We are witnessing significant climatic changes and increasingly frequent extreme weather conditions affecting every part of the globe. In order to reduce and stop these unfavourable climate changes, there has been a shift to the use of renewables, and in this sense, a significant contribution of the photovoltaic (PV) power plant is planned. This paper analyses the safety, reliability, and resilience of PV systems to extreme weather conditions such as wind storms, hail, lightning, high temperatures, fire, and floods. In addition to using available information from the literature, temperature measurements were also carried out on the rooftop PV power plant in Slavonski Brod, as well as a numerical stress analysis at extreme wind speeds using Ansys software. The results of the analysis show that existing PV systems are very resilient to extreme weather conditions. Utility-scale PV systems can usually withstand wind speeds of up to 50 m/s without any problems, and only at higher speeds do local stresses occur in certain parts of the structure that are higher than permissible. Resistance to hail is also very high, and manufacturers guarantee resistance to hail up to 25 mm in size. At high air temperatures, the temperature of the panel frame can reach about 70 °C, the panel temperature up to 85 °C, and the temperature of the cable insulation over 60 °C, as measurements have shown. Such high temperatures lead to a drop in electricity production up to 30% but do not pose a fire hazard to the cables and the roof if the roof insulation is conducted correctly. Forest fires do not usually pose a direct threat to PV systems, but the smoke that spreads over a large area reduces the solar radiation reaching the PV panel. It can also cause an unfavourable “wiggle effect”. Lightning strikes to a PV panel are not common, although they are possible. With built-in safeguards, no major damage should occur. Flooding is always a possibility, but with properly designed drainage systems, the damage is minimal in most cases.

Rapid decarbonization and decentralization of power systems are driving the integration of renewable generation, energy storage and digital technologies into unified energy ecosystems. In this context, photovoltaic (PV) systems combined with battery and hydrogen storage and blockchain-based platforms represent a promising pathway toward sustainable and transparent energy management. This study evaluates the techno-economic performance and operational feasibility of integrated PV systems combining battery and hydrogen storage with a blockchain-based peer-to-peer (P2P) energy trading platform. A simulation framework was developed for two representative consumer profiles: a scientific–educational institution and a residential household. Technical, economic and environmental indicators were assessed for PV systems integrated with battery and hydrogen storage. The results indicate substantial reductions in grid electricity demand and CO₂ emissions for both profiles, with hydrogen integration providing additional peak-load stabilization under current cost constraints. Blockchain functionality was validated through smart contracts and a decentralized application, confirming the feasibility of P2P energy exchange without central intermediaries. Grid electricity consumption is reduced by up to approximately 45–50% for residential users and 35–40% for institutional buildings, accompanied by CO₂ emission reductions of up to 70% and 38%, respectively, while hydrogen integration enables significant peak-load reduction. Overall, the results demonstrate the synergistic potential of integrating PV generation, battery and hydrogen storage and blockchain-based trading to enhance energy independence, reduce emissions and improve system resilience, providing a comprehensive basis for future pilot implementations and market optimization strategies.

The increasing implementation of renewable energy sources (RES), along with the diversity of energy source types, has additionally imposed significant operational and management problems in distribution networks. These problems are manifested in volt...

This paper describes a numerical study of the optimal distribution of energy between fuel cells and auxiliary energy storages in the hybrid train. Internal combustion engines (ICEs) are currently under pressure from environmental agencies due to their harmful gas emissions, and pure battery vehicles have a short range; a hybrid train powered by fuel cells, batteries, and supercapacitors can provide a viable propulsion solution. In this study, special energy management on the mountain railway with optimal power distribution and minimum hydrogen consumption is proposed. Considering the characteristics of the mountain railway, the vehicle uses recuperation of regenerative braking energy and thus charges additional power devices, and hybridization optimization gives favorable power to each power source device with a minimum consumption of hydrogen in the fuel cell. In this study, a simulation model was created in a Matlab/Simulink environment for the optimization of hybridized power systems on trains, and it can be easily modified for the hybridization of any type of train. Optimization was performed by using Sequential quadratic programming (SQP). The results show that this hybrid train topology has the ability to recover battery and supercapacitor state of charge (SOC) while meeting vehicle speed and propulsion power requirements. The effect of battery and supercapacitor parameters on power distribution and fuel consumption was also simulated.

This article introduces a new method of permanent inspection of technical active power losses. It is based on analytical computer code calculation with examples from the actual power system. Based on the incoming data from the remote control supervising system as well as technical and commercial information data, the result of the calculation is technical losses for every single element of the network. Further calculation result analysis enables global understanding of commercial losses as well as more quality planning and operating of the distribution network

Calibration of low-cost humidity sensors such as the HTS221TR is critical for accurate measurements, especially in smart devices. This study compares two calibration methods: machine learning (PyTorchNeural Network regression model) and optimization...