scholarly journals A holistic approach for the optimization of offshore wind farm layouts considering cable layouts

2019 ◽  
Vol 122 ◽  
pp. 04005
Author(s):  
Ilayda Ulku ◽  
Cigdem Alabas-Uslu
Keyword(s):  
Total Energy ◽  
Wind Farm ◽  
Holistic Approach ◽  
Offshore Wind ◽  
Total Power ◽  
Holistic Model ◽  
Electrical Cable ◽  
Proposed Model ◽  
Wake Effects

A wind farm, mainly, is composed of a set of turbines, one or more transmitters and a set of electrical cable connections between turbines and transmitters. Determination of turbine locations within the farm to maximize total power generation is called turbine location (TL) problem. Relative turbine positions affect the amount of overall energy because of wake effects. Determination of cable connections among turbines and transmitters to collect produced energy by turbines at transmitters is called cable layout (CL) problem. While TL problem is directly effective on the total energy production in the farm, CL problem indirectly affects the total energy due to the power losses. In the literature, TL and CL problems are solved sequentially where the layout found by solving of TL is used as an input of CL problem. To minimize wake effects in TL problem, distances between turbine pairs should be increased, however, as the distances are increased the cable cost increases in CL problem. A new mathematical model is developed to deal with simultaneously solving of TL and CL problems. A set of test instances are used to show the performance of the proposed model. The experiments show the practical use of the proposed holistic model.

Geometry-Based Models for Studying the Effects of Wind Farm Layout

2016 ◽  
Vol 33 (3) ◽  
pp. 481-501 ◽  
Author(s):  
Niranjan S. Ghaisas ◽  
Cristina L. Archer
Keyword(s):  
Wind Farm ◽  
Linear Models ◽  
Orientation Angle ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Farm Layout ◽  
Prevailing Wind Direction ◽  
Wake Effects ◽  
Well Correlation ◽  
Large Wind

AbstractLayout studies are critical in designing large wind farms, since wake effects can lead to significant reductions in power generation. Optimizing wind farm layout using computational fluid dynamics is practically unfeasible today because of their enormous computational requirements. Simple statistical models, based on geometric quantities associated with the wind farm layout, are therefore attractive because they are less demanding computationally. Results of large-eddy simulations of the Lillgrund (Sweden) offshore wind farm are used here to calibrate such geometry-based models. Several geometric quantities (e.g., blockage ratio, defined as the fraction of the swept area of a wind turbine that is blocked by upstream turbines) and their linear combinations are found to correlate very well (correlation coefficient of ~0.95) with the power generated by the turbines. Linear models based on these geometric quantities are accurate at predicting the farm-averaged power and are therefore used here to study layout effects in large wind farms. The layout parameters that are considered include angle between rows and columns, angle between incoming wind and columns (orientation), turbine spacings, and staggering of alternate rows. Each can impact wind power production positively or negatively, and their interplay is complex. The orientation angle is the most critical parameter influencing wake losses, as small changes in it can cause sharp variations in power. In general, for a prevailing wind direction, the orientation angle should be small (7.5°–20°) but not zero; staggering and spacing are beneficial; and nonorthogonal layouts may outperform orthogonal ones. This study demonstrates the utility of simple, inexpensive, and reasonably accurate geometry-based models to identify general principles governing optimal wind farm layout.

Regional Impacts of Offshore Windfarming on the Hydro- and Ecosystem Dynamics in the North Sea

2020 ◽  
Author(s):  
Nils Christiansen ◽  
Ute Daewel ◽  
Corinna Schrum ◽  
Jeff Carpenter ◽  
Bughsin Djath ◽  
...  
Keyword(s):  
North Sea ◽  
Wind Farm ◽  
Ecosystem Dynamics ◽  
Ocean Dynamics ◽  
Offshore Wind ◽  
Tidal Mixing ◽  
Surface Boundary ◽  
The North ◽  
Wake Effects ◽  
The North Sea

<p>The production of renewable offshore wind energy in the North Sea increases rapidly, including development in ecologically significant regions. Recent studies identified implications like large-scale wind wake effects and mixing of the water column induced by wind turbines foundations. Depending on atmospheric stability, wind wakes imply changes in momentum flux and increased turbulence up to 70 km downstream, affecting the local conditions (e.g. wind speed, cloud development) near offshore wind farms. Atmospheric wake effects likely translate to the sea-surface boundary layer and hence influence vertical transport in the surface mixing layer. Changes in ocean stratification raise concerns about substantial consequences for local hydrodynamic and biogeochemical processes as well as for the marine ecosystem.<br>Using newly developed wind wake parametrisations together with the unstructured-grid model SCHISM and the biogeochemistry model ECOSMO, this study addresses windfarming impacts in the North Sea for future offshore wind farm scenarios. We focus on wind wake implications on ocean dynamics as well as on changes in tidal mixing fronts near the Dogger Bank and potential ecological consequences. At this, we create important knowledge on how the cross-scale wind farm impacts can be modelled suitably on the system scale.</p>

scholarly journals Research on a Comprehensive Maintenance Optimization Strategy for an Offshore Wind Farm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 965
Author(s):  
Yang Lu ◽  
Liping Sun ◽  
Yanzhuo Xue
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Weather Conditions ◽  
Offshore Wind ◽  
Monte Carlo Algorithm ◽  
Maintenance Cost ◽  
Offshore Wind Turbine ◽  
Offshore Wind Farm ◽  
Proposed Model ◽  
Maintenance Activities

Offshore wind is considered a crucial part in the future energy supply. However, influenced by weather conditions, the maintenance of offshore wind turbine system (OWTs) equipment is challenged by poor accessibility and serious failure consequences. It is necessary to study the optimized strategy of comprehensive maintenance for offshore wind farms, with consideration of the influences of incomplete equipment maintenance, weather accessibility and economic relevance. In this paper, a Monte Carlo algorithm-improved factor is presented to simulate the imperfect preventive maintenance activity, and waiting windows were created to study the accessibility of weather conditions. Based on a rolling horizon approach, an opportunity group maintenance model of an offshore wind farm was proposed. The maintenance correlations between systems and between equipment as well as breakdown losses, maintenance uncertainty, and weather conditions were taken into account in the model, thus realizing coordination of maintenance activities of different systems and different equipment. The proposed model was applied to calculate the maintenance cost of the Dafengtian Offshore Wind Farm in China. Results proved that the proposed model could realize long-term dynamic optimization of offshore wind farm maintenance activities, increase the total availability of the wind power system and reduce total maintenance costs.

scholarly journals Improved Evaluation of The Wind Power Potential of a Large Offshore Wind Farm Using Four Analytical Wake Models

2021 ◽  
Vol 11 (1) ◽  
pp. 35-48
Author(s):  
Mohammed Amine Hassoine ◽  
Fouad Lahlou ◽  
Adnane Addaim ◽  
Abdessalam Ait Madi
Keyword(s):  
Wind Direction ◽  
Power Output ◽  
Power Loss ◽  
Wind Farm ◽  
Offshore Wind ◽  
Total Power ◽  
Offshore Wind Farm ◽  
Wake Effect ◽  
Power Deficit ◽  
Total Power Output

The objective of this paper is to investigate the ability of analytical wake models to estimate the wake effects between wind turbines (WTs). The interaction of multiple wakes reduces the total power output produced by a large offshore wind farm (LOFWF). This power loss is due to the effect of turbine spacing (WTS), if the WTs are too close, the power loss is very significant. Therefore, the optimization of turbine positions within the offshore wind farm requires an understanding of the interaction of wakes inside the wind farm. To better understand the wake effect, the Horns Rev 1 offshore wind farm has been studied with four wake models, Jensen, Larsen, Ishihara, and Frandsen. A comparative study of the wake models has been performed in several situations and configurations, single and multiple wakes are taken into consideration. Results from the Horns Rev1 offshore wind farm case have  been evaluated and compared to observational data, and also  with the previous studies. The power output of a row of WTs is sensitive to the wind direction. For example, if a row of ten turbines is aligned with the 270° wind direction, the full wake condition of WTs is reached and the power deficit limit predicted by Jensen model exceeds 70%. When a wind direction changes only of  10° (260° and 280°), the deficit limit reduces to 30%. The obtained results show that a significant power deficit occurs when the turbines are arranged in an aligned manner. The findings also showed that all four models gave acceptable predictions of the total power output. The comparison between the calculated and reported power output of Horns Revs 1 showed that the differences ranged from - 8.27 MW (12.49%) to 15.27 MW (23.06%) for the Larsen and Frandsen models, respectively.

scholarly journals A Calibration Procedure for an Analytical Wake Model Using Wind Farm Operational Data

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3537
Author(s):  
Jian Teng ◽  
Corey D. Markfort
Keyword(s):  
Wind Turbine ◽  
Analytical Approach ◽  
Wind Farm ◽  
Renewable Energy Sources ◽  
Calibration Procedure ◽  
Total Power ◽  
Wake Effects ◽  
Wake Model ◽  
Operational Data ◽  
Wake Modeling

Wind energy is one of the fastest growing renewable energy sources in the U.S. Wind turbine wakes change the flow field within wind farms and reduce power generation. Prior research has used experimental and computational methods to investigate and model wind farm wake effects. However, these methods are costly and time-consuming to use commercially. In contrast, a simple analytical approach can provide reasonably accurate estimates of wake effects on flow and power. To reducing errors in wake modeling, one must calibrate the model based on a specific wind farm setting. The purpose of this research is to develop a calibration procedure for wind farm wake modeling using a simple analytical approach and wind turbine operational data obtained from the Supervisory Control And Data Acquisition (SCADA) system. The proposed procedure uses a Gaussian-based analytical wake model and wake superposition model. The wake growth rate varies across the wind farm based on the local streamwise turbulence intensity. The wake model was calibrated by implementing the proposed procedure with turbine pairs within the wind farm. The performance of the model was validated at an onshore wind farm in Iowa, USA. The results were compared with the industry standard wind farm wake model and shown to result in an approximate 1% improvement in sitewide total power prediction. This new SCADA-based calibration procedure is useful for real-time wind farm operational optimization.

scholarly journals An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects

2017 ◽  
Vol 2 (2) ◽  
pp. 477-490 ◽  
Author(s):  
Niko Mittelmeier ◽  
Julian Allin ◽  
Tomas Blodau ◽  
Davide Trabucchi ◽  
Gerald Steinfeld ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Average Power ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Partial Load ◽  
Wake Effects

Abstract. For offshore wind farms, wake effects are among the largest sources of losses in energy production. At the same time, wake modelling is still associated with very high uncertainties. Therefore current research focusses on improving wake model predictions. It is known that atmospheric conditions, especially atmospheric stability, crucially influence the magnitude of those wake effects. The classification of atmospheric stability is usually based on measurements from met masts, buoys or lidar (light detection and ranging). In offshore conditions these measurements are expensive and scarce. However, every wind farm permanently produces SCADA (supervisory control and data acquisition) measurements. The objective of this study is to establish a classification for the magnitude of wake effects based on SCADA data. This delivers a basis to fit engineering wake models better to the ambient conditions in an offshore wind farm. The method is established with data from two offshore wind farms which each have a met mast nearby. A correlation is established between the stability classification from the met mast and signals within the SCADA data from the wind farm. The significance of these new signals on power production is demonstrated with data from two wind farms with met mast and long-range lidar measurements. Additionally, the method is validated with data from another wind farm without a met mast. The proposed signal consists of a good correlation between the standard deviation of active power divided by the average power of wind turbines in free flow with the ambient turbulence intensity (TI) when the wind turbines were operating in partial load. It allows us to distinguish between conditions with different magnitudes of wake effects. The proposed signal is very sensitive to increased turbulence induced by neighbouring turbines and wind farms, even at a distance of more than 38 rotor diameters.

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