As the low-carbon economy continues to evolve, the energy structure adjustment of using renewable energies to replace fossil fuel energies has become an inevitable trend. To increase the ratio of renewable energies in the electric power system and improve the economic efficiency of power generation systems based on renewables with hydrogen production, in this paper, an operation optimization model of a wind–solar hybrid hydrogen energy storage system is established based on electrochemical energy storage and hydrogen energy storage technology. The adaptive simulated annealing particle swarm algorithm is used to obtain the solution, and the results are compared with the standard particle swarm algorithm. The results show that the day-ahead operation scheme solved by the improved algorithm can save about 28% of the system operating cost throughout the day. The analytical results of the calculation example revealed that the established model had fully considered the actual operational features of devices in the system and could reduce the waste of wind and solar energy by adjusting the electricity purchased from the power grid and the charge and discharge powers of the storage batteries under the mechanism of time-of-use electricity price. The optimization of the day-ahead scheduling of the system achieved the minimization of daily system operation costs while ensuring that the hydrogen-producing power could meet the hydrogen demand.

A three‐dimensional (3D) model simulating the flow, heat, and mass transfer characteristics of a high‐temperature solid oxide electrolytic cell (SOEC) is developed based on the coupling of gas flow in channels, the electrochemical reaction in the triple‐phase boundary of electrodes and component diffusion in the porous media in electrodes. The model comprises several equations such as mass equation, momentum equation, energy equation, mass transport equation, and electrochemical reaction equation. The computational fluid dynamics method is adopted to simulate the operating performance (current density distribution, temperature distribution, and gas component distribution) of the 3D electrolytic cell model under different operating conditions (electrolytic voltage, gas temperature at the inlet, flow rate and flow pattern). The simulation results show that gas temperature at the inlet obviously influences electrolytic hydrogen production by SOEC. In the case of lower voltage, a higher temperature can be selected to effectively increase the current density. When the current density is high enough, the supply flow of water vapor needs to be increased to prevent the inhibition reaction of low‐concentration reactants, but this may lead to the reduction of water vapor conversion. In practical operation, a lower voltage and higher gas temperature should be selected, and the flow rate of water vapor appropriately increased to ensure hydrogen production and improve operating efficiency.

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This paper proposed an optimized day-ahead generation model involving hydrogen-load demand-side response, with an aim to make the operation of an integrated wind−photovoltaic−energy storage hydrogen production system more cost-efficient. Considering the time-of-use electricity pricing plan, demand for hydrogen load, and the intermittency of renewable energy, the model has the ambition to achieve minimum daily cost of operating a hydrogen production system. The model is power-balanced, fit for energy storage devices, and developed through adaptive simulated annealing particle swarm optimization. Analysis results showed that the proposed optimized scheduling model helped avoid the significant purchase of electric power at peak times and reduced the cost of running the hydrogen production system, ensuring that the daily hydrogen energy produced could meet the daily demand for the gas load. This justified how the model and its algorithm were correctly and efficiently applied.