Informed mutation of wind farm layouts to maximise energy harvest

Michael Mayo and Maisa Daoud

Renewable Energy, 2016, vol. 89, issue C, 437-448

Abstract: Correct placement of turbines in a wind farm is a critical issue in wind farm design optimisation. While traditional “trial and error”-based approaches suffice for small layouts, automated approaches are required for larger wind farms with turbines numbering in the hundreds. In this paper we propose an evolutionary strategy with a novel mutation operator for identifying wind farm layouts that minimise expected velocity deficit due to wake effects. The mutation operator is based on constructing a predictive model of velocity deficits across a layout so that mutations are inherently biased towards better layouts. This makes the operator informed rather than randomised. We perform a comprehensive evaluation of our approach on five challenging simulated scenarios using a simulation approach acceptable to industry [1]. We then compare our algorithm against two baseline approaches including the Turbine Displacement Algorithm [2]. Our results indicate that our informed mutation approach works effectively, with our approach identifying layouts with the lowest aggregate velocity deficits on all five test scenarios.

Keywords: Wind farm; Layout optimisation; Velocity deficit; Wake effect; Evolutionary strategy; Informed mutation operator; Turbine displacement algorithm (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:89:y:2016:i:c:p:437-448

DOI: 10.1016/j.renene.2015.12.006

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