Mixed Logical Dynamical Nonlinear Model Predictive Controller for Large‐Scale Solar Fields

2019 ◽  
Vol 21 (4) ◽  
pp. 1881-1891 ◽  
Author(s):  
Tiago Araújo Elias ◽  
Paulo Renato Costa Mendes ◽  
Júlio Elias Normey‐Rico
Keyword(s):  
Nonlinear Model ◽  
Large Scale ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Mixed Logical Dynamical

scholarly journals Large-eddy simulation of airborne wind energy farms

2021 ◽  
Author(s):  
Thomas Haas ◽  
Jochem De Schutter ◽  
Moritz Diehl ◽  
Johan Meyers
Keyword(s):  
Power Generation ◽  
Wind Energy ◽  
Large Scale ◽  
Flow Conditions ◽  
Model Predictive Controller ◽  
Large Eddy ◽  
Power Yield ◽  
Predictive Controller ◽  
Utility Scale ◽  
Turbulent Flow Conditions

Abstract. The future utility-scale deployment of airborne wind energy technologies requires the development of large-scale multi-megawatt systems. This study aims at quantifying the interaction between the atmospheric boundary layer (ABL) and large-scale airborne wind energy systems operating in a farm. To that end, we present a virtual flight simulator combining large-eddy simulations to simulate turbulent flow conditions and optimal control techniques for flight-path generation and tracking. The two-way coupling between flow and system dynamics is achieved by implementing an actuator sector method that we pair to a model predictive controller. In this study, we consider ground-based power generation pumping-mode AWE systems (lift-mode AWES) and on-board power generation AWE systems (drag-mode AWES). For the lift-mode AWES, we additionally investigate different reel-out strategies to reduce the interaction between the tethered wing and its own wake. Further, we investigate AWE parks consisting of 25 systems organized in 5 rows of 5 systems. For both lift- and drag-mode archetypes, we consider a moderate park layout with a power density of 10 MW km−2 achieved at a rated wind speed of 12 m s−1. For the drag-mode AWES, an additional park with denser layout and power density of 28 MW km−2 is also considered. The model predictive controller achieves very satisfactory flight-path tracking despite the AWE systems operating in fully waked, turbulent flow conditions. Furthermore, we observe significant wake effects for the utility-scale AWE systems considered in the study. Wake-induced performance losses increase gradually through the downstream rows of systems and reach in the last row of the parks up to 17 % for the lift-mode AWE park and up to 25 % and 45 % for the moderate and dense drag-mode AWE parks, respectively. For an operation period of 60 minutes at a below-rated reference wind speed of 10 m s−1, the lift-mode AWE park generates about 84.4 MW of power, corresponding to 82.5 % of the power yield expected when AWE systems operate ideally and interaction with the ABL is negligible. For the drag-mode AWE parks, the moderate and dense layouts generate about 86.0 MW and 72.9 MW of power, respectively, corresponding to 89.2 % and 75.6 % of the ideal power yield.

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