Abstract This paper presents a general spatio-temporal model of energy systems comprising technologies for generation/conversion, transport and storage and infrastructures for transport. The model determines the optimal network structure (e.g. location and size of technologies and their interconnections through transport infrastructures) and its operation (e.g. rate of utilisation of technologies and transport flows) considering simultaneously the short-term dynamics and a long-term planning horizon. Here, we address one of the main challenges of solving a large scale MILP model: tractability. This issue is mainly caused by the need to include a wide range of time scales in the model: yearly (or decadal) intervals to include investment decisions; seasonal intervals to account for e.g. seasonal variations in demand and availability of resources; and hourly (or shorter) intervals to model the dynamics of storage technologies and to account for intermittency of renewable resources and demand. To exacerbate the problem, the spatial aspects also need to be fine enough to locate and size the technologies properly and to model the transport of resources, which depend on the location of demand and availability of resources. The model uses an efficient representation of time that exploits periodicity in system properties via a non-uniform hierarchical time discretisation. A decomposition method is also proposed wherein the large problem is broken down into 3 sub-problems that are then solved iteratively until the objective function is no longer improved. These methods significantly improve the computational efficiency without sacrificing temporal and spatial detail. The applicability of the model is illustrated using a case study in which the least-cost design and operation of a hydrogen network is determined such that the hourly transport demand of the different regions of an island is met by the intermittent and remotely located wind energy.