Assessment of offshore wind energy potential using mesoscale model and geographic information system

Yamaguchi, Atsushi; Ishihara, Takeshi

doi:10.1016/j.renene.2014.02.024

Cited by 45 publications

(31 citation statements)

References 7 publications

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“…Furthermore, we are using pressure and virtual temperature to normalize the wind speed at the turbine heights considering the contribution of the air density. We only know one work that uses a similar method [10], but they use an older generation of an atmospheric model. As we clarify in the next section 5, the air density can show important seasonal variations and our seasonal CF maps ( Figure 7) contain this contribution that is often ignored.…”

Section: Discussionmentioning

confidence: 99%

“…However, even the use of satellite SAR data has not solved the problem of calculating the wind speed at hub height. It is usually solved through the assumption of a simplistic logarithmic wind shear (Equation 3) [10,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

“…It provides a wind resource map [10,11,15], and has already been applied to coastlines on the Iberian Peninsula [14]. The mesoscale model's wind speed data should be verified properly before estimating the wind energy potential, and in the case of the ocean this involves comparing the model's results to a buoy's anemometer observations, remote sensing observations, or similar analyses [16].…”

Section: Introductionmentioning

confidence: 99%

“…Wind potential depends not only on meteorology but also on legal criteria, such as the distance from the coastline, or economic and technical criteria, such as water depth [10]. These aspects have also been taken into account in our research to calculate different approximate scenarios of wind energy resource assessment.…”

Section: Introductionmentioning

confidence: 99%

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Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean

et al. 2017

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In this article, offshore wind energy potential is measured around the West Mediterranean using the WRF meteorological model without 3DVAR data assimilation (the N simulation) and with 3DVAR data assimilation (the D simulation). Both simulations have been checked against the observations of six buoys and a spatially distributed analysis of wind based on satellite data (second version of Cross-Calibrated Multi-Platform, CCMPv2), and compared with ERA-Interim (ERAI). Three statistical indicators have been used: Pearson's correlation, root mean square error and the ratio of standard deviations. The simulation with data assimilation provides the best fit, and it is as good as ERAI, in many cases at a 95% confidence level. Although ERAI is the best model, in the spatially distributed evaluation versus CCMPv2 the D simulation has more consistent indicators than ERAI near the buoys. Additionally, our simulation's spatial resolution is five times higher than ERAI. Finally, regarding the estimation of wind energy potential, we have represented the annual and seasonal capacity factor maps over the study area, and our results have identified two areas of high potential to the north of Menorca and at Cabo Begur, where the wind energy potential has been estimated for three turbines at different heights according to the simulation with data assimilation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean

et al. 2017

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“…Areas where wind speeds are above 5 m/s can be considered suitable (Baban and Parry 2001). The water depth has an economic implication on the choice wind farm site as it affects its installation cost (Higgins and Foley 2014;Yamaguchi and Ishihara 2014). On the other hand, installation in shallow water can affect the benthic ecology (Mee 2006).…”

Section: Selection Criteriamentioning

confidence: 99%

Gis-Based Wind Farm Site Selection Model Offshore Abu Dhabi Emirate, Uae

Saleous¹,

Issa²,

Mazrouei³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The United Arab Emirates (UAE) government has declared the increased use of alternative energy a strategic goal and has invested in identifying and developing various sources of such energy. This study aimed at assessing the viability of establishing wind farms offshore the Emirate of Abu Dhabi, UAE and to identify favourable sites for such farms using Geographic Information Systems (GIS) procedures and algorithms. Based on previous studies and on local requirements, a set of suitability criteria was developed including ocean currents, reserved areas, seabed topography, and wind speed. GIS layers were created and a weighted overlay GIS model based on the above mentioned criteria was built to identify suitable sites for hosting a new offshore wind energy farm. Results showed that most of Abu Dhabi offshore areas were unsuitable, largely due to the presence of restricted zones (marine protected areas, oil extraction platforms and oil pipelines in particular). However, some suitable sites could be identified, especially around Delma Island and North of Jabal Barakah in the Western Region. The environmental impact of potential wind farm locations and associated cables on the marine ecology was examined to ensure minimal disturbance to marine life. Further research is needed to specify wind mills characteristics that suit the study area especially with the presence of heavy traffic due to many oil production and shipping activities in the Arabian Gulf most of the year.

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GIS‐based multiregional potential evaluation and strategies selection framework for various renewable energy sources: a case study of eastern coastal regions of China

Sun

Wang

et al. 2017

Energy Science & Engineering

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Evaluation of available potential and suitable alternative options for various renewable energy sources (RES) requires the explicit consideration of interregional disparities and site-specific conditions (e.g., environmental, socioeconomic, and resources features) for a larger scale region or country. This paper presents a novel multiregional analytical framework considering interregional suitability difference and inter-RES competitiveness at a given location in order to support efficient and sustainable RES spatial planning strategies. A GIS-based multicriteria evaluation technique was used to identify the geospatial potential/appropriate sites for various RES through combination of satellitederived information, reanalysis data, ground measurements, and statistical data. The composite index (CI) considering location suitability and sustainability of renewable energy technology was then used to compare different RES options, and as a selection criteria to define suitable strategies for each specific region. A real case study concerning the China's eastern coastal 10 provinces of RES planning issue demonstrates the applicability of the proposed approach. The present results illustrate how to coordinate the competing relationships between inter-regions and inter-RES for a given location in the eastern coastal regions of China. This could provide useful insight into plan the long-term RES developing paths and facilitate the determination of optimal energy mix for the other same type area. 124

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Assessment of offshore wind energy potential using mesoscale model and geographic information system

Cited by 45 publications

References 7 publications

Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean

Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean

Gis-Based Wind Farm Site Selection Model Offshore Abu Dhabi Emirate, Uae

GIS‐based multiregional potential evaluation and strategies selection framework for various renewable energy sources: a case study of eastern coastal regions of China

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