Perovskite solar cells have rapidly emerged as a leading third-generation photovoltaic technology, driven by their high power-conversion efficiencies and compatibility with low-temperature processing. However, dominant lead–halide perovskite systems continue to face significant challenges related to toxicity and limited operational stability. Scientists are therefore investigating lead-free alternatives, such as the all-inorganic chalcogenide perovskite SrZrS₃. This material has strong optical absorption and has both thermal and chemical stability. Nevertheless, their photovoltaic performance remains constrained by transport-layer selection and interface- and defect-induced recombination losses. Thus, in this work, an ultra-thin all-inorganic SrZrS₃ device with the planar architecture of FTO/ZnO/α-SrZrS₃/NiOX/Ni has been numerically optimized using SCAPS-1D under AM1.5G illumination (100 mW/cm²) at 300 K. Unlike previous SrZrS₃ simulation studies that primarily focused on transport-layer or contact optimization, the novelty of the present work lies in the development of a comprehensive and defect-sensitive optimization framework that systematically integrates ETL selection, band alignment, interface-defect engineering, resistive-loss analysis, and absorber optimization to provide a more physically realistic assessment of device performance. With NiOX serving as the hole transport layer, a systematic screening of ETL candidates (ZnO, TiO₂, IGZO, and SnO₂) was conducted. Due to its favorable band alignment and superior device performance, ZnO has been identified as the most suitable ETL. The optimized structure maintains ZnO thickness, α-SrZrS3 thickness, ZnO donor density, and absorber doping density of 0.08 µm, 0.8 µm, 1016 cm-3, and 5 × 1017 cm-3 respectively. Keeping the layer defect density at 1014 cm-3, interface defect densities at 1014 cm-2, series resistance at 3 Ω. cm2 and shunt resistance at 5000 Ω. cm2, the refined configuration demonstrated an outstanding simulated performance, having VOC, JSC, FF, and peak efficiency of 0.997 V, 25.78 mA/cm2, 79.16%, and 20.35% respectively. Overall, this study demonstrates that a holistic optimization strategy encompassing systematic ETL selection, defect engineering, and thorough parametric tuning, offers an effective route for the development of high-performance α-SrZrS₃ chalcogenide perovskite solar cells.
This work presents a technical enhancement of the open-source Micro Grid User Energy Planning Tool Library (MiGUEL) to simulate hybrid renewable energy system (HRES) integrating green hydrogen. MiGUEL, originally developed for Photovoltaic generation (PV)+Diesel Generator+Battery Energy Storage System (BESS) microgrids, was expanded to include electrolysers, hydrogen storage, and fuel cells. These components were integrated into MiGUEL to enable energetic, economic, and environmental assessments. The updated tool was assessed using a case study of the remote community of Caiambé-Tefé in the Brazilian Amazon, based on measured load data. Simulation results were benchmarked against those obtained with HOMER Pro (Hybrid Optimization of Multiple Energy Resources), a widely used commercial software package for hybrid energy system optimisation. Evaluated at the same configuration of 3.5 MWp of PV, 5 MWh of battery, a 0.5 MW electrolyser, 4500 kg of hydrogen storage and a 0.5 MW fuel cell, the two tools agree closely: MiGUEL obtains a levelized cost of energy (LCOE) of 0.53 USD/kWh and a total capital cost of 16.07 million USD, against 0.515 USD/kWh and 15.31 million USD in HOMER Pro, differences of 2.9 and 4.7 per cent respectively. The residual difference comes from the representation of the hydrogen conversion chain, which HOMER Pro models with a constant efficiency and MiGUEL with a part-load curve, and from the battery replacement schedule. The integration of hydrogen into MiGUEL enables the modelling of CO₂-free seasonal storage systems in remote microgrids and provides a flexible, accessible, and transparent tool for planning sustainable electrification in isolated regions.
Directly coupling photovoltaic (PV) arrays to proton exchange membrane (PEM) electrolysers is among the cheapest routes to green hydrogen, but without a power converter the operating point is mismatched to the solar maximum-power point, and that mismatch shifts minute-to-minute with cloud cover. Which coupling strategy maximises hydrogen yield under realistic solar variability, and whether the answer holds across climates, has remained unresolved, because dynamic PV-array reconfiguration and sub-second electrochemical transients have never been modelled together. This work addresses both within a single Python–Simulink co-simulation of seven DC-coupled PV–PEM architectures, spanning battery-less and battery-integrated designs, driven by minute-level irradiance from a hybrid K-nearest-neighbours downscaling algorithm and evaluated across four sites from polar Svalbard to the Namib Desert. The dominant lever on hydrogen output is found to be array reconfiguration rather than added power electronics: string switching alone raises solar-to-hydrogen efficiency to 14.9%, matching converter-based maximum-power-point tracking without a converter, and, combined with active battery management, gives the highest yield of all seven topologies. Temporal resolution proves decisive, as hourly-averaged data suppress almost 200 daily reconfiguration events and misrank the topologies. Crucially, the performance hierarchy is invariant from pole to desert, yet the benefit of reconfiguration is strongly climate-dependent, falling from ＋24% in the low, variable light of the polar site to ＋4% under the abundant, steady irradiance of the desert. The framework provides a validated, climate-aware benchmark for PV–PEM system design.
This study develops and evaluates a practical workflow for estimating annual PV energy yield in remote, data-scarce Andean locations, and tests it at two climatically contrasting sites in southern Peru: Cota Cota (4240 m a.s.l.) and Pampas de Majes (1498 m a.s.l.). Three years of local irradiance and temperature measurements were combined with ERA5 reanalysis, using machine learning to correct systematic biases and generate a 25-year hourly climatology, from which irradiance components and PV energy yield were estimated across the full range of tilt and azimuth configurations. Bias correction performed well at both sites, and optimal annual yields reached approximately 1042 kWh per panel at Majes and 1018 kWh at Cota Cota, with low sensitivity to small deviations from the ideal orientation. The analysis also revealed a systematic, altitude-dependent divergence between two independent estimates of the diffuse fraction: at Cota Cota, the Engerer2 decomposition and the satellite-based Solargis product differ by over 25 percentage points, a gap considerably smaller at Majes. As both are model-based estimates and no in-situ component measurements were available, the true bias cannot be resolved from this comparison, and substituting the clear-sky model did not reduce the divergence. These findings highlight both the value and the current limitations of the workflow, and underscore the need for in-situ component measurements to validate and locally calibrate irradiance decomposition models.
The rapid growth of renewable energy penetration has intensified the need for robust and intelligent energy management strategies capable of addressing the intermittency of solar and wind resources. This study presents a simplified fuzzy logic controller (FLC)–based energy management system (EMS) for a grid-connected PV/Wind hybrid system integrated with battery energy storage. A fuzzy logic–based Maximum Power Point Tracking (MPPT) scheme is developed for the photovoltaic subsystem to ensure efficient power extraction under variable irradiance and temperature conditions. MATLAB/Simulink-based investigations reveal that the proposed FLC-MPPT achieves a tracking efficiency of 98.6%, with a settling time reduced by 32% compared to conventional perturb-and-observe methods. The hybrid EMS effectively coordinates power flow among PV, wind, battery, and grid, reducing grid dependency by 41% and improving battery state-of-charge regulation within ±5% limits under dynamic load conditions. Battery cycling stress is reduced by 27%, enhancing storage lifespan. Furthermore, the integration of a Unified Power Flow Controller (UPFC) significantly improves system resilience under grid disturbances, achieving 38% voltage sag mitigation and 45% reduction in power oscillations during simulated fault conditions. The results confirm that the proposed simplified FLC-based EMS enhances energy utilization, system stability, and power quality in grid-linked hybrid renewable systems. This work directly supports UN SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action) by enabling efficient, reliable integration of renewable energy sources into smart power systems.
