The potential of both long-term (hydrogen storage) and short-term (batteries and thermal) storage systems in decentralized neighbourhoods are assessed using a multi-objective optimization approach that minimizes both costs and CO₂ emissions. A method is developed, which evaluates the performance of long and short-term storage systems in the future based on multi-objective optimization. More specifically, hydrogen storage is investigated for its future potential to be used as a long-term storage in a decentralized context and it is compared with short-term storage systems such as batteries and thermal storage. In order to analyze potential future developments, a scenario approach is deployed based on the Intergovernmental Panel of Climate Change's 'Special Report on Emissions Scenarios'. Three future scenarios are defined and simulated for the years of 2015, 2020, 2035, and 2050 for both a rural and an urban neighbourhood in Switzerland. Based on the scenarios, the energy demand and renewable potential projections until 2050 are simulated including retrofitted buildings and renewable potential in the neighbourhoods. The Pareto front of solutions is then benchmarked against national carbon and energy targets from 2020 until 2050. In addition, a range of parameter assumptions (e.g., for economic variables, policy changes, environmental conditions) are used in each scenario to incorporate uncertainty into the analysis. The long-term storage potential of hydrogen, in particular, is evaluated for its capability to shift renewable surpluses in summer towards demand later in the year. From the results, it is predicted that neighbourhoods with high renewable surpluses (i.e., in rural settings) should consider the advantages of a hydrogen storage system from 2035 to 2050. For neighbourhoods with low surpluses, short-term battery and thermal storage systems are predicted to be sufficient for load shifting. It is also observed that a high feed-in remuneration undermines on-site consumption, thus resulting in lower levels of storage deployment due the selling of production back to the centralized electricity grid. Lastly, it is concluded that both an increase in renewable technology deployment and in the retrofit rate of buildings will both be required to meet energy targets for the two case studies. As the renewable potential in urban contexts is limited, it is particularly important for older building stock to be retrofitted at a high rate (more than 2% of buildings per year) in order to reduce the end energy demand of the buildings. The approach used in this article is widely applicable both in spatial scope (e.g., other decentralized energy systems, geographies) and temporal scope (e.g., different years, scenarios) and allows for an optimization with a range of objective functions, thus making it an effective approach to identify the renewable and storage technologies that can contribute to most of the decarbonization of the building stock in the future.