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Open Access Thesis
Master of Science in Mechanical Engineering (M.S.M.E.)
Year Degree Awarded
Month Degree Awarded
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.
Matthew A Lackner
Pfeiffer, Timothy A., "INCORPORATING SEASONAL WIND RESOURCE AND ELECTRICITY PRICE DATA INTO WIND FARM MICROSITING" (2017). Masters Theses. 531.