Offshore wind energy assessment for the Iberian coast with a regional atmospheric model

Salvação, N.; Soares, C

doi:10.1201/b18973-33

Cited by 3 publications

(2 citation statements)

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“…Average wave height and period for both locations were taken from Silva et al [14], whereas current speed was assumed considering the conclusions of Sauvaget et al [37]. More detailed local resource information can be found in [38,39] for waves and [40,41] for wind. …”

Section: Locationmentioning

confidence: 99%

Methodology to Calculate the Costs of a Floating Offshore Renewable Energy Farm

2016

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This paper establishes a general methodology to calculate the life-cycle cost of floating offshore renewable energy devices, applying it to wave energy and wind energy devices. It is accounts for the contributions of the six main phases of their life-cycle: concept definition, design and development, manufacturing, installation, exploitation and dismantling, the costs of which have been defined. Moreover, the energy produced is also taken into account to calculate the Levelized Cost of Energy of a floating offshore renewable energy farm. The methodology proposed has been applied to two renewable energy devices: a floating offshore wave energy device and a floating offshore wind energy device. Two locations have been considered: Aguçadoura and São Pedro de Moel, both in Portugal. Results indicate that the most important cost in terms of the life-cycle of a floating offshore renewable energy farm is the exploitation cost, followed by the manufacturing and the installation cost. In addition, the best area in terms of costs is the same independently of the type of floating offshore renewable energy considered: Aguçadoura. However, the results in terms of Levelized Cost of Energy are different: Aguçadoura is better when considering wave energy technology and the São Pedro de Moel region is the best option when considering floating wind energy technology. The method proposed aims to give a direct approach to calculate the main life-cycle cost of a floating offshore renewable energy farm. It helps to assess its feasibility and evaluating the relevant characteristics that influence it the most.

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Section: Locationmentioning

confidence: 99%

Methodology to Calculate the Costs of a Floating Offshore Renewable Energy Farm

2016

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“…The values corresponding to each regime for these variables are summarized in Table 1. These values were chosen because the average PM2.5 concentration of Chennai is 33 µg m -3 (Chen et al, 2020); the traffic volume in the stretch during peak hour is around 2300 vehicles h -1 (Sampathkumar et al, 2018); and, the wind speed referring to Beaufort scale of light and gentle breeze in low wind speed category, moderate breeze in medium wind speed category, and Fresh and strong breeze in high wind speed category (Salvacao et al, 2015). In addition to these input parameters, the effect of traffic composition on the vertical differences in PM2.5 was also explored.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Analysis of the Near-road Fine Particulate Exposure to Pedestrians at Varying Heights

Patra

Vanajakshi

2021

Aerosol Air Qual. Res.

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Scientific literature has overlooked how PM 2.5 concentrations vary with varying pedestrian heights near a roadway. Understanding this is important because walking is an essential commuting element of a sustainable transportation system, and pedestrians' height varies widely.Therefore, the focus of the current study is to bridge this gap using results from CALINE 4 model and mobile PM2.5 measurements. In CALINE 4, a simple pedestrian pathway depicting the selected study site located near the Sardar Patel Road, Chennai, India, was simulated. The PM2.5 concentrations were estimated on this pathway at varying heights (0.1-1.8 m) in 135 simulated runs. Subsequently, the sensitivity of the PM2.5 exposure difference across heights was explored with varying ambient PM2.5 concentrations, wind speed, traffic volume, and traffic compositions.Results indicated that the PM2.5 concentrations reduced with increasing heights of pedestrians in all the modelled runs. When this PM2.5 exposure difference was investigated with varying surrounding conditions, it was found that the difference in PM2.5 exposure across heights was influenced by the wind speed, traffic volume, and traffic composition. Ambient PM2.5 concentrations had no discernible effect on it. Car-dominated traffic with a higher mode share of heavy commercial vehicles was marked with the highest PM2.5 exposure difference across heights.For traffic volume, it was observed that for every 100 vehicles hr -1 increase in traffic volume, the PM2.5 exposure difference increased by 0.13 µg m -3 m -1 in the range of pedestrian's height. For wind speed, calculations suggested that for every 1 m s -1 increase in wind speed, the PM2.5 exposure difference was reduced by 0.095 µg m -3 m -1 in the range of pedestrian's height. Finally, to bolster the modelling results, mobile PM2.5 measurements (using portable, low-cost optical particle sensors) were conducted near a busy urban roadway at two different heights, 80 cm and 150 cm, during peak and off-peak hours. The results of mobile measurements were found to be consistent with CALINE 4 modelled results.

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