An optimization method based on random fork tree coding for the electrical networks of offshore wind farms. (March 2020)
- Record Type:
- Journal Article
- Title:
- An optimization method based on random fork tree coding for the electrical networks of offshore wind farms. (March 2020)
- Main Title:
- An optimization method based on random fork tree coding for the electrical networks of offshore wind farms
- Authors:
- Wang, Long
Wu, Jianghai
Wang, Tongguang
Han, Ran - Abstract:
- 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. Highlights: A novel method proposed for optimization of wind farm electrical network. The random fork tree coding proposed for connection topology for the first time. A union-finding algorithm for loop identifications integrated into optimization. Electrical network optimization of aAbstract: 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. Highlights: A novel method proposed for optimization of wind farm electrical network. The random fork tree coding proposed for connection topology for the first time. A union-finding algorithm for loop identifications integrated into optimization. Electrical network optimization of a discrete wind farm successfully achieved. Optimal economical topology solutions obtained through the proposed method. … (more)
- Is Part Of:
- Renewable energy. Volume 147(2020)Part 1
- Journal:
- Renewable energy
- Issue:
- Volume 147(2020)Part 1
- Issue Display:
- Volume 147, Issue 1, Part 1 (2020)
- Year:
- 2020
- Volume:
- 147
- Issue:
- 1
- Part:
- 1
- Issue Sort Value:
- 2020-0147-0001-0001
- Page Start:
- 1340
- Page End:
- 1351
- Publication Date:
- 2020-03
- Subjects:
- Wind farm -- Electrical network -- Coding scheme -- Union-finding algorithm
Renewable energy sources -- Periodicals
Power resources -- Periodicals
Énergies renouvelables -- Périodiques
Ressources énergétiques -- Périodiques
333.794 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09601481 ↗
http://www.elsevier.com/journals ↗
http://www.journals.elsevier.com/renewable-energy/ ↗ - DOI:
- 10.1016/j.renene.2019.09.100 ↗
- Languages:
- English
- ISSNs:
- 0960-1481
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7364.187000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 12351.xml