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AIMS Energy, 2017, 5(2): 173-192. doi: 10.3934/energy.2017.2.173. Research article Topical Section

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Optimization of regular offshore wind-power plants using a non-discrete evolutionary algorithm

Angel G. Gonzalez-Rodriguez, Manuel Burgos Payan, Jesús Riquelme Santos, Javier Serrano Gonzalez

1 Department of Electronic Engineering and Automation, University of Jaen, Jaen, Spain
2 Department of Electrical Engineering, University of Seville, Camino de los Descubrimientos s/n, Seville, Spain

Received: , Accepted: , Published:

Topical Section: Wind Energy

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Offshore wind farms (OWFs) often present a regular configuration mainly due to aesthetical considerations. This paper presents a new evolutionary algorithm that optimizes the location, configuration and orientation of a rhomboid-shape OWF. Existing optimization algorithms were based on dividing the available space into a mess of cells and forcing the turbines to be located in the centre of a cell. However, the presented algorithm searches for the optimum within a continuous range of the eight parameters that define the OWF, which allows including a gradient-based local search operator to improve the optimization process. The study starts from a review of the economic data available in the bibliography relative to the most significant issues influencing the profitability of the investment in terms of the Internal Rate of Return (IRR). In order to address the distinctive characteristics of OWFs, specific issues arise which have been solved. The most important ones are: interpretation of nautical charts, utilization of the seabed map with different load-bearing capacities, and location of the shoreline transition.

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Keywords: wind energy; offshore; non-discrete evolutionary algorithm; continuous evolutionary algorithm; IRR; regular patterns; optimal configuration; gradient-based local search

Citation: Angel G. Gonzalez-Rodriguez, Manuel Burgos Payan, Jesús Riquelme Santos, Javier Serrano Gonzalez. Optimization of regular offshore wind-power plants using a non-discrete evolutionary algorithm. AIMS Energy, 2017, 5(2): 173-192. doi: 10.3934/energy.2017.2.173

References:

1. Tesauro A, Réthoré P E, and Larsen G C. (2012) State of the art in wind farm optimization. Proc EWEA, 1–11.
2. Mosetti G, Poloni C, and Diviacco B. (1994) Optimization of wind turbine positioning in large wind farms by means of a genetic algorithm. J Wind Eng Ind Aerod 51: 105–116.
3. González J, Gonzalez A G, Castro J, et al. (2010) Optimization of wind farm turbines layout using an evolutive algorithm. Renew Energ 35: 1671–1681.
4. Nielsen P. (2003) Offshore wind energy projects feasibility study guidelines seawind. Tech Rep, Altener Project.
5. Hajela P. (1990) Genetic search-An approach to the nonconvex optimization problem. Aiaa J 28: 1205–1210.
6. Serrano J, Burgos M, and Riquelme J M. (2011) An improved evolutive algorithm for large offshore wind farm optimum turbines layout. Power Tech 2011 IEEE Trondheim, 1–6.
7. Réthoré P E, Fuglsang P, Larsen G C, et al. (2014) Topfarm: multi-fidelity optimization of wind farms. Isope 17: 1797–1816.
8. Gonzalez A G. (2017) Review of offshore wind farm cost components. Energ Sust Dev, 10–19.
9. Douglas W. (2010) Offshore wind asessment for Norway. Tech Rep, Norway.
10. Green J, Bowen A, Fingersh L J, et al. (2007) Electrical collection and transmission systems for offshore wind power. National Renewable Energy Laboratory (U.S.), 1–7. Available from http://www.nrel.gov/docs/fy07osti/41135.pdf.
11. Offshore development information statement (ODIS). (2009) Appendices. Tech Rep, National Grid.
12. Hou P, Hu W, Soltani M, et al. (2015) Optimized placement of wind turbines in large-scale offshore wind farm using particle swarm optimization algorithm. IEEE T Sust Energ 6: 1272–1282.
13. Gonzalez A G, Burgos M, Riquelme J, et al. (2015) Reducing computational effort in the calculation of annual energy produced in wind farms. Renew Sust Energ Rev 43: 656–665.
14. Pérez B, Mínguez R, and Guanche R. (2013) Offshore wind farm layout optimization using mathematical programming techniques. Renew Energ 53: 389–399.
15. Offshore Design Engineering (ODE) Limited. (2007) Study of the costs of offshore wind generation. Tech Rep URN number 07/779, Renewables Advisory Board (RAB) & DTI. Available from http://webarchive.nationalarchives.gov.uk/+/http:/www.berr.gov.uk/files/file38125.pdf.
16. Morgan C A, Snodin H M, and Scott N C. (2003) Offshore wind economies of scale, engineering resource and load factors. Tech Rep, Garrad Hassan.
17. Slengesol I, De Miranda W P, Birch N et al. (2010) Offshore wind experiences: a bottom-up review of 16 projects. Tec Rep, OceanWind.
18. Wind Energy-The facts. (2009) Economics: the cost of energy generated by offshore wind power. EWEA. Available from https://www.wind-energy-the-facts.org/images/chapter3.pdf.

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Copyright Info: © 2017, Angel G. Gonzalez-Rodriguez, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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