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The inherently variable nature of renewable energy sources makes them storage-dependent when providing a reliable and continuous energy supply. One feasible energy-storage option that could meet this challenge is storing surplus renewable energy in the form of hydrogen. In this context, storage of hydrogen electrochemically in porous carbon-based electrodes is investigated. Measurements of hydrogen storage capacity, proton conductivity, and capacitance due to electrical double layer of several porous activated carbon electrodes are reported. The hydrogen storage capacity of the tested electrodes is found in the range of 0.61−1.05 wt.%, which compares favorably with commercially available metal hydride-based hydrogen storage, lithium polymer batteries, and lithium ion batteries in terms of gravimetric energy density. The highest obtained proton conductivity was 0.0965 S/cm, which is near to that of the commercial polymer-based proton conductor, nafion 117, under fully hydrated conditions. The obtained capacitance due to double-layers of the tested electrodes was in the range of 28.3–189.4 F/g. The relationship between specific surface area, micropore volume and hydrogen storage capacity of the carbon electrodes is discussed. The contribution of capacitance to the equivalent hydrogen storage capacity of carbon electrodes is reported. The implications of the obtained experimental results are discussed.

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Purpose The purpose of the proposed hybrid method aims to increase population efficiency, and a local search is used to further improve the value of the global best solution. An experimental observation suggests that the model’s statistical outcomes are more aligned with the real-time experimental findings. Design/methodology/approach A novel metaheuristic efficient hybrid algorithm, i.e. hybrid particle swarm optimization rat search algorithm, is introduced and applied for parameter extraction of hybrid energy system. This proposed hybrid method rules out the chances of local minima, hence enhancing the precision of the parametric estimation. The parameter extraction and error is calculated for the solar photovoltaic (PV)–fuel cell system using the proposed algorithm. Findings Nonparametric statistical tests are also conducted to indicate the findings of the outcome parameters using various metaheuristic algorithms. The proposed algorithm is better than the rest of the compared algorithms in the study. Originality/value The authors proposed a novel algorithm, and this proposed algorithm is implemented on hybrid solar PV and fuel cell-based system for parameter extraction. The nonparametric test results clearly suggest that the proposed algorithm is far more effective for parameter estimation of the test system.