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Computing the power profiles for an Airborne Wind Energy system based on large-scale wind data

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  • Malz, E.C.
  • Verendel, V.
  • Gros, S.

Abstract

Airborne Wind Energy (AWE) is a new power technology that harvests wind energy at high altitudes using tethered wings. Studying the power potential of the system at a given location requires evaluating the local power production profile of the AWE system. As the optimal operational AWE system altitude depends on complex trade-offs, a commonly used technique is to formulate the power production computation as an Optimal Control Problem (OCP). In order to obtain an annual power production profile, this OCP has to be solved sequentially for the wind data for each time point. This can be computationally costly due to the highly nonlinear and complex AWE system model. This paper proposes a method how to reduce the computational effort when using an OCP for power computations of large-scale wind data. The method is based on homotopy-path-following strategies, which make use of the similarities between successively solved OCPs. Additionally, different machine learning regression models are evaluated to accurately predict the power production in the case of very large data sets. The methods are illustrated by computing a three-month power profile for an AWE drag-mode system. A significant reduction in computation time is observed, while maintaining good accuracy.

Suggested Citation

  • Malz, E.C. & Verendel, V. & Gros, S., 2020. "Computing the power profiles for an Airborne Wind Energy system based on large-scale wind data," Renewable Energy, Elsevier, vol. 162(C), pages 766-778.
    • Handle: RePEc:eee:renene:v:162:y:2020:i:c:p:766-778
    DOI: 10.1016/j.renene.2020.06.056
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    References listed on IDEAS

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    1. Malz, E.C. & Hedenus, F. & Göransson, L. & Verendel, V. & Gros, S., 2020. "Drag-mode airborne wind energy vs. wind turbines: An analysis of power production, variability and geography," Energy, Elsevier, vol. 193(C).
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    6. Archer, Cristina L. & Delle Monache, Luca & Rife, Daran L., 2014. "Airborne wind energy: Optimal locations and variability," Renewable Energy, Elsevier, vol. 64(C), pages 180-186.
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    8. Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2020. "Wind turbine power curve modeling for reliable power prediction using monotonic regression," Renewable Energy, Elsevier, vol. 147(P1), pages 214-222.
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    Cited by:

    1. Eijkelhof, Dylan & Schmehl, Roland, 2022. "Six-degrees-of-freedom simulation model for future multi-megawatt airborne wind energy systems," Renewable Energy, Elsevier, vol. 196(C), pages 137-150.
    2. Jochem De Schutter & Rachel Leuthold & Thilo Bronnenmeyer & Elena Malz & Sebastien Gros & Moritz Diehl, 2023. "AWEbox : An Optimal Control Framework for Single- and Multi-Aircraft Airborne Wind Energy Systems," Energies, MDPI, vol. 16(4), pages 1-32, February.

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