This paper presents HRES4Ports, an open-source computational framework for supporting investment decisions on renewable

energy systems in container terminals. The framework combines OpSEASim, a Python-based discrete-event simulation model of

a container terminal, with an HRES sizing and optimisation tool, which constitutes the core contribution of this work. OpSEASim

enables the characterisation of highly dynamic electricity demand at a 1-minute temporal resolution while accounting for the

stochastic nature of terminal operations. A case study is presented in which multiple high-activity scenarios are generated by

varying stochastic factors, with their mean demand profile subsequently used as input for HRES sizing. The sizing problem is

formulated to characterise the trade-off between HRES investment cost and grid dependency. Using the ϵ-constraint method, the

multi-objective problem is reformulated as a sequence of single-objective optimisation problems and solved using the pymoo

genetic algorithm framework, yielding the corresponding Pareto front. The results demonstrate the applicability of the frame-

work and provide insights into the implications of different sizing decisions. In particular, even the solution with the lowest

grid dependency exhibits periods of insufficient renewable generation, while increasing renewable capacity may substantially

increase curtailment. This highlights the potential for utilising surplus energy through on-site green-fuel production or electricity

export. Future work will investigate additional use cases, enhanced mathematical formulations, multi-objective approaches, and

alternative optimisation algorithms.