Installation planning for an offshore wind farm: a hybrid modelling framework of integrating simulation and optimisation with a Markov Chain

Offshore wind energy has been rapidly gaining traction more recently and the development of offshore wind farms continues to rise on a global scale in search of a sustainable future energy source. In the meantime, the capital-intensive project of installing wind turbines offshore involves a multitude of logistics activities and maritime operations. The high-risk installation process becomes more complex, in particular, under the uncertain weather conditions at sea such as wind speed and wave height. We present in this paper a hybrid modelling framework that minimises the overall installation time required to build an offshore wind farm, and develop a mixed-integer linear program submodule embedded on the discrete event simulation model. In addition, to take stochastic weather states into consideration, a Markov-Chain Monte Carlo method is used to generate weather instances with uncertainty. The weather occurrence serves as an integrated look-ahead viewpoint in making turbine component installation decisions. Further, the experiments of sensitivity analysis are designed to draw insights regarding the effects that the number of turbines, the number of vessels, and the seasonality have on the total project timespan. The computational results demonstrate the efficacy of our proposed hybrid solution approach, tested on the case of Southwest Offshore Wind Farm in South Korea.