Wind Farm Power Production Research Articles

The influence of incoming turbulence on the dynamic modes of an NREL-5MW wind turbine wake

Knowledge of the dynamics of wind turbine wakes and its dependence on the incoming boundary layer is fundamental to optimize and control the power production of wind farms. This work aims at investigating the effect of inflow turbulence on the wake of the NREL-5MW wind turbine. Sparsity-Promoting Dynamic Mode Decomposition (SP-DMD) is performed on snapshots extracted from large-eddy simulations of the turbine wake, for detecting the most dynamically-relevant flow structures in the presence or absence of inflow turbulence. We demonstrate that inflow turbulence generated by a precursor simulation radically changes the most dynamically-relevant flow structures. For the laminar-inflow case the DMD modes selected by the SP algorithm have high wavenumbers and are spatially localized. When turbulence is added at the inflow, these high-frequency modes are superseded by low-frequency modes lying in the frequency range of the wake meandering and filling the whole domain, mostly corresponding to those dynamically relevant for the precursor simulation. These results show that, in the presence of inflow turbulence, coherent structures linked to endogenous mechanisms such as tip and root vortices loose their dynamical relevance in favour of those exogenously excited by turbulence, indicating that low-dimensional models of turbine wakes should take into account atmospheric turbulence.

Decentralised optimisation for large offshore wind farms using a sparsified wake directed graph

Considering the wake effects, developing an efficient wake-based graph clustering algorithm is essential for the decentralised coordinated online control of large offshore wind farms. In this study, the idea of graph sparse is introduced into the algorithm, in which the sparsified wake-directed graph is generated while preserving the original wake-directed graph critical wake coupling relationship between turbines. The key is to construct the original wake-directed graph by quantifying wake intensity and applying the graph sparseness constraints algorithm to achieve an ultra-sparse wake subgraph. Based on the sparsified wake-directed graph and wake intensity weighting matrix, the wind farm is split into almost uncoupled cluster subsets to establish decentralised sparse communication architectures. By doing so, a decentralised sequential quadratic programming optimisation strategy is proposed to solve a nonconvex optimisation problem, in which the thrust load problem is converted into a power controlling optimisation problem, and the thrust load distribution is balanced using the power margin. Simulation results reveal that the proposed scheme can maximise wind farm power production while minimising thrust loads in various turbulence intensities, making it functional for real-time operations on a large-scale wind farm.

Time-Dependent Upper Limits to the Performance of Large Wind Farms Due to Mesoscale Atmospheric Response

A prototype of a new physics-based wind resource assessment method is presented, which allows the prediction of upper limits to the performance of large wind farms (including the power loss due to wind farm blockage) in a site-specific and time-dependent manner. The new method combines the two-scale momentum theory with a numerical weather prediction (NWP) model to assess the “extractability” of wind, i.e., how high the wind speed at a given site can be maintained as we increase the number of turbines installed. The new method is applied to an offshore wind farm site in the North Sea to demonstrate that: (1) Only a pair of NWP simulations (one without wind farm and the other with wind farm with an arbitrary level of flow resistance) are required to predict the extractability. (2) The extractability varies significantly from time to time, which may cause more than 30% of change in the upper limit of the performance of medium-to-high-density offshore wind farms. These results suggest the importance of considering not only the natural wind speed but also its extractability in the prediction of (both long- and short-term) power production of large wind farms.

Impact of ocean waves on offshore wind farm power production

Offshore wind energy has seen a steady increase in the latest years as it serves as an ideal alternative energy source to meet our renewable energy goals. Due to this increase in interest and in installations of offshore wind farms a better understanding of the impact of waves on the power production of wind farms in necessary. This wave-wind farm power production interaction has scarcely been examined, however, some exceptions of small scale large-eddy-simulations exist. Unfortunately, these studies are not able to take multiple wind farms under real weather conditions into account. Here, we show the power production and wake lengths of 1250 offshore wind turbines located in the German Bight simulated by a stand-alone atmospheric (WRF) model and a coupled atmosphere-wave (WRF-SWAN) model. The coupled atmosphere-wave model estimates larger (smaller) power production in case of waves and wind traveling in the same (opposed) direction compared to the stand-alone atmospheric model. The relative difference between the two models can be as much as 20% for the two-week averaged grid power production, while the difference in total power over this two-week period is equal to 9%. Moreover, the wind farm wake lengths of waves and wind traveling in the same (opposed) direction are longer (shorter) for the coupled atmosphere-wave model compared to the stand-alone atmospheric model. Here, the relative difference of the mean wake length between the two models can be up to 25%. This shows the importance of waves as a part of the offshore wind environment and can be an important factor in future wind assessment or power production estimation studies.

Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production

Cooperative yaw control of wind turbines can substantially increase the total power production of wind farms, especially for the ones with aligned turbines. However, a generalized and effective method to quickly determine the turbines’ yaw angles for the wind farm control is rather limited. To this end, a systematic study was carried out on the cooperative yaw control of a single column of turbines. It is found that the optimized yaw angles are not sensitive to the turbine number. All the turbines should yaw to the identical direction for power maximization except the last turbine for which no yaw is required. The cooperative yaw control strategy can be simplified to a solution space only containing two yaw angles, i.e., the yaw angle of the first turbine and the angle for the downstream yawed turbines. Then, two algebra formulas are proposed to quickly determine the two yaw angles for the wind farm control only using three available parameters, i.e., the thrust coefficient of the rotor, the wake decay coefficient and the turbine spacing. To validate the proposed formulas, high-fidelity large-eddy simulations are performed. The results demonstrate that the proposed control strategy can increase the power production significantly for cases with strong wake effects. For one of the cases with five aligned turbines, the total power can be enhanced by 17.5%.

Enhanced wind-farm performance using windbreaks

The flow speed-up generated by windbreaks can be used to increase the power production of wind turbines. However, due to the increased drag imposed by the windbreaks, their use in large wind turbine arrays has been questioned. We use large eddy simulations to show that windbreaks can increase the power production of large wind farms. A crucial finding is that windbreaks in a wind farm should be much lower than for a single turbine case. In fact, the optimal windbreak for an isolated turbine can reduce wind farm performance. The optimal windbreak height in a wind farm namely depends on the right balance between flow speed-up over the windbreak and the drag imposed by all windbreaks in the farm. The increased performance is a result of the favorable total pressure flux created by the windbreaks.

Wind farm yaw control set-point optimization under model parameter uncertainty

Wake steering, the intentional yaw misalignment of certain turbines in an array, has demonstrated potential as a wind farm control approach to increase collective power. Existing algorithms optimize the yaw misalignment angle set-points using steady-state wake models and either deterministic frameworks or optimizers that account for wind direction and yaw misalignment variability and uncertainty. Wake models rely on parameterizations of physical phenomena in the mean flow field, such as the wake spreading rate. The wake model parameters are uncertain and vary in time at a wind farm depending on the atmospheric conditions, including turbulence intensity, stability, shear, veer, and other atmospheric features. In this study, we develop a yaw set-point optimization approach that includes model parameter uncertainty in addition to wind condition variability and uncertainty. To enable computationally efficient online set-point optimization under model parameter uncertainty, a simplified, approximate parameter distribution estimation method is used. The optimization is tested in open-loop control numerical experiments using utility-scale wind farm operational data for which the set-point optimization framework with parametric uncertainty has a statistically significant impact on the wind farm power production for certain wind turbine layouts at low turbulence intensity, but the results are not significant for all layouts considered nor at higher turbulence intensity. The set-point optimizer is also tested for closed-loop wake steering control of a model wind farm in large eddy simulations of a convective atmospheric boundary layer (ABL). The yaw set-point optimization with model parameter uncertainty reduced the sensitivity of the closed-loop wake steering control to increases in the yaw controller update frequency. Increases in wind farm power production were not statistically significant due to the high ambient power variability in the turbulent, convective ABL.

An overall performance index for wind farms: a case study in Norway Arctic region

Wind farms (WFs) experience various challenges that affect their performance. Mostly, designers focus on the technical side of WFs performance, mainly increasing the power production of WFs, through improving their manufacturing and design quality, wind turbines capacity, their availability, reliability, maintainability, and supportability. On the other hand, WFs induce impacts on their surroundings, these impacts can be classified as environmental, social, and economic, and can be described as the sustainability performance of WFs. A comprehensive tool that combines both sides of performance, i.e. the technical and the sustainability performance, is useful to indicate the overall performance of WFs. An overall performance index (OPI) can help operators and stakeholders rate the performance of WFs, more comprehensively and locate the weaknesses in their performance. The performance model for WFs, proposed in this study, arranges a set of technical and sustainability performance indicators in a hierarchical structure. Due to lack of historical data in certain regions where WFs are located, such as the Arctic, expert judgement technique is used to determine the relative weight of each performance indicator. In addition, scoring criteria are predefined qualitatively for each performance indicator. The weighted sum method makes use of the relative weights and the predefined scoring criteria to calculate the OPI of a specific WF. The application of the tool is illustrated by a case study of a WF located in the Norwegian Arctic. Moreover, the Arctic WF is compared to another WF located outside the Arctic to illustrate the effects of Arctic operating conditions on the OPI.

Are wind turbines a mafia windfall? The unintended consequences of green incentives

Over the past decades, national and supranational governments have amply incentivised wind farm power production. In this paper, we reconstruct investments in the wind sector over twenty years of national support schemes in Italy, and we uncover unintended consequences of wind incentives exploited by criminal organization groups. We show that Italian mafia-exposed municipalities register a larger take-up of incentives for wind energy production, measured with the number of wind farms. The differential effect appears when the national energy services authority started withdrawing the excess supply of tradeable Green Certificates. With the end of the national intervention in the market, when the system of incentives became more competitive, installed power capacity sharply decreased. Further, we do not observe any differential in the take-up of mafia-exposed municipalities based on local physical constraints related to the wind potential nor spillover effects in neighboring municipalities. Our analysis highlights the risks of criminal infiltration in the green energy sector, especially when incentive schemes are insufficiently competitive and overly generous.

Exploring the application of reinforcement learning to wind farm control

Optimal control of wind farms to maximize power is a challenging task since the wake interaction between the turbines is a highly nonlinear phenomenon. In recent years the field of Reinforcement Learning has made great contributions to nonlinear control problems and has been successfully applied to control and optimization in 2D laminar flows. In this work, Reinforcement Learning is applied to wind farm control for the first time to the authors’ best knowledge. To demonstrate the optimization abilities of the newly developed framework, parameters of an already existing control strategy, the helix approach, are tuned to optimize the total power production of a small wind farm. This also includes an extension of the helix approach to multiple turbines. Furthermore, it is attempted to develop novel control strategies based on the control of the generator torque. The results are analysed and difficulties in the setup in regards to Reinforcement Learning are discussed. The tuned helix approach yields a total power increase of 6.8% on average for the investigated case, while the generator torque controller does not yield an increase in total power. Finally, an alternative setup is proposed to improve the design of the problem.

Spatial Management for Solar and Wind Energy in Kuwait

Kuwait situated at the northwestern part of the Arabian Gulf experiences severe arid environmental conditions. Kuwait is facing many challenges in the sustainable development of the energy sector. These challenges are: deposited dust, increase of carbonate and mud percentages in dust, lower solar radiation, high relative humidity and low wind speed. These challenges that affects more the photovoltaic (PV) power stations were carefully monitored in order to special manage the solar and wind energy in Kuwait. Using 14 weather stations, solar radiation, wind speed and directions were monitored during the period (2010-2017). The annual deposited dust represents one of the highest in the region with 216 t km-2 as an average in 2009-2010 and 339 t km-2 in 2010-2011. Also, carbonates and mud content within deposited dust represent the highest percentages compared to the surrounding regions. The annual power production of wind farms in Kuwait is 2.3 times higher than PV unit with similar capacity. Finally, maps that were produces for all environmental challenges for solar and wind energy in this present paper show that the northwestern regions of Kuwait are the most appropriate areas for setting up a wind and/or a PV power stations.

Co‐optimisation of wind farm micro‐siting and cabling layouts

Wind farm layout optimisation (WFLO) is carried out in this study considering the wake effect, and cabling connections and losses. The wind farm micro-siting optimisation problem is formulated with the aid of Jensen's wake model. Cabling between the wind turbines and the point of common coupling is an important aspect of the wind farm design as it affects the capital investment as well as income over the lifetime of the wind farm. The cabling layout must satisfy the connection of the wind turbines to the point of common coupling in such a way that the total cable length is reduced while reliability is maintained. Introducing the cabling layout optimisation to the WFLO, further complicates the optimisation problem. An integrated tool is developed to optimise the wind farm layout and cabling simultaneously. The main contribution of this work is the development of an integrated tool that maximizes the energy production of the wind farm via optimal allocation of wind turbines with optimal cable routing. This tool considers the capital cost of wind turbines and cabling, wind farm power production, and power losses in the cabling over the lifetime of the wind farm. The proposed co-optimisation problem is solved using genetic algorithm. The decision variables are the wind farm layout, cable paths and sizes, and the location of the point of common coupling within the land perimeter. A case study incorporating a multi-speed and multi-direction wind profile is carried out to demonstrate the applicability of the proposed approach. Moreover, the proposed methodology is compared to the separate optimisation method where the WFLO and cabling optimisation are solved sequentially with two separate steps. It is shown that the co-optimisation method is superior in terms of cable power losses, overall wind farm cost, and compactness (land use).

Artificial Neural Networks based wake model for power prediction of wind farm

In the wind industry, power prediction of wind farm is commonly implemented by analytical wake models, which is low-cost but insufficient in accuracy for high-turbulent wake modelling. In this study, a novel machine-learning-based wake model is developed to improve the power prediction of wind farms. The presented model can reproduce the velocity and turbulence fields in turbine wakes commensurate to the high-fidelity Computational Fluid Dynamics (CFD) simulations while achieving good computational efficiency. Driven by massive CFD simulation dataset, the implicit relationship between inflows and wake flows is established using the Artificial Neural Networks (ANN) technique based on back-propagation algorithm. The reduced-order method Actuator Disk Model with Rotation (ADM-R) and modified k−ε turbulence model are implemented into RANS simulations to save the computational costs dramatically in producing the big-data of wake flows. The ANN wake model is deployed in the Horn Rev wind farm, and validated against LES, onsite measurement, and analytical wake models. The conclusions show that the ANN model can appreciably improve the power predictions compared with the existing analytical models and match the LES and measurement data well. The validated model is also adopted to investigate the influence of wind direction and turbine layout on power production of wind farms.

Are Wind Turbines a Mafia Windfall? The Unintended Consequences of Green Incentives

Over the past decades, national and supranational governments have amply incentivized wind farm power production. In this paper, we reconstruct investments in the wind sector over twenty years of national support schemes in Italy, and we uncover unintended consequences of wind incentives exploited by criminal organization groups. We show that Italian mafia-exposed municipalities register a larger take-up of incentives for wind energy production, measured with the number of wind farms. The differential effect appears when the national energy services authority started withdrawing the excess supply of tradeable Green Certificates. With the end of the national intervention in the market, when the system of incentives became more competitive, installed power capacity sharply decreased. Further, we do not observe any differential in the take-up of mafia-exposed municipalities based on local physical constraints related to the wind potential nor spillover effects in neighboring municipalities. Our analysis highlights the risks of criminal infiltration in the green energy sector, especially when incentive schemes are insufficiently competitive and overly generous.

Wake‐effect aware optimal online control of wind farms: An explicit solution

Wake effects impose significant aerodynamic interactions among wind turbines. To improve the wind farm operating performance, practical wind farm online control considering wake effects becomes very important. To achieve online optimal wind farm control while responding to grid demands, this paper proposes a novel optimal wind farm supervisory control (SC) model and its explicit solutions. From the controller modelling perspective, the two major wind farm operating modes, the maximum power point tracking mode and the set-point tracking mode, are first analysed and unified in one optimisation model while considering wake effects. In this way, wind farm power production and rotor kinetic energy reserve can be simultaneously considered to conveniently modify the operation mode in response to different grid demands. Aside from controller modelling, the collocation method is first introduced to address the online application problem of such wake-effect aware optimal WF control. Although a few optimisation algorithms have been proposed to find the optimum offline, online optimal control is still challenging because of the computational complexity brought by wake model non-linearity and non-convexity. The proposed collocation method explicitly approximates the optimal solutions to the proposed supervisory control model, through which only a direct algebraic operation is required for online optimal control instead of repeated optimisations. Case studies are carried out on different wind farms under various wind conditions, showing that the wind farm power production potential and releasable power reserve are improved compared to traditional greedy control in both modes. The accuracy of the collocation method is verified. A detailed analysis of the wind farm production capacity under different wind speeds and directions is also provided.

Effect of low-level jet height on wind farm performance

Low-level jets (LLJs) are the wind maxima in the lowest 50 to 1000 m of atmospheric boundary layers. Due to their significant influence on the power production of wind farms, it is crucial to understand the interaction between LLJs and wind farms. In the presence of a LLJ, there are positive and negative shear regions in the velocity profile. The positive shear regions of LLJs are continuously turbulent, while the negative shear regions have limited turbulence. We present large eddy simulations of wind farms in which the LLJ is above, below, or in the middle of the turbine rotor swept area. We find that the wakes recover relatively quickly when the LLJ is above the turbines. This is due to the high turbulence below the LLJ and the downward vertical entrainment created by the momentum deficit due to the wind farm power production. This harvests the jet's energy and aids wake recovery. However, when the LLJ is below the turbine rotor swept area, the wake recovery is very slow due to the low atmospheric turbulence above the LLJ. The energy budget analysis reveals that the entrainment fluxes are maximum and minimum when the LLJ is above and in the middle of the turbine rotor swept area, respectively. Surprisingly, we find that the negative shear creates a significant entrainment flux upward when the LLJ is below the turbine rotor swept area. This facilitates energy extraction from the jet, which is beneficial for the performance of downwind turbines.

Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity

A new multiple wake model is developed for wind farm power prediction and wind farm control. First, numerical simulations are conducted for two wind turbines under different layout sets, and the characteristics of mean velocity and turbulence intensity in multiple wakes are systematically investigated. A new multiple wake model considering the local effective turbulence on the rotor and the wake interaction effects is proposed. The proposed model can favorably predict the mean velocity and turbulence intensity distributions in multiple wake regions, as well as the power production in wind farm comparing with numerical simulations and field measurements. Finally, the new proposed multiple wake model is applied to wind farm modelling and optimization framework, which enables the maximization of wind farm power production by wake steering control. The wind sector width of 2° with the wind speed bin of 0.5 m/s is proposed for the lookup-table-based wake steering optimization. The proposed values reduce the prediction error of annual energy production gain from 34.5% to 3.2% comparing with the conventional values of 5° and 1 m/s. In addition, the yaw offset limit of ±15° is recommended to satisfy both the maximization of power production and the safety requirement of International Electrotechnical Commission (IEC) standard.

Data analysis and simulation of the Lillgrund wind farm

AbstractThe power production of the Lillgrund wind farm has been investigated by means of SCADA data covering almost 10 years of operation, wake models and numerical simulation tools, such as WindSim, OpenFOAM and ORFEUS. The analysis of the SCADA data provided the quantification of wake losses and of blockage effects, results that enable a benchmark to assess the uncertainty of other numerical methods of industrial use. The results have been analysed in terms of global array efficiency for different wind directions, wind velocity and stratification conditions. A comparison of the present results with the Jensen and Ainsle models indicated that the two used wake models are affected by an error of about 8–10% in the estimation of the array efficiency, while the error does not vary significantly when using numerical tools such as ORFEUS, WindSim and OpenFOAM. By comparing wind statistics available before the park construction, an assessment of the blockage of the Lillgrund farm was performed with contrasting results from the other numerical tools, highlighting how challenging is to assess farm blockage with current industrial settings.

Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment

The intentional yaw misalignment of leading, upwind turbines in a wind farm, termed wake steering, has demonstrated potential as a collective control approach for wind farm power maximization. The optimal control strategy and the resulting effect of wake steering on wind farm power production are in part dictated by the power degradation of the upwind yaw misaligned wind turbines. In the atmospheric boundary layer, the wind speed and direction may vary significantly over the wind turbine rotor area, depending on atmospheric conditions and stability, resulting in freestream turbine power production which is asymmetric as a function of the direction of yaw misalignment and which varies during the diurnal cycle. In this study, we propose a model for the power production of a wind turbine in yaw misalignment based on aerodynamic blade elements, which incorporates the effects of wind speed and direction changes over the turbine rotor area in yaw misalignment. The proposed model can be used for the modeling of the angular velocity, aerodynamic torque, and power production of an arbitrary yaw misaligned wind turbine based on the incident velocity profile, wind turbine aerodynamic properties, and turbine control system. A field experiment is performed using multiple utility-scale wind turbines to characterize the power production of yawed freestream operating turbines depending on the wind conditions, and the model is validated using the experimental data. The resulting power production of a yaw misaligned variable speed wind turbine depends on a nonlinear interaction between the yaw misalignment, the atmospheric conditions, and the wind turbine control system.

Wind farm power output optimization using cooperative control methods

AbstractWe study the application of cooperative control and game theoretic approaches to wind farm optimization. The conventional (greedy) wind farm control strategy seeks to individually maximize each turbine power. However, this strategy does not maximize the overall power production of wind farms due to the aerodynamic interactions (wake effect) between the turbines. We formulate the wind farm power optimization problem as an identical interest game which can also be used to solve other cooperative control problems. Two model‐free learning algorithms are developed to obtain the optimal axial induction factors of the turbines and maximize power production. The algorithms are simulated for a four‐turbine wind farm and the Princess Amalia wind farm and are compared to a learning algorithm that uses a game‐theoretic approach. It is shown that the proposed algorithms improve upon benchmark algorithm in terms of both performance and actuation effort.