Computationally efficient analytical O&M model for strategic decision-making in offshore renewable energy systems

To boost the deployment of all offshore renewable energy technologies, it is fundamental to adopt convenient long-term strategic operation and maintenance (O&M) decisions. Due to the lack of experience and reliable information, performing extensive sensitivity analysis is a key factor for supporting strategic O&M decision-making. By evaluating various scenarios, sensitivity analyses provide valuable insights to identify critical factors and enhance decision confidence. To that end, the development of computationally efficient O&M models, where accessibility, availability, energy, and economic aspects are adequately articulated is crucial. Simulation-based O&M models, i.e. based on Monte Carlo methods, have been widely used to incorporate those fours aspects. However, the computational burden of simulation-based O&M models is prohibitive, limiting the feasibility of conducting extensive sensitivity analyses. In view of this, this study presents a computationally efficient analytical O&M model based on Markov Chains. This analytical O&M model is compared with two case studies presented in the literature, where simulation-based O&M models are employed, studying a floating offshore wind and a wave energy farm. Results demonstrate that the analytical O&M model achieves the same level of fidelity as simulation-based models (within 10% deviation), while reducing the computational burden by at least five orders of magnitude.