This thesis introduces a model designed to provide port operators with insights into necessary infrastructure and adequate scheduling approaches when ships use electricity as their main power source. The operator manages a shipping port equipped with charging stations and provides a selection of electricity-powered freight cargo ships with container batteries. The model is designed for an arbitrary single-port waterway system. The focus is directed toward inland waterway systems, a choice influenced by the limited capacity of container batteries. Optional external revenue streams in the form of grid balancing and two core uncertainties of the maritime sector are incorporated into the model, namely energy consumption and arrival time uncertainty.
Optimization approaches are formulated on the model to find the optimal number of batteries, charging stations, and grid balancing stints. The following six approaches are employed: approximation algorithm, MIP formulation, rolling horizon, probabilistic constraint, extreme value analysis, and value at risk. The strategies all produce a schedule that guides the operator in managing the infrastructure optimally in a different context. The approaches are tested on a simulated waterway system. The approximation algorithm is a great first step. The MIP formulation provides a valuable next step in insight into the complexities of the system. However, it scales too poorly to extend to bigger data cases or to integrate uncertainties.
In scenarios involving uncertainty, the rolling horizon approach is recommended due to its adaptability and realistic modeling. Valuable insights about the limits of the system can be obtained by implementing values at risk and extreme value analyses. The probabilistic constraint approach is a suitable alternative if normally distributed uncertainty is inherent to the uncertainty data.
The model with these optimization methods provides port operators with essential insights into the system they are managing.