Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/77464
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Building Services Engineering | - |
dc.creator | Sun, H | - |
dc.creator | Yang, H | - |
dc.creator | Gao, X | - |
dc.date.accessioned | 2018-08-28T01:32:31Z | - |
dc.date.available | 2018-08-28T01:32:31Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/77464 | - |
dc.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 | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2017 The Authors. | en_US |
dc.rights | The following publication Sun, H., Yang, H., & Gao, X. (2017). Study on offshore wind farm layout optimization based on decommissioning strategy. Energy Procedia, 143, 566-571 is available athttps://dx.doi.org/10.1016/j.egypro.2017.12.728 | en_US |
dc.subject | Cost of energy | en_US |
dc.subject | Decommissioning strategy | en_US |
dc.subject | Multi-population genetic algorithm | en_US |
dc.subject | Offshore wind farm layout optimization | en_US |
dc.title | Study on offshore wind farm layout optimization based on decommissioning strategy | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 566 | - |
dc.identifier.epage | 571 | - |
dc.identifier.volume | 143 | - |
dc.identifier.doi | 10.1016/j.egypro.2017.12.728 | - |
dcterms.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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Energy procedia, 2017, v. 143, no. , p. 566-571 | - |
dcterms.isPartOf | Energy procedia | - |
dcterms.issued | 2017 | - |
dc.identifier.scopus | 2-s2.0-85040832369 | - |
dc.relation.conference | World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference [WES-CUE] | - |
dc.identifier.eissn | 1876-6102 | - |
dc.description.validate | 201808 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Sun_Offshore_Wind_Farm.pdf | 569.07 kB | Adobe PDF | View/Open |
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