Project description
Harnessing the wind with smarter control
Wind farms are much more than just groups of turbines. They interact with the atmosphere on a large scale and affect weather patterns and energy production. Traditional wind farm flow control relies on simplified models that miss the larger atmospheric effects that influence performance. Real-time control using simulations has been too slow for practical use. The ERC-funded REALTOWIND project aims to combine large-eddy simulations (LES) with predictive control techniques. This method models turbulent airflow and wind farm interactions in nearly real time, promising to improve energy output and efficiency. By addressing challenges, such as chaotic flow dynamics and limited atmospheric data, REALTOWIND seeks to change how wind farms generate power while adjusting to complex environmental conditions.
Objective
Current Wind-Farm flow Control (WFC) focuses on the interaction of wind turbines through their wakes, and relies on fast heuristic wake models that are mostly considered in an open-loop control framework. However, modern wind farms interact with the atmosphere at much larger scales, such, e.g. as their excitation of atmospheric gravity waves. WFC response is thereby not only governed by intra-farm turbine wakes, but possibly even more by the interaction between the larger atmospheric mesoscales and the farm operation. The only models that realistically capture these aspects down to the wake scale are large-eddy simulations (LES), which are generally run on high-performance computers, yet considered orders of magnitude too slow for use in real-time model predictive control.
Recently however, we have shown that coarse-grid LES integrated in a time-decoupled model predictive control (TDMPC) framework, is about a factor three too slow only for real-time use, while potentially still being effective at realizing the WFC objective. With wind turbines being the largest manmade “flow actuators” existing today, and smaller-sized systems exhibiting faster time scales, the wind farm will be the first turbulent flow system in which LES can be used as a real-time control model.
We aim at inducing a paradigm shift in the use of LES, by developing a first fully integrated LES-based TDMPC and demonstrate it in a high-fidelity emulator environment, as well as, in part, using field data. This raises following fundamental research challenges: diverging sensitivities of perturbations in turbulent flows (chaotic systems) over long time horizons, the sparse nature of measurements in the atmosphere required for state estimation in the control loop, the limited understanding of wind-farm atmosphere interaction in non-neutral stratification, and the efficient emulation of WFC using high-performance computing. My pioneering work in these fields will enable us to tackle these challenges.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
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(opens in new window) ERC-2024-ADG
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3000 LEUVEN
Belgium
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