor

Matthew A Lackner

Second Advisor

Erin Baker

Third Advisor

James Manwell

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