Wind Farm Configurations Research Articles

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.

Are steady-state wake models and lookup tables sufficient to design profitable wake steering strategies? A Large Eddy Simulation investigation

We leverage Large Eddy Simulation (LES) of wind farms to study the performances of an open-loop wake steering controller. This global controller is elaborated based on Lookup Table (LUT) generated using FLORIS. Our objectives are to highlight the potential problems that can arise from model errors due to its steady-state formulation and from difficulties to properly define the ambient flow state. We consider two wind farm configurations: a first theoretical layout of six machines, and a second layout representing a commercial wind farm that is currently studied for wake steering strategy, the King Plains wind farm. We represent our wind farm environment using LES within which the turbines are modeled as actuator disks. In addition to the standard local controller, the wind farm utilizes a LUT to compute the optimal yaw offset angles. Before its integration into the controller, this LUT is first populated using the yaw optimizer integrated into FLORIS. The primary input parameters for the LUT are derived from the freestream atmospheric conditions, which are parametrized in terms of TI, hub height wind speed, and wind direction. In this work, we investigate different strategies for estimating the ambient wind direction fed as input to the LUT during the control and their impact on the dynamics of the yaw actuation. While results confirm the potential of this type of controller, they also highlight the importance of a proper sensing to determine the freestream state.

A control-oriented load surrogate model based on sector-averaged inflow quantities: capturing damage for unwaked, waked, wake-steering and curtailed wind turbines

This paper presents a novel approach to constructing a load surrogate model. The surrogate estimates the damage equivalent loads (DELs) of a wind turbine of a given type regardless of its position within a wind farm, and under various farm control actions. The model relies solely on local inflow quantities (sector-averaged wind speeds and turbulence intensities) and local control parameters (rotor speed, pitch angle, and yaw misalignment). Despite its highly simplified representation of the complex behavior of the turbulent wind field, wake effects, and controller dynamics, these quantities prove sufficient to characterize DELs. The paper demonstrates the training of this load model within a simulation environment. Validation results using a different wind farm configuration indicate that the surrogate can accurately predict fatigue loads for both unwaked and waked turbines, encompassing scenarios of wake steering and induction control.

Spatial planning offshore wind energy farms in California for mediating fisheries and wildlife conservation impacts

Achieving a blue economy will require reconciling the value of emerging ocean uses with their impacts on the seascape and sectors with historical access to marine resources and areas. To meet this challenge, we developed an analytical framework for conducting marine spatial planning through tradeoff analysis, and applied it to prospective offshore wind energy development in the ∼974 km2 Morro Bay, California, USA Wind Energy Area (WEA). We generated spatial data layers estimating MW power production and impacts on fisheries value and marine wildlife conservation (seabird and cetacean populations) from wind farm development. We then quantified each sector's response to plans of development across the WEA and inside three leases recently acquired by the energy industry for prospective development. Finally, we integrated the sector response data into an analytical framework for mitigating sector tradeoffs with novel spatial planning solutions (maps of wind farm size, location, and configuration) that optimally maximize value to the emergent energy sector (MW power) while minimizing impacts to historical (fisheries and wildlife) sectors. We found that western sites in the WEA had the highest potential power production concurrent with the lowest impact on the historical sectors, revealing the eastern lease to be less efficient at optimally balancing the sector's objectives relative to the development of the central or western leases or the optimal spatial plans identified in the tradeoff analysis. Within a lease, tradeoff analysis found spatial planning able to generate out-sized savings in fisheries value with only modest losses in MW power – for example, by avoiding development in just 5% of the eastern lease to preserve nearly half its fisheries value and still generate 95% its total power potential. Small-scale development opportunities (e.g., a pilot project) with significant power potential and no fisheries impact were also identified, in this case by placing turbines in an area in the western lease with no fisheries value and high power production potential. These plans would also have a relatively low impact on the wildlife conservation sectors, due to decreases in vulnerability levels of both seabird and cetacean populations to turbines going from east to west across the WEA. Our results can inform site evaluation and permitting processes for wind energy development in the Morro Bay WEA. We also expect the tradeoff analysis framework we developed to provide a simple and actionable analytical tool for supporting marine spatial planning of offshore wind energy and other emerging blue economy activities from a balanced perspective that values emerging uses of marine resources alongside existing socio-economic and conservation interests.

A New Study on the Effect of the Partial Wake Generated in a Wind Farm

In this article, we present an investigative study on the often-overlooked partial wake phenomenon in previous studies concerning wind farm configurations. A partial wake occurs when a portion of the actuator disk of a downstream wind turbine is affected by the wake of another upstream turbine. This phenomenon occurs in addition to the full wake, where the entire upstream turbine is affected by the wake of the frontal turbine, also leading to a decrease in wind speed and consequently a reduction in power production. The proposed study is based on measuring the power generated by the area swept by the wake of an array of turbines in a wind farm. To accomplish this, we integrate the linear wake model of Jensen, the specifications of the ENERCON E2 wind turbine, and the wind farm data into Matlab-developed software (version 18) to perform the calculations. In a concrete application, this proposed method is validated by reproducing the previous works that neglected the partial wake in wind farm configurations. The simulation results obtained are analyzed, compared, and discussed under similar operational conditions.

Optimal design and operation of a wind farm/battery energy storage considering demand side management

AbstractBalancing electricity demand and sustainable energy generation like wind energy presents challenges for the smart grid. To address this problem, the optimization of a wind farm (WF) along with the battery energy storage (BES) on the supply side, along with the demand side management (DSM) on the consumer side, should be considered during its planning and operation stages. An optimization framework with two levels to simultaneously decide the layout and operation of the WF/BES is put forward in this paper. The first‐level model consists of determining the WF/BES capacities, the WF configuration, and the connection buses. It is tackled by the mixed‐discrete particle swarm optimization algorithm. The multi‐objective optimization problem (MOOP) model in the second level determines the operation schedule of the WF/BES and other generators taking the DSM into consideration. The MOOP model in the second level is transformed to a single‐objective optimization problem via the maximum fuzzy satisfaction method, and is then solved by the genetic algorithm. The proposed model and the strategy are verified by the Barrow offshore WF test case, which is integrated into the IEEE‐118 system. Simulation results indicate that the wind and load patterns, the DSM and the BES price are the three key factors influencing the WF/BES design optimization.

Bidding against the wind: A choice experiment in green energy, green jobs and offshore views in North Carolina, USA

Offshore wind development is in its nascent stages in the United States. Recent research indicates that the visual impacts of offshore wind farms are viewed negatively by the general population. This North Carolina application is the first US-focused discrete choice experiment that explicitly asks respondents to consider the positive local and global benefits from offshore wind development, such as job creation and greenhouse gas emission reductions, simultaneously with their visual impacts. We find significant willingness to pay (WTP) for reducing the visual impacts of offshore wind farms, and that the extent of disamenity varies in the population and with placement along developed tourist towns (as much as $783/year for three years) or preserved coastlines (as much as $451/year for three years). We also find that some preference classes value projects that create permanent jobs and reduce carbon emissions. We use our estimates of preferences for the positive and negative attributes to explore specific wind farm configurations and locations that could achieve positive consensus in a heterogenous population.

Numerical simulations for a parametric study of blockage effect on offshore wind farms

AbstractThe paper presents a study of the upstream influence of wind farms on the wind speed, which is called blockage effect. A Reynolds Averaged Navier–Stokes (RANS) numerical model using an actuator disc method was devised and validated using the SCADA data from a Horns Rev 1 wind farm. The maximum difference between the average power in the first row for SCADA and the numerical model was 7.8%. The model was used to determine the impact of blockage effect on the wind farm parameters and the extent to which the wind speed and the power generation were reduced. A reference wind farm was defined, with a modified size, spacing, turbine height, and diameter that were used for comparison with other wind farm configurations. The results of the investigation of the wind farm parameter effects on the upstream wind speed reduction are presented in the paper. It has been established that increasing the turbine spacing from 5D to 6.7D reduces the power loss due to blockage by two. Blockage losses are almost eliminated when the spacing is increased two times. Similarly, the wind turbine thrust coefficient (CT) has a large impact on blockage, which is more pronounced, when CT is higher. In fact, the velocity deficit due to blockage is proportional to CT. The turbine tower height has small impact on blockage effect—the power reduction was changed by 0.3% due to blockage for the investigated range. The number of turbines in a row (with a constant number of turbines in a row) does not affect blockage significantly.

A low-order simulation model for wind turbine infrasound prediction

Wind energy is one of the fastest-growing renewable energy technologies. One of the environmental concerns associated with wind energy development is noise emission from wind energy facilities, especially the low-frequency noise and infrasound that allegedly raises public health concerns. In order to analyze the noise emission from wind turbines and to aid future wind energy development, a numerical simulation tool for the prediction of low-frequency noise and infrasound emission from wind turbines is developed. The simulation tool is a low-order model developed by combining aerodynamic noise theories and basic working principles of wind turbines. 2-D airfoil aerodynamics calculation is coupled with a subroutine that solves Ffowcs Williams-Hawkings (FW-H) equation in time domain. By omitting unsteady aeroacoustics mechanisms, the low-order simulation model is capable of computing infrasound emissions from multiple wind turbines simultaneously. By utilizing the low-order simulation model, the effects of wind directions, wind turbine locations, and phases of wind turbine rotations on the consequent aerodynamic infrasound are studied. Results of aerodynamic infrasound computation imply that wind farm configuration or wind turbine phase adjustment can help reduce the noise level at certain locations, which makes the low-order simulation model a useful tool to aid wind farm siting and controlling.

CFD modeling of vertical-axis wind turbine wake interaction

Since wind turbines placed in wind farms need to minimize their footprint on the ground, the effects of the wake must be considered. Placement optimization, turbine spacing, and direction of rotation are known to affect the performance of vertical-axis wind turbines (VAWTs). However, rigorous numerical modeling methodologies that investigate the influence of these characteristics are lacking, especially in the case of large wind turbines. The goal of this study is to analyze turbine configurations that might enhance the power production of VAWT farms using two-dimensional CFD models based on the Star CCM+ package. The novelty of this work is to analyze wind farm configurations for very large turbines. This is important because large turbines are much more performant than small turbines and have a high power coefficient. Results show that CFD simulations adequately capture the performance of wind turbines in farms with multiple VAWTs. In general, if a second rotor is spaced more than 10 turbine diameters downstream of the first rotor, the effect of the wake is less significant. Furthermore, a specific farm configuration with five VAWTs is investigated and shows a 20% increase in power output compared with the same number of turbines operating in isolation.

Predictive digital twin for offshore wind farms

As wind turbines continue to grow in size, they are increasingly being deployed offshore. This causes operation and maintenance of wind turbines becoming more challenging. Digitalization is a key enabling technology to manage wind farms in hostile environments and potentially increasing safety and reducing operational and maintenance costs. Digital infrastructure based on Industry 4.0 concept, such as digital twin, enables data collection, visualization, and analysis of wind power analytic at either individual turbine or wind farm level. In this paper, the concept of predictive digital twin for wind farm applications is introduced and demonstrated. To this end, a digital twin platform based on Unity3D for visualization and OPC Unified Architecture (OPC-UA) for data communication is developed. The platform is completed with the Prophet prediction algorithm to detect potential failure of wind turbine components in the near future and presented in augmented reality to enhance user experience. The presentation is intuitive and easy to use. The limitations of the platform include a lack of support for specific features like electronic signature, enhanced failover, and historical data sources. Simulation results based on the Hywind Tampen floating wind farm configuration show our proposed platform has promising potentials for offshore wind farm applications.

A Level Set-Based Actuator Disc Model for Turbine Realignment in Wind Farm Simulation: Meshing, Convergence and Applications

We present a novel meshing and simulation approach for wind farms, featuring realignment and mesh adaptation. The turbines are modeled with actuator discs, which are discretized by means of an adaptation process to represent a level set function. The level-set-based simulation framework is combined with an adaptation cycle to capture both the solution and the actuator discs. In addition, we devise a turbine realignment process which takes into account the actual flow in the actuator disc configuration. Several results are presented to highlight the features of the approach. First, the adaptive simulation approach is validated, fulfilling the theoretical convergence rates and improving the accuracy of the boundary tight representations. Second, the adaptive simulation process is applied to a full wind farm configuration featuring 219 turbines, illustrating that is it well devised for complex wind farm configurations. Third, the turbine reorientation process is validated in a one turbine scenario. Finally, the realignment simulation framework is applied in a wind farm featuring 115 turbines. The presented results outline the significance of the proposed work, enabling turbine realignment and mesh adaptation to perform accurate simulations of complex wind farm configurations.

Development of Reactive Power Allocation Method for Radial Structure Wind Farm Considering Multiple Connections

In recent years, the number of wind farms consisting of type 3 and type 4 wind turbines located within the distribution system has been growing rapidly. Wind turbines can be utilized as a continuous reactive power source to support the system voltage by taking advantage of their reactive power control capability. This paper aims to further develop the reactive power assignment strategy in order to minimize losses in wind farms described in the published paper. We introduce the method of reconfiguration and numbering to apply the algorithm to the wind farm structure and develop the previously-defined allocation ratio into two types of allocation ratios. The goal is to apply the loss minimization algorithm to a wind farm configuration with up to two wind turbines connected to one ring main unit (RMU). The proposed strategy reduces power loss and increases the real power flow in the wind farm by allocating reactive power to connected wind turbines taking into account the resistance value. The proposed allocation technique is validated in a Real Time Digital Simulator (RTDS)-based Hardware-in-the-loop Simulation (HILS) environment considering the Dongbok wind farm configuration in Jeju, South Korea. In the simulation, a Raspberry Pi acts as a wind farm controller sending a reactive power dispatch signal to each wind turbine via Modbus TCP/IP protocol. The simulation results mean that, applying the proposed algorithm, we can expect loss reduction effects in the wind farm.

Power Curtailment Analysis of DC Series–Parallel Offshore Wind Farms

This paper analyzes one of the most important power capture challenges of the DC series–parallel collection system, called the power curtailment losses. The wind speed difference between the series-connected turbines causes over- and under-voltage conditions in the output voltage of the MVDC (medium-voltage DC) converters of the wind turbine. The power curtailment losses caused by the upper-voltage tolerance levels of the MVDC converters of the wind turbines are analyzed considering a redundancy-based upper-voltage limiting condition. This analysis emphasizes the importance of choosing suitable voltage tolerance levels for the MVDC converters of wind turbines based on the wind farm configuration. The annual energy curtailment losses are quantified and evaluated by a comparative case study performed on a DC series–parallel-connected wind farm rated at 200 MW with the redundancy-based upper-voltage limiting condition.

A wall-modeled approach accounting for wave stress in Large Eddy Simulations of offshore wind farms

In the context of offshore wind farms, accurate predictions of surface fluxes in the marine atmospheric boundary layer are critical for Large Eddy Simulations (LES) of airflow over waves. The effect of the waves on the airflow is often modeled by prescribing the roughness length in the framework of Monin-Obukhov similarity theory. However, such approaches lack generalizability over different wave conditions due to reliance on model coefficients tuned to specific datasets. Wave phase-resolving simulations on the other hand have higher accuracy but also a higher computational cost. In this work, a sea surface-based hydrodynamic drag model is applied to model the pressure-based surface drag felt by the wind due to the waves. An offshore wind farm configuration is simulated using a wall-modeled LES, with the effect of the waves represented using the wave-drag model and the wind turbines represented by an actuator disk model. The approach is validated with data from high fidelity wave-resolving Large Eddy Simulations. The effect of the waves and the farm configuration on the mean velocity profiles power production, and kinetic energy budget are quantified.

Model for evaluation of technical and economic indicators of offshore wind farms

THE PURPOSE. An urgent problem in the development of offshore wind energy is the high cost of generating electricity, which is due to large capital investments. The solution to this problem is possible by increasing efficiency while reducing costs as much as possible, which requires optimal design of offshore wind farms.GOAL. Development of model for the technical and economic indicators of offshore wind farms based on configuration data, taking into account the factors of climatic conditions and the topography of the seabed at the site of the planned wind farm location.METHODS. Mathematical modeling using Matlab software environment.RESULTS. The model evaluates the impact of wake and electrical losses in the main components of the electrical system on the operation of an offshore wind farm, and also allows to take into account the influence of the seabed relief on the economic characteristics of wind turbine foundations. The model was tested on the example of calculating two existing offshore wind farms «Horns Rev 1» and «Horn Rev 2» by comparing the calculated indicators of the average annual electricity generation, capacity factor, capital expenditures and normalized cost of electricity with the actual indicators obtained during their operation. The comparison results show slight deviations within 5% of the actual values.CONCLUSION. The model for assessing the technical and economic indicators of offshore wind farms was developed and tested on the basis of data on the wind farm configuration and layout, as well as factors of climatic conditions and terrain. Evaluation of the computational speed showed a sufficiently high efficiency of the algorithm, which allows the model to be applied to optimize large offshore wind farms.

Wind Farm Layout Optimization with Different Hub Heights in Manjil Wind Farm Using Particle Swarm Optimization

Nowadays, optimizing wind farm configurations is one of the biggest concerns for energy communities. The ongoing investigations have so far helped increasing power generation and reducing corresponding costs. The primary objective of this study is to optimize a wind farm layout in Manjil, Iran. The optimization procedure aims to find the optimal arrangement of this wind farm and the best values for the hubs of its wind turbines. By considering wind regimes and geographic data of the considered area, and using the Jensen’s method, the wind turbine wake effect of the proposed configuration is simulated. The objective function in the optimization problem is set in such a way to find the optimal arrangement of the wind turbines as well as electricity generation costs, based on the Mossetti cost function, by implementing the particle swarm optimization (PSO) algorithm. The results reveal that optimizing the given wind farm leads to a 10.75% increase in power generation capacity and a 9.42% reduction in its corresponding cost.

Wind farm cumulative induction zone effect and the impact on energy yield estimation

The cumulative induction zone effect arising from the operation of multiple wind turbine generators and their interactions within a wind farm is investigated within the framework of computational fluid dynamics. A computational fluid dynamic model is benchmarked and validated against measured lidar data from an offshore wind farm. A range of wind farm array configurations are investigated and the results are used to advise adjustments relevant to wind farm energy production assessments. Based upon the offshore wind farm array configurations investigated, the outcomes indicate that the cumulative induction zone effect results in a reduction in the induction zone wind velocity of approximately 0.3–1.6%, with a corresponding reduction in energy yield of 0.4–1.2% depending on the wind farm configuration, the inflow wind velocity and the atmospheric stability conditions. In the case of larger wind farms, investigations to date indicate that the cumulative induction zone effect is more pronounced with the appropriate energy adjustment factor predicted to be increased by a factor of up to two. Additionally, wind farm configurations and wind speed distributions differing from those considered within this work may result in cumulative induction zone effect values outside those stated.

The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries

This work introduces the area localized coupled (ALC) model, which extends the applicability of approaches that couple classical wake superposition models and atmospheric boundary layer models to wind farms with arbitrary layouts. Coupling wake and top–down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with turbine-specific regions of the flow that need to be specified. The ALC model uses Voronoi tessellation to define this local area around each turbine. A top–down description of a developing internal boundary layer is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top–down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The wake model in the present implementation takes into account variations in wind farm inflow velocity and represents the wake profile behind each turbine as a super-Gaussian function that smoothly transitions between a top-hat shape in the region immediately following the turbine to a Gaussian profile downstream. Detailed comparisons to large-eddy simulation (LES) data from two different wind farms demonstrate the efficacy of the model in accurately predicting both wind farm power output and local turbine hub-height velocity for different wind farm geometries. These validations using data generated from two different LES codes demonstrate the model's versatility with respect to capturing results from different simulation setups and wind farm configurations.

A Generic Power Converter Sizing Framework for Series-Connected DC Offshore Wind Farms

Wind farms featuring a series-connected dc collection system have been shown to offer advantages in terms of total conversion efficiency and amount of offshore-deployed equipment. To ensure proper exploitation of these benefits, it is necessary to determine the ratings of wind turbine converters. These ratings must be sufficient to cover the expected operating conditions over the life of the wind farm, without unnecessarily oversizing the equipment. As shown in this article, the variable string current results in an interdependence of operating points among wind turbines that has to be considered for sizing these converters. This article proposes a generic sizing framework for such single-string, series-connected dc wind farms. This framework is applied to three wind farm configurations featuring differential power processing, voltage-source converters, and diode-bridge rectifiers and buck converters. Finally, a case study for a 450 MW offshore wind farm demonstrates the implementation of this sizing methodology and quantifies the design tradeoffs between converter ratings and energy production enabled from those converters. In two of the studied wind farm configurations, significant rating reductions are achievable while still avoiding energy curtailment for >99.7% of the energy production of an equivalent ac wind farm.