Wind Farm Layout Research Articles

Floating offshore wind potential for Mediterranean countries

Floating offshore wind is a promising renewable energy source for several Mediterranean Countries. The exploitation of this resource will contribute to reducing carbon dependence and support the clean energy transition towards a climate neutral Europe. This work presents a novel methodology for estimating spatially-resolved Levelised Cost of Energy and offshore wind energy potential to provide optimal design of floating offshore wind farms in the Mediterranean Sea. For the first time, each site is optimised based on electrical grid cable design and wind farm layout optimisation using the Jensen wake model. The largest technical capacity potentials are obtained in Libya, Tunisia, Italy and Greece, accounting for 72.2 % of the total Mediterranean potential with a total installed capacity of 782 GW. The average LCOE is 93.4 €/MWh and the average capacity factor is 31.8 %, while 67.5 % of the technical potential has LCOE below 90 €/MWh which demonstrates that floating offshore wind in the Mediterranean could become soon competitive with other renewable energies. Optimal floating wind farm design parameters show the prevalence of a wind farm array of 10x10 wind turbines with a preferred rated power of 15 MW and the HVDC export cable connection. Among the selected floating platforms, Hywind outperforms WindFloat and GICON-SOF in 59.2 % of the suitable areas due to the lower structure material. Policymakers and stakeholders will primarily benefit from this study, which provides them with important information for careful marine spatial planning and the development of floating offshore wind farms in the Mediterranean.

Optimization of the Offshore Wind Turbines Layout Using Cuckoo Search Algorithm

Wind turbines have gained popularity as one of the efficient micro grid energy generation sources over the years. However, wind energy yield has been greatly impacted by the wind farm wake effect, especially when the size of the wind farm becomes very large. One way to address this challenge is through wind turbine arrangements and their scalability. Developing effective means of optimizing wind farm configurations is a major concern for energy communities. This has continuously led to increased power generation and a corresponding cost reduction. As such, this research article developed an optimal large-scale offshore wind turbine placement based on wind farm layout design. The system developed focuses on wind turbine placement in wind farm layouts that have a negative influence on the capital investment due to the increasing wake effect thereby reducing energy production. A multi-objective function consisting of wake effect and component costs within the wind farm is formulated. After-which a cuckoo search algorithm technique is employed in the wind farm model to optimize the optimization problem (minimizing the wake effect while improving power output and cost). Four test case scenarios were considered when implementing the wind model. The results obtained from the developed scheme were compared with those obtained when other optimization techniques were used, using power output and cost as performance metrics. The obtained power output and cost for the test case scenario in the ideal wind turbine position on the wind farm are 18288.3KW, 19680.1KW, 18879.9KW, 21105.8KW and 26.9, 28.7, 26.9, and 29.8KW, respectively. This shows that the result obtained from the developed scheme outperformed that obtained from PSO and that of WOA.

Study on Aerodynamic Performance and Wake Characteristics of a Floating Offshore Wind Turbine in Wind–Wave Coupling Field

Floating offshore wind turbines (FOWTs) exhibit complex motion with multiple degrees of freedom due to the interaction of wind and waves. The aerodynamic performance and wake characteristics of these turbines are highly intricate and challenging to accurately capture. In this study, dynamic fluid body interaction (DFBI) and overset grid technology are employed to investigate the dynamic motion of a 5 MW FOWT. We use the volume of fluid (VOF) method and improved delayed detached eddy simulation (IDDES) model to investigate the aerodynamic performance and wake evolution mechanism for various wave periods and heights. According to the findings, the magnitude of the pitch motion increases with the period and height of the waves, leading to a decrease in both the power output and thrust; the maximum power was reduced by nearly 6.8% compared to a wind turbine without motion. The value of power and thrust reduction varies for different wave periods and heights, and is influenced by the relative speed and pitch angle, which play a crucial role. Wind–wave coupling has a significant impact on the evolution of both wake and vortex structures for FOWT. The wake shape downstream is also dynamically influenced by the waves. In the presence of wind and wave coupling, the interaction between the wind turbine and the wake is heightened, leading to the merger of two unstable vortex rings into a single, larger vortex ring. The research unveils a comprehensive picture of the offshore wind energy dynamics and wake field, which holds immense significance for the design of floating wind turbines and the optimization of wind farm layout.

Design optimization of offshore wind farm layout based on game theory

In offshore wind farm (OWF) planning, traditional methods usually use global optimal strategies. In this paper, game theory is applied to the multi-objective optimization of wind turbine (WT) micro-siting and cabling in an OWF. This method considers the real situation where two different teams may choose the global optimal or the local optimal solution. Firstly, the wind speed deficit in the OWF is estimated using the Gaussian wake model. The objective function and constraints of this optimization problem are formulated. Secondly, the game strategies are exhaustively enumerated. The strategies are allocated to the game space matrix and the payoffs are calculated. The above steps are repeated after redistributing the design permissions. Finally, the Nash front is formed and compared with the Pareto optimum. From simulation results, it can be observed that different Nash solutions can be obtained using different strategy permission allocation methods. So the final layout of the OWF should be determined according to different strategy allocation methods when planning the OWF.

Experimental study on the periodicity of wake flow of a vertical staggered wind turbine fleet

To optimize wind farm layout and micro-siting, this study analyzes the impact of different numbers of small wind turbines on the wake region of a large wind turbine at varying yaw angles. It also explores the periodic characteristics of wind speed signals at different time scales. The study involves one large and nine scaled-down small wind turbines in a 3 × 3 arrangement to simulate real-world interactions. The results reveal that increasing small turbine numbers significantly amplify wake deficits downstream, intensifying periodic oscillations, providing crucial theoretical support for wind farm design optimization. These findings contribute to understanding complex wind turbine dynamics in wind farms and supporting future efficient and sustainable designs.

Integrated floating wind farm layout design and mooring system optimization to increase annual energy production

As we cluster wind turbines in wind farms to gain energy from sites with high wind speeds, wake losses occur within the wind farm. Wake loss is a term used to describe the lower energy production of a downwind turbine that is totally or partially in the wake of an upwind turbine. To decrease wake losses inside the wind farm, the wind farm’s layout is optimized. However, a variety of factors constrain the wind farm layout optimization, such as the size of the lease area relative to the number of turbines to be placed, or the shape of the lease area. Therefore, many wind farms end up with a regular grid layout, such as the Horns Rev 1 wind farm in the North Sea. The ability of a floating offshore wind turbine (FOWT) to change its position based on the wind direction and its mooring system design presents an opportunity to further decrease wake losses in floating wind farms. In this work, we integrate the design of the FOWT mooring systems with the floating wind farm layout design with the goal of increasing the farm’s annual energy production. We use the Horns Rev 1 wind farm as a case study to demonstrate our method. The results show that allowing the FOWT to relocate can decrease wake losses up to 18%. Moreover, the newly developed mooring systems are less stiff and therefore allow larger motion of the FOWT; hence, the material cost of the mooring system decreases by an estimated 17%.

Machine learning to rapidly predict turbine yaw angles for wake steering

Wake steering is an important control strategy to boost power production of a wind farm. Because of computational expense and problem complexity, wind farm layouts are typically optimized assuming they will operate without wake steering. However, performance gains are possible by simultaneously optimizing wind farm layout and the yaw angles for wake steering. In this paper, we present a method to train a machine learning model to predict turbine yaw angles as a function of their position relative to other turbines in the wind farm and the inflow wind speed. This model is able to predict turbine yaw angles with an R2 value of 0.98. The model also produces turbine yaw angles with wind farm power production that is similar to yaw angles that have been directly optimized. This method to rapidly compute optimal turbine yaw angles for wake steering enables control co-design of wind farms and the associated performance increase.

Evaluation of LES-based time-decoupled model-predictive control in different wind farm layouts

Very recently, Janssens and Meyers (Wind Energy Sci., vol. 9, 65–95, 2024) proposed a time-decoupled model-predictive control (TD-MPC) framework for wind farm power optimization using a control model based on coarse-grid large-eddy simulation (LES). In a receding-horizon fashion, by computing the controls for the next time window based on a prediction of the flow (so ahead of time), they achieved (almost) real-time computational speed. The current paper further explores the potential of this approach in the context of dynamic yaw control, under the assumption of exact state knowledge. To that end, the LES-based, TD-MPC framework is validated on a reference wind farm for three different wind directions, for which we observe gains ranging from 4 (for staggered configurations) to 42% (for aligned configurations w.r.t. the wind direction). The LES-based controller always outperforms a Betz optimal controller and a static (wake model based) yaw controller at near real-time speed. An analysis of the yaw angles reveals that the framework is very effective in steering the wakes away from downstream turbines in upstream and downstream regions, synchronized to the turbulent inflow. By comparing the performance of the LES-based controllers (based on the actuator disc turbine model) against a reference simulation that uses a more accurate actuator sector model, we illustrate the robustness of the approach in the presence of the additional model error.

A two-step topology and layout wind farm optimization approach

A novel two-step approach is presented to optimize the placement of wind turbines within a wind farm. The goal is to maximize the annual energy production, while the topology and layout of the wind turbines are optimized in sequence with a two-step procedure using gradient-based algorithms. In particular, in the first step the topology of the wind farm is optimized. A grid of potential wind turbine locations is defined, and continuous density variables ranging from zero to one are assigned to each location. The variables indicate the presence or absence of a wind turbine. Throughout the topology optimization process, we penalize the intermediate values of these variables, implicitly pushing the algorithm towards crisp 0-1 final layouts. Constraints are imposed on the minimum and maximum number of wind turbines and the spacing between them. Subsequently, in the second step, we refine the wind farm layout by optimizing the coordinates (x, y) of the wind turbines, which are initialized based on the optimal topology obtained in the first step. Numerical findings demonstrate the capability of the proposed approach to identify non-intuitive optimized wind farm layouts, with reasonable computational resources and time.

Floating wind farm design using social and environmental constraints

This work proposes a wind farm design methodology, integrating several design variables from different design aspects into an optimization problem, such as turbine power, number of turbines, cable types, wind farm layout and site location. A mixed integer genetic algorithm is employed to combine discrete and continuous design variables to find the design with minimum Levelized Cost of Energy (LCoE). The wind farm LCoE is calculated using a wind farm model, which combines detailed cost functions for different cost elements and engineering wake models to estimate Annual Energy Production. The design is constrained not only by technical aspects such as total power, farm area, but also by environmental and social constraints. These include the wind farm visibility, which is a popular concern among the people living along the coast of nearby wind farms, and availability, implying the compliance with the designated exclusion zones. To demonstrate the sensitivity of design constraints, two floating wind farms are sited and optimized as a case study, using different visibility constraints. The developed design tool within this study aims to bridge the gap between wind farm developers and local authorities who are responsible for permitting and zoning of development areas.

Optimizing wind farm layout for enhanced electricity extraction using a new hybrid PSO-ANN method

With the growing need for renewable energy, wind farms are playing an important role in generating clean power from wind resources. The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency. This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization (PSO) and artificial neural networks (ANNs). The PSO method was used to explore the solution space and develop preliminary turbine layouts, and the ANN model was used to fine- tune the placements based on the predicted energy generation. The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits. The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches, giving exciting prospects for developing more efficient and sustainable wind farms. The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques. Furthermore, this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency. A notable increase in power generation is observed across various scenarios. The percentage increase in the power generation ranged from approximately 7.7% to 11.1%. Owing to its versatility and adaptability to site-specific conditions, the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.

Need For Speed: Fast Wind Farm Optimization

The Wind in my Backyard (WIMBY) project is developing a web interface to aid communities in siting wind energy projects. As part of this siting tool, users will be able to find realistic wind farm layouts for any proposed site in Europe, given certain constraints. When designing this tool, there arises a need for speed: realistic layouts must be designed in computational times that are appropriate for a web interface. In this study, we compare two optimization algorithms: a gradient-based algorithm, referred to as stochastic gradient descent (SGD), and a gradient-free method, referred to as smart-start. The trade-offs between the optimal energy yield and optimization computational time are characterized via a parameter sweep, considering a site in Denmark. This analysis considered farms with 10, 25, and 50 turbines. We find that smart-start yielded the best results for very short computational times, and that SGD yielded layouts with higher energy yields when considering larger computational times.

Effects of wind turbine rotor tilt on large-scale wind farms

Recent studies have explored the use of rotor tilt adjustments to reduce wake losses in wind farms. While downward wake deflection in aligned wind farms has shown promise for significant power gains, the impact of wind farm layout on the effectiveness of tilt strategies is not yet fully understood. Additionally, the effect on downstream farms remains unclear. Our large eddy simulations reveal that a rotor tilt of 20 degrees significantly reduces wake losses in aligned wind farms. For wind farms with 8 turbine rows, we observe an overall increase in wind farm productivity of up to 11%. However, tilting the rotors may decrease power production in staggered wind farms, where wake losses are inherently lower due to the increased spacing between turbines. Our findings also suggest that a downstream wind farm might benefit from an upstream farm implementing rotor tilt, although this advantage is primarily observed in the first row of the downstream farm.

Advancements in wind farm layout optimization: a comprehensive review of artificial intelligence approaches

Advancements in wind farm layout optimization: a comprehensive review of artificial intelligence approaches

A blind test on wind turbine wake modelling based on wind tunnel experiments: Phase I – The benchmark case

Wind turbine wake and modelling is crucial to optimizing future wind farm layouts and hence reducing the cost of energy. This paper presents the first phase of a blind test on modelling controlled and uncontrolled wind turbine wakes. The blind test is based on wind tunnel experiments of two model scale wind turbines (D = 1.1 m) one downstream of the other. The exercise is split into two phases and the first one is presented here, where participants are invited to simulate the baseline case, in which both turbines are aligned with the flow and there is no control on the either turbine. The objective of this phase is to ensure all participants can benchmark their numerical approach against a baseline open data set, where no wake control is applied. Experimental measurements include inflow velocity, turbine power and loads for a range of tip speed ratios. In the second phase, not presented here, the wake of the upstream turbine will be controlled and the performance of the downstream one will be recorded. This will be a blind test with the data not released prior to submissions. The present paper gives an overview of the initial, open benchmark case, including its objectives, methodology and experimental results.

Exploring the impact of different inflow conditions on wind turbine wakes using Large-Eddy Simulations

The ever-growing demand for renewable energy, driven by cost-effectiveness and minimal ecological impacts, has resulted in the deployment of larger wind turbines with rotor diameters surpassing 200 m. This underscores the importance of a thorough understanding of flow dynamics to optimize operational efficiency in diverse atmospheric inflow scenarios. Understanding the intricate impact of atmospheric conditions, including wind shear and turbulence, on wind turbine wakes is crucial for optimizing wind farm layouts and performance, influencing wake evolution, turbine loads, and power output. This research focuses on bridging the gap between idealized inflow scenarios and real-world atmospheric inflow conditions by systematically integrating linear shear, turbulence and the logarithmic wind shear profile into the uniform inflow conditions and analyzing the wake behind the IEA-15 MW wind turbine. To specifically examine inflow effects, a constant hub height wind speed was maintained through a velocity controller. The study focuses on analyzing the wake’s flow field and providing insights into its recovery process. It was found that turbulence plays a critical role in a faster wake recovery as well as increasing the power production of the turbine for sheared inflows and the wind speed selected.

Towards a general tool chain integration platform for multi-disciplinary analysis and optimization in wind energy

To enable easier uptake and flexible usage for the wind energy community, a general tool chain integration platform for multi-disciplinary analysis and optimization, TopDesign, is proposed and developed. This work describes its concept and the initial implementation. The platform is written in Python and provided as an open source framework. The design of this platform puts emphasis on flexibility, usability and extensibility. General model classes with standard interfaces, which can wrap custom tools/models in different formats based on different programming languages, will be provided together with examples that are easy to understand and modify. Coupling tools/models into a system thus becomes an easy task of connecting models by inputs and outputs and defining workflow with a directed graph. To demonstrate the usage of the proposed platform, we implement a wind farm evaluation system by coupling the PyWake model, the FLORIS model and self-written constraint models in the platform and apply it in solving wind farm layout optimization problems with two algorithms, as implemented in an in-house optimization Python library. A case study for a wind farm composed of 9 turbines shows the potential of the proposed platform and methodology.

Passive Mooring-based Turbine Repositioning Technique for Wake Steering in Floating Offshore Wind Farms

Power loss due to wake effects from upstream wind turbines is an important factor in the design of floating wind farms. These wake disturbances also increase fatigue loads on downstream units. Economic-driven wind farm layout optimization may culminate in non-standard, irregular configurations of offshore wind farms that deviate from conventional engineering practices. Consequently, wind farm developers are inclined towards a more geometrically coherent layout that are sub-optimal. Floating wind turbines horizontal offsets can be used to overcome wake losses. In this study, a novel method suggesting various mooring orientation methods to passively reposition the wind turbines in the farm to maximize annual energy yield. Preliminary parametric optimization demonstrates the effectiveness of the proposed method for standard floating wind farm designs to reduce wake effects by as much as 30% at rated wind speed compared to a baseline case, while preserving the farm’s overall uniformity.

Ranking multi-fidelity model performances in reproducing internal and external wake impacts at neighbouring offshore wind farms

Performances of multi-fidelity numerical models in reproducing the impacts of internal and external wakes on power production are evaluated against SCADA data from two operational offshore wind farms in the North Sea. Results obtained for two engineering wake models, Jensen and Cumulative Curl (default and tuned settings), and a higher-fidelity Reynolds-averaged Navier-Stokes (RANS) setup are presented here. Tuned engineering models were configured against RANS results for an idealized wind farm layout consisting of several turbine setups. Input data for the model evaluation scenarios was obtained from a met mast located between the wind farms and binned according to atmospheric stability, wind speed and direction, and turbulence intensity. Results show that the higher fidelity of RANS is most advantageous for inflows that generate more complex internal wakes (e.g., deep array effects) and in replicating external wake impacts. The tuned setups of the engineering models, modified according to stability, outperform their default setups. Default model properties are defined in accordance with developer recommendations. One of the tuned models shows close farm-level agreements with RANS and even exceeds its performances in some of the cases of lower flow complexity.

Evaluating wind-farm power generation using a new direct integration of axisymmetric turbine wake

Engineering wake models enable fast calculation of wind-farm power generation including wake-turbine interactions, which is critical for annual energy yield calculations and for farm optimisation either at the design phase or during operation. The operating point of a turbine is determined based on its rotor-averaged wind speed, which is impacted by upstream wakes. The rotor-averaged deficit is typically calculated numerically by using a set of averaging points across the rotor disk, for which a Gaussian shape function is commonly evaluated. This numerical approach can have uncertainty based on the distribution of the averaging points. Also, a large number of averaging points can increase computational cost, which is detrimental for studies that rely on many individual calculations of farm power generation, e.g. layout optimisation. To avoid these drawbacks, we present a novel analytical solution to the circular-disk integration of a two-dimensional axisymmetric Gaussian function depicting upstream wakes for an arbitrary lateral offset between the rotor and the wake source. The analytical expression is compared against numerical averaging of an upstream wake, and is applied to the Horns Rev 1 wind farm showing excellent accuracy. Wind-farm layout optimisation models, especially methods based on gradient-descent optimisation, can benefit from the presented expression significantly, as this provides an analytically differentiable formulation for the rotor-averaged wind speed.