An optimization method based on random fork tree coding for the electrical networks of offshore wind farms

Long Wang, Jianghai Wu, Tongguang Wang and Ran Han

Renewable Energy, 2020, vol. 147, issue P1, 1340-1351

Abstract: The electrical network optimization is an important aspect of reducing the development cost of offshore wind farm because it accounts for nearly 25% of total investment. This paper presents an integration of the random fork tree coding scheme, a union-finding algorithm and electrical parameter calculating models for electrical network optimization. The coding scheme is developed for the first time for integrated optimization of the tree connection topology, substation positions, and cable cross sections to solve the defects of conventional step-by-step approaches such as minimum spanning tree, to achieve the most economical solution. The case studies clearly show that the proposed method, when coupled with the Omni-optimizer, achieves optimal economical matching solutions and can be applied for optimization of the tree-structure electrical network with any number of wind turbines and substations. However, the proposed method is not applicable for other topological structures, where the coding schemes need to be re-modelled. Also, gradient algorithms may be used in the optimization for convergence enhancement for hundreds of decision variables of the large-scale offshore wind farm.

Keywords: Wind farm; Electrical network; Coding scheme; Union-finding algorithm (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148119314387
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:147:y:2020:i:p1:p:1340-1351

DOI: 10.1016/j.renene.2019.09.100

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().