Coastal Wind Measurements and Power Assessment Using a LIDAR on a Pier

Nassif, Felipe Barros; Pimenta, Felipe M.; D'Aquino, Carla de Abreu; Assireu, Arcilan Trevenzoli; Garbossa, Luis Hamilton Pospissil; Passos, Júlio César

doi:10.1590/0102-7786351007

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…Wind records refer to 10 min averages at 110 m above the mean sea level. Data were reduced to hourly averages; a description of the dataset is available in [33,34].…”

Section: Observational Datamentioning

confidence: 99%

Exploring the Complementarity of Offshore Wind Sites to Reduce the Seasonal Variability of Generation

et al. 2022

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Wind energy is a powerful resource contributing to the decarbonization of the electric grid. However, wind power penetration introduces uncertainty about the availability of wind energy. This article addresses the complementarity of remote offshore wind sites in Brazil, demonstrating that strategic distribution of wind farms can significantly reduce the seasonality and the risk of periods without generation and reduce dependence on fossil sources. Field observations, atmospheric reanalysis, and simplified optimization methods are combined to demonstrate generation improvement considering regions under environmental licensing and areas not yet considered for offshore development. Aggregated power results demonstrate that with the relocation of wind turbines, a 68% reduction of the grid seasonal variability is possible, with a penalty of only 9% of the generated energy. This is accomplished through optimization and the inclusion of the northern region, which presents negative correlations with all other stations. More specifically, the north and northeast of Brazil have large seasonal amplitudes. However, out-of-phase wind regimes with a strong negative correlation (R < −0.6) and high-capacity factors (CF) during the peak seasons occur in Jan-Feb-Mar in the north (CF > 0.5) and in Aug-Sep-Oct in the northeast (CF > 0.7). These complementary regimes allow for the introduction of the concept of Reserve Wind Power (RWP) plants, wind farms that can be viewed as “reserve sources” for energy security. These can replace the contracts of thermal reserve plants, with resulting economic and environmental advantages. Our analysis suggests that RWP plants can be 20 to 32% cheaper than thermal reserves in the current market.

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“…Wind records refer to 10 min averages at 110 m above the mean sea level. Data were reduced to hourly averages; a description of the dataset is available in [33,34].…”

Section: Observational Datamentioning

confidence: 99%

Exploring the Complementarity of Offshore Wind Sites to Reduce the Seasonal Variability of Generation

et al. 2022

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“…Two pioneer studies monitored winds on the coast of Piauí [29,30], one for Espírito Santo State [31]. Thus, A preliminary analysis of the first 6 months of LIDAR wind data was conducted by Correa (2018) [17], Nassif et al (2020) [18] and Pires (2019) [19]. Here we extend the period of analysis, and cover two years of data from January 2017 to December 2018.…”

Section: Figurementioning

confidence: 99%

“…where D p (W m −2 ) is the power density, ρ a is the local air density (kg m −3 ), and U is the horizontal wind speed at a height of 110 m. Air density ρ a was calculated with air temperature T ( • C), relative humidity RH (%), and atmospheric pressure P (hPa) records from BOOA, following [18]. The data gaps were covered by information from the Torres meteorological station, supplied by the National Institute of Meteorology (INMET) (Figure 1).…”

Section: Power Densitymentioning

confidence: 99%

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Coastal Wind Power in Southern Santa Catarina, Brazil

et al. 2020

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A light detection and ranging (LIDAR) wind profiler was used to estimate the wind speed in the southern coast of Santa Catarina State, Brazil. This profiler was installed on a coastal platform 250 m from the beach, and recorded wind speed and direction from January 2017 to December 2018. The power generation from three wind turbines was simulated, to obtain estimations of the average power, energy generation and capacity factor, as well as to assess the performance of a hypothetical wind farm. The scale and shape parameters of the Weibull distribution were evaluated and compared with those of other localities in the state. The prevailing winds tend to blow predominantly from the northeast and southwest directions. Wind magnitudes are higher for the NE and SW ocean sectors where the average wind power density can reach 610–820 W m−2. The Vestas 3.0 turbine spent the largest percentage of time in operation (>76%). The higher incidence of strong northeasterly winds in 2017 and more frequent passage of cold fronts in 2018 were attributed to the cycle of the South Atlantic subtropical high. The results demonstrate a significant coastal wind power potential, and suggest that there is a significant increase of resources offshore.

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“…First, a preliminary solution was obtained, and then the shoreline was extracted using contour evolution technology. In addition, some studies [13][14][15][16] have used airborne LiDAR data to estimate and analyze the location of the shoreline and then evaluated other marine elements, such as coastal protection and wind force. Although these studies did not use airborne LiDAR data to extract the shoreline, they used the shoreline obtained from an LiDAR data source to examine other marine elements.…”

Section: Introductionmentioning

confidence: 99%

A Method for Optimal Estimation of Shoreline in Cliff Zones Based on Point Cloud Segmentation and Centroid Calculation

Liu

Qin

2022

Applied Sciences

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The integrity of shoreline is disrupted by cliffs, posing obstacles to marine surveying and chart mapping, particularly in research related to the cliff section of the sea area. The present study proposes a method to solve this problem. In the proposed method, we first extract the boundary of the cliff to segment the point cloud according to a designed rule, then calculate the centroid coordinates of each point cloud block, followed by the coordinates of side points on both sides of the boundary of each block from the centroid, and finally create a side point cloud projected to the water surface corrected for elevation. The corrected point cloud is considered a point cloud dataset of the innermost shoreline. Combined with a point cloud projected from the boundary of the cliff section to the water surface, we developed a calculation method for the optimal shoreline position. Our experiment proved that the method could effectively extract the shoreline of the study area. Moreover, compared with that of the commonly used shoreline extraction method, the absolute error of the isoline tracking method was very large, up to several meters. However, the proposed method achieved smaller standard deviation and variance (0.1254 and 0.0157, respectively) than the isoline tracking method (0.9837 and 0.9677, respectively).

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Coastal Wind Measurements and Power Assessment Using a LIDAR on a Pier

Cited by 7 publications

References 26 publications

Exploring the Complementarity of Offshore Wind Sites to Reduce the Seasonal Variability of Generation

Exploring the Complementarity of Offshore Wind Sites to Reduce the Seasonal Variability of Generation

Coastal Wind Power in Southern Santa Catarina, Brazil

A Method for Optimal Estimation of Shoreline in Cliff Zones Based on Point Cloud Segmentation and Centroid Calculation

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