The intermittent nature of renewable energy sources has driven development of energy storage solutions and emerging energy carriers like hydrogen. This degree project investigated optimization of a multi-energy system integrating a solar power plant, battery storage, hydrogen production and storage. Focus was placed on repurposing non-arable land as the site of the multi-energy system. The case study site of Lilla Nyby in Eskilstuna, Sweden was used to model support of a transportation fleet of battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs) thus introducing additional demand. A co-optimization strategy combined Genetic Algorithm (GA) and Mixed-Integer Linear Programming (MILP) to determine optimal component sizes and operational strategy. Seasonal variation in demand and supply affected the operation of the energy storages as solar production is critical for system performance. The results revealed a trade-off between self-sufficiency and life-cycle costs of the system where the increase in self-sufficiency can be achieved at larger capacities. Further investments to improve self-sufficiency require disproportional high investments at high self-sufficiencies. Concluding that full autonomy from the grid is economically impractical in most cases unless self-sufficiency and autonomy is a desired system requirement for energy security.