Offshore Wind Resource Assessment Research Articles

Towards sustainable energy: integrating ERA5 data for offshore wind farm planning in India

India’s pursuit of offshore wind energy development, particularly along the Tamil Nadu coastline within its Exclusive Economic Zone (EEZ), faces significant spatial and economic challenges due to existing industrial activities and water depth limitations. Motivated by the need to optimize offshore wind resources and bridge the gap between onshore and offshore wind energy achievements, this study hypothesizes that ERA5 reanalysis wind data can be relied upon for assessing offshore wind resources and optimizing wind farm layouts, especially in the absence of measured data. The research employs methods such as comparing ERA5 data with empirical wind measurements, generating wind rose diagrams for layout optimization, and conducting a comprehensive financial analysis to estimate the Levelized Cost of Energy (LCoE). The results confirm the efficacy of ERA5 data, identifying an optimal wind farm layout with a capacity density of 5.17 MW per square kilometer, turbine spacing of 7.22 meters, and an LCoE of Rs. 8.84 per kWh. The study concludes that significant financial support and strategic policy interventions are essential for minimizing wake losses, optimizing capacity density, and fostering the long-term growth of India’s offshore wind energy sector, providing critical insights for policymakers, developers, and investors. The novelty of this research lies in its detailed exploration of spatial optimization and cost-effective strategies to enhance the techno-economic viability of offshore wind projects in India.

A novel decomposition-based approach for non-stationary hub-height wind speed modelling

An accurate description of hub-height wind speed characteristics is indispensable to offshore wind resource assessment and structure reliability analysis. However, given the assumption of stationarity in wind speeds that is violated, the commonly used stationary statistical models will lead to bias, and a non-stationary frequency analysis is required. In this paper, a novel decomposition-based non-stationary modelling approach was proposed. To decompose time series into deterministic and stochastic components, a procedure was designed by combining signal decomposition methods and recurrence quantification analysis and the performances of seven signal decomposition methods were evaluated under various represent non-stationary scenarios via numerical experiments. Then the non-stationary model was established by aggregating the modelled two components. Compared with other methods, the proposed approach is superior, which is of good self-adaption to data and relies on no hypotheses and explanatory covariates, guaranteeing the simplicity and reliability of the constructed models. Additionally, the SSA-based procedure is capable of capturing complicated non-stationary patterns while preserving the higher-order moments of the underlying stochastic process. The capacity of the proposed approach was verified using wind speed data at six positions distributed along China's coastline. Results emphasize the importance of the consideration of non-stationarity and the necessity of this study.

Enhancing offshore wind resource assessment with LIDAR-validated reanalysis datasets: A case study in Gujarat, India

Enhancing offshore wind resource assessment with LIDAR-validated reanalysis datasets: A case study in Gujarat, India

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Abstract. As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer timescales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting model (WRF) 4.2.1 to different SST products. We first evaluate three different data sets: the Multiscale Ultrahigh Resolution (MUR25) SST analysis, a daily, 0.25∘ × 0.25∘ resolution product; the Operational Sea Surface Temperature and Ice Analysis (OSTIA), a daily, 0.054∘ × 0.054∘ resolution product; and SSTs from the Geostationary Operational Environmental Satellite 16 (GOES-16), an hourly, 0.02∘ × 0.02∘ resolution product. GOES-16 is not processed at the same level as OSTIA and MUR25; therefore, the product requires gap-filling using an interpolation method to create a complete map with no missing data points. OSTIA and GOES-16 SSTs validate markedly better against buoy observations than MUR25, so these two products are selected for use with model simulations, while MUR25 is at this point removed from consideration. We run the model for June and July of 2020 and find that for this time period, in the Mid-Atlantic, although OSTIA SSTs overall validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height (140 m) wind characterization performance on monthly timescales. Additionally, during hours-long flagged events (< 30 h each) that show statistically significant wind speed deviations between the two simulations, both simulations once again demonstrate similar validation performance (differences in bias, earth mover's distance, correlation, and root mean square error on the order of 10−1 or less), with GOES-16 winds validating nominally better than OSTIA winds. With a more refined GOES-16 product, which has been not only gap-filled but also assimilated with in situ SST measurements in the region, it is likely that hub-height winds characterized by GOES-16-informed simulations would definitively validate better than those informed by OSTIA SSTs.

Offshore wind resource assessment based on scarce spatio-temporal measurements using matrix factorization

In the pre-construction of wind farms, wind resource assessment is of paramount importance. Measurements by lidars are a source of high-fidelity data. However, they are expensive and sparse in space and time. Contrarily, Weather Research and Forecasting models generate continuous data with relatively low fidelity. We propose a hybrid approach combining measurements and output from numerical simulations for the assessment of offshore wind. Firstly, the datasets were fed onto a matrix, with columns representing the spatial lidar and WRF points, and the rows representing the time steps. Entries of the matrix reflect the wind speed, empty entries represent unobserved data. Then, matrix factorization using Gaussian process was employed for filling the missing entries with statistically calculated estimates. The model was optimized with stochastic gradient descent to apply GP without approximation methods. To evaluate the method, wind speed data along the coast of Denmark were used. The proposed technique, evaluated using two experiments, resulted in 58% more accurate results than the industrial standard method with trivial increase of computational cost. The RMSE of the proposed method ranges between 0.35 and 0.52 m/s.

Offshore wind resource assessment by characterizing weather regimes based on self-organizing map

As offshore wind power is continuously integrated into the electric power systems in around the world, it is critical to understand its variability. Weather regimes (WRs) can provide meteorological explanations for fluctuations in wind power. Instead of relying on traditional large-scale circulation WRs, this study focuses on assessing the dependency of wind resources on WRs in the tailored region clustered based on the finer spatial scale. For this purpose, we have applied self-organizing map algorithm to cluster atmospheric circulations over the South China Sea (SCS) and characterized wind resources for the classified WRs. Results show that WRs at mesoscale can effectively capture weather systems driving wind power production variability, especially on multi-day timescale. Capacity factor reconstruction during four seasons illustrates that WRs highly influence most areas in winter and southern part of SCS in summer, and WRs can serve as a critical source of predicting the potential of wind resources. In addition, we further qualify the wind power intermittency and complementarity under different WRs, which have not been assessed associated with WRs. During WRs with changeable atmosphere conditions, the high complementarity over coastal areas can reduce the impact of intermittency on wind power generation. The proposed approach is able to be implemented in any region and may benefit wind resource evaluation and characterization.

Offshore wind resource assessment off the coast of Daejeong, Jeju Island using 30-year wind estimates

Long-term offshore wind resource assessment was performed for the offshore site off the Daejeong coast on Jeju Island of South Korea. A jacket type offshore met mast was installed at the site to measure wind and meteorological conditions for 1 year from 1 July 2015 to 30 June 2016. The wind conditions were measured at 32.5 m, 72.5 m and 100.5 m above mean sea level (AMSL). Fundamental wind characteristics such as average wind speed, wind direction frequency, turbulence intensity and so on were analyzed utilizing 1-year wind measurements. In order to correct the limited period of 1-year measurements to long-term conditions, the Measure-Correlate-Predict (MCP) method was applied to estimate 30-year wind conditions using nearby Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data. Also, wind turbine classes suitable for the offshore site were determined based on extreme wind speed estimations and the measured ambient turbulence intensity. Five commercial wind turbines were chosen for estimation of the 30-year annual energy productions (AEPs) and capacity factors (CFs). As a result, it was estimated that the 30-year average wind speed was 7.86 m/s at 100.5 m AMSL. The wind turbine class II rating was estimated to be suitable for enduring extreme wind speed for a return period of 50 years. For the 30-year results, the minimum CF of 30.48% was estimated, while the maximum nearly reached 45%.

High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 synthetic aperture radar (SAR) data and machine learning

Abstract. This paper presents a method for estimating offshore extractable wind power at hub height using Sentinel-1 synthetic aperture radar (SAR) data and machine learning. The method was tested in two areas off the Dutch coast, where measurements from Doppler wind lidars installed at the sea surface were available and could be used as a reference. A first machine learning algorithm improved the accuracy of SAR sea surface wind speeds by using geometrical characteristics of the sensor and metadata. This algorithm was trained with wind data measured by a large network of weather buoys at 4 m above sea level. After correction, the bias in SAR wind speed at 4 m versus buoys was 0.02 m s−1, with a standard deviation of error of 0.74 m s−1. Corrected surface wind speeds were then extrapolated to hub height with a second machine learning algorithm, which used meteorological parameters extracted from a high-resolution numerical model. This algorithm was trained with lidar vertical wind profiles and was able to extrapolate sea surface wind speeds at various altitudes up to 200 m. Once wind speeds at hub height were obtained, the Weibull parameters of their distribution were estimated, taking into account the satellites' irregular temporal sampling. Finally, we assumed the presence of a 10 MW turbine and obtained extractable wind power with a 1 km spatial resolution by multiplying the Weibull distribution point by point by its power curve. Accuracy for extractable wind power versus lidars was ± 3 %. Wind power maps at hub height were presented and compared with the outputs of the numerical model. The maps based on SAR data had a much higher level of detail, especially regarding coastal wind gradient. We concluded that SAR data combined with machine learning can improve the estimation of extractable wind power at hub height and provide useful insights to optimize siting and risk management. The algorithms presented in this study are independent and can also be used in a more general context to correct SAR surface winds, extrapolate surface winds to higher altitudes, and produce instantaneous SAR wind fields at hub height.

A revised offshore wind resource assessment and site selection along the Indian coast using ERA5 near-hub-height wind products

The present study aims to re-assess offshore wind resources and analyse the wind climate along the Indian coast using the latest ERA5 near-hub-height wind products. Further, proposed an index to identify the hotspots for offshore wind farm (OWF) establishment. The effect of air density variation is considered while estimating wind power density (WPD) and actual power generation with the two latest wind turbines. Results show that maximum wind speed (7–10 m/s) and WPD (500–650 W/m2) are along the southern coast. The peak frequency of exploitable wind energy (>60%) is observed along the Tamil Nadu coast. Further, it is observed that consideration of standard air density overestimates the WPD with 5–7.5%, and wind turbines power output (2.5–5.1%) than actual air density. The suggested hotspots for OWFs have the potential of generating 1.84–3.96 GWh/year with a V164/9500 wind turbine. The methodology presented in this study will aid decision-makers in rapidly identifying and characterising OWF sites for the Indian coast and other regions.

Climatic Impacts of Wind-Wave-Wake Interactions in Offshore Wind Farms

Accurate wind resource assessments are necessary for cost effective offshore wind energy developments. The wind field offshore depends on the sea state. In coastal areas, where wind farms are usually built today, wind and waves are often not in full balance. In addition, wind farms modify their surrounding wind and turbulence field, especially downwind. These wind farm wakes, in turn, interact with the wave field, creating a complex dynamical system. To fully capture the dynamics in such a system in a realistic way, a coupled atmosphere-wave modelling system equipped with a wind farm parameterization should be applied. However, most conventional resource assessment relies on standalone atmosphere model simulations. We compare the wind-wave-wake climate predicted from a coupled modelling system, to one predicted from a standalone atmosphere model. Using a measurement-driven statistical-dynamical downscaling method, we show that about 180 simulation days are enough to represent the wind- and wave-climate, as well as the relation between those two, for the German Bight. We simulate these representative days with the atmosphere-wave coupled and the uncoupled modelling system. We perform simulations both without wind farms as well as parameterizing the existing wind farms as of July 2020. On a climatic average, wind resources derived from the coupled modelling system are reduced by 1% in 100 m over the sea compared to the uncoupled modelling system. In the area surrounding the wind farm the resources are further reduced. While the climatic reduction is relatively small, wind speed differences between the coupled and uncoupled modelling systems differ by more than ±20% on a 10-min time-scale. The turbulent kinetic energy derived from the coupled system is higher, which contributes to a more efficient wake dissipation on average and thus slightly smaller wake-affected areas in the coupled system. Neighbouring wind farms reduce wind resources of surrounding farms by up to 10%. The wind farm wakes reduce significant wave height by up to 3.5%. The study shows the potential of statistical-dynamical downscaling and coupled atmosphere-wave-wake modelling for offshore wind resource assessment and physical environmental impact studies.

Offshore wind resource assessment using reanalysis data

The growth rate of offshore wind is increasing due to technological advancement and reduction in cost. An approach using mast measured data at coastline and reanalysis data is proposed for offshore wind resource assessment, especially for developing countries. The evaluation of fifth generation European Reanalysis (ERA5) data was performed against measured data using statistical analysis. ERA5 data slightly underestimates wind speed and wind direction with percentage bias of less than 1%. Wind resource assessment of region in Exclusive Economic Zone (EEZ) of Pakistan was performed in terms of wind speed and Wind Power Density (WPD). The range of monthly mean wind speed and WPD in the region was 4.03–8.67 m/second and 73–515 W/m2 respectively. Most-probable wind speed and dominating wind direction on corners and center of the region were found using probability distributions and wind rose diagrams respectively. Most-probable wind speed ranges 4.41–7.64 m/second and dominating wind direction is southwest.

Influence of the WRF model and atmospheric reanalysis on the offshore wind resource potential and cost estimation: A case study for Rio de Janeiro State

The growing increase of offshore wind implementations has been made this modality subject to many resource assessment works. However, the variety of available wind datasets and the difficulty of the validation process over the ocean still leave uncertainty about the most relevant data for offshore wind resource assessment and how much they affect the energy estimation. This study investigates the influence of ERA5, CFSv2, MERRA2 atmospheric reanalyses, and the WRF model on the offshore wind resource potential and cost estimation of offshore wind projects. The coastline of the Rio de Janeiro state of Brazil is considered as the focus of the study region. Wind results are validated with in-situ measurements from three meteo-oceanographic buoys by the SIMCosta and PNBOIA programs, an anemometer placed on a floating vessel for production storage and offloading (FPSO), and seven weather stations positioned inland near the coastline from the National Meteorological Institute of Brazil (INMET). Then, energy production and cost estimation are calculated employing the IEA-15 MW wind turbine, and the differences between the models are analyzed. The comparative analysis reveals significant differences in the annual energy production, mainly near to the coastline, impacting the cost estimation of the projects in the planning phase.

Evaluating the suitability of Sentinel-1 SAR data for offshore wind resource assessment around Cyprus

Offshore wind offers an excellent opportunity for domestic renewable energy production with a vast potential for future energy systems. Offshore wind resource assessment, however, can be challenging. Remote sensing data e.g., Synthetic Aperture Radar (SAR), provide high spatial resolution detailed information on the spatial variability of offshore wind and have been used for wind resource assessment, as well as for the long-term validation of wind speed estimates from other sources (e.g. Numerical Weather Prediction models). This paper focuses on the evaluation of a 26-month time-series of Sentinel-1 SAR Level 2 OCN products for wind resource assessment in the offshore areas around Cyprus. Sentinel data were evaluated against a 10-year regional reanalysis dataset (UERRA) time-series and wind measurements from 5 coastal meteorological stations in Cyprus. Comparison revealed an overall agreement between the fitted stations and Sentinel Weibull distributions while discrepancies exist between the two data sources and UERRA. Bias observed between Sentinel and UERRA Weibull-derived statistics appears to be spatially dependent. Preliminary wind power assessment results indicate a significant wind power potential for the southwestern offshore areas of Cyprus, surpassing 400 W/m2 on average, offering thus economically viable solutions in terms of a future offshore wind power project development.

GIS-based multi-criteria analysis of offshore wind farm development in Morocco

• GIS-based model using the FAHP for offshore wind farm site suitability analysis. • Different technical, socio-economic and environmental criteria are considered. • Morocco has a great potential for offshore wind farm development. This investigation focuses on offshore wind resource assessment in Morocco using an integrated methodology consisting of Geographic Information System and the Fuzzy Analytic Hierarchy Process. Following the published literature and regional necessities, a set of suitability criteria is defined including eight different site selection criteria and six exclusion factors. A weighted overlay analysis is conducted in order to map the ideal locations for offshore wind energy development in Morocco. The study results highlight a wide offshore area that is most suited for wind farm development. Three high suitable areas are shortlisted within the Atlantic Ocean boundary of Morocco, in proximity to the cities of Essaouira, Boujdour, and Dakhla. The three selected areas are capable of generating around 13.3 GW of electrical power. The suggested methodology can be of immense aid for locating the most suitable areas for offshore wind farms in different regions having equivalent geo-factors.

A hybrid solution for offshore wind resource assessment from limited onshore measurements

In wind resource assessments, which are critical to the pre-construction of wind farms, measurements by LiDARs or masts are a source of high-fidelity data, but are expensive and scarce in space and time, particularly for offshore sites. On the other hand, numerical simulations, using for example the Weather Research and Forecasting (WRF) model, generate temporally and spatially continuous data with relatively low-fidelity. A hybrid approach is proposed here to combine the merit of measurements and simulations for the assessment of offshore wind. Firstly a temporal data fusion using deep Multi Fidelity Gaussian Process Regression (MF-GPR) is performed to combine the intermittent measurement and the continuous simulation data at an onshore location. Then a spatial data fusion using a neural network with Non-linear Autoregression (NAR) and Non-linear Autoregression with external input (NARX) are conducted to project the wind from onshore to offshore. The numerical and measured wind speeds along the west coast of Denmark were used to evaluate the method. We show that the proposed data fusion technique using a gappy onshore measurement results in accurate offshore wind resource assessment within a 2% margin error.

Influence of the WRF Model and Atmospheric Reanalysis on the Offshore Wind Resource Potential and Cost Estimation: A Case Study for Rio De Janeiro State

The growing increase of offshore wind implementations has been made this modality subject to many resource assessment works. However, the variety of available wind datasets and the difficulty of the validation process over the ocean still leave uncertainty about the most relevant data for offshore wind resource assessment and how much they affect the energy estimation. This study investigates the influence of ERA5, CFSv2, MERRA2 atmospheric reanalyses, and the WRF model on the offshore wind resource potential and cost estimation of offshore wind projects. The coastline of the Rio de Janeiro state of Brazil is considered as the focus of the study region. Wind results are validated with in-situ measurements from three meteo-oceanographic buoys by the SIMCosta and PNBOIA programs, an anemometer placed on a floating vessel for production storage and offloading (FPSO), and seven weather stations positioned inland near the coastline from the National Meteorological Institute (INMET) of Brazil. Then, energy production and cost estimation are calculated employing the IEA-15 MW wind turbine, and the differences between the models are analyzed. The comparative analysis reveals significant differences in the annual energy production, mainly near to the coastline, impacting the cost estimation of the projects in the planning phase.

Offshore wind power assessment on the western coast of Thailand

This paper presents an offshore wind resource assessment and offshore wind power feasibility analysis on the western coast of Thailand using coupled mesoscale atmospheric and microscale Computational Fluid Dynamics wind flow modeling, along with a climatic database. Offshore wind maps at elevations of 80 m, 90 m and 120 m above mean sea level (amsl) are presented and validated with measured wind datasets gathered from nine met masts installed along the coast of the Andaman Sea and the Strait of Malacca on the western part of Thailand. Results show that, at an elevation of 120 m amsl, the northern region near Myanmar is characterized by wind speeds in the range of 5.5 to 6.0 m/s, while the area around Phuket Island has wind speeds in the range of 4.6 to 5.3 m/s. In line with the Thai energy policies for wind power plant development, the annual energy production, the capacity factors, the levelized cost of energy (LCOE), the CO2eq emission avoidance, and the required Feed-in-Tariff (FiT) are analyzed for wind power plants integrating 3.3 MW, 8.0 MW and 9.5 MW offshore wind turbine generators. Results show that offshore wind power plants with 3.3 MW wind turbine generators could produce over 13 GWh of electricity per year under the Very Small Power Producer (VSPP) scenario for 10 MW wind power plants. For its part, 50 MW wind power plants under the Small Power Producer (SPP) scenario could produce 68 GWh/yr, and nearly 123 GWh/yr for SPP wind power plants with 90 MW of installed capacities. With capacity factors in the order of 16%, the lowest LCOE is 0.188 USD/kWh and the CO2eq emission avoidances reach 774 tonnes CO2eq per year. Finally, the required FiT to sustain the development of VSPP offshore wind power plants in the vicinity of Phuket Island is in the range of 0.314 to 0.688 USD/kWh.

Offshore wind resource assessment using MCP combing short-term Lidar observations and long-term WRF simulations

Offshore wind resource assessment using MCP combing short-term Lidar observations and long-term WRF simulations

Vehicular Motion Experiment and Data Retrieval of a Compact Floating Lidar System

Accurate and rapid observation of sea surface wind is important for the research of ocean dynamic prediction model, offshore wind resource assessment, air-sea interaction and flux. A compact floating coherent Doppler lidar system named WindMast 350-M was developed by Ocean University of China (OUC) and Leice Transient Technology Co. LTD (LEICE) for the observations of sea surface wind profiles. As an observation device installed on buoy platforms, the first vehicular motion experiment was conducted at Laoshan campus of Ocean University of China(120.49°E , 36.16°N) on 06 and 12 March, 2019. During the first experiment, the wind profiles measured by the WindMast 350-M were compared with the results from a well calibrated Ground-based Coherent Doppler lidar WindMast WP350. In this contribution, the systematic design and the specifications of 350-M are presented in detail. The preliminary results of the vehicular motion experiment are discussed as well.

Progress of NEDO project Bottom fixed offshore wind farm development support project (Establishment of offshore wind resource assessment method)

Progress of NEDO project Bottom fixed offshore wind farm development support project (Establishment of offshore wind resource assessment method)