A reference model for airborne wind energy systems for optimization and control

Malz, Elena; Koenemann, Jonas; Sieberling, S.; Gros, Sébastien

doi:10.1016/j.renene.2019.03.111

Cited by 33 publications

(47 citation statements)

References 8 publications

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“…We consider a 6 degree of freedom (DOF) rigid-wing aircraft model. It uses pre-computed quadratic approximations of the aerodynamic coefficients which are controlled via aileron, elevator and rudder deflection rates (Malz et al, 2019). The tether is controlled by the tether jerk ( ... l tether ) from which tether acceleration (l tether ), speed (l tether = v tether ) and length (l tether ) are derived.…”

Section: Optimization Model Overviewmentioning

confidence: 99%

“…Malz et al, 2019;Ampyx) for comparison with other publications and since no other AWES data were available 3 .Figure 9(left) visualizes the implemented aircraft lift c L and drag coefficient c D .…”

mentioning

confidence: 99%

“…Left: dimensionless Ampyx AP2(Malz et al, 2019;Ampyx), lift cL and drag coefficients cD over angle of attack α used in this study. Vertical dashed black lines visualize the angle of attack constraints.…”

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confidence: 99%

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Offshore and onshore ground-generation airborne wind energy power curve characterization

Sommerfeld

Dörenkämper

Schutter³

et al. 2020

Preprint

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Abstract. Airborne wind energy systems (AWESs) aim to operate at altitudes well above conventional wind turbines (WTs) and harvest energy from stronger winds aloft. While multiple AWES concepts compete for entry into the market, this study focuses on ground-generation AWES. Various companies and researchers proposed power curve characterizations for AWES, but no consensus for an industry-wide standard has been reached. An universal description of a ground-generation AWES power curve is difficult to define because of complex tether and drag losses as well as alternating flight paths over changing wind conditions with altitude, as compared to conventional WT with winds at fixed hub height and rotor area normalization. Therefore, this study determines AWES power and annual energy prediction (AEP) based on the awenox optimal control model for two AWES sizes, driven by representative 10-minute onshore and offshore mesoscale WRF wind data. The wind resource is analyzed with respect to atmospheric stability as well as annual and diurnal variation. The wind data is categorized using k-means clustering, to reduce the computational cost. The impact of changing wind conditions on AWES trajectory and power cycle is investigated. Optimal operating heights are below 400 m onshore and below 200 m offshore. Efforts are made to derive AWES power coefficients similar to conventional WT to enable a simple power and AEP estimation for a given site and system. This AWES power coefficient decreases up to rated power due to the increasing tether length with wind speed and the accompanying tether losses. A comparison between different AEP estimation methods shows that a low number of clusters with three representative wind profiles within the clusters yields the highest AEP, as other wind models average out high wind speeds which are responsible for a high percentage of the overall AEP.

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Section: Optimization Model Overviewmentioning

confidence: 99%

mentioning

confidence: 99%

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Offshore and onshore ground-generation airborne wind energy power curve characterization

Sommerfeld

Dörenkämper

Schutter³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The accuracy of the models is critical in order to be able to project the results to reality. The aircraft model has been validated to some extent as described in [11,12], but especially for quick changes in the wind conditions the aerodynamic model is probably too aggressive because changes in the local flow immediately change the resulting lift force. It is expected that with a more realistic aerodynamic model an additional time delay between changes in the local flowfield around the aircraft and the resulting change of the tether force is present, which might alter the presented results in the previous section.…”

Section: Discussion Model Validity and Future Workmentioning

confidence: 99%

“…The aircraft is modeled as a six-degree-of-freedom rigid body, and its geometric and aerodynamic properties are based on the values in [11,12]. The translational dynamics are given by…”

Section: A Aircraft Ground Station and Wind Modelsmentioning

confidence: 99%

Enhancing Resilience of Airborne Wind Energy Systems Through Upset Condition Avoidance

Rapp

Schmehl

2021

Journal of Guidance, Control, and Dynamics

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Airborne wind energy (AWE) systems are tethered flying devices that harvest wind resources at higher altitudes, which are not accessible to conventional wind turbines. To become a viable alternative to other renewable energy technologies, AWE systems are required to fly reliably and autonomously for long periods of time while being exposed to atmospheric turbulence and wind gusts. In this context, the present paper proposes a three-step methodology to improve the resilience of an existing baseline control system toward these environmental disturbances. In the first step, upset conditions are systematically generated that lead to a failure of the control system using the subset simulation method. In the second step, the generated conditions are used to synthesize a surrogate model that can be used to predict upsets beforehand. In the final step an avoidance maneuver is designed that keeps the AWE system operational while minimizing the impact of the maneuver on the average pumping cycle power. The feasibility of the methodology is demonstrated on the example of tether rupture during pumping cycle operation. As an additional contribution a novel transition strategy from retraction to traction phase is presented that can reduce the probability of tether rupture significantly.

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The value of airborne wind energy to the electricity system

et al. 2021

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Airborne wind energy (AWE) is a new power generation technology that harvests wind energy at high altitudes using tethered wings. The potentially higher energy yield, combined with expected lower costs compared to traditional wind turbines (WTs), motivates interest in further developing this technology. However, commercial systems are currently unavailable to provide more detailed information on costs and power generation. This study estimates the economic value of AWE in the future electricity system, and by that indicates which cost levels are required for AWE to be competitive. A specific focus is put on the relation between AWE systems (AWESs) and WTs. For this work, ERA‐5 wind data are used to compute the power generation of the wind power technologies, which is implemented in a cost‐minimizing electricity system model. By forcing a certain share of the annual electricity demand to be supplied by AWESs, the marginal system value (MSV) of AWE is investigated. The MSV is found to be affected by the AWE share, the wind resource, and the temporal distribution of the AWES's electricity generation. The MSV of AWE is location‐ and system‐dependent and ranges between 1.4 and 2.2 M€false/MW at a low share of AWE supply (0%–30%). At higher shares, the MSV drops. The power generation of WTs and AWESs are related, implying that the wind technologies present a similar power source and can be used interchangeably. Thus, the introduction of AWESs will have a low impact on the cost‐optimal wind power share in the electricity system, unless an AWES cost far below the system‐specific MSV is attained.

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A reference model for airborne wind energy systems for optimization and control

Cited by 33 publications

References 8 publications

Offshore and onshore ground-generation airborne wind energy power curve characterization

Offshore and onshore ground-generation airborne wind energy power curve characterization

Enhancing Resilience of Airborne Wind Energy Systems Through Upset Condition Avoidance

The value of airborne wind energy to the electricity system

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