scholarly journals Clustering wind profile shapes to estimate airborne wind energy production

2020 ◽  
Author(s):  
Mark Schelbergen ◽  
Peter C. Kalverla ◽  
Roland Schmehl ◽  
Simon J. Watson
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Energy Production ◽  
Wind Profile ◽  
Atmospheric Stability ◽  
Reanalysis Data ◽  
Offshore Wind ◽  
Height Range ◽  
The North ◽  
Wind Resource

Abstract. Airborne wind energy (AWE) systems typically harness energy in an altitude range up to 500 m above the ground. To estimate the annual energy production (AEP), measured wind speed statistics close to the ground are commonly extrapolated to higher altitudes, introducing substantial uncertainties. This study proposes a clustering procedure for obtaining wind statistics for an extended height range from reanalysis data or long-term LiDAR measurements that include the vertical variation of the wind speed and direction. K-means clustering is used to identify a set of prevailing wind profile shapes that characterise the wind resource. The methodology is demonstrated using the Dutch Offshore Wind Atlas and LiDAR observations for the locations of the met masts IJmuiden and Cabauw, 85 km off the Dutch coast in the North Sea and in the center of the Netherlands, respectively. The resulting wind profile shapes and the corresponding temporal cycles, wind properties, and atmospheric stability are in good agreement with literature. Finally, it is demonstrated how a set of wind profile shapes and their statistics can be used to estimate the AEP of a pumping AWE system. For four or more clusters, the site specific AEP error is within a few percent of the converged value.

scholarly journals Clustering wind profile shapes to estimate airborne wind energy production

2020 ◽  
Vol 5 (3) ◽  
pp. 1097-1120 ◽  
Author(s):  
Mark Schelbergen ◽  
Peter C. Kalverla ◽  
Roland Schmehl ◽  
Simon J. Watson
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Energy Production ◽  
Wind Profile ◽  
Atmospheric Stability ◽  
Offshore Wind ◽  
Small Scale ◽  
Height Range ◽  
The North ◽  
The Difference

Abstract. Airborne wind energy (AWE) systems harness energy at heights beyond the reach of tower-based wind turbines. To estimate the annual energy production (AEP), measured or modelled wind speed statistics close to the ground are commonly extrapolated to higher altitudes, introducing substantial uncertainties. This study proposes a clustering procedure for obtaining wind statistics for an extended height range from modelled datasets that include the variation in the wind speed and direction with height. K-means clustering is used to identify a set of wind profile shapes that characterise the wind resource. The methodology is demonstrated using the Dutch Offshore Wind Atlas for the locations of the met masts IJmuiden and Cabauw, 85 km off the Dutch coast in the North Sea and in the centre of the Netherlands, respectively. The cluster-mean wind profile shapes and the corresponding temporal cycles, wind properties, and atmospheric stability are in good agreement with the literature. Finally, it is demonstrated how a set of wind profile shapes is used to estimate the AEP of a small-scale pumping AWE system located at Cabauw, which requires the derivation of a separate power curve for each wind profile shape. Studying the relationship between the estimated AEP and the number of site-specific clusters used for the calculation shows that the difference in AEP relative to the converged value is less than 3 % for four or more clusters.

scholarly journals An intercomparison of mesoscale models at simple sites for wind energy applications

2017 ◽  
Vol 2 (1) ◽  
pp. 211-228 ◽  
Author(s):  
Bjarke T. Olsen ◽  
Andrea N. Hahmann ◽  
Anna Maria Sempreviva ◽  
Jake Badger ◽  
Hans E. Jørgensen
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Farm ◽  
Weather Prediction ◽  
Atmospheric Stability ◽  
Resource Assessment ◽  
Offshore Wind ◽  
Grid Spacing ◽  
Energy Applications ◽  
Wind Resource

Abstract. Understanding uncertainties in wind resource assessment associated with the use of the output from numerical weather prediction (NWP) models is important for wind energy applications. A better understanding of the sources of error reduces risk and lowers costs. Here, an intercomparison of the output from 25 NWP models is presented for three sites in northern Europe characterized by simple terrain. The models are evaluated using a number of statistical properties relevant to wind energy and verified with observations. On average the models have small wind speed biases offshore and aloft (< 4 %) and larger biases closer to the surface over land (> 7 %). A similar pattern is detected for the inter-model spread. Strongly stable and strongly unstable atmospheric stability conditions are associated with larger wind speed errors. Strong indications are found that using a grid spacing larger than 3 km decreases the accuracy of the models, but we found no evidence that using a grid spacing smaller than 3 km is necessary for these simple sites. Applying the models to a simple wind energy offshore wind farm highlights the importance of capturing the correct distributions of wind speed and direction.

scholarly journals Offshore Wind Energy Resource Atlas of Asturias (N Spain)

Proceedings ◽  
2018 ◽  
Vol 2 (23) ◽  
pp. 1416
Author(s):  
Mario López ◽  
Noel Rodríguez-Fuertes ◽  
Rodrigo Carballo
Keyword(s):  
Wind Speed ◽  
Numerical Model ◽  
Wind Energy ◽  
Energy Resource ◽  
Offshore Wind ◽  
Power Curve ◽  
Offshore Wind Energy ◽  
The North ◽  
North Of Spain ◽  
First Time

This work assesses for the first time the offshore wind energy resource in Asturias, a region in the North of Spain. Numerical model and observational databases are used to characterize the gross wind energy resource at different points throughout the area of study. The production of several wind turbines is then forecasted on the basis of each technology power curve and the wind speed distributions. The results are mapped for a better interpretation and discussion.

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

Energies ◽  
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.

Analysis of Environmental Conditions for the Conceptual Design of a 200 MW Floating Offshore Wind Farm in the East Sea, Korea

2019 ◽  
Author(s):  
Hyunkyoung Shin ◽  
Youngjae Yu ◽  
Thanh Dam Pham ◽  
Hyeonjeong Ahn ◽  
Byoungcheon Seo ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Wind Energy ◽  
Environmental Conditions ◽  
Early Stage ◽  
Measurement Data ◽  
Reanalysis Data ◽  
Offshore Wind ◽  
East Sea ◽  
Wind Resource

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.

scholarly journals Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine

2020 ◽  
Vol 10 (24) ◽  
pp. 9017
Author(s):  
Andoni Gonzalez-Arceo ◽  
Maitane Zirion-Martinez de Musitu ◽  
Alain Ulazia ◽  
Mario del Rio ◽  
Oscar Garcia
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Energy Production ◽  
Cost Effective ◽  
Reanalysis Data ◽  
Small Scale ◽  
Wide Range ◽  
Wind Measurements ◽  
Scale Experiment ◽  
Wind Resource

In this work, a cost-effective wind resource method specifically developed for the ROSEO-BIWT (Building Integrated Wind Turbine) and other Building Integrated Wind Turbines is presented. It predicts the wind speed and direction at the roof of an previously selected building for the past 10 years using reanalysis data and wind measurements taken over a year. To do so, the reanalysis wind speed data is calibrated against the measurements using different kinds of quantile mapping, and the wind direction is predicted using random forest. A mock-up of a building and a BIWT were used in a wind tunnel to perform a small-scale experiment presented here. It showed that energy production is possible and even enhanced over a wide range of attack angles. The energy production estimations made with the best performing kind of calibration achieved an overall relative error of 6.77% across different scenarios.

scholarly journals Can Reanalysis Products Outperform Mesoscale Numerical Weather Prediction Models in Modeling the Wind Resource in Simple Terrain?

2021 ◽  
Author(s):  
Vincent Pronk ◽  
Nicola Bodini ◽  
Mike Optis ◽  
Julie K. Lundquist ◽  
Patrick Moriarty ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Stability ◽  
Negative Bias ◽  
Numerical Weather ◽  
Wind Resource ◽  
Mesoscale Numerical Weather Prediction

Abstract. Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at site in Oklahoma, United States, and in a U.S. Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (~ −1 m s−1 ) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a slight negative bias (~ −0.5 m s−1 ) offshore and a slight positive bias (~ +0.5 m s−1) at the land-based site. Surprisingly, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED’s higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle both onshore and offshore.

Long-term climate change effects in the European offshore wind energy 

2021 ◽  
Author(s):  
Stefano Susini ◽  
Melisa Menendez
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Climate Change Impact ◽  
Energy Production ◽  
Offshore Wind ◽  
Change Impact

&lt;p&gt;Climate change and offshore renewable energy sector are connected by a double nature link. Even though energy generation from clean marine sources is one of the strategies to reduce climate change impact within next decades, it is expected that large scale modification of circulation patterns will have in turn an impact on the spatial and temporal distribution of the wind fields. Under the WINDSURFER project of the ERA4CS initiative, we analyse the climate change impact on marine wind energy resource for the European offshore wind energy sector. Long-term changes in specific climate indicators are evaluated over the European marine domain (e.g. wind power density, extreme winds, operation hours) as well as local indicators (e.g. gross energy yield, capacity factor) at several relevant operating offshore wind farms.&lt;/p&gt;&lt;p&gt;Adopting an ensemble approach, we focus on the climate change greenhouse gases scenario RCP8.5 during the end of the century (2081-2100 period) and analyze the changes and uncertainty of the resulting multi-model from seven high resolution Regional Climate Models (RCM) realized within Euro-Cordex initiative (EUR-11, ~12.5km). ERA5 reanalysis and in-situ offshore measurements are the historical data used in present climate.&lt;/p&gt;&lt;p&gt;Results indicate a small decrease of wind energy production, testified by reduction of the climatological indicators of wind speed and wind power density, particularly in the NW part of the domain of study. The totality of the currently operating offshore windfarms is located in this area, where a decrease up to 20% in the annual energy production is expected by the end of the century, accompanied by a reduction of the operation hours between 5 and 8%. Exceptions are represented by Aegean and Baltic Sea, where these indicators are expected to slightly increase. Extreme storm winds however show a different spatial pattern of change. The wind speed associated to 50 years return period decreases within western Mediterranean Sea and Biscay Bay, while increases in the remaining part of the domain (up to 15% within Aegean and Black Sea). Finally, the estimated variations in wind direction are relevant on the Biscay Bay region.&lt;/p&gt;

scholarly journals Offshore Wind Energy Resource Assessment from Satellite Data Observations and WRF in Porto Santo Island

2020 ◽  
Author(s):  
Fabiola S. Pereira ◽  
Carlos S. Silva
Keyword(s):  
Wind Energy ◽  
Weibull Distribution ◽  
Energy Production ◽  
Energy Resource ◽  
Resource Assessment ◽  
Offshore Wind ◽  
Electricity Production ◽  
Offshore Wind Energy ◽  
Wind Resource Assessment ◽  
Wind Resource

Abstract. The vast majority of isolated electricity production systems such as Islands depends on fossil fuels. Porto Santo Island, a Portuguese UNESCO Biosphere Reserve candidate from Madeira Archipelago situated in the Atlantic Ocean, aims to become a sustainable territory in order to reduce its carbon footprint. A sustainable pathway goes through the integration of renewable energy in the electricity production system, in particular, the potential of offshore wind energy. The scope of this work has three main purposes: (1) the offshore wind resource assessment in Porto Santo Island, (2) the determination of a zone of interest regarding the combination of different parameters such us the bathymetry, distance to the coastline and integrated in the national situation plan of maritime space (3) the estimation of the annual energy production from the best-fitted Weibull Distribution. In the first place, a methodology for data analysis was defined processing netcdf data regarding a ten year wind hindcast from WRF (Weather Research and Forecasting) atmospheric model at 100 m above mean sea level from Ocean Observatory, annual and monthly mean offshore wind energy resource maps were created and a comparison with about 20 year times series of surface winds derived from remotely satellite scatterometer observations at different locations was made. Results show that the average annual mean wind speeds reach the range of 6.6–7.6 m/s in specific areas, situated in the northern part of Porto Santo Island with a Weibull distribution shape parameter (k) of 2.4–2.9. Based on the results, the wind resource assessment, the estimation of the annual wind energy production and capacity factors were calculated from the best-fitted Weibull distribution for each of the geographical coordinates selected. Comparisons with observational data show that WRF model is a proficient wind generating tool. The technical energy production potential and a priority zoning for offshore wind power development is performed using wind turbine generators of 3.3 MW–8.0 MW capacity, that could generate between 12 and 26 GWh of energy per year, while avoiding CO2 emissions. The results show that an offshore wind farm plan is an eligible choice, with an average annual wind power density reaching about 300  W/m2 at 100 m height in the north region.

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