The transition to more sustainable operations is being widely adapted in order to reduce the green house gas emissions and meet future sustainability requirements. This transition most often utilizes electrification as a means to reduce emissions and utilize renewable energy sources. This transition comes with extra burden on container terminal authorities who have to manage their power demands and transmission and distribution system operators who have to keep up with providing this growing electricity demand.

This comes with extra costs as distribution system operators have to build and maintain a larger network and larger power capacities can not always be ensured for consumers such as container terminal authorities due to grid congestion. To achieve electrification for container terminals these distribution system operator costs as well as electricity costs and a congesting grid should be taken into account. To combat this, this thesis will analyze the electrification for a container terminal with a case study considering these factors.

However, scheduling power demands for container terminals is not trivial as they operate in a very dynamic and uncertain environment. This stochasticity is caused by uncertainty due to for example uncertain energy generation or uncertainty in operations, such as arrival time of ships. To ensure a container terminal has sufficient electric capacity and can manage its power demand for the day-ahead around this uncertain arrival time, a two-stage stochastic power optimization is modeled.

This optimization takes into account the flexible resources which a container terminal could benefit from, such as a battery energy storage system and flexible cooling of refrigerated containers. The charging decisions for the electric yard fleet as well as charging and discharging of battery energy storage system and cooling of reefers are scheduled for the next day. Power such as shore power and crane power for berthed ships which are loading or unloading are considered uncertain due to the uncertainty in arrival
and its deviation from the estimated time of arrival will be taken into account.

In this two-stage optimization where the aforementioned uncertain loads are second stage decisions, while decision such as when to charge batteries or cool refrigerated containers are made beforehand and therefore belonging to the first stage decisions. This stochastic two-stage optimization with uncertain ship arrival time is then solved with the progressive hedging algorithm, which decomposes the possible ship arrival scenarios in to individual solvable problems. These solutions are then pushed towards a common decision value through a penalty term.

With this model it is found that with the current electric contracted capacity, full electrification of the port equipment will not be a viable option. The necessary capacity is then optimized considering the flexible resources and electricity pricing. Dynamic electricity pricing will utilize a higher capacity to benefit from the lower electricity prices by charging and cooling at these times, despite the cost for a higher capacity. Despite these higher distribution costs, the total costs for electricity for a dynamic electricity price contract is significantly lower, minimally 23.65 % lower for the same configuration. A Time Constraint Transport Right is also analyzed, which could work for container terminals with many flexible loads, but this does not provide more incentive compared to a regular contracted capacity.