Hydrogen production using solar energy is an important way to obtain hydrogen energy. However, the inherent intermittent and random characteristics of solar energy reduce the efficiency of hydrogen production. Therefore, it is necessary to add an energy storage system to the photovoltaic power hydrogen production system. This paper establishes a model of a photovoltaic power generation hydrogen system and optimizes the capacity configuration. Firstly, the mathematical model is modeled and analyzed, and the system is modeled using Matlab/Simulink; secondly, the principle of optimal configuration of energy storage capacity is analyzed to determine the optimization strategy, we propose the storage capacity configuration algorithm based on the low-pass filtering principle, and optimal time constant selection; finally, a case study is conducted, whose photovoltaic installed capacity of 30 MW, verifying the effectiveness of the proposed algorithm, analyzing the relationship between energy storage capacity and smoothing effect. The results show that as the cut-off frequency decreases, the energy storage capacity increases and the smoothing effect is more obvious. The proposed algorithm can effectively reduce the 1 h maximum power variation of PV power generation. In which the maximum power variation of PV generation 1 h before smoothing is 4.31 MW. We set four different sets of time constants, the maximum power variation of PV generation 1 h after smoothing is reduced to 0.751, 0.389, 0.078, and 0.04 MW, respectively.

The use of renewable energy sources, such as wind, photovoltaics (PV), and hydropower, to supply facility agriculture may effectively mitigate food and environmental pollution problems and ensure continuity of the energy supply. The operating conditions of a hybrid system are complex, so the operating strategy is very important for system configuration and scheduling purposes. In the current study, first, a hybrid wind/PV/small-hydropower/facility-agricultural system was constructed. Then, the chaotic particle swarm method was applied to optimize hybrid system operation, and a scheduling strategy of the hybrid system was proposed. Finally, combined with an example, according to wind and PV power output and load curves, supply-to-load curves for wind, PV, and small hydropower were obtained. The operational strategy proposed in this study maximizes the utilization of wind and solar resources and rationally allocates hydropower resources. The aforementioned operational strategy provides a basis for hybrid system capacity allocation and scheduling.

The unbalanced forces generated by pumped storage units operating under non-ideal operating conditions can cause pressure pulsations. Due to the noise interference, the feature information reflecting the operating state of the unit in the pressure pulsation is difficult to extract. Therefore, this paper proposes a noise reduction method based on sparrow search algorithm (SSA) optimized variational mode decomposition (VMD) combined with singular value decomposition (SVD). Firstly, SSA is used to realize the adaptive optimization of VMD parameters for ideal decomposition of the signal. Then, the noise reduction of the decomposed signal is performed by using the sensitivity of the Permutation Entropy (PE) for small mutations. The noise reduction and reconstruction of the decomposed signal are carried out again by using SVD. The experimental and comparison results show that the mean square error of the signal after VMD-SVD feature extraction is reduced from 1.0068 to 0.0732 and the correlation coefficient is increased from 0.2428 to 0.9614. It is proved that the method achieves better results in the pressure pulsation signal of pumped storage units and has some application significance for the fault diagnosis of pumped storage units.