Assessment of wave energy resource of the Black Sea based on 15-year numerical hindcast data Adem Akpınar a,⇑ , Murat _ Ihsan Kömürcü b a Gümüs ßhane University, Civil Engineering Department, 29000 Gümüs ßhane, Turkey b Karadeniz Technical University, Civil Engineering Department, 61080 Trabzon, Turkey highlights " This study deals with variability of wave energy resource in the Black Sea. " The wave parameters hindcasts were performed using Simulated WAves Nearshore – SWAN. " The areas with the highest wave energy resource were determined. " The south-west coasts of the Black Sea are suggested as the best site. article info Article history: Received 1 February 2012 Received in revised form 16 May 2012 Accepted 6 June 2012 Available online 21 July 2012 Keywords: SWAN wave prediction model Wave energy resource Wave power Black Sea abstract In this study, the existence and variability of wave energy resource potential of the Black Sea based on 15-year hindcast data is described in detail. The hindcasts of wave parameters were carried out by using the third generation wave prediction model (Simulated WAves Nearshore – SWAN), which is one of the most popular numerical wave models and has been widely used for estimating ocean waves. The model was forced with the ECMWF ERA Interim wind fields and applied with a spatial resolution of about 0.0167°  0.0167° and a model time step of 6 h to resolve efficiently offshore and nearshore wave condi- tions. The results were presented in the form of charts of the spatial distribution of significant wave height and wave power, on a monthly, seasonal and annual basis. Annual energy was calculated in the study region with the hindcast data set covering 15 years (1995–2009). The areas with the highest wave energy resource were determined and the south west coasts of the Black Sea are suggested as the best site for the installation of a wave farm. It was determined the western parts of the Black Sea (especially the south-west) are exposed to energetic waves more than the eastern parts. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction The sustainable development of economic activities in the mar- ine environment requires long term data about environmental conditions such as waves [1]. Availability of long-term wind and wave data in offshore and coastal areas is important to a number of marine applications and operations, such as the design and con- struction of offshore and coastal structures, the management and protection of coastal environment, tourist and land-planning development of coastal areas and islands, vulnerability and risk analysis of inhabited coastal areas, as well as the feasibility analy- sis for wind and wave energy utilization in specific sea areas [2]. However, field measurements of waves over a long period are extremely difficult and expensive. Therefore, there is a lack of such information in many regions, or in most cases, little measuring wave data are available for engineering purposes. For a region, long-term wind data can be generally reached more easily than long-term wave data. The desired sea-state information or long- term information of wave parameters has thus been obtained by using reliable wave models. Previously, several approaches have been proposed for wave prediction, which include empirical-based, soft-computing-based, and numerical-based approaches [3,4]. In this study, the SWAN third generation numerical model, one of the most popular numerical wave models and widely used for esti- mating ocean waves, was utilized for hindcasting of desired wave parameters. The ocean is a vast repository of energy that can be extracted from its motion, temperature, or chemistry. Ocean energy can be recovered from waves, tides, marine currents, thermal gradients, and differences in salinity. Among all these options, the most 0306-2619/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.apenergy.2012.06.005 ⇑ Corresponding author. Tel.: +90 456 233 7425x1127; fax: +90 456 233 7427. E-mail addresses: aakpinar@ktu.edu.tr, aakpinar@gumushane.edu.tr (A. Akpınar). Applied Energy 101 (2013) 502–512 Contents lists available at SciVerse ScienceDirect Applied Energy journal homepage: www.elsevier.com/locate/apenergy