On the Environmental Variability of Offshore Wind Power
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Power curve is widely used to estimate power output for planning and operational purposes. Existing methods for constructing power curves have three main limitations: (i) they mostly rely on wind speed as the sole input, thus ignoring the secondary, yet possibly significant effects of other environmental factors, (ii) they largely overlook the complex marine environment in which offshore wind turbines operate, potentially compromising their value in offshore wind energy applications, and (ii) they solely focus on the first-order properties of wind power, with little information about the variation around the mean behavior, which is important for ensuring reliable grid integration, asset health monitoring, and energy storage, among others. In light of that, this study investigates the impact of several wind- and wave-related factors on offshore wind power variability, with the ultimate goal of accurately predicting its first two moments. Our approach couples OpenFAST-a high-fidelity stochastic multi-physics simulator-with Gaussian Process (GP) regression to reveal the underlying relationships governing offshore weather-to-power conversion. We find that a multi-input power curve which captures the combined impact of wind speed, direction, and air density, can provide double-digit improvements relative to univariate methods which rely on wind speed as the sole explanatory variable. Wave-related variables are found not important for predicting the average power output, but appear to be extremely relevant in describing the fluctuation of the offshore power around its mean. Tested on real-world data collected at the New York/New Jersey bight, our proposed multi-input models demonstrate a high explanatory power in predicting the first two moments of offshore wind power, testifying their potential value to the offshore wind industry.
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