801 resultados para Renewable energy. Offshore wind power. LCOE


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Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.

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The rapid increase in renewable energy generation from wind has increased concerns about the impacts that wind arrays have on the marine environment and what these impacts mean for society. One method for identifying the impacts of offshore wind farms (OWFs) on human welfare is through the assessment and valuation of ecosystem services. Using an ecosystem services approach, this paper reviews the impacts of OWFs on the ecosystem services delivered by marine environments. During the construction phase, supporting services such as reduced energy capture and nutrient cycling are changed due to the introduction of hard substrate and the reduction in soft sediment habitat at turbine bases. This may lead to changes in all other ecosystem services, both negative and positive. Quantifying these changes, however, is a challenge partly due to data limitations and a lack of clear understanding of the impacts of OWFs on the marine ecosystems. Scientific effort needs to quantitatively explore the impacts of OWFs on ecosystem functionality and the gathering of data that enables the assessment of changes to ecosystem services. Data needed to better quantify and value the impacts of OWFs on ecosystem services are suggested. The development of methods which integrate socioeconomic valuation of ecosystem services into the evaluation of renewable energy devices compliments efforts in assessing the environmental impacts and should enable a holistic assessment of the impact of renewable energy production and greenhouse gas mitigation technologies on the U. K. carbon footprint.

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High level environmental screening study for offshore wind farm developments – marine habitats and species This report provides an awareness of the environmental issues related to marine habitats and species for developers and regulators of offshore wind farms. The information is also relevant to other offshore renewable energy developments. The marine habitats and species considered are those associated with the seabed, seabirds, and sea mammals. The report concludes that the following key ecological issues should be considered in the environmental assessment of offshore wind farms developments: • likely changes in benthic communities within the affected area and resultant indirect impacts on fish, populations and their predators such as seabirds and sea mammals; • potential changes to the hydrography and wave climate over a wide area, and potential changes to coastal processes and the ecology of the region; • likely effects on spawning or nursery areas of commercially important fish and shellfish species; • likely effects on mating and social behaviour in sea mammals, including migration routes; • likely effects on feeding water birds, seal pupping sites and damage of sensitive or important intertidal sites where cables come onshore; • potential displacement of fish, seabird and sea mammals from preferred habitats; • potential effects on species and habitats of marine natural heritage importance; • potential cumulative effects on seabirds, due to displacement of flight paths, and any mortality from bird strike, especially in sensitive rare or scarce species; • possible effects of electromagnetic fields on feeding behaviour and migration, especially in sharks and rays, and • potential marine conservation and biodiversity benefits of offshore wind farm developments as artificial reefs and 'no-take' zones. The report provides an especially detailed assessment of likely sensitivity of seabed species and habitats in the proposed development areas. Although sensitive to some of the factors created by wind farm developments, they mainly have a high recovery potential. The way in which survey data can be linked to Marine Life Information Network (MarLIN) sensitivity assessments to produce maps of sensitivity to factors is demonstrated. Assessing change to marine habitats and species as a result of wind farm developments has to take account of the natural variability of marine habitats, which might be high especially in shallow sediment biotopes. There are several reasons for such changes but physical disturbance of habitats and short-term climatic variability are likely to be especially important. Wind farm structures themselves will attract marine species including those that are attached to the towers and scour protection, fish that associate with offshore structures, and sea birds (especially sea duck) that may find food and shelter there. Nature conservation designations especially relevant to areas where wind farm might be developed are described and the larger areas are mapped. There are few designated sites that extend offshore to where wind farms are likely to be developed. However, cable routes and landfalls may especially impinge on designated sites. The criteria that have been developed to assess the likely marine natural heritage importance of a location or of the habitats and species that occur there can be applied to survey information to assess whether or not there is anything of particular marine natural heritage importance in a development area. A decision tree is presented that can be used to apply ‘duty of care’ principles to any proposed development. The potential ‘gains’ for the local environment are explored. Wind farms will enhance the biodiversity of areas, could act as refugia for fish, and could be developed in a way that encourages enhancement of fish stocks including shellfish.

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In the last fifty years, Nunavut has developed a deep dependence on diesel for virtually all of its energy needs, including electricity. This dependence has created a number of economic, environmental and health related challenges in the territory, with an estimated 20% of the territory’s annual budget being spent on energy, thereby limiting the Government of Nunavut’s ability to address other essential infrastructure and societal needs, such as education, nutrition and health care and housing. One solution to address this diesel dependency is the use of renewable energy technologies (RETs), such as wind, solar and hydropower. As such, this thesis explores energy alternatives in Nunavut, and through RETScreen renewable energy simulations, found that solar power and wind power are technically viable options for Nunavut communities and a potentially successful means to offset diesel-generated electricity in Nunavut. However, through this analysis it was also discovered that accurate data or renewable resources are often unavailable for most Nunavut communities. Moreover, through qualitative open-ended interviews, the perspectives of Nunavut residents with regards to developing RETs in Nunavut were explored, and it was found that respondents generally supported the use of renewable energy in their communities, while acknowledging that there still remains a knowledge gap among residents regarding renewable energy, stemming from a lack of communication between the communities, government and the utility company. In addition, the perceived challenges, opportunities and gaps that exist with regards to renewable energy policy and program development were discussed with government policy-makers through further interviews, and it was discovered that often government departments work largely independently of each other rather than collaboratively, creating gaps and oversights in renewable energy policy in Nunavut. Combined, the results of this thesis were used to develop a number of recommended policy actions that could be undertaken by the territorial and federal government to support a shift towards renewable energy in order to develop a sustainable and self-sufficient energy plan in Nunavut. They include: gathering accurate renewable resource data in Nunavut; increasing community consultations on the subject of renewable energy; building strong partnerships with universities, colleges and industry; developing a knowledge sharing network; and finally increasing accessibility to renewable energy programs and policies in Nunavut.

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Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised. (C) 2011 Elsevier Ltd. All rights reserved.