Communication Dans Un Congrès Année : 2023

A New Energy Yield Assessment Model with Reduced Uncertainty for Wind Farms Repowering

Résumé

This paper presents an ”adjustment” model and its application for wind plant projects’ energy yield assessment (EYA) in a repowering context. The outcomes of energy yield assessment (EYA) are critical in the decision process of financial investment in new wind farm projects. In particular, reducing energy yield uncertainty is a lever to improve the project’s economic yield. Greenfield wind farm projects have motivated the development of well-established EYA models. These ”theoretical” models are based on a sequence of equations that solve the physics, starting from the wind flow modeling to wind turbine power conversion. They are calibrated upon on-site wind measurements. These EYA models can have up to 12% uncertainty in simple terrain in the annual yield prediction. The paradigm of EYA differs for repowering projects since long historical measurements from the operating plant are available. This data can be integrated into the EYA model to estimate the annual yield contributing to the otherwise limited history of wind campaigns used in greenfield projects. In the literature, this is done through either adjusting models inputs or outputs, calibrating the flow models or replacing the theoretical model with a surrogate machine learning model. The main drawback of such models is the absence of a framework to assess their uncertainty: they only qualitatively improve uncertainty. The uncertainty assessment is only possible with extensive testing of models on a vast, often unavailable database. Besides, the uncertainty models are generally semi-empirical and cannot characterize the added benefit of calibrated parameters or adjusted values. The so-called ”adjustment model” proposed here solves this issue by providing a quantitative assessment of the uncertainty along with an annual energy production (AEP) assessment equation. This paper presents the proposed adjustment model. The adjustment model builds upon the theoretical models. The work demonstrates that the adjustment model consistently outperforms theoretical models regarding uncertainty. We illustrate the adjustment model by applying it to several real-world repowering or extension projects, where we compare AEP established from the theoretical and the adjustment models.

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Dates et versions

hal-04162063 , version 1 (14-07-2023)

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Identifiants

  • HAL Id : hal-04162063 , version 1

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Paul Mazoyer, Duc Thomas, Andreas Bechmann, Georges Kariniotakis. A New Energy Yield Assessment Model with Reduced Uncertainty for Wind Farms Repowering. WindEurope Annual Event 2023, WindEurope, Apr 2023, Copenhagen, Denmark. ⟨hal-04162063⟩
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