Hydrogen is being explored in several industrial applications to produce various chemicals products, including ammonia and methanol. However, conventional hydrogen production is associated with harmful emissions and relies heavily on depleting fossil fuels. Renewable energy sources have been offered as alternative energy supplies to minimize fossil fuels and ensure lower emission levels, therefore, providing a sustainable energy transition. Renewable energy sources, such as solar photovoltaics (PV) and wind turbines (WT), are gaining wide attention for the production of green hydrogen. This article focuses on the storage of renewable energy generation to enhance the production process of green hydrogen. One of the main obstacles to the rapid development of renewable energy is the need for inexpensive, highly efficient energy storage. Integrating renewable energy sources into green hydrogen production stands out as a promising solution to this problem. This work aims to evaluate the potential of hydrogen production by electrolysis from solar PV and wind energies. The implemented methodology is a theoretical model of a caustic electrolyzer connected to a solar PV panel and a wind generator. The study assessed the input/output power of solar and wind energy and calculated the green hydrogen production rate to underline the efficiency of the MPPT controller and renewable-based hydrogen production systems. According to the simulation results obtained using the MATLAB/Simulink environment, the hydrogen production system performs effectively, especially in terms of energy efficiency. Taking into account the final results, the Wind energy demonstrates superior performance in green hydrogen production, achieving 90% efficiency and a rate of 1.2 Nm³/h, significantly outperforming solar photovoltaics’ 82% efficiency and 0.66 Nm³/h.

With power demands continuously growing, the penetration of renewable energy resources, particularly solar photovoltaic (PV) systems, across the residential sector has been extensive. A voltage source inverter (VSI) is the key element for efficiently processing energy conversion and connecting PV systems to home loads or utility grids. The operation of this inverter relies heavily on power-switching devices, which suffer from larger power losses due to the conventional semiconductors used based on silicon (Si) material. The new materials of wide bandgap (WBG) semiconductors, for example, gallium nitride (GaN) and silicon carbide (SiC), provide remarkably distinct characteristics of semiconductor devices to minimize power loss and boost the inverter’s operational capabilities. This research paper assesses the effects of integrating SiC-MOSFET devices into VSIs in order to improve the switching behavior and efficiency level. An experimental double-pulse testing (DPT) circuit was configured and set up for investigating the switching characterization of SiC-MOSFETs compared to the widely used Si-IGBTs. Under various operating circumstances, the switching behavior of two different types of power transistors was tested while their turning-on and turning-off losses were measured. The VSI based on SiC and Si transistors was simulated to examine the performance of the inverter. The results reveal that incorporating SiC-MOSFETs into the VSI substantially enhances the switching operation and reduces total power losses while increasing the efficiency compared to the inverter based on Si-IGBTs.

The day-ahead scheduling of multi-stack Power-to-X (PtX) plants must simultaneously cope with stack degradation under variable loading and with compound uncertainty in renewable generation and electricity prices. Existing scheduling frameworks address these two challenges in isolation, since degradation-aware models remain deterministic and stochastic models treat the electrolyser as a constant-efficiency device. This work develops a degradation-aware two-stage stochastic mixed-integer linear programming (MILP) framework that closes this gap. First-stage binaries fix the commitment and startup decisions of every stack, while second-stage scenario-indexed variables capture the dispatch, the hydrogen output, the shortfall, and the load-dependent and start–stop cycling degradation cost monetised at the stack level through a piecewise linear epigraph. Joint wind price uncertainty is represented by a Gaussian copula fitted on empirical CDF marginals and reduced to twenty representative scenarios via k-medoids clustering. The framework is fully implemented in MATLAB R2024a with the Optimization Toolbox, using the built-in intlinprog and linprog solvers. On a 100 MW reference plant with ten heterogeneous PEM stacks, out-of-sample evaluation against four formal benchmarks demonstrates the lowest LCOH at EUR 24/kg, the highest demand reliability at 85.0%, the highest hydrogen delivery at 7.68 t/day, and up to 50% total cost reduction over deterministic baselines, with end-to-end runtime under two minutes on standard workstation hardware.

The nonlinear and intermittent nature of Photovoltaic (PV) systems introduces dynamic disturbances that negatively impact the stability of the DC bus voltage (Vdc) between PV sources and shunt active power filters (SAPFs). These fluctuations pose significant challenges to the performance of SAPFs, especially when the reference DC bus voltage (Vdc*) is constant and not adapted to the instantaneous operating conditions. In this study, a Perturb and Observe (P&O) algorithm is employed within the PV subsystem to perform Maximum Power Point Tracking (MPPT), further contributing to the time-varying behavior of Vdc. To address this problem, this paper proposes a real-time optimization strategy based on the Mantis Shrimp Optimization Algorithm (MShOA) for continuous Vdc* adjustment. This method relies on real-time Total Harmonic Distortion (THD) feedback to dynamically determine the optimal Vdc*, thereby improving harmonic mitigation and maintaining voltage stability. The effectiveness of the proposed method in accurately determining the optimal reference DC bus voltage (Vdc) has been thoroughly verified through simulation results. Comparative analysis with conventional theoretical methods confirms the superior performance of the MShOA-based strategy in enhancing power quality and reducing THD in dynamic operating environments.