Unified engineering models for the performance and cost of Ground-Gen and Fly-Gen crosswind Airborne Wind Energy Systems

Trevisi, Filippo; Gaunaa, Mac; McWilliam, Michael

doi:10.1016/j.renene.2020.07.129

Cited by 15 publications

(29 citation statements)

References 13 publications

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“…At V w = 8 m/s, the reel-out tether force is T out = 61 kN; the component of the reel-out force along the Z R axis (see Figure 2) is T out cos Φ = 58 kN. This latter value should be compared with the analytic expression [10] In Figure 6 the reel-out factor γ ro and the reel-out velocity V ro as function of wind speed are shown. Until 9 m/s, γ ro has a linear trend.…”

Section: T-glide Results: Finding Optimal Working Set-pointsmentioning

confidence: 95%

“…At V w = 8 m/s, the reel-out power is P out = 139 kW. The analytic expression for the optimal power production is [10]…”

Section: T-glide Results: Finding Optimal Working Set-pointsmentioning

confidence: 99%

“…to the design are computed accordingly. The cost model available at this stage of development was previously introduced in [10], while a new updated cost model is being developed and will be presented in future works. Finally, the reel-in phase is modelled with a simplified method [11] assuming a straight trajectory with a constant reel-in speed.…”

Section: Modelmentioning

confidence: 99%

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Sensitivity analysis of a Ground-Gen Airborne Wind Energy System design

Trevisi

Riboldi

Croce

2022

J. Phys.: Conf. Ser.

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The architecture of a new multidisciplinary design and optimization framework for rigid wing Airborne Wind Energy Systems, named T-GliDe, is introduced and a Ground-Gen AWES is analyzed to illustrate the features of the design approach. T-GliDe features an optimization module and an uncertainty quantification module, allowing for a number of algorithm-based design techniques. T-GliDe performs an AWES design optimizing market-based objective functions, while satisfying constraints related to fight stability. The fight dynamics is modelled with analytical aerodynamic theories, allowing for fast and consistent design evaluations, without the use of time simulation codes and thus of active control. In this work, a reduced optimization problem is run to find optimal working-set points of a Ground-Gen AWES with respect to power production. The reel-out factor and the pitch angle as function of wind speed are considered as design variables and output trends are shown. A variance based sensitivity analysis is then run to investigate how the rigid body eigenvalues are influenced by a set of independent variables. Among the chosen independent variables, a sub-set of variables which bear an impact on the dynamics is identified and they will be considered in future design and optimization activities as design variables.

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Section: T-glide Results: Finding Optimal Working Set-pointsmentioning

confidence: 95%

“…At V w = 8 m/s, the reel-out power is P out = 139 kW. The analytic expression for the optimal power production is [10]…”

Section: T-glide Results: Finding Optimal Working Set-pointsmentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity analysis of a Ground-Gen Airborne Wind Energy System design

Trevisi

Riboldi

Croce

2022

J. Phys.: Conf. Ser.

Self Cite

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“…where the induction a is found by setting the thrust given by momentum theory (T d = 1 2 ρA t (4a(1−a)) |v r | v r ) equal to D t , as in Trevisi et al (2020b), and A t is the total turbine area.…”

Section: Flight Dynamic Modelmentioning

confidence: 99%

Flight trajectory optimization of Fly-Gen airborne wind energy systems through a harmonic balance method

et al. 2022

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Abstract. The optimal control problem for flight trajectories of Fly-Gen airborne wind energy systems (AWESs) is a crucial research topic for the field, as suboptimal paths can lead to a drastic reduction in power production. One of the novelties of the present work is the expression of the optimal control problem in the frequency domain through a harmonic balance formulation. This allows the potential reduction of the problem size by solving only for the main harmonics and allows the implicit imposition of periodicity of the solution. The trajectory is described by the Fourier coefficients of the dynamics (elevation and azimuth angles) and of the control inputs (onboard wind turbine thrust and AWES roll angle). To isolate the effects of each physical phenomenon, optimal trajectories are presented with an increasing level of physical representation from the most idealized case: (i) if the mean thrust power (mechanical power linked to the dynamics) is considered as the objective function, optimal trajectories are characterized by a constant AWES velocity over the loop and a circular shape. This is done by converting all the gravitational potential energy into electrical energy. At low wind speed, onboard wind turbines are then used as propellers in the ascendant part of the loop; (ii) if the mean shaft power (mechanical power after momentum losses) is the objective function, a part of the potential energy is converted into kinetic and the rest into electrical energy. Therefore, the AWES velocity fluctuates over the loop; (iii) if the mean electrical power is considered as the objective function, the onboard wind turbines are never used as propellers because of the power conversion efficiency. Optimal trajectories for case (ii) and (iii) have a circular shape squashed along the vertical direction. The optimal control inputs can be generally modeled with one harmonic for the onboard wind turbine thrust and two for AWES roll angle without a significant loss of power, demonstrating that the absence of high-frequency control is not detrimental to the power generated by Fly-Gen AWESs.

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“…Due to the large size of the swept area of the kite trajectory -typically an annulus or a lemniscate on the spherical surface around the ground station -AWE systems typically lead to lower induction effects than conventional wind turbines. However, induction effects are particularly relevant for the optimization of multiple kite systems (De Schutter, Leuthold, Bronnenmeyer, Paelinck, & Diehl, 2019;Zanon, Gros, Andersson, & Diehl, 2013), and their inclusion into modeling and optimal control of AWE systems is the subject of ongoing research (Haas, De Schutter, Diehl, & Meyers, 2019;Leuthold et al, 2017;Trevisi, Gaunaa, & McWilliam, 2020).…”

Section: Fundamental Principles and Aerodynamic Considerationsmentioning

confidence: 99%