scholarly journals Do Black-naped Hares Lepus nigricollis (Mammalia: Lagomorpha: Leporidae) have synanthropic association with wind farms?

2018 ◽  
Vol 10 (7) ◽  
pp. 11925 ◽  
Author(s):  
V. Anoop ◽  
P.R. Arun ◽  
Rajah Jaypal

Wind energy is an upcoming major source of clean energy.  The unprecedented proliferation of wind farms across landscapes has raised concerns on the environmental impacts.  Generally reported direct impacts of wind farms include collision of birds and bats with turbines, habitat alterations, noise pollution from the turbines, aesthetic impact on landscapes and displacement of faunal species.  Here we report our preliminary results indicating an apparent positive association of Indian Hares Lepus nigricollis with a wind farm in a scrub forest area.  This study was conducted at Harada Reserve Forest near Harapanahalli of Davangere District, Karnataka, India.  The pellet count method was used for comparing the abundance of the species between areas.  The abundance of Indian Hares in wind farm area was significantly higher than in the surrounding forest area without turbines.  The factors that might be affecting this pattern of preferential use of wind farm area by the Indian Hare are discussed and the scope for further studies also highlighted. 

2020 ◽  
Vol 68 (2) ◽  
pp. 157-167
Author(s):  
Gino Iannace ◽  
Amelia Trematerra ◽  
Giuseppe Ciaburro

Wind energy has been one of the most widely used forms of energy since ancient times, with it being a widespread type of clean energy, which is available in mechanical form and can be efficiently transformed into electricity. However, wind turbines can be associated with concerns around noise pollution and visual impact. Modern turbines can generate more electrical power than older turbines even if they produce a comparable sound power level. Despite this, protests from citizens living in the vicinity of wind farms continue to be a problem for those institutions which issue permits. In this article, acoustic measurements carried out inside a house were used to create a model based on artificial neural networks for the automatic recognition of the noise emitted by the operating conditions of a wind farm. The high accuracy of the models obtained suggests the adoption of this tool for several applications. Some critical issues identified in a measurement session suggest the use of additional acoustic descriptors as well as specific control conditions.


2017 ◽  
Vol 1 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Nina Lansbury Hall ◽  
Jarra Hicks ◽  
Taryn Lane ◽  
Emily Wood

The wind industry is positioned to contribute significantly to a clean energy future, yet the level of community opposition has at times led to unviable projects. Social acceptance is crucial and can be improved in part through better practice community engagement and benefit-sharing. This case study provides a “snapshot” of current community engagement and benefit-sharing practices for Australian wind farms, with a particular emphasis on practices found to be enhancing positive social outcomes in communities. Five methods were used to gather views on effective engagement and benefit-sharing: a literature review, interviews and a survey of the wind industry, a Delphi panel, and a review of community engagement plans. The overarching finding was that each community engagement and benefit-sharing initiative should be tailored to a community’s context, needs and expectations as informed by community involvement. This requires moving away from a “one size fits all” approach. This case study is relevant to wind developers, energy regulators, local communities and renewable energy-focused non-government organizations. It is applicable beyond Australia to all contexts where wind farm development has encountered conflicted societal acceptance responses.


2019 ◽  
Vol 21 (2) ◽  
pp. 745-754
Author(s):  
Otávio Augusto de Oliveira Lima Barra ◽  
Fábio Perdigão Vasconcelos ◽  
Danilo Vieira dos Santos ◽  
Adely Pereira Silveira

O Brasil é um país com uma extensa linha de costa, são cerca de 7.367 km de extensão do seu litoral, com um potencial natural para a geração de energia eólica. O estado do Ceará é um dos maiores produtores de energia eólica para o país, obtendo notoriedade e a necessidade de manutenção dos seus parques eólicos, especialmente se instalados em zonas de costa, onde há uma grande dinâmica natural. O presente trabalho, busca o acompanhamento das dinâmicas morfológicas na praia de Volta do Rio, localizada em Acaraú/CE, que fica a cerca de 238 km de Fortaleza/CE. Os dados coletados em idas à campo, constataram que há um forte processo erosivo atuante na praia de Volta do Rio, o que alerta para a contenção do avanço marinho sob o parque eólico presente no local. A erosão é um fenômeno natural que trabalha na modelação de demasiadas formas terrestres. No litoral, isso não é diferente, por ser um ambiente altamente dinâmico onde há a interação entre continente, atmosfera e oceano, sendo possível encontrar diversos atuantes que podem intensificar os processos erosivos, sejam eles o vento, maré, ou por intervenções humanas, como construções e ocupações indevidas ao longo da linha de costa.Palavras Chave: Volta do Rio; Energia Eólica; Erosão. ABSTRACTBrazil is a country with an extensive coastline, about 7,367 km of coastline, with a natural potential for wind power generation. The state of Ceará is one of the largest producers of wind energy for the country, obtaining notoriety and required maintenance of its wind farms, especially if located in coastal areas, where there is a great natural dynamic. The present work seeks the movement of morphological dynamics in the beach of Volta do Rio, located in Acaraú/CE, which is about 238 km from Fortaleza/CE. The data collected in the field found that there is a strong erosive process on the Beach of Volta do Rio, which warns about the expansion of advanced marine on the wind farm present on site. Erosion is a natural phenomenon that works in the modeling of many hearth forms. On the coast, this is not different, considering a highly dynamic environment in which there is an interaction between continent, atmosphere and ocean, being possible to find many factors that can intensify the erosive processes, such as wind, tide, or human intervention, as constructions and improper occupations along the coast line.Key words: Volta do Rio; Wind Energy; Erosion. RESUMENBrasil es un país con una extensa costa, cerca de 7.367 km de costa, con un potencial natural para la generación de energía eólica. El estado del Ceará es uno de los mayores productores de energía eólica del país, ganando notoriedad y la necesidad de mantener sus parques eólicos, especialmente si está instalado en zonas costeras, donde existe una gran dinámica natural. La presente investigación tiene como objetivo monitorear la dinámica morfológica en la playa de Vuelta del Rio, ubicada en Acaraú / CE, que está a unos 238 km de Fortaleza / CE. Los datos recopilados en los viajes de campo, encontraron que hay un fuerte proceso erosivo en la playa de Vuelta del Rio, que advierte sobre la contención del avance marino bajo el parque eólico presente en el sitio. La erosión es un fenómeno natural que funciona en el modelado de muchas formas terrestres. En la costa, esto no es diferente, ya que es un entorno altamente dinámico donde existe la interacción entre el continente, la atmósfera y el océano, permitiendo encontrar varios actores que pueden intensificar los procesos erosivos, ya sea viento, marea o intervenciones humanas, como edificios y ocupaciones inadecuadas a lo largo de la costa.Palabras clave: Vuelta del Río; Energía Eólica; Erosión.


Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 693
Author(s):  
Anna Dóra Sæþórsdóttir ◽  
Margrét Wendt ◽  
Edita Tverijonaite

The interest in harnessing wind energy keeps increasing globally. Iceland is considering building its first wind farms, but its landscape and nature are not only a resource for renewable energy production; they are also the main attraction for tourists. As wind turbines affect how the landscape is perceived and experienced, it is foreseeable that the construction of wind farms in Iceland will create land use conflicts between the energy sector and the tourism industry. This study sheds light on the impacts of wind farms on nature-based tourism as perceived by the tourism industry. Based on 47 semi-structured interviews with tourism service providers, it revealed that the impacts were perceived as mostly negative, since wind farms decrease the quality of the natural landscape. Furthermore, the study identified that the tourism industry considered the following as key factors for selecting suitable wind farm sites: the visibility of wind turbines, the number of tourists and tourist attractions in the area, the area’s degree of naturalness and the local need for energy. The research highlights the importance of analysing the various stakeholders’ opinions with the aim of mitigating land use conflicts and socioeconomic issues related to wind energy development.


2018 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie

Abstract. Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind turbine hub-heights is poorly constrained and maybe overestimated by the 6 % standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the long-term wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6 %. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90 % of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.


2021 ◽  
pp. 0309524X2110438
Author(s):  
Carlos Méndez ◽  
Yusuf Bicer

The present study analyzes the wind energy potential of Qatar, by generating a wind atlas and a Wind Power Density map for the entire country based on ERA-5 data with over 41 years of measurements. Moreover, the wind speeds’ frequency and direction are analyzed using wind recurrence, Weibull, and wind rose plots. Furthermore, the best location to install a wind farm is selected. The results indicate that, at 100 m height, the mean wind speed fluctuates between 5.6054 and 6.5257 m/s. Similarly, the Wind Power Density results reflect values between 149.46 and 335.06 W/m2. Furthermore, a wind farm located in the selected location can generate about 59.7437, 90.4414, and 113.5075 GWh/y electricity by employing Gamesa G97/2000, GE Energy 2.75-120, and Senvion 3.4M140 wind turbines, respectively. Also, these wind farms can save approximately 22,110.80, 17,617.63, and 11,637.84 tons of CO2 emissions annually.


2021 ◽  
Vol 55 (4) ◽  
pp. 72-87
Author(s):  
Travis Miles ◽  
Sarah Murphy ◽  
Josh Kohut ◽  
Sarah Borsetti ◽  
Daphne Munroe

Abstract The U.S. East Coast has 1.7 million acres of federal bottom under lease for the development of wind energy installations, with plans for more than 1,500 foundations to be placed. The scale of these wind farms has the potential to alter the unique and delicate oceanographic conditions along the expansive Atlantic continental shelf, a region characterized by a strong seasonal thermocline that overlies cold bottom water, known as the “Cold Pool.” Strong seasonal stratification traps cold (typically less than 10°C) water above the ocean bottom sustaining a boreal fauna that represents vast fisheries, including the most lucrative shellfish fisheries in the United States. This paper reviews the existing literature and research pertaining to the ways in which offshore wind farms may alter processes that establish, maintain, and degrade stratification associated with the Cold Pool through vertical mixing in this seasonally dynamic system. Changes in stratification could have important consequences in Cold Pool setup and degradation, processes fundamental to high fishery productivity of the region. The potential for these multiple wind energy arrays to alter oceanographic processes and the biological systems that rely on them is possible; however, a great deal of uncertainty remains about the nature and scale of these interactions. Research should be prioritized that identifies stratification thresholds of influence, below which turbines and wind farm arrays may alter oceanographic processes. These should be examined within context of spatial and seasonal dynamics of the Cold Pool and offshore wind lease areas to identify potential areas of further study.


2020 ◽  
Vol 12 (14) ◽  
pp. 5761 ◽  
Author(s):  
Chakib El Mokhi ◽  
Adnane Addaim

Wind energy is currently one of the fastest-growing renewable energy sources in the world. For this reason, research on methods to render wind farms more energy efficient is reasonable. The optimization of wind turbine positions within wind farms makes the exploitation of wind energy more efficient and the wind farms more competitive with other energy resources. The investment costs alone for substation and electrical infrastructure for offshore wind farms run around 15–30% of the total investment costs of the project, which are considered high. Optimizing the substation location can reduce these costs, which also minimizes the overall cable length within the wind farm. In parallel, optimizing the cable routing can provide an additional benefit by finding the optimal grid network routing. In this article, the authors show the procedure on how to create an optimized wind farm already in the design phase using metaheuristic algorithms. Besides the optimization of wind turbine positions for more energy efficiency, the optimization methods of the substation location and the cable routing for the collector system to avoid cable losses are also presented.


Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4246 ◽  
Author(s):  
Guglielmo D’Amico ◽  
Giovanni Masala ◽  
Filippo Petroni ◽  
Robert Adam Sobolewski

Because of the stochastic nature of wind turbines, the output power management of wind power generation (WPG) is a fundamental challenge for the integration of wind energy systems into either power systems or microgrids (i.e., isolated systems consisting of local wind energy systems only) in operation and planning studies. In general, a wind energy system can refer to both one wind farm consisting of a number of wind turbines and a given number of wind farms sited at the area in question. In power systems (microgrid) planning, a WPG should be quantified for the determination of the expected power flows and the analysis of the adequacy of power generation. Concerning this operation, the WPG should be incorporated into an optimal operation decision process, as well as unit commitment and economic dispatch studies. In both cases, the probabilistic investigation of WPG leads to a multivariate uncertainty analysis problem involving correlated random variables (the output power of either wind turbines that constitute wind farm or wind farms sited at the area in question) that follow different distributions. This paper advances a multivariate model of WPG for a wind farm that relies on indexed semi-Markov chains (ISMC) to represent the output power of each wind energy system in question and a copula function to reproduce the spatial dependencies of the energy systems’ output power. The ISMC model can reproduce long-term memory effects in the temporal dependence of turbine power and thus understand, as distinct cases, the plethora of Markovian models. Using copula theory, we incorporate non-linear spatial dependencies into the model that go beyond linear correlations. Some copula functions that are frequently used in applications are taken into consideration in the paper; i.e., Gumbel copula, Gaussian copula, and the t-Student copula with different degrees of freedom. As a case study, we analyze a real dataset of the output powers of six wind turbines that constitute a wind farm situated in Poland. This dataset is compared with the synthetic data generated by the model thorough the calculation of three adequacy indices commonly used at the first hierarchical level of power system reliability studies; i.e., loss of load probability (LOLP), loss of load hours (LOLH) and loss of load expectation (LOLE). The results will be compared with those obtained using other models that are well known in the econometric field; i.e., vector autoregressive models (VAR).


Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2284 ◽  
Author(s):  
Rômulo Lemos Bulhões ◽  
Eudemário Souza de Santana ◽  
Alex Álisson Bandeira Santos

Electricity generation via renewable sources is emerging as a possible solution to meet the growing demand for electricity worldwide. Additionally, the need to produce clean energy, with little or no pollutants or greenhouse gas emission is paramount. Due to these factors, wind farms are noticeably increasing in number, especially in Brazil. However, the vast size of the country and the poor quality of its infrastructure are among several factors that make it difficult for effective decision-making to accelerate the growth of this segment in Brazil. With the purpose of assisting government agencies, regulatory agencies and other institutions in this area, the use of a multi-criteria selection method called the analytic hierarchy process is proposed here to assist in decision-making and to select priority regions for implementing wind farms. This work focuses on a case study of the state of Bahia, in which 27 territories were selected for an installation priority evaluation. Computational tools were used to hierarchize these chosen territories, including Matlab, for the construction of the computational algorithm. The results indicate the priority pf the regions according to the established criteria, which allows installation locations to be mapped—these could serve as a basis for regional investment.


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