scholarly journals Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models

2021 ◽  
Author(s):  
Jana Fischereit ◽  
Kurt Schaldemose Hansen ◽  
Xiaoli Guo Larsén ◽  
Maarten Paul van der Laan ◽  
Pierre-Elouan Réthoré ◽  
...  
Keyword(s):  
High Resolution ◽  
Wind Energy ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wake Effect ◽  
Disk Model ◽  
Computational Costs ◽  
Rans Simulations ◽  
Wind Resource

Abstract. Numerical wind resource modelling across scales from mesoscale to turbine scale is of increasing interest due to the expansion of offshore wind energy. Offshore, wind farm wakes can last several tens kilometres downstream and thus affect the wind resources of a large area. So far, scale-specific models have been developed and it remains unclear, how well the different model types can represent intra-farm wakes, farm-to-farm wakes as well as the wake recovery behind a farm. Thus, in the present analysis the simulation of a set of wind farm models of different complexity, fidelity, scale and computational costs are compared among each other and with SCADA data. In particular, two mesoscale wind farm parameterizations implemented in the mesoscale Weather Research and Forecasting model (WRF), the Explicit Wake Parameterization (EWP) and the Wind Farm Parameterization (FIT), two different high-resolution RANS simulations using PyWakeEllipSys equipped with an actuator disk model, and three rapid engineering wake models from the PyWake suite are selected. The models are applied to the Nysted and Rødsand II wind farms, which are located in the Fehmarn Belt in the Baltic Sea. Based on the performed simulations, we can conclude that average intra-farm variability can be captured reasonable well with WRF+FIT using a resolution of 2 km, a typical resolution of mesoscale models for wind energy applications, while WRF+EWP underestimates wind speed deficits. However, both parameterizations can be used to estimate median wind resource reduction caused by an upstream farm. All considered engineering wake models from the PyWake suite simulate intra-farm wakes comparable to the high fidelity RANS simulations. However, they considerably underestimate the farm wake effect of an upstream farm although with different magnitudes. Overall, the higher computational costs of PyWakeEllipSys and WRF compared to PyWake pay off in terms of accuracy for situations when farm-to-farm wakes are important.

Optimization of Large Offshore Wind Farm Layout Considering Reliability and Wake Effect

2020 ◽  
Author(s):  
Xiangyu Li ◽  
Cuong D. Dao ◽  
Behzad Kazemtabrizi ◽  
Christopher J. Crabtree
Keyword(s):  
Wind Energy ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Development Trend ◽  
Offshore Wind ◽  
Total Output ◽  
Wake Effect ◽  
Offshore Wind Farms ◽  
Wind Farm Layout

Abstract Nowadays, the increasing demand of electricity and environmental hazards of the greenhouse gas lead to the requirement of renewable energies. The wind energy has been proved as one of the most successful sustainable energies. Recently, the development trend of the wind energy is to build large offshore wind farms (OWFs) with hundreds of wind turbines, which could generates more power in one wind farm. In the large OWF, the wake effect is a very important impact factor to the wind farms, especially for those with close spacing. Therefore, the wind farm layout, the location of the wind turbines (WTs) is very essential to the performance of the whole wind farm, especially for large OWFs. In this research, we focus on the optimization of the large OWF layout by considering performance of the OWF, such as the total output energy. Firstly, the model for wind farm performance evaluation is established by incorporating historical wind speed data and the wake effect which can affect the total wind farm output. Then, by using the metaheuristic algorithms, the genetic algorithm (GA), the OWF layout is optimized. This study can offer useful information to the wind farm manufactures in the large OWF design phase.

Offshore Wind Energy and the Mid-Atlantic Cold Pool: A Review of Potential Interactions

2021 ◽  
Vol 55 (4) ◽  
pp. 72-87
Author(s):  
Travis Miles ◽  
Sarah Murphy ◽  
Josh Kohut ◽  
Sarah Borsetti ◽  
Daphne Munroe
Keyword(s):  
Wind Energy ◽  
Wind Farm ◽  
Vertical Mixing ◽  
Wind Farms ◽  
The United States ◽  
Offshore Wind ◽  
Cold Pool ◽  
Ocean Bottom ◽  
Offshore Wind Farms ◽  
Seasonal Thermocline

Abstract The U.S. East Coast has 1.7 million acres of federal bottom under lease for the development of wind energy installations, with plans for more than 1,500 foundations to be placed. The scale of these wind farms has the potential to alter the unique and delicate oceanographic conditions along the expansive Atlantic continental shelf, a region characterized by a strong seasonal thermocline that overlies cold bottom water, known as the “Cold Pool.” Strong seasonal stratification traps cold (typically less than 10°C) water above the ocean bottom sustaining a boreal fauna that represents vast fisheries, including the most lucrative shellfish fisheries in the United States. This paper reviews the existing literature and research pertaining to the ways in which offshore wind farms may alter processes that establish, maintain, and degrade stratification associated with the Cold Pool through vertical mixing in this seasonally dynamic system. Changes in stratification could have important consequences in Cold Pool setup and degradation, processes fundamental to high fishery productivity of the region. The potential for these multiple wind energy arrays to alter oceanographic processes and the biological systems that rely on them is possible; however, a great deal of uncertainty remains about the nature and scale of these interactions. Research should be prioritized that identifies stratification thresholds of influence, below which turbines and wind farm arrays may alter oceanographic processes. These should be examined within context of spatial and seasonal dynamics of the Cold Pool and offshore wind lease areas to identify potential areas of further study.

scholarly journals Optimization of Wind Turbine Interconnections in an Offshore Wind Farm Using Metaheuristic Algorithms

2020 ◽  
Vol 12 (14) ◽  
pp. 5761 ◽  
Author(s):  
Chakib El Mokhi ◽  
Adnane Addaim
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Farm ◽  
Renewable Energy Sources ◽  
Wind Farms ◽  
Optimization Methods ◽  
Network Routing ◽  
Metaheuristic Algorithms ◽  
Offshore Wind ◽  
Offshore Wind Farms

Wind energy is currently one of the fastest-growing renewable energy sources in the world. For this reason, research on methods to render wind farms more energy efficient is reasonable. The optimization of wind turbine positions within wind farms makes the exploitation of wind energy more efficient and the wind farms more competitive with other energy resources. The investment costs alone for substation and electrical infrastructure for offshore wind farms run around 15–30% of the total investment costs of the project, which are considered high. Optimizing the substation location can reduce these costs, which also minimizes the overall cable length within the wind farm. In parallel, optimizing the cable routing can provide an additional benefit by finding the optimal grid network routing. In this article, the authors show the procedure on how to create an optimized wind farm already in the design phase using metaheuristic algorithms. Besides the optimization of wind turbine positions for more energy efficiency, the optimization methods of the substation location and the cable routing for the collector system to avoid cable losses are also presented.

scholarly journals Offshore wind energy potential for Bahrain via multi-criteria evaluation

2020 ◽  
pp. 0309524X2092539
Author(s):  
Mohamed Elgabiri ◽  
Diane Palmer ◽  
Hanan Al Buflasa ◽  
Murray Thomson
Keyword(s):  
Wind Energy ◽  
Geographical Information Systems ◽  
Wind Farm ◽  
Electricity Consumption ◽  
Pairwise Comparison ◽  
Wind Farms ◽  
Offshore Wind ◽  
Geographical Information ◽  
Energy Yield ◽  
Energy Potential

Current global commitments to reduce the emissions of greenhouse gases encourage national targets for renewable generation. Due to its small land mass, offshore wind could help Bahrain to fulfil its obligations. However, no scoping study has been carried out yet. The methodology presented here addresses this research need. It employs analytical hierarchy process and pairwise comparison methods in a geographical information systems environment. Publicly available land use, infrastructure and transport data are used to exclude areas unsuitable for development due to physical and safety constraints. Meteorological and oceanic opportunities are ranked and then competing uses are analyzed to deliver optimal sites for wind farms. The potential annual wind energy yield is calculated by dividing the sum of optimal areas by a suitable turbine footprint to deliver maximum turbine number. In total, 10 favourable wind farm areas were identified in Bahrain’s territorial waters, representing about 4% of the total maritime area, and capable of supplying 2.68 TWh/year of wind energy or almost 10% of the Kingdom’s annual electricity consumption. Detailed maps of potential sites for offshore wind construction are provided in the article, giving an initial plan for installation in these locations.

The Application of Wind Speed Numerical Simulation in Wind Power Generation

2013 ◽  
Vol 380-384 ◽  
pp. 3370-3373 ◽  
Author(s):  
Li Yang Liu ◽  
Jun Ji Wu ◽  
Shao Liang Meng
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Wind Farm ◽  
Power Grid ◽  
Wind Farms ◽  
Stable Operation ◽  
Wind Gust ◽  
Gradual Change ◽  
Wake Effect

With the massive development and application of wind energy, wind power is having an increasing proportion in power grid. The changes of the wind speed in a wind farm will lead to fluctuations in the power output which would affect the stable operation of the power grid. Therefore the research of the characteristics of wind speed has become a hot topic in the field of wind energy. In the paper, the wind speed at the wind farm was simulated in a combination of wind speeds by which wind speed was decomposed of four components including basic wind, gust wind, stochastic wind and gradient wind which denote the regularity, the mutability, the gradual change and the randomness of a natural wind respectively. The model is able to reflect the characteristics of a real wind, easy for engineering simulation and can also estimate the wind energy of a wind farm through the wind speed and wake effect model. This paper has directive significance in the estimation of wind resource and the layout of wind turbines in wind farms.

Solving the Turbine Positioning Problem for Large Offshore Wind Farms by Simulated Annealing

2009 ◽  
Vol 33 (3) ◽  
pp. 287-297 ◽  
Author(s):  
Rajai Aghabi Rivas ◽  
Jens Clausen ◽  
Kurt S. Hansen ◽  
Leo E. Jensen
Keyword(s):  
Simulated Annealing ◽  
Wind Farm ◽  
Simulated Annealing Algorithm ◽  
Real Life ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Wind Climate ◽  
The Difference ◽  
Wind Resource

The current paper is concerned with determining the optimal layout of the turbines inside large offshore wind farms by means of an optimization algorithm. We call this the Turbine Positioning Problem. To achieve this goal a simulated annealing algorithm has been devised, where three types of local search operations are performed recursively until the system converges. The effectiveness of the proposed algorithm is demonstrated on a suite of real life test cases, including Horns Rev offshore wind farm. The results are verified using a commercial wind resource software indicating that this method represents an effective strategy for the wind turbine positioning problem. The findings enable the comparison of the optimized and the grid layouts and the study of the wake differences between these configurations. It is seen that for very large offshore wind farms the difference in wake losses is negligible while, as the wind farm's size reduces, the differences start becoming significant. A sensitivity analysis is also performed showing that greater density of turbines in the perimeter of the optimized wind farm reduces the wake losses even if the wind climate changes.

scholarly journals Wind Resource Assessment for Alaska’s Offshore Regions: Validation of a 14-Year High-Resolution WRF Data Set

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2780
Author(s):  
Jared A. Lee ◽  
Paula Doubrawa ◽  
Lulin Xue ◽  
Andrew J. Newman ◽  
Caroline Draxl ◽  
...  
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Regional Climate ◽  
Wind Farms ◽  
Resource Assessment ◽  
Small Sample ◽  
Offshore Wind ◽  
Data Set ◽  
Wind Resource Assessment ◽  
Wind Resource

Offshore wind resource assessments for the conterminous U.S. and Hawai’i have been developed before, but Alaska’s offshore wind resource has never been rigorously assessed. Alaska, with its vast coastline, presents ample potential territory in which to build offshore wind farms, but significant challenges have thus far limited Alaska’s deployment of utility-scale wind energy capacity to a modest 62 MW (or approximately 2.7% of the state’s electric generation) as of this writing, all in land-based wind farms. This study provides an assessment of Alaska’s offshore wind resource, the first such assessment for Alaska, using a 14-year, high-resolution simulation from a numerical weather prediction and regional climate model. This is the longest-known high-resolution model data set to be used in a wind resource assessment. Widespread areas with relatively shallow ocean depth and high long-term average 100-m wind speeds and estimated net capacity factors over 50% were found, including a small area near Alaska’s population centers and the largest transmission grid that, if even partially developed, could provide the bulk of the state’s energy needs. The regional climate simulations were validated against available radiosonde and surface wind observations to provide the confidence of the model-based assessment. The model-simulated wind speed was found to be skillful and with near-zero average bias (−0.4–0.2 m s−1) when averaged over the domain. Small sample sizes made regional validation noisy, however.

scholarly journals Offshore Wind Energy Potential for Bahrain via Multi-criteria Evaluation

2020 ◽  
Author(s):  
Diane Palmer ◽  
Mohamed Elgabiri ◽  
Hanan Al Buflasa ◽  
Murray Thomson
Keyword(s):  
Wind Energy ◽  
Geographical Information Systems ◽  
Wind Farm ◽  
Electricity Consumption ◽  
Pairwise Comparison ◽  
Wind Farms ◽  
Offshore Wind ◽  
Geographical Information ◽  
Energy Yield ◽  
Energy Potential

Current global commitments to reduce emissions of greenhouse gases encourage national targets for renewable generation. Due to its small land mass, offshore wind could help Bahrain to fulfill its obligations. However, no scoping study has yet been carried out. The methodology presented here addresses this research need. It employs Analytical Hierarchy Process and pairwise comparison methods in a Geographical Information Systems environment. Publicly available land use, infrastructure and transport data are used to exclude areas unsuitable for development due to physical and safety constraints. Meteorological and oceanic opportunities are ranked, then competing uses are analyzed to deliver optimal sites for wind farms. The potential annual wind energy yield is calculated by dividing the sum of optimal areas by a suitable turbine footprint, to deliver maximum turbine number. Ten favourable wind farm areas were identified in Bahrain’s territorial waters, representing about 4% of the total maritime area, and capable of supplying 2.68 TWh/yr of wind energy or almost 10% of the Kingdom’s annual electricity consumption. Detailed maps of potential sites for offshore wind construction are provided in the paper, giving an initial plan for installation in these locations.

Layout optimization for a large offshore wind farm using Genetic Algorithm

2020 ◽  
Author(s):  
K Narender Reddy ◽  
S Baidya Roy
Keyword(s):  
Genetic Algorithm ◽  
Wind Energy ◽  
Wind Farm ◽  
Selection Process ◽  
Wind Farms ◽  
Cost Effective ◽  
Layout Optimization ◽  
Offshore Wind ◽  
Previous Generation ◽  
Large Wind

<p>Wind Farm Layout Optimization Problem (WFLOP) is an important issue to be addressed when installing a large wind farm. Many studies have focused on the WFLOP but only for a limited number of turbines (10 – 100 turbines) and idealized wind speed distributions. In this study, we apply the Genetic Algorithm (GA) to solve the WFLOP for large wind farms using real wind data.</p><p>The study site is the Palk Strait located between India and Sri Lanka. This site is considered to be one of the two potential hotspots of offshore wind in India. An interesting feature of the site is that the winds here are dominated by two major monsoons: southwesterly summer monsoon (June-September) and northeasterly winter monsoon (November to January). As a consequence, the wind directions do not drastically change, unlike other sites which can have winds distributed over 360<sup>o</sup>. This allowed us to design a wind farm with a 5D X 3D spacing, where 5D is in the dominant wind direction and 3D is in the transverse direction (D- rotor diameter of the turbine - 150 m in this study).</p><p>Jensen wake model is used to calculate the wake losses. The optimization of the layout using GA involves building a population of layouts at each generation. This population consists of, the best layouts of the previous generation, crossovers or offspring from the best layouts of the previous generation and few mutated layouts. The best layout at each generation is assessed using the fitness or objective functions that consist of annual power production by the layout, cost incurred by layout per unit power produced, and the efficiency of the layout. GA mimics the natural selection process observed in nature, which can be summarised as survival of the fittest. At each generation, the layouts performing the best would enter the next generation where a new population is created from the best performing layouts.</p><p>GA is used to produce 3 different optimal layouts as described below. Results show that:</p><p>A ~5GW layout – has 738 turbines, producing 2.37 GW of power at an efficiency of 0.79</p><p>Layout along the coast – has 1091 turbines, producing 3.665 GW of power at an efficiency of 0.82.</p><p>Layout for the total area – has 2612 turbines, producing 7.82 GW of power at an efficiency of 0.74.</p><p>Thus, placing the turbines along the coast is more efficient as it makes the maximum use of the available wind energy and it would be cost-effective as well by placing the turbines closer to the shores.</p><p>Wind energy is growing at an unprecedented rate in India. Easily accessible terrestrial resources are almost saturated and offshore is the new frontier. This study can play an important role in the offshore expansion of renewables in India.</p>

Wind energy potential assessment in western Mediterranean

2020 ◽  
Author(s):  
Platon Patlakas ◽  
Christos Stathopoulos ◽  
Ariadni Gavriil ◽  
George Galanis ◽  
George Kallos
Keyword(s):  
Wind Energy ◽  
Power Plants ◽  
Wind Farm ◽  
Structural Characteristics ◽  
Wind Farms ◽  
Western Mediterranean ◽  
Offshore Wind ◽  
Energy Potential ◽  
Safety Level ◽  
Marine Area

<p>Wind energy investments have met a quick growth during the last decades due to the stricter climate policies, the need for energy independence and the higher profits coming from the smaller costs of such applications. Moreover the evolution of technology leads to the characterization of more areas as suitable for energy applications. Offshore wind farms are a nice example of how to build bigger, more efficient and resistant in extreme conditions wind power plants.</p><p>The present work is focused on the determination of the suitability of an offshore marine area for the development of wind farm structures. More specifically the region of interest is the marine area on the south of France including the Gulf of Leon. For the needs of the study a 10-year database, produced employing state of the art atmospheric and wave models, is utilized. The wind and wave parameters used, have a spatial resolution of 6 km and a frequency of one hour.</p><p>Wind speed and power probability distribution characteristics are discussed in different heights throughout the domain. Particular locations are selected for a more comprehensive analysis. At the same time extreme wind and wave conditions and their 50-years return period are analyzed and used to define the safety level of the wind farms structural characteristics. The outcome could lead to a review of the area suitability for wind farm development, providing a new tool for technical/research teams and decision makers.</p>

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