A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty

Li, Mingxin and Jiang, Xiaoli and Carroll, James and Negenborn, Rudy R. (2022) A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty. Applied Energy, 321. 119284. ISSN 0306-2619 (https://doi.org/10.1016/j.apenergy.2022.119284)

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While offshore wind energy is showing enormous potential, effective approaches to enhance its economics are being sought at the same time. The design of maintenance strategy is a type of strategic decision-making for offshore wind farms, aiming to improve energy production and reduce maintenance expenses. As a complicated and challenging task, the maintenance decision-making is confronted with various types of uncertainty in the model. The presence of uncertainty affects the estimation of maintenance performance, and renders the determined maintenance decisions sub-optimal or even inappropriate. In this paper, the authors propose an integrated decision-making framework incorporating i) a maintenance model which is applied to estimate maintenance performance, including maintenance costs and production losses, ii) a probabilistic uncertainty modelling approach which is used to characterize different types of uncertainty and a Monte Carlo method is adopted to generate stochastic scenarios, and iii) a multi-objective optimization method used to find the optimal decisions in the presence of conflict between multiple objectives. The uncertainties considered in the model include the stochastic attributes of time to failure, deviation between real and predicted failure times of components, and uncertain maintenance consequences. The proposed framework was applied in a generic 150MW-offshore wind farm located in the North sea. Results demonstrate that the deterministic scenario underestimates the maintenance costs and production losses, leading to the consequence that the developed maintenance strategy becomes unsatisfactory. A new series of solutions including priority solutions and tradeoffs is provided for decision-makers to satisfy different goals while involving uncertainty. In addition, the influence of different uncertainties on the maintenance performance is quantified to assess the significance. The proposed optimization framework constitutes a useful decision-making tool to instruct the long-term maintenance strategy for offshore wind farms in a practical environment involving a high degree of uncertainty.