Optimization Model for Installation Logistic of Offshore Wind Farms

Abstract

This thesis presents the development and application of a Mixed Integer Linear Programming (MILP) model for optimizing the logistics of offshore wind turbine installations. The objective is to identify strategies that not only meet turbine installation targets but also minimize associated costs. This thesis evaluates various vessel strategies, focusing on the impacts of weather windows and CO2 emission costs.

When conducting experiments, the MILP model is applied to two different graph instances to simulate potential routing paths. The experiments demonstrate the model's capabilities and limitations, revealing significant optimality gaps as the problem size increases.

Further analysis explores the sensitivity of installation strategies to weather disruptions and the economic implications of CO2 emissions. The results show that substantial delays caused by adverse weather can lead to strategic adjustments, such as opting for vessels with higher operational efficiency despite greater initial costs. The thesis also addresses the economic impact of CO2 emission costs, suggesting that under the experimental conditions, current market prices for CO2 allowances are too low to motivate shifts towards lower emissions without significant adjustments.