Optimal design of neighbouring offshore wind farms: A co-evolutionary approach

Javier Serrano González, Manuel Burgos Payán and Jesús Manuel Riquelme Santos

Applied Energy, 2018, vol. 209, issue C, 140-152

Abstract: This paper presents a new approach for the optimization of neighbouring offshore wind farms. Offshore wind energy is one of the most promising and developed low-carbon generation technologies. However, the high capital costs, which are strongly dependent on seabed depth, currently limit the geographical expansion of this technology to areas with relatively shallow waters and appropriate wind resource. This, along with the advantages of sharing a submarine transmission system among several projects, leads to a high concentration of offshore wind farms in certain zones, as happens, for example, in the North Sea. The presence of other neighbouring offshore wind farms has to be taken into account when a developer plans a new project, since the wake effect of wind turbines belonging to other neighbouring wind farms will affect the annual energy production and, consequently, the profitability of the project under study. However, not only already operating or installed neighbouring projects have to be borne in mind, but also the possible design of future neighbouring wind farms yet to be developed. In order to tackle this issue, an innovative co-evolutionary algorithm is proposed in this paper with the objective of determining a Nash equilibrium solution that would provide the best possible configuration of the wind farm under study by taking into account and limiting the disturbance introduced by other neighbouring projects. The performance of the proposed methodology has been successfully tested through the analysis of a realistic case and compared with other collaborative approaches and the classic single-project optimization methods already existing in the literature.

Keywords: Co-evolutionary algorithm; Games theory; Genetic algorithm; Micro-siting; Neighbouring offshore wind farm (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:209:y:2018:i:c:p:140-152

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DOI: 10.1016/j.apenergy.2017.10.120

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