(25) An average turbine load of 8,760 h per year was assumed. (24) Losses due to rotor blade soiling (1–2%), wind hysteresis (1%) and losses for the grid connection (1–3%) were assumed to amount to 5% (η losses). A generator in the nacelle converts the captured energy into electric energy with a reported average mechanical-electrical efficiency (η generator) of 94%. (22, 23) All calculations in this paper were based on a low wind speed site with an annual wind speed of 5 m/s ( v 1) at 10 m height ( h 1) and a wind shear gradient n of 1/7. As a general rule a vertical wind shear gradient described by the Hellman exponent of 1/7 is applied, which is at the lower range of wind shear gradients reported in the literature (between 0.15 and 0.25 for onshore regions). (21) Average wind speed at hub height depends on height ( h), swept area A, and wind shear n. The captured kinetic wind power depends on the air density ρ, swept area of the rotor A and the wind speed v at hub height h. (17) The progress rate (PR) describes the rate at which the costs reduce with every doubling of the production, (3) The formula used is (2)where C cum is the cost per unit C 0 the cost of the first produced unit Cum the cumulative production z is the experience index. (16) An experience curve is a function of the cumulative production and if plotted on a log–log scale, the experience curve becomes linear. (12-15) A study for wind energy showed that due to the global cumulative experience the investment costs of a wind farm display a progress rate of 81%, meaning that costs decrease by 19% each time the cumulative production doubles. (10, 11) Experience curves are commonly derived from empirical studies and widely applied in different energy sectors. Few studies have tried to disentangle scaling from learning, relevant examples come from photovoltaic technologies. (3, 9) Combining scaling and learning is commonly practiced due to the difficult separation of the two effects. (7)Īn approach quantifying both these mechanisms together, scaling and learning, is the experience curve concept, which estimates the investment costs at a certain cumulative installed capacity, without having detailed product specifications or cost indications. (5, 6) Scaling factors between 0.5 and 1 have also been found for the environmental impacts from the production phase of energy conversion equipment. Commonly cost scaling factors between 0.5 and 1 are applied, however a scaling factor of 0.6 is recommended if no data is available, meaning that a 1% size increase, results in a 0.6% cost increase. (4) Cost scaling laws estimate the costs of bigger or smaller equipment based on the costs of a known equipment size, (1)where C 2 is the investment cost of unknown equipment C 1 is the investment cost of known equipment X 2 is the capacity of unknown equipment X 1 is the capacity of known equipment and b is the scaling factor. (3) Size effects are described in the form of a power law and are commonly developed to estimate properties at size X when no measurements or data are available. The factors responsible for the cost reduction can be grouped in size and learning effects. With an increased cumulative production of wind turbines, manufacturers gain experience with the technology, which is commonly reflected in a reduction of the investment costs. The parameters, hub height and rotor diameter were identified as Environmental Key Performance Indicators that can be used to estimate the environmental impacts for a generic turbine. The environmental progress rate was 86%, indicating that for every cumulative production doubling, the global warming potential per kWh was reduced by 14%. This effect was caused by pure size effects of the turbine (micro level) as well as learning and experience with the technology over time (macro level). The results showed that the larger the turbine is, the greener the electricity becomes. Previously published life cycle inventories were combined with an engineering-based scaling approach as well as European wind power statistics. This study quantifies whether the trend toward larger turbines affects the environmental profile of the generated electricity. In life cycle assessment, scaling and progress rates are seldom applied to estimate the environmental impacts of wind energy. Size scaling in the form of a power law, experience curves and progress rates are used to estimate the cost development of ever-larger turbines. The investment costs of wind turbines have decreased over the years, making wind energy economically competitive to conventionally produced electricity. Facebook Icon Twitter Icon Youtube Icon Messenger Icon Linkedin Icon Instagram Icon Shared Link Icon Checkmark Icon Chevron Icon Close Icon Add Icon Increase Icon Arrow Oblique Icon Arrow Down Icon Search Icon energy is a fast-growing and promising renewable energy source.
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