Advancing Offshore Wind Resource Characterization Using Buoy-Based Observations

2020 ◽  
Vol 54 (6) ◽  
pp. 37-43
Author(s):  
Alicia M. Gorton ◽  
Will J. Shaw

AbstractAs countries continue to implement sustainable and renewable energy goals, the need for affordable low-carbon technologies, including those related to offshore wind energy, is accelerating. The U.S. federal government recognizes the environmental and economic benefits of offshore wind development and is taking the necessary steps to overcome critical challenges facing the industry to realize these benefits. The U.S. Department of Energy (DOE) is investing in buoy-mounted lidar systems to facilitate offshore measurement campaigns that will advance our understanding of the offshore environment and provide the observational data needed for model validation, particularly at hub height where offshore observations are particularly lacking. On behalf of the DOE, the Pacific Northwest National Laboratory manages a Lidar Buoy Program that facilitates meteorological and oceanographic data collection using validated methods to support the U.S. offshore wind industry. Since being acquired in 2014, two DOE lidar buoys have been deployed on the U.S. east and west coasts, and their data represent the first publicly available multi-seasonal hub height data to be collected in U.S. waters. In addition, the buoys have undergone performance testing, significant upgrades, and a lidar validation campaign to ensure the accuracy and reliability of the lidar data needed to support wind resource characterization and model validation (the lidars were validated against a reference lidar installed on the Air-Sea Interaction Tower operated by the Woods Hole Oceanographic Institution). The Lidar Buoy Program is providing valuable offshore data to the wind energy community, while focusing data collection on areas of acknowledged high priority.

2020 ◽  
Vol 54 (6) ◽  
pp. 44-61
Author(s):  
Lindsay M. Sheridan ◽  
Raghavendra Krishnamurthy ◽  
Alicia M. Gorton ◽  
Will J. Shaw ◽  
Rob K. Newsom

AbstractThe offshore wind industry in the United States is gaining strong momentum to achieve sustainable energy goals, and the need for observations to provide resource characterization and model validation is greater than ever. Pacific Northwest National Laboratory (PNNL) operates two lidar buoys for the U.S. Department of Energy (DOE) in order to collect hub height wind data and associated meteorological and oceanographic information near the surface in areas of interest for offshore wind development. This work evaluates the performance of commonly used reanalysis products and spatial approximation techniques using lidar buoy observations off the coast of New Jersey and Virginia, USA. Reanalysis products are essential tools for setting performance expectations and quantifying the wind resource variability at a given site. Long-term accurate observations at typical wind turbine hub heights have been lacking at offshore locations. Using wind speed observations from both lidar buoy deployments, biases and degrees of correspondence for the Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2), the North American Regional Reanalysis (NARR), ERA5, and the analysis system of the Rapid Refresh (RAP) are examined both at hub height and near the surface. Results provide insights on the performance and uncertainty of using reanalysis products for long-term wind resource characterization. A slow bias is seen across the reanalyses at both deployment sites. Bias magnitudes near the surface are on the order of 0.5 m s−1 greater than their hub height counterparts. RAP and ERA5 produce the highest correlations with the observations, around 0.9, followed by MERRA-2 and NARR.


Author(s):  
Hyunkyoung Shin ◽  
Youngjae Yu ◽  
Thanh Dam Pham ◽  
Hyeonjeong Ahn ◽  
Byoungcheon Seo ◽  
...  

Abstract Due to global climate change, concern regarding the environment is greater than ever. Also, the energy industry is constantly developing and investing in new and renewable energy to reduce carbon emissions. Korea is planning to increase the proportion of renewable energy generation to 20% by 2030, in accordance with the 3020 renewable energy policy. This will involve 16.5 GW (34%) from wind energy, with a capacity from offshore wind energy of approximately 13 GW. Considering domestic technological wind resource potential (33.2 GW), it seems to be a sufficient target amount. However, in order to start the wind power generation business, the installation area must be analyzed for environmental information, for the evaluation of the wind resource and the early-stage concept design. Because it is difficult to conduct long-term measurements of the entire sea area, the environmental conditions are generally estimated from short-term measurement data and long-term reanalysis data. In this study, the environmental conditions of the East Sea of Korea were selected, and a comparative analysis was performed on the meteorological agency’s oceanic meteorology buoy data, ERA-5 reanalysis data obtained from ECMWF, and NASA’s MERRA-2 data. The extreme sea states of 50 years and 100 years were analyzed by extreme statistical analysis. Finally, environmental conditions required for the basic design of wind turbines were selected following IEC and DNV standards.


Author(s):  
Amy N. Robertson ◽  
Jason M. Jonkman ◽  
Andrew J. Goupee ◽  
Alexander J. Coulling ◽  
Ian Prowell ◽  
...  

The DeepCwind consortium is a group of universities, national labs, and companies funded under a research initiative by the U.S. Department of Energy (DOE) to support the research and development of floating offshore wind power. The two main objectives of the project are to better understand the complex dynamic behavior of floating offshore wind systems and to create experimental data for use in validating the tools used in modeling these systems. In support of these objectives, the DeepCwind consortium conducted a model test campaign in 2011 of three generic floating wind systems: a tension-leg platform (TLP), a spar-buoy (spar), and a semi-submersible (semi). Each of the three platforms was designed to support a 1/50th-scale model of a 5-MW wind turbine and was tested under a variety of wind/wave conditions. The focus of this paper is to summarize the work done by consortium members in analyzing the data obtained from the test campaign and its use for validating the offshore wind modeling tool, FAST.


2020 ◽  
Author(s):  
Philipp C Beiter ◽  
Jessica K Lau ◽  
Joshua E Novacheck ◽  
Qing Yu ◽  
Gordon W Stephen ◽  
...  

2021 ◽  
Author(s):  
Jana Fischereit ◽  
Kurt Schaldemose Hansen ◽  
Xiaoli Guo Larsén ◽  
Maarten Paul van der Laan ◽  
Pierre-Elouan Réthoré ◽  
...  

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.


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