Self-reconfiguration simulations of turbines to reduce uneven farm degradation

Sam Brooks, Minhal Mahmood, Rajkumar Roy, Marinos Manolesos and Konstantinos Salonitis

Renewable Energy, 2023, vol. 206, issue C, 1301-1314

Abstract: This study explored the concept of a floating offshore wind farm (FOWF) that self-reconfigures wind turbine (WT) positions. The self-reconfiguration (SR) mechanism moves degraded turbines to different farm positions to delay failures occurring and reduce power loss. A 40-turbine agent-based simulation was created utilising wind and turbine performance data. For the first time, the SR mechanism was designed and optimised with principles of self-engineering complexity. The paper demonstrates the effectiveness of the SR mechanism through an agent-based simulation approach. The optimised SR was used in FOWF simulations of 50 years of operation; SR balanced fatigue across the FOWF and led to an increased income of £5–40M at years 27–33 of operation; however, outside of these years, there is no net positive income, and by year 50 SR has cost the FOWF £4–7M more in movement costs. Lastly, simulations with repair and maintenance restricted to 10 months or less were conducted. SR delayed turbine failures, so they occurred in months when repair could be conducted. The SR reduced power loss and increased net income by up to £20M, indicating that SR could be useful when repair and maintenance times are limited. In the absence of significant operational data, a qualitative validation with experts confirmed the approach and the validity of the simulation model for a range of FOWF scenarios.

Keywords: Self-engineering; Self-reconfiguring; Wind turbines; Floating offshore wind farm; Offshore wind energy (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:206:y:2023:i:c:p:1301-1314

DOI: 10.1016/j.renene.2023.02.064

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