Hydrogen has been identified as a very promising vector for energy storage, especially for heavy mobility applications. For this reason, France is making significant investments in this field, and use cases need to be evaluated as they are sprouting. In this paper, the relevance of H2 in two storage applications is studied: a domestic renewable electricity production system connected to the grid and a collective hydrogen production for the daily bus refill. The investigation consists of the sizing of the system and then the evaluation of its performance according to several criteria depending on case. Optimizations are made using Bayesian and gradient-based methods. Several variations around a central case are explored for both cases to give insights on the impact of the different parameters (location, pricing, objective, etc.) on the performance of the system.Our results show that domestic power-to-power applications (case 1) do not seem to be competitive with electrochemical storage. Meanwhile, without any subsidies or incentives, such configuration does not allow prosumers to save money (+16% spendings compared to non-equipped dwelling). It remains interesting when self-sufficiency is the main objective (up to 68% of energy is not exchanged). The power-to-gas application (case 2, central case), with a direct use of hydrogen for mobility, seems to be more relevant according to our case study, we could reach a production cost of green H2 around 5 €/kg, similar to the 3–10 $/kg found in literature, for 182 houses involved. In both cases, H2 follows a yearly cycle, charging in summer and discharging in winter (long term storage) due to low conversion efficiency.

With the increasing constraints on energy and resource markets and the non-decreasing trend in energy demand, the need for relevant clean energy generation and storage solutions is growing and is gradually reaching the individual home. However, small-scale energy storage is still an expensive investment in 2022 and the risk/reward ratio is not yet attractive enough for individual homeowners. One solution is for homeowners not to store excess clean energy individually but to produce hydrogen for mutual use. In this paper, a collective production of hydrogen for a daily filling of a bus is considered. Following our previous work on the subject, the investigation consists of finding an optimal buy/sell rule to the grid, and the use of the energy with an additional objective: mobility. The dominant technique in the energy community is reinforcement learning, which however is difficult to use when the learning data is limited, as in our study. We chose a less data-intensive and yet technically well-documented approach. Our results show that rulebooks, different but more interesting than the usual robust rule, exist and can be cost-effective. In some cases, they even show that it is worth punctually missing the H2 production requirement in exchange for higher economic performance. However, they require fine-tuning as to not deteriorate the system performance.