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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

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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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