On the most suitable sites for wind farm development in Nigeria.
Ontology highlight
ABSTRACT:
SUBMITTER: Ayodele TR
PROVIDER: S-EPMC5992976 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Wind farms can help to mitigate increasing atmospheric carbon (C) emissions. However, disturbance caused by wind farm development must not have lasting deleterious impacts on landscape C sequestration. To understand the effects of wind farm development on peatlands, we monitored streamwater at Europe's second largest onshore wind farm (539 MW), Whitelee, Scotland, for 31 months. Using nested catchment sampling to understand impacts on water quality, increasing macronutrient concentrations and exports were associated with wind farm development, particularly forest-felling and borrow pits. Low/poor water quality occurred in small headwater catchments most disturbed by development. At the site exit, dissolved organic C and soluble reactive phosphorus (SRP) concentrations increased during construction, though [SRP] recovery occurred within 2 years. Since C was lost and streamwater quality negatively affected, we propose future good practice measures for wind farm development, including limiting total disturbance within individual catchments and locating borrow pits, where deemed necessary, off site avoiding peatlands.
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Project description:The purpose of this paper is to provide a high level, holistic overview of the work being undertaken in the wind energy industry. It summarises the main techniques used to simulate both aerodynamic and structural issues associated with wind turbines and farms. The motivation behind this paper is to provide new researchers with an outlook of the modelling and simulation landscape, whilst highlighting the trends and direction research is taking. Each section summarises an individual area of simulation and modelling, covering the important historical research findings and a comprehensive analysis of recent work. This segregated approach emphasises the key components of wind energy. Topics range in geometric scales and detail, ranging from atmospheric boundary layer modelling, to fatigue and fracture in the turbine blades. More recent studies have begun to combine a range of scales and physics to better approximate real systems and provide higher fidelity and accurate analyses to manufacturers and companies. This paper shows a clear trend towards coupling both scales and physics into singular models utilising high performance computing system.
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Project description:Global power production increasingly relies on wind farms to supply low-carbon energy. The recent Intergovernmental Panel on Climate Change (IPCC) Special Report predicted that renewable energy production must leap from [Formula: see text] of the global energy mix in 2018 to [Formula: see text] by 2050 to keep global temperatures from rising 1.5°C above preindustrial levels. This increase requires reliable, low-cost energy production. However, wind turbines are often placed in close proximity within wind farms due to land and transmission line constraints, which results in wind farm efficiency degradation of up to [Formula: see text] for wind directions aligned with columns of turbines. To increase wind farm power production, we developed a wake steering control scheme. This approach maximizes the power of a wind farm through yaw misalignment that deflects wakes away from downstream turbines. Optimization was performed with site-specific analytic gradient ascent relying on historical operational data. The protocol was tested in an operational wind farm in Alberta, Canada, resulting in statistically significant ([Formula: see text]) power increases of 7-[Formula: see text] for wind speeds near the site average and wind directions which occur during less than [Formula: see text] of nocturnal operation and 28-[Formula: see text] for low wind speeds in the same wind directions. Wake steering also decreased the variability in the power production of the wind farm by up to [Formula: see text] Although the resulting gains in annual energy production were insignificant at this farm, these statistically significant wake steering results demonstrate the potential to increase the efficiency and predictability of power production through the reduction of wake losses.
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Project description:We have been the first to investigate whether long-lived geese and ducks can detect and avoid a large offshore wind farm by tracking their diurnal migration patterns with radar. We found that the percentage of flocks entering the wind farm area decreased significantly (by a factor 4.5) from pre-construction to initial operation. At night, migrating flocks were more prone to enter the wind farm but counteracted the higher risk of collision in the dark by increasing their distance from individual turbines and flying in the corridors between turbines. Overall, less than 1% of the ducks and geese migrated close enough to the turbines to be at any risk of collision.
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Project description:In this work, the numerical data related to wind turbine micrositing problem is presented. The data is acquired using the differential evolution algorithm (DEA) at different wind speeds. The data obtained through DEA include total dissipated power, cost per installation of unit turbine, and the efficiency of algorithm after installation of any particular number turbines; and are depicted versus number of turbines. The data provided in this paper can be used directly without having to spend weeks of computational time to simulate the results; and can readily be used for comparison with other existing (Massan et al. [1] and Rajper et al. [2], etc.) and forthcoming algorithms in future.
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Project description:Worldwide growth of offshore renewable energy production will provide marine organisms with new hard substrate for colonization in terms of artificial reefs. The artificial reef effect is important when planning offshore installations since it can create habitat enhancement. Wind power is the most advanced technology within offshore renewable energy sources and there is an urgent need to study its impacts on the marine environment. To test the hypothesis that offshore wind power increases the abundance of reef species relative to a reference area, we conduct an experiment on the model species common shore crab (Carcinus maenas).Overall, 3962 crabs were captured, observed, marked and released in 2011 and 1995 crabs in 2012. Additionally, carapace size, sex distribution, color morphs and body condition was recorded from captured crabs. We observed very low recapture rates at all sites during both years which made evaluating differences in population sizes very difficult. However, we were able to estimate population densities from the capture record for all three sites. There was no obvious artificial reef effect in the Lillgrund wind farm, but a spill-over effect to nearby habitats cannot be excluded. We could not find any effect of the wind farm on either, morphs, sex distribution or condition of the common shore crab. Our study found no evidence that Lillgrund wind farm has a negative effect on populations of the common shore crab. This study provides the first quantitative and experimental data on the common shore crab in relation to offshore wind farms.
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Project description:Energy systems need decarbonisation in order to limit global warming to within safe limits. While global land planners are promising more of the planet's limited space to wind and solar photovoltaic, there is little information on where current infrastructure is located. The majority of recent studies use land suitability for wind and solar, coupled with technical and socioeconomic constraints, as a proxy for actual location data. Here, we address this shortcoming. Using readily accessible OpenStreetMap data we present, to our knowledge, the first global, open-access, harmonised spatial datasets of wind and solar installations. We also include user friendly code to enable users to easily create newer versions of the dataset. Finally, we include first order estimates of power capacities of installations. We anticipate these data will be of widespread interest within global studies of the future potential and trade-offs associated with the global decarbonisation of energy systems.
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Project description:Quantifying the likely effects of offshore wind farms on wildlife is fundamental before permission for development can be granted by any Determining Authority. The effects on marine top predators from displacement from important habitat are key concerns during offshore wind farm construction and operation. In this respect, we present evidence for no significant displacement from a UK offshore wind farm for two broadly distributed species of conservation concern: common guillemot (Uria aalge) and harbor porpoise (Phocoena phocoena). Data were collected during boat-based line transect surveys across a 360 km2 study area that included the Robin Rigg offshore wind farm. Surveys were conducted over 10 years across the preconstruction, construction, and operational phases of the development. Changes in guillemot and harbor porpoise abundance and distribution in response to offshore wind farm construction and operation were estimated using generalized mixed models to test for evidence of displacement. Both common guillemot and harbor porpoise were present across the Robin Rigg study area throughout all three development phases. There was a significant reduction in relative harbor porpoise abundance both within and surrounding the Robin Rigg offshore wind farm during construction, but no significant difference was detected between the preconstruction and operational phases. Relative common guillemot abundance remained similar within the Robin Rigg offshore wind farm across all development phases. Offshore wind farms have the potential to negatively affect wildlife, but further evidence regarding the magnitude of effect is needed. The empirical data presented here for two marine top predators provide a valuable addition to the evidence base, allowing future decision making to be improved by reducing the uncertainty of displacement effects and increasing the accuracy of impact assessments.
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