Intra-annual wave resource characterization for energy exploitation: A new decision-aid tool

被引：47

作者：

Carballo, R. ^{[1
]}

Sanchez, M. ^{[1
]}

Ramos, V. ^{[1
]}

Fraguela, Ja ^{[2
]}

Iglesias, G. ^{[3
]}

机构：

[1] Univ Santiago de Compostela, Hydraul Eng, Lugo 27002, Spain

[2] Univ A Coruna, Naval Architecture & Ocean Eng, EPS, Ferrol 15403, Spain

[3] Univ Plymouth, Sch Marine Sci & Engn, Plymouth PL4 8AA, Devon, England

来源：

ENERGY CONVERSION AND MANAGEMENT | 2015年 / 93卷

关键词：

Intra-annual variability; Power performance; High-resolution; iWEDGE; WIND ENERGY; CONVERSION; ISLAND; SPAIN; PERFORMANCE; CONVERTERS; HINDCAST; COAST; SEAS; FARM;

D O I：

10.1016/j.enconman.2014.12.068

中图分类号：

O414.1 [热力学];

学科分类号：

070201 [理论物理];

摘要：

The wave energy resource is usually characterized by a significant variability throughout the year. In estimating the power performance of a Wave Energy Converter (WEC) it is fundamental to take into account this variability; indeed, an estimate based on mean annual values may well result in a wrong decision making. In this work, a novel decision-aid tool, iWEDGE (intra-annual Wave Energy Diagram GEnerator) is developed and implemented to a coastal region of interest, the Death Coast (Spain), one Of the regions in Europe with the largest wave resource. Following a comprehensive procedure, and based on deep water wave data and high-resolution numerical modelling, this tool provides the monthly high-resolution characterization matrices (or energy diagrams) for any location of interest. In other words, the information requited for the accurate computation of the intra-annual performance of any WEC at any location within the region covered is made available. Finally, an application of iWEDGE to the site of a proposed wave farm is presented. The results obtained highlight the importance of the decision-aid tool herein provided for wave energy exploitation. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：1 / 8

页数：8

共 32 条

[1]

Assessment of wave energy resource of the Black Sea based on 15-year numerical hindcast data [J].