Among the decarbonization pathways for greener ports, electrification is emerging as a future industry standard. Electrification contributes not only to the reduction of greenhouse gas emissions but also improves energy efficiency, lowers energy costs, and facilitates automation. This transformation is particularly critical for container terminals, which are energy-intensive and have traditionally relied on diesel power.

A key challenge in this transition is ensuring a secure and reliable electricity supply capable of accommodating increased demand while supporting decarbonization objectives. This introduces significant complexities in the design of local, low-carbon energy systems due to the variability and uncertainty of the new electricity demand. While conventional approaches to renewable energy and storage sizing typically rely on aggregated or historical load profiles, these methods may prove inadequate for container terminals. This insufficiency stems from two critical factors: first, the lack of representative historical data prior to the completion of the electrification process, which makes robust forecasting methods essential; and second, the unique operational dynamics of container terminals, which produce energy demand patterns that are significantly less predictable than those found in other industries.

This work proposes a simulation-based methodology that builds on a discrete-event simulation (DES) model of terminal operations to generate high-resolution, event-driven electricity demand profiles. The DES is designed to capture the stochastic nature of logistics processes (including vessel arrivals, crane operations, and yard activities), enabling the characterization of demand variability and peak formation mechanisms in electrified terminals under different electrification levels. Multiple demand scenarios are generated to reflect different operational conditions and levels of terminal utilization. These demand scenarios are used as inputs to a scenario-based evaluation framework developed to assess the performance of different energy system configurations. The system integrates photovoltaic generation, wind power, and battery energy storage, and the simulation-based optimization problem is formulated to determine the optimal installed capacities. The objective is to minimize system cost while ensuring robust performance across demand scenarios and aiming for a high degree of self-sufficiency.

The results indicate that the temporal structure of demand, as captured by the DES, has a dominant impact on system design. In particular, simultaneous operational events, specially those related with electric cranes, lead to pronounced demand peaks that significantly increase storage requirements compared to estimates based on smoothed profiles. The analysis further shows that solutions optimized for a single demand realization may perform poorly under realistic variability, highlighting the importance of robust design.