Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77464
Title: Study on offshore wind farm layout optimization based on decommissioning strategy
Authors: Sun, H 
Yang, H 
Gao, X
Keywords: Cost of energy
Decommissioning strategy
Multi-population genetic algorithm
Offshore wind farm layout optimization
Issue Date: 2017
Publisher: Elsevier
Source: Energy procedia, 2017, v. 143, p. 566-571 How to cite?
Journal: Energy procedia 
Abstract: In recent years, along with the first generation of wind power reaching retirement age, the high decommissioning cost arises with more and more attention all around the word. To reduce this huge cost, an innovative offshore wind farm layout optimization method based on decommissioning strategy is presented in this paper. In the optimization method, the decommissioning strategy means that the foundations can be reused after the retirement of the first generation of wind turbines, and then smaller second-generation wind turbines will be installed on the original foundations. The optimization process is based on the Multi-Population Genetic Algorithm (MPGA). A conceptual two-dimensional (2D) wake model is adopted to calculate wind losses caused by wake effect. The Cost of Energy (COE) is regarded as the criteria to judge the effectiveness of this new method. The way to estimate costs will also be introduced in this study. Finally, a case study in Waglan sea area in Hong Kong is analyzed and discussed. From the case results, Hong Kong is an ideal region to develop the offshore wind industry, and the proposed optimization method can reduce the COE down to 1.02 HK$/kWh.
Description: 1st Joint Conference on World Engineers Summit - Applied Energy Symposium and Forum: Low Carbon Cities and Urban Energy, WES-CUE 2017, Singapore, 19-21 2017
URI: http://hdl.handle.net/10397/77464
EISSN: 1876-6102
DOI: 10.1016/j.egypro.2017.12.728
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