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Assessment method of offshore wind resource based on a multi-dimensional indexes system

Abstract

Traditional assessment indexes could not fully describe offshore wind resource for the meteorological properties of offshore are more complex than onshore. And as a result, the uncertainty of offshore wind power projects would be increased and final economic benefits would be affected. Therefore, a study on offshore wind resource assessment is carried out, including three processes of "studying data sources, conducting multi-dimensional indexes system and proposing offshore wind resource assessment method based on Analytic Hierarchy Process (AHP)". Firstly, measured wind data and two kinds of reanalysis data are used to analyze the characteristics and reliability of data sources. Secondly, indexes such as effective wind speed occurrence, affluent level occurrence, coefficient of variation, neutral state occurrence have been proposed to depict availability, richness, and stability of offshore wind resource, respectively. And combined with the existing parameters (wind power density, dominant wind direction occurrence, water depth, distance to coast), a multi-dimensional indexes system has been built and based on the above indexes system, an offshore wind energy potential assessment method has been proposed. Furthermore, the proposed method is verified by the annual energy production of five offshore wind turbines and practical operating data of four offshore wind farms in China. This study also compares the ranking results of the AHP model to two multi-criteria decision making (MCDM) models including a Weighted Aggregated Sum Product Assessment (WASPAS) and Multi-Attribute Ideal Real Comparative Analysis (MAIRCA). The results show that the proposed method gains well in practical engineering applications, where the economic score values has been considered based on the offshore reasonable utilization hours of the whole life cycle in China.
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Category

Academic article

Client

  • Research Council of Norway (RCN) / 304229

Language

English

Author(s)

  • Xiaomei Ma
  • Yongqian Liu
  • Jie Yan
  • Shuang Han
  • Li Li
  • Hang Meng
  • Muhammet Deveci
  • Konstanze Kölle
  • Umit Cali

Affiliation

  • North China Electric Power University
  • Qinghai Normal University
  • Imperial College London
  • Turkish Naval Academy
  • SINTEF Energy Research / Energisystemer
  • Norwegian University of Science and Technology

Year

2022

Published in

CSEE Journal of Power and Energy Systems (JPES)

ISSN

2096-0042

Volume

10

Issue

1

Page(s)

76 - 87

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