Wind farm operators utilize various financial agreements to generate revenue and mitigate risk. These agreements are often based on some estimate of the energy production from the wind farm. A power purchase agreement (PPAs), which is a long-term fixed volume fixed price arrangement, was the most common type of agreement for much of the growth of wind energy in the U.S. Recently, wind turbine power production estimations are relying less on fixed production volumes and PPAs as the basis for energy estimation in financial agreements and more on proxy generation, or an estimate of what the wind farm should make given a set of inflow conditions. These newer types of financial agreements are shifting the focus to when power is produced rather than just how much, and so it is imperative to understand and analyze the errors arising in proxy generation and how it may impact the financial agreements that use proxy generation. This work quantifies the errors in proxy generation and compares two methods of estimating power production, examining the financial impacts of both, for one wind project. These two methods are the nacelle transfer function (NTF) method and the reanalysis data method, which may be used if onsite data is unavailable. The different methods of estimating power production have varying impacts on the financial outcome of the project. Errors in power production estimates that coincide with large price events can result in significant financial impacts for the wind project, and this is more likely to occur with the reanalysis method compared to the NTF method. The results show that the Nacelle Transfer Function (NTF) method of estimating power production via onsite measurements has much less risk of being impacted by a price excursion than the reanalysis data method.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:masters_theses_2-2145 |
| Date | 20 October 2021 |
| Creators | Fan, Shanon |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses |
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