Application of WRF-OML Model for Offshore Wind Resource Prediction

This paper, to the best of author's knowledge, presents the first wind resource assessment for offshore-wind energy off the mainland coasts of Saudi Arabia, despite the onshore wind resource being known. The study utilized wind speed data from, in effect, an offshore meteorological station to study the annual and seasonal variation of wind speed, wind speed frequency distribution, energy yield and consequent opportunity for reduction in green house gases (GHG) emissions. These results were compared with contemporaneous data from a mainland location ∼ 10 km inland at the same longitude Energy yields were calculated using HOMER and RetScreen models. The annual mean wind measured at Abu Ali Island, the offshore location, was 5.43 m/s. This is larger than the 4.9 m/s measured over the same period at Abu Kharuf, the nearby inland location. Larger wind speeds were found in winter months than in summer months at both locations. At Abu Ali Island, the power of the wind could be extracted for 75% of the time at hub-height of 60 meters using modern wind machines of cut-in-speed 4 m/s, in comparison with 60% of time at Abu Kharuf. The prevailing wind direction was found to be North (N), North West (NNW) and North East (NNE).

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Mesoscale modeling of coastal low-level jets: implications for offshore wind resource estimation

Wind Energy ◽

10.1002/we.1628 ◽

2013 ◽

Vol 17 (8) ◽

pp. 1199-1216 ◽

Cited By ~ 43

Author(s):

Christopher G. Nunalee ◽

Sukanta Basu

Keyword(s):

Mesoscale Modeling ◽

Offshore Wind ◽

Resource Estimation ◽

Low Level ◽

Low Level Jets ◽

Wind Resource

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Advancing Offshore Wind Resource Characterization Using Buoy-Based Observations

Marine Technology Society Journal ◽

10.4031/mtsj.54.6.5 ◽

2020 ◽

Vol 54 (6) ◽

pp. 37-43

Author(s):

Alicia M. Gorton ◽

Will J. Shaw

Keyword(s):

Data Collection ◽

Wind Energy ◽

Model Validation ◽

Performance Testing ◽

Pacific Northwest National Laboratory ◽

Economic Benefits ◽

Department Of Energy ◽

Offshore Wind ◽

Wind Resource ◽

The U.S

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.

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Validation of Reanalysis-Based Offshore Wind Resource Characterization Using Lidar Buoy Observations

Marine Technology Society Journal ◽

10.4031/mtsj.54.6.13 ◽

2020 ◽

Vol 54 (6) ◽

pp. 44-61

Author(s):

Lindsay M. Sheridan ◽

Raghavendra Krishnamurthy ◽

Alicia M. Gorton ◽

Will J. Shaw ◽

Rob K. Newsom

Keyword(s):

Pacific Northwest ◽

National Laboratory ◽

Pacific Northwest National Laboratory ◽

The United States ◽

Department Of Energy ◽

Offshore Wind ◽

Approximation Techniques ◽

The North ◽

Wind Resource

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.

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Verification of the Reliability of Offshore Wind Resource Prediction Using an Atmosphere–Ocean Coupled Model

Energies ◽

10.3390/en13010254 ◽

2020 ◽

Vol 13 (1) ◽

pp. 254

Author(s):

Minhyeop Kang ◽

Kyungnam Ko ◽

Minyeong Kim

Keyword(s):

Wind Speed ◽

Energy Production ◽

Prediction Models ◽

Wrf Model ◽

Coupled Model ◽

Sea Surface ◽

Offshore Wind ◽

Wind Effect ◽

Observation Data ◽

Wind Resource

An atmosphere–ocean coupled model is proposed as an optimal numerical prediction method for the offshore wind resource. Meteorological prediction models are mainly used for wind speed prediction, with active studies using atmospheric models. Seawater mixing occurring at sea due to solar radiation and wind intensity can significantly change the sea surface temperature (SST), an important variable for predicting wind resources and energy production, considering its wind effect, within a short time. This study used the weather research forecasting and ocean mixed layer (WRF-OML) model, an atmosphere–ocean coupled model, to reflect time-dependent SST and sea surface fluxes. Results are compared with those of the WRF model, another atmospheric model, and verified through comparison with observation data of a meteorological mast (met-mast) at sea. At a height of 94 m, the wind speed predicted had a bias and root mean square error of 1.09 m/s and 2.88 m/s for the WRF model, and −0.07 m/s and 2.45 m/s for the WRF-OML model, respectively. Thus, the WRF-OML model has a higher reliability. In comparing to the met-mast observation data, the annual energy production (AEP) estimation based on the predicted wind speed showed an overestimation of 15.3% and underestimation of 5.9% from the WRF and WRF-OML models, respectively.

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