Assessment of the Wave Energy in the Black Sea Based on a 15-Year Hindcast with Data Assimilation

Abstract

Abstract:The principal target of the present work is to assess the wave energy potential in the Black Sea, identifying also some relevant energetic features and possible patterns. A wave prediction system based on the Simulating Waves Nearshore model (SWAN) has been implemented and intensively tested in the entire sea basin. Moreover, considering an optimal interpolation technique, an assimilation scheme of the satellite data has been developed, leading to a visible improvement of the wave model predictions in terms of significant wave heights and, consequently, also in terms of wave power. Using this wave prediction system with data assimilation, simulations have been performed for a 15-year period (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013). Considering the results of this 15-year wave hindcast, an analysis of the wave energy conditions in the basin of the Black Sea has been carried out. This provided a more comprehensive picture concerning the wave energy patterns in the coastal environment of the Black Sea focused on the average wave conditions that might be expected in this sea. Following the results presented, it can be concluded that the wave energy extraction in the Black Sea can become an issue of interest, especially from the perspective of the hybrid solutions.

Analysis of the Effect of a Marine Energy Farm to Protect a Biosphere Reserve

“…Previous analyses performed in the Black Sea [3,13] put in evidence that the western part of the sea is its most energetic part. Thus, every winter at least one or two storms occur characterised by significant wave heights greater than 7 meters.…”

“…In order to provide reliable information concerning the wave conditions in the Black Sea, a multi-level wave prediction system, SWAN based was implemented [3,4]. SWAN is a spectral phase averaged wave model, which integrates the spectral action balance equation in time, geographical and spectral spaces.…”

“…The advantage is that one single model covers the full scale of the modelling process, although the physical processes considered in each computational domain might be rather different from one computational level to another. Moreover, in order to improve the reliability of the wave predictions, some data assimilation technique have been also implemented, considering either satellite data [3], or in situ measurements [5].…”

Estimation of Downtime and of Missed Energy Associated with a Wave Energy Converter by the Equivalent Power Storm Model

“…The efforts of several research groups were oriented toward the determination of the most attractive sites and of the most reliable technologies for wave energy exploitation. The former analyses were conducted in several areas, such as the Mediterranean Sea [3][4][5][6][7][8][9], the Black Sea [10,11], the USA [12][13][14] and in various Asian regions [15][16][17]. The latter analyses involved the conception of a variety of devices and theoretical and experimental tests on both small-scale and full-scale devices [18][19][20].…”

Spatial and temporal variations of wave energy in the nearshore waters of the central west coast of India

“…Ocean waves have the potential to become a commercially viable renewable energy source (Clement et al, 2002). Globally, wave energy resource assessments have been made for the Baltic Sea, the Black Sea, the Hawaiian islands, the North Sea, the Persian Gulf and for the seas around Australia, Brazil, Canada, California, Canary Islands, China, India, Iran, Ireland, Malaysia, Portugal, Taiwan, the United Kingdom and the United States (Barstow et al, 2008;Defne et al, 2009;Stopa et al, 2011;Saket and Etemad-Shahidi, 2012;Kamranzad et al, 2013;Soares et al, 2014;Appendini et al, 2015;Contestabile et al, 2015;Rusu, 2015;Sanil Kumar and Anoop, 2015;Gallagher et al, 2016). Most of the studies on the assessment of wave power are carried out either through the wave data obtained from numerical model or reanalysis data; ERA-40 or ERA-Interim (Dee et al, 2011) of the European Centre for Medium-Range Weather Forecasts (ECMWF).…”