Global Wind Research Articles

Power Maximisation of Wind Energy Using Wind Speed Sensors on Stewart Island.

This paper evaluates the feasibility of using wind power for power supply to coastal communities isolated from the main supply grid. The case study is Stewart Island, where the cost of electricity provided by a central diesel power station is higher than the grid network in New Zealand. The Princeton Ocean Model (POM) conducted by MetOcean Solutions Limited (MSL) is used to find Foveaux as an optimized site for generating wind power. Global Wind Atlas is used to plot the wind rose of current wind patterns in New Zealand. In the next step, wind speed data from each site are imported from the NASA database to WRPLOT view software and Homer Pro to find wind frequency distribution and output power in the area. The maximum annual power can be seen in WSW (32,299 kW hours/year), SW (20,111 kW hours/year) and W (15,622 kW hour/year) directions, respectively.

Exploration of the avenues for adoption of the thermal based food processing technologies in manufacturing arena

ABSTRACT Winds of globalisation and the growing population have prompted food industries to look for effective and efficient production systems. For the same, various technological upgrades need to be ramped up, having the potential to upscale the procedurals involving the processing, preservation, and quality of food products. Thermal processing is one among the highly opted procedural in the food industry and various field advancements allied with it need to be analysed for exploration of the avenues of intelligent manufacturing. Presented work glimpses the various novel thermal technologies clustered from the insights of research literature, to be enacted in the nowadays food processing arena. Various perspectives allied with its adoption are suited to develop a relational hierarchical framework, which is further contemplated by the fundamentals of probability-based hesitant fuzzy numbers. Outcomes secure the primacies allied with the different thermal technologies under consideration, accomplishing towards its adoption. Evaluated values are further checked for the dimension of robustness and reliability by sensitivity analysis, strengthening its implication perspectives.

Problems in the Adoption of Climate-Resilient Technologies by the Farmers of Telangana State, India

Climate change refers to significant changes in global temperature, precipitation, and wind patterns. Climate change hasa serious impact on the natural resources available on the earth. Climate change adversely affects human health and quality of life. A majority of the population depends on agriculture directly or indirectly and it is more vulnerable to climate change. To mitigate the adverse effects of climate change, climate-resilient agriculture has become a boon to the farmers in areas that are vulnerable to climatic effects like drought, floods, etc., National Innovations on Climate Resilient Agriculture (NICRA), is a project launched by the Indian Council of Agricultural Research (ICAR) with the aim to enhance the resilience of Indian agriculture to climate change and climate variability through strategic research and technology demonstrations. Krishi Vigyan Kendras (KVKs) is the main implementing agency of NICRA. With the help of KVKs, the interventions of NICRA were accessible throughout the country. The present study was conducted in the Nalgonda district and Khammam district of Telangana. The ex-post-facto research design was used for the study. A sample of 120 respondents from NICRA implemented villages and 60 farmers from non-NICRA implemented villages were selected randomly for the study. The major problems in the adoption of CRA technologies were limited knowledge of CRA technologies (91.66%) followed by inadequate trainings regarding climate-resilient practices (90.00%),inadequate weather based agro advisory services (88.33%), limited outreach of extension services about climate-resilient technologies to (76.66%), non -availability of required implements in custom hiring centres at the time of heavy demand (73.33%), etc., In case of non-NICRA implemented villages, the major problems faced by the respondents were lack of confidence among the farmers to adopt the new CRA technologies due to the fear of failure (93.33%), lack of awareness on climate-resilient technologies among non-NICRA farmers (90.00%), Difficulty in the understanding of CRA technology (86.67%), adoption of CRA technologies demand more resources like water, labour and money (83.33%), less number of need-based training on CRA technologies (75.00%), lack of access to improved crop varieties and other inputs (70.00%), etc.

Spatial pattern prediction of forest wildfire susceptibility in Central Yunnan Province, China based on multivariate data

Wildfires are an important disturbance factor in forest ecosystems. Assessing the probability of forest wildfires can assist in forest wildfire prevention, control, and supervision. The logistic regression model is widely used to forecast the probability, spatial patterns, and drivers of forest wildfires. This study used logistic regression to establish a spatial prediction model for forest wildfire susceptibility, which was applied to evaluate the risk of forest wildfires in Central Yunnan Province (CYP), China. A forest wildfire risk classification was implemented for CYP using forest burn scar data for 2001 to 2020 and the logistic spatial prediction model for forest wildfire susceptibility. Climate, vegetation, topographical, human activities, and location were selected as forest wildfire prediction variables. The results showed that: (1) The distributions of temperature, vegetation coverage, distance to water bodies, distance to roads, and precipitation were positively correlated with the occurrence of forest wildfires. Elevation, relative humidity, the global vegetation moisture index, wind speed, slope, latitude, and distance to residential areas were negatively correlated with the occurrence of forest wildfires. (2) The results of the logistic spatial prediction model for forest wildfire susceptibility showed a good fit to wildfire data, with an overall simulation probability of 81.6%. The optimal threshold for spatial prediction for forest wildfire susceptibility in CYP was determined to be 0.414. A significance level of a selected model variable of < 0.05 resulted in an area under the receiver operating characteristic curve (AUC) of 0.882–0.890. (3) Forest wildfire prevention efforts should focus on Southwest Yuxi City and southern Qujing City accounted for a high proportion of the areas at high risk of forest wildfires. Other localities should adjust forest wildfire prevention measures according to local conditions and strengthen existing wildfire prevention and emergency resource planning and allocation. (4) Some factors contributing to forest wildfires where different among the different areas. Forest wildfire risk factors had different degrees of impact under different spatial and temporal scales. The spatial relationships between wildfire disasters and influencing factors should be established in areas with heterogeneous environmental conditions for the selection of relevant factors.

Evaluation of global terrestrial near-surface wind speed simulated by CMIP6 models and their future projections.

We evaluate the performance of Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the observed global terrestrial near-surface wind speed (NSWS) and project its future changes under three different Shared Socioeconomic Pathways (SSPs). Results show that the CESM2 has the best ability in reproducing the observed NSWS trends, although all models examined are generally not doing well. Based on projections of CESM2, the global NSWS will decrease from 2021 to 2100 under all three SSPs. The projected NSWS declines significantly over the north of 20°N, especially across North America, Europe, and the mid-to-high latitudes of Asia; meanwhile, it increases over the south of 20°N. Under SSP585, there would be more light-windy days and fewer strong-windy days than those under SSP245, which leads to a significant global NSWS decline. Robust hemispheric-asymmetric changes in the NSWS could be due to the temperature gradient in the two hemispheres under global warming, with -1.2%, -3.5%, and -4.1% in the Northern Hemisphere, and 0.8%, 1.0%, and 1.5% in the Southern Hemisphere, for the near-term (2021-2040), mid-term (2041-2060), and long-term (2081-2100), respectively.

Current status and grand challenges for small wind turbine technology

Abstract. While modern wind turbines have become by far the largest rotating machines on Earth with further upscaling planned for the future, a renewed interest in small wind turbines (SWTs) is fostering energy transition and smart grid development. Small machines have traditionally not received the same level of aerodynamic refinement as their larger counterparts, resulting in lower efficiency, lower capacity factors, and therefore a higher cost of energy. In an effort to reduce this gap, research programs are developing worldwide. With this background, the scope of the present study is 2-fold. In the first part of this paper, an overview of the current status of the technology is presented in terms of technical maturity, diffusion, and cost. The second part of the study proposes five grand challenges that are thought to be key to fostering the development of small wind turbine technology in the near future, i.e. (1) improving energy conversion of modern SWTs through better design and control, especially in the case of turbulent wind; (2) better predicting long-term turbine performance with limited resource measurements and proving reliability; (3) improving the economic viability of small wind energy; (4) facilitating the contribution of SWTs to the energy demand and electrical system integration; (5) fostering engagement, social acceptance, and deployment for global distributed wind markets. To tackle these challenges, a series of unknowns and gaps are first identified and discussed. Based on them, improvement areas are suggested, for which 10 key enabling actions are finally proposed.

Potential impact of global stilling on wind energy production in China

The rapid increase in world's installed wind energy capacity may have masked the power loss caused by declining global surface wind speed (termed ‘global stilling’), particularly for China with huge wind energy investments. Here, we estimated the potential impact of global stilling on wind energy production in China, using data from 1226 meteorological stations between 1971 and 2015. We show that surface wind speeds have on average declined at a rate of 5.5% decade−1, while the corresponding wind power density dropped by 24.5% decade−1. Although the decline in wind speeds has slowed after 1991, the decline in wind power density did not show this slowdown. This was attributed to the absence of slowdown in the declining trend of strong winds. Compared with the wind speed levels in the 2000s, the mean capacity factor would have dropped from 20.7% in 2001 to 14.9% in 2015, with the largest absolute change in the “Three North” regions. Furthermore, wind electricity generation would have declined by 40 TWh/a using the installed capacity of 2015, representing a decrease of 16%. The correlation analysis indicates that the decreased wind speed is most likely caused by changes in the large-scale atmospheric circulation rather than increased surface roughness.

Improvements in wind field hindcast for storm surge predictions in the Bay of Bengal: A case study for the tropical cyclone Varadah

Forecasting the storm surges is crucial to prevent the enormous devastation of coastal ecosystems, habitats, and economic losses. Further, estimating ocean wave parameters in tropical areas is difficult due to the complexity of wind fields associated with tropical cyclones (TCs). Usually, parametric and global model wind fields are the two primary sources to acquire wind and pressure fields to estimate the storm surges. The parametric models usually depict a better wind field near the cyclone center. Unlike the parametric models, the wind fields from global models have better accuracy in the areas far away from the eye of the cyclone. Owing to this inconsistency, a blended wind field approach has been introduced. However, there are no consensus on selecting parametric and background wind models due to the lack of a rigorous assessment of different wind models in the Bay of Bengal (BoB) region. Hence, in this study, five well-known parametric wind models, namely, Jelesnianski (SLOSH), rankine vortex (RV), Willoughby (WL06), Holland (HL80), and generalized asymmetric holland model (GAHM); and, two global wind models, namely, european center for medium-rangeweather forecasts (ECMWF), and weather research forecast (WRF) models were investigated with the available predictions and measurements in BoB. After investigating the best wind model in this region, a detailed analysis was carried out for the TC Varadah in the BoB region. Further, this study provides insight into the deviations in the resulting simulated surge, wave characteristics, and inundation induced by the different atmospheric forcing wind models. The results reveal that a blended wind model based on GAHM and WRF can better simulate the background wind field and the internal structure of the cyclone for the BoB region.

Wind energy integration in Africa: development, impacts and barriers

&lt;span&gt;The African renewable energy initiative (AREI), adopted in 2015 by nearly half of the African countries, planned to install 10 GW of renewable energy by the end of 2020 and 300 GW by 2030, of which 100 GW would be wind. These countries have each adopted their own national energy strategy defining their rate of renewable electricity capacity, particularly wind, in the overall energy mix by 2020 and/or 2030. This article aims to assess the implementation of these strategies by evaluating the up-to-date achievements in regards to wind energy and thus infer the AREI realization rate by the end of 2020. It focuses on the wind energy investments of the major African countries while comparing their effective realization rates with those targeted by their national strategies. This article also covers the impact of wind energy integration and the barriers to its development in Africa. Taking into account the recent study published in 2020 by the Global Wind Energy Council which assessed the wind energy potential in Africa at 59 TW, the obtained results show that the huge wind power potential in Africa is still far from being exploited and that only Morocco, Egypt and South Africa are on the right track.&lt;/span&gt;

A cluster analysis of the global wind power industry: Insights for renewable energy business stakeholders and environmental policy decision makers

AbstractIn light of an ongoing climate change and an increasing fragility of fossil energy supply due to global political tensions, the adoption of renewable energy sources is of utmost importance for economic and social prosperity. This study targets to endeavor major value chain configurations within the global wind power industry network based on a data set of 326 relationships established by the 10 globally leading wind turbine firms covering a time span of 15 years. We discuss the demand side and provide an overview of the main wind power investment target countries and compare the importance of different regions. On the supply side, we analyze and identify eight important firm clusters within the global wind power business network. Concerning our sample, we elaborate horizontal and vertical relationship links, their collaboration patterns, regional partner preferences, and their value chain competences, thus providing valuable insights into the competitive structures of the wind power industry. By doing so, we open the debate on appropriate and efficient firm strategies within renewable industries. As consequence, we provide robust empirical evidence on global wind power industry architectures and corresponding competitive firm forces for the future. As we found that the industry is currently divided into eight main global industry clusters, our study delivers valuable industry network insights addressing managerial, political, and socioeconomic decision makers in order to secure future ecological and economic prosperity in a challenging world.

Experimental investigation on noise characteristics of small scale vertical axis wind turbines in urban environments

Noise pollution from wind turbines has been receiving significant attention with the rapid increase in global wind power installation, especially in urban environments where population density is high. Several countries tried to quantify the relation between wind turbine design characteristics and noise impact levels. To date, no quantitative results have been presented, while it is believed that the vertical axis wind turbine has advantages compared to horizontal axis wind turbines in certain applications. With the purpose to enhance understanding of noise pollution caused by vertical axis wind turbines in the urban environment, a systematic experimental test campaign in an anechoic wind tunnel was conducted. The results demonstrate that low-frequency noise increasingly affects humans when the wind speed increases. The blade-passing frequency is identified as a major parameter in the generation of low-frequency noise. More importantly, the noise pollution from various designs is discussed to analyze the impacts on humans and various animals. These designs are analyzed according to national standards from different countries. It is found that an optimal design of a vertical axis wind turbine is suitable for the urban environment with minimal noise impact.

Wind turbine wakes can impact down-wind vegetation greenness

Global wind energy has expanded 5-fold since 2010 and is predicted to expand another 8–10-fold over the next 30 years. Wakes generated by wind turbines can alter downwind microclimates and potentially downwind vegetation. However, the design of past studies has made it difficult to isolate the impact of wake effects on vegetation from land cover change. We used hourly wind data to model wake and non-wake zones around 17 wind facilities across the U.S. and compared remotely-sensed vegetation greenness in wake and non-wake zones before and after construction. We located sampling sites only in the dominant vegetation type and in areas that were not disturbed before or after construction. We found evidence for wake effects on vegetation greenness at 10 of 17 facilities for portions of, or the entire growing season. Evidence included statistical significance in Before After Control Impact statistical models, differences >3% between expected and observed values of vegetation greenness, and consistent spatial patterns of anomalies in vegetation greenness relative to turbine locations and wind direction. Wakes induced both increases and decreases in vegetation greenness, which may be difficult to predict prior to construction. The magnitude of wake effects depended primarily on precipitation and to a lesser degree aridity. Wake effects did not show trends over time following construction, suggesting the changes impact vegetation greenness within a growing season, but do not accrue over years. Even small changes in vegetation greenness, similar to those found in this study, have been seen to affect higher trophic levels. Given the rapid global growth of wind energy, and the importance of vegetation condition for agriculture, grazing, wildlife, and carbon storage, understanding how wakes from wind turbines impact vegetation is essential to exploit or ameliorate these effects.

An artificial intelligence reconstruction of global gridded surface winds

An artificial intelligence reconstruction of global gridded surface winds

Radiation error of air temperature measurements: Minimization of the error by a new reference system and comparison with conventional measurement systems, diurnal and annual variation of their errors as a function of radiation and wind speed

Abstract The measurement of air temperature is associated with inaccuracies resulting from radiation errors. Liquid thermometers and automatic sensors are therefore housed in screens. Mechanical ventilation also helps to increase the accuracy of readings. Nonetheless, temperature measurements are not completely accurate. This spurred the development of a measuring system that features improved radiation protection and a modified mechanical ventilation system through a new type of screen. Parallel measurements with this new measurement system and two conventional screens of multiple plastic cones were taken at a location in north-east Germany with a temperate climate. Of the two traditional screens, one was ventilated and the other not. Based on previous findings concerning the seasonality of radiation errors, our analysis of their dependency on global radiation and wind speed was focused on the months of May to August, when global radiation is at its strongest. It was found that the not insignificant differences in Δ T \Delta T display a characteristic diurnal variation. With the new measurement system, in comparison with the two conventional measurung systems, the average air temperature of the four surveyed months during daylight hours was 0.43 K and 0.58 K lower. The differences in Δ T \Delta T are greatest not in the middle of the day but when the sun is low in the sky, as radiation is then reflected into the screen. The findings contribute to the understanding of the temporal variability of radiation errors in modern weather stations in dependence on global radiation and wind speed. The technical innovations presented here allow radiation errors to be largely avoided.

Microscale Wind Assessment, Comparing Mesoscale Information and Observed Wind Data

One of the most common problems in wind resource assessment is that measured data are not always available at the site of interest. That is why, in several studies, reanalysis data have been used as an alternative, which, in some cases, have been validated by measured data. Mexico is no exception, since there are not many measurement towers in the country that provide valid records throughout the country. In view of the above, in this study a comparison was made between the measurements observed in six anemometric towers, located in different locations in the United Mexican States; data from the MERRA-2 and ERA-5 reanalysis; and data from the generalized wind climates (GWC), available in the Global Wind Atlas. The study was conducted at 80 m, which is the highest height at which data were recorded on the measurement towers at each site. In the case of the MERRA-2 and ERA-5 data, extrapolation of the data series to 80 m was required. In the case of the towers, a comparison of the two data sets measured at 80 m and the height at which two anemometers were available, was performed. This analysis was supported by Windographer version 4 software designed by the company UL solutions, from which *.tab files were exported at 80 m, which were then imported from the WAsP 10.0 program to perform the microscale modeling. The comparison variable was the mean power density, for which the relative deviations between the measured values and those obtained from the reanalysis data and the GWCs were determined. For a better interpretation of the relative errors calculated, an analysis of the orographic characteristics of all the sites was performed using the roughness index (RIX). The results obtained showed that the behavior of the reanalysis and the GWC data was not homogeneous in the sites studied; therefore, an adequate relationship between the magnitudes of the ΔRIX and the relative deviations was not observed, especially for the ERA5 and GWC. The ERA5 data were the furthest from the measured data, with relative deviations greater than 50% at five of the six sites; however, the MERRA-2 and GWC data were the closest to the measured data. The MERRA-2 data showed deviations of less than 11%, except at the La Venta site, where it was 29.5%—a site where the GWC also had a high deviation of 139.4%. The latter is attributable to the effects caused by the nearby wind farms on the wind flow measured by the La Venta station. In general, the MERRA-2 data are an alternative to performing a pre-analysis of the wind resource in Mexico.

Wind-wave forecasting in enclosed basins using statistically downscaled global wind forcing

Accurate wind-wave forecasting in enclosed and semi-enclosed basins is a challenging task, demanding primarily for high-resolution wind forcing at regional scale. This is generally obtained with dynamical downscaling from a low-to-mid resolution atmospheric model. In this context, a new wave forecasting system for the marginal Adriatic Sea is herein presented aimed at proposing an alternative strategy for accurate wind-wave forecasting in (semi-) enclosed basins that does not require an ad-hoc regional atmospheric model. The system is based on the state-of-the-art WAVEWATCH III® spectral wave model forced by the global IFS-ECMWF forecast. At first, wind speed is quantile-corrected to account for the systematic underestimation over the Adriatic Sea. Then, the significant wave height in the target region and for regimes associated with marine storms is calibrated following standard procedure. Wind and wave observations from different sources are used for calibration and validation of the wave forecasts, which achieve satisfactory scores. We also compare results with those of other forecasting systems in the area, highlighting the importance of the wind forcing accuracy and the wave model calibration. Doing so, we discuss the challenges that characterise (semi-) enclosed environments in order to propose effective solutions for them and future developments.

DeepOWT: a global offshore wind turbine data set derived with deep learning from Sentinel-1 data

Abstract. Offshore wind energy is at the advent of a massive global expansion. To investigate the development of the offshore wind energy sector, optimal offshore wind farm locations, or the impact of offshore wind farm projects, a freely accessible spatiotemporal data set of offshore wind energy infrastructure is necessary. With free and direct access to such data, it is more likely that all stakeholders who operate in marine and coastal environments will become involved in the upcoming massive expansion of offshore wind farms. To that end, we introduce the DeepOWT (Deep-learning-derived Offshore Wind Turbines) data set (available at https://doi.org/10.5281/zenodo.5933967, Hoeser and Kuenzer, 2022b), which provides 9941 offshore wind energy infrastructure locations along with their deployment stages on a global scale. DeepOWT is based on freely accessible Earth observation data from the Sentinel-1 radar mission. The offshore wind energy infrastructure locations were derived by applying deep-learning-based object detection with two cascading convolutional neural networks (CNNs) to search the entire Sentinel-1 archive on a global scale. The two successive CNNs have previously been optimised solely on synthetic training examples to detect the offshore wind energy infrastructures in real-world imagery. With subsequent temporal analysis of the radar signal at the detected locations, the DeepOWT data set reports the deployment stages of each infrastructure with a quarterly frequency from July 2016 until June 2021. The spatiotemporal information is compiled in a ready-to-use geographic information system (GIS) format to make the usability of the data set as accessible as possible.

Personal exposure to black carbon in Stockholm, Sweden, compared to fixed-site monitoring and dispersion modelling

INTRODUCTION AND AIM Short-term studies of health effects from ambient air pollution usually rely on fixed site monitoring data or spatio-temporal models for exposure characterization, but the relation to personal exposure is often not known. We aimed to explore this relation for black carbon (BC) in central Stockholm. METHODS Families (n=36) with an infant, one parent working and one parent on leave, agreed to perform personal measurements of BC for the parents, with battery-operated aethalometers for 7 days. Routine monitoring data were from rural background (RB) and urban background (UB). Outdoor levels at home and work were estimated in 24h periods by dispersion modelling with real-time meteorological data. Global radiation, air pressure, precipitation, temperature, and wind speed data were achieved from an urban background station. All families lived within 4 km from the city center. RESULTS The average level of 24h personal BC was 370 (s.d. 200) ng/m3 for parents on leave, and 357 (s.d. 175) ng/m3 for working parents. The corresponding fixed-site monitoring observations were 148 (s.d. 139) at RB and 317 (s.d. 149) at UB. Modelled BC at home and at work were 493 (s.d. 228) and 331 (s.d. 173) ng/m3, respectively. UB, RB and air pressure explained only 21% of personal 24h BC variability for parents on leave and 25% for working parents. Modelled home BC and air pressure explained 23% of personal BC, and adding modelled BC at work increased the explanation to 34% for the working parents. CONCLUSION Both routine monitoring and modeled data explained less than 35% of variability in personal 24h observations of BC exposure. Any associated short-term health effects are likely to be underestimated in studies using such exposure data. KEYWORDS Black carbon, Personal exposure, Dispersion modelling

Do green bonds offer a diversification opportunity during COVID-19?—an empirical evidence from energy, crypto, and carbon markets

The present study is a novel attempt to unravel the connectedness of the green bond with energy, crypto, and carbon markets using the S&P green bond index (RSPGB). We consider MAC global solar energy index (RMGS) and ISE global wind energy index (RIGW) as proxies of the energy market and use bitcoin and the European energy exchange carbon index (REEX) for the cryptocurrency and carbon market. Employing the Diebold and Yilmaz (2012), Baruník and Krehlík (2018), and wavelet coherence econometric techniques, we find that the energy market (RMGS) has the highest connectedness derived from other asset classes, and bitcoin (RBTC) has the least connectedness. Concurrently, we find that the risk transmission is heterogeneous in different scales as the short period has less connectedness than the medium and long run. We conclude that the overall diversification opportunity among green bonds, energy stock, bitcoin, and the carbon market is more in the short-run than in the medium and long-run. In summary, our findings on the green bond market will provide investors, portfolio managers, and policymakers with critical insight into ensuring a sustainable financial market.

Improving the Accuracy of the Cross-Calibrated Multi-Platform (CCMP) Ocean Vector Winds

The Cross-Calibrated Multi-Platform (CCMP) Ocean vector wind analysis is a level-4 product that uses a variational method to combine satellite retrievals of ocean winds with a background wind field from a numerical weather prediction (NWP) model. The result is a spatially complete estimate of global ocean vector winds on six-hour intervals that are closely tied to satellite measurements. The current versions of CCMP are fairly accurate at low to moderate wind speeds (&lt;15 m/s) but are systematically too low at high winds at locations/times where a collocated satellite measurement is not available. This is mainly because the NWP winds tend to be lower than satellite winds, especially at high wind speed. The current long-term CCMP version, version 2.0, also shows spurious variations on interannual to decadal time scales caused by the interaction of satellite/model bias with the varying amount of satellite measurements available as satellite missions begin and end. To alleviate these issues, here we explore methods to adjust the source datasets to more closely match each other before they are combined. The resultant new CCMP wind analysis agrees better with long-term trend estimates from satellite observations and reanalysis than previous versions.