Principal The Renewables Consulting Group, an ERM Group company Boston, Massachusetts
Presentation Description: With up to seven lease auctions expected over the next three years, understanding the potential wind resource is important driver in understanding a lease area's potential and its cost. The first lease auctions off the U.S. east coast relied mainly on model data, whereas the recent New York auctions had the advantage of a robust measurement campaign prior to auction, providing better certainty and a more competitive environment. While its unlikely new measurement systems will be deployed in time for each wind development area, there now exists significantly more measurement data then in years past that can be used to understand the performance of available model data sources.
The mesoscale model data sources often relied upon in the U.S. for early-stage offshore wind development include NREL's WIND ToolKit and the Global Wind Atlas, both available publicly. In this presentation we will present findings comparing these two popular industry model datasets to that of measured data from various floating lidars sited offshore in the west and east coasts of the U.S. The comparison will include a review of key parameters including annual wind speed, shear, and frequency distribution, highlighting potential biases and unique characteristics of the measured data not shown in the model data. Based on the results we will provide recommendations on incorporating the data into a wind resource assessment and assess the wind resource uncertainty of the proposed wind energy areas
Learning Objectives:
Upon completion, participant will be able to determine how popular mesoscale model data sources perform compared to wind resource measurement data.
Upon completion, participant will be able to input recommendations on incorporating model data in a wind resource assessment in absence of measurements.
Upon completion, participant will be able to will understand the uncertainty of the wind resource for the proposed wind energy areas.