Uncertainty Quantification in Wind Plant Energy Estimation

January 29, 2019

Data characterizing a wind power plant’s production are important for the validation, calibration, and refinement of the wake loss models used to simulate performance for preconstruction energy estimates. In this process, there is the potential for significant variation in the methods used by an analyst for, first, the preparation of the operational production data for comparison to model data and, second, the generation of analogous production data from simulations. In this work, the uncertainty introduced by these method variations is quantified by considering ensembles of analysis methods. Specifically, 1920 method variations were developed for preparing the operational data as a predictable production benchmark and simulations taking as input sample sets of the observed wind resource ranging in size from 4% to 125% of a full year were run using 3 engineering wake loss models in simulations run in the FLOw Redirection and Induction in Steady State software package.

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Advanced Power

Advanced Power is a privately owned company that develops, owns and manages power generation and related infrastructure projects throughout Europe and North America. We have a clear vision for cleaner energy, and we seek better ways to a cleaner future. Always. We’re a leader in the movement for more efficient and more sustainable energy. Through our expertise in development, financial structuring and asset management, we’re advancing toward our fundamental goal of rapidly reducing global emissions.

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Advanced Power is a privately owned company that develops, owns and manages power generation and related infrastructure projects throughout Europe and North America. We have a clear vision for cleaner energy, and we seek better ways to a cleaner future. Always. We’re a leader in the movement for more efficient and more sustainable energy. Through our expertise in development, financial structuring and asset management, we’re advancing toward our fundamental goal of rapidly reducing global emissions.

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