| 09 Mar 2026
Modeling wind farm response: a modular, integrated, and multi-stakeholder approach
Abstract. Accurate and computationally efficient modeling of wind farm response is essential to support a wide range of stakeholders, including research institutes, wind turbine and wind farm designers, operators, and control algorithm developers. This paper presents a modular and integrated framework for modeling wind farm response, enabling consistent and multi-purpose predictions across turbine- and farm-level applications. The proposed approach combines computationally efficient and site-specific wind farm flow modeling, high-fidelity aero-servo-elastic simulations, wake-resolved inflow characterization, and data-driven response surrogates within a flexible architecture that allows individual components to be independently developed, validated, and exchanged.
Within this framework, key novelties are introduced such as a modular and holistic wind farm model, as well as a wake-slice methodology to represent local waked inflow conditions in a compact and physically meaningful form, enabling efficient training of response surrogate models using single-turbine simulations. Artificial neural network surrogates are developed to predict individual turbine responses based on a reduced set of local inflow and control descriptors, allowing the effects of wakes, turbulence, and operational strategies to be captured without resorting to full farm-level aeroelastic simulations. Another key feature of the proposed framework is its ability to consistently model multiple turbine types as well as a wide range of operational modes (power production, start-up, shut-down and parking) combined with several control modes (normal operation, yaw-steering, derating, down-regulation and noise-curtailment) within a single formulation. To this end, the methodology employs location-agnostic load surrogates, applicable to a given turbine type irrespective of its position within a farm and at any site. The overall framework is wind farm agnostic, with a modular structure that enables application to arbitrary farm layouts, environmental conditions, and operating modes without structural modification.
The framework is tested using one open-source reference turbine and two anonymized commercial turbines. For each turbine type, surrogates were developed using a single holistic library of inflow profiles representing clean and waked conditions. The performances are evaluated through an exemplary wind farm configuration composed of six turbines, demonstrating the location agnosticism of the proposed approach. Furthermore, the framework is systematically evaluated through surrogate validation and analysis across different turbine types, environmental conditions, and operational and control modes. The results demonstrate that the proposed toolchain accurately reproduces the load variations induced by wake interactions, operational modes and control modes, while maintaining a low computational cost. By combining modular physics-based modeling with scalable data-driven surrogates, the framework provides a multi-stakeholder solution for wind farm response modeling, supporting applications ranging from design analysis to operational assessment and wind farm control studies.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.
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The manuscript presents a framework for surrogate modeling of wind farms. While not providing breakthroughs compared to previous loads surrogate work, it is a good overview and can be a useful standard reference paper.
The paper lacks evidence for some of the claims in my opinion:
- The toolchain is claimed to have a low computational cost, but I cannot find that numbers were given to support the claim directly (although of course we can trust it is relatively quick compared to the models providing the training data). How quick/low computational cost are the simulations with the surrogate?
- The toolchain is claimed to be fully modular and it is said that blocks can be independently enhanced / updated (line 100-105). There is no clear evidence given imo. Further it is mentioned that wake parameters are to be calibrated in line 186-187, referring to Braunbehrens, 2023. Indeed, the assumption mentioned in line 192-194 is very critical. My own experience is that the turbine loads surrogate models need to be retrained when changing the flow model to get reliable results. Hence maybe the the first statement on independent modular model building is too broad/optimistic and we need to be very careful with changing modeling blocks (retraining/calibration may be needed)?
Furthermore in relation with the previous point, the methodology is not quite clear to me in Section 3.4. The wake model used now is a FLORIS model instead of FAST.FARM DWM as used for training the surrogate. Was any recalibration of the FLORIS wake model parameters applied? How do the FLORIS results of Figure 13 compare to FAST.FARM model results? That to me seems a very relevant verification of the framework?
Further, I think the statement in Line 70 generalizes too much in the way it is formulated, considering that many types of wind farm models are applied in industry and research. In the same paragraph, widely used turbulence models are mentioned, but we can also think of electrical modelling, environmental impact modeling etc.
Table B1 has a formatting problem (does not respect the page margins).