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AWEbox : An Optimal Control Framework for Single- and Multi-Aircraft Airborne Wind Energy Systems

Author

Listed:
  • Jochem De Schutter

    (Systems Control and Optimization Laboratory, Department of Microsystems Engineering, University of Freiburg, 79110 Freiburg, Germany)

  • Rachel Leuthold

    (Systems Control and Optimization Laboratory, Department of Microsystems Engineering, University of Freiburg, 79110 Freiburg, Germany)

  • Thilo Bronnenmeyer

    (Kiteswarms GmbH, 79379 Müllheim, Germany)

  • Elena Malz

    (Department of Electrical Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden)

  • Sebastien Gros

    (Department of Engineering Cybernetics, Norwegian University of Science and Technology, 7034 Trondheim, Norway)

  • Moritz Diehl

    (Systems Control and Optimization Laboratory, Department of Microsystems Engineering, University of Freiburg, 79110 Freiburg, Germany
    Department of Mathematics, University of Freiburg, 79104 Freiburg, Germany)

Abstract

In this paper, we present AWEbox, a Python toolbox for modeling and optimal control of multi-aircraft systems for airborne wind energy (AWE). AWEbox provides an implementation of optimization-friendly multi-aircraft AWE dynamics for a wide range of system architectures and modeling options. It automatically formulates typical AWE optimal control problems based on these models, and finds a numerical solution in a reliable and efficient fashion. To obtain a high level of reliability and efficiency, the toolbox implements different homotopy methods for initial guess refinement. The first type of method produces a feasible initial guess from an analytic initial guess based on user-provided parameters. The second type implements a warm-start procedure for parametric sweeps. We investigate the software performance in two different case studies. In the first case study, we solve a single-aircraft reference problem for a large number of different initial guesses. The homotopy methods reduce the expected computation time by a factor of 1.7 and the peak computation time by a factor of eight, compared to when no homotopy is applied. Overall, the CPU timings are competitive with the timings reported in the literature. When the user initialization draws on expert a priori knowledge, homotopies do not increase expected performance, but the peak CPU time is still reduced by a factor of 5.5. In the second case study, a power curve for a dual-aircraft lift-mode AWE system is computed using the two different homotopy types for initial guess refinement. On average, the second homotopy type, which is tailored for parametric sweeps, outperforms the first type in terms of CPU time by a factor of three. In conclusion, AWEbox provides an open-source implementation of efficient and reliable optimal control methods that both control experts and non-expert AWE developers can benefit from.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1900-:d:1068468
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    References listed on IDEAS

    as
    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.
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    3. Cherubini, Antonello & Papini, Andrea & Vertechy, Rocco & Fontana, Marco, 2015. "Airborne Wind Energy Systems: A review of the technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1461-1476.
    4. Malz, E.C. & Koenemann, J. & Sieberling, S. & Gros, S., 2019. "A reference model for airborne wind energy systems for optimization and control," Renewable Energy, Elsevier, vol. 140(C), pages 1004-1011.
    5. 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.
    6. 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.
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