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The article presents the results of theoretical research in the organization of the construction of a diagnostic system, microprocessor blocks of a typesetting group of railway automation and telemechanics. Products made with the use of microcontrollers, using mathematical expressions to determine the criteria for evaluating the diagnostic process, were subject to research. A graph of the theory of Petri nets of transitions of the diagnostic system of blocks of a set group has been developed and analyzed to determine the readiness indicator.

Following the massive power cut in Spain on 28 April 2025, questions have been raised about the reliability of energy generation infrastructure in the face of the variability of renewable energy sources. On the other hand, the market penetration of electric vehicles and their charging requirements implies the need for knowledge about the availability of electric generation technologies. This research work presents a macro-level analysis of the availability factor of electricity generation mix, applied to the case of Spain and based on data collected between 2019 and 2024. Using hourly generation and installed capacity data, a methodology is developed to estimate the seasonal and daily availability of the main generation technologies: photovoltaic, solar thermal, wind, hydroelectric, nuclear, combined cycle, coal and others. The analysis reveals that conventional sources, such as nuclear and combined cycles, exhibit low variability, with daily fluctuation of less than 1%. In contrast, renewable sources show significant variability. Photovoltaic availability increases from 22.6% ± 1.3% in the early morning to 57.8% ± 0.6% during summer afternoons, while solar thermal energy reaches a maximum of 78.5% ± 1.3% under the same conditions. The results highlight the uncertainty generated by the high penetration of renewable energy and the challenges posed by balancing generation with demand, particularly given new consumption patterns influenced, for example, by electric vehicles, battery storage and green hydrogen, among others. The integration of probabilistic planning frameworks into infrastructure development and the extension of this analysis to the provincial level, together with the incorporation of restriction and self-consumption scenarios involving constraints and self-consumption, will help to ensure the robust operation of the grid in the future.

The present work proposes a Monte Carlo Simulation (MCS) to obtain availability projections for Hydroelectric Power Plants (HPP), based mainly on regulatory aspects involving the Availability Factor (AFA). The main purpose of the simulation is to generate scenarios to obtain statistics for risk analysis and decision-making in relation to the HPP. The proposed methodology consists of two steps, firstly, the optimization of the maintenance schedule of the hydroelectric plant is carried out, in order to allocate the mandatory maintenance in the simulation horizon. Then, for the MCS, scenarios of forced shutdowns of the Generating Units (GU) will be generated, which directly influence the operation and, consequently, the availability of the HPP. The scenarios will be inserted into an operation optimization model, which considers the impact of forced shutdown samples on the MCS. The proposed modeling was applied using real data from the Santo Antônio HPP, which is one of the largest hydroelectric plants in Brazil.

Biomass has emerged as an important and promising energy source, particularly in developing countries, owing to continuous research for sustainable energy sources that do not interfere with food, water or land needs. This study introduces the surplus availability factor (SAF), minimum, average and maximum biogas production technique in the assessment of crop production data in 2020 to provide a more precise and current estimate of Cameroon's crop residue and municipal solid waste (MSW) bioenergy potential. Crop residues contributed roughly 96% while MSW contributed the remaining 4% of the total bioenergy potential of 606 PJ per year. The bioenergy potential was calculated using crop production statistics derived from the FAOSTAT database of the Food and Agriculture Organization, while the residue-to-product ratio (RPR) and surplus availability factors (SAF) were found from related studies. The study concludes that crop residues and MSW have significant energy potential capable of meeting the country's electricity, transport fuel and biogas demand while simultaneously mitigating climate change through the capture of about 1.6 billion kg of CO 2 through biogas recovery. It also highlights the lack of accurate and up-to-date data on the country's biomass potential and recommends ground data collection and geospatial mapping of areas with enormous potential for these resources to guide policymakers and investment plans.

Purpose. To build a graph of states and transitions of the surveying-geodetic network (SGN), which includes 16 points. To study the functioning of constructed discrete-continuous stochastic Markov models of the surveying-geodetic network with full and current recovery. To perform a numerical calculation of reliability, safety and efficiency indicators: readiness ratio, limit probability states, mean time to failure, mean time between failures. Methodology. A model of SGN functioning is built in the form of a graph of states and transitions with current and full recovery. Based on the model in the Mathcad software, the availability factor, mean time to failure, mean time between failures are calculated. The following graphs are built: readiness functions, probabilities of operation until the first failure, and frequency of getting into an emergency situation. Findings. Constructed discrete-continuous stochastic Markov reliability models of the boundary probability states, mean time between failures, mean time to the first failure have been analyzed. The probability of fault-free operation is presented graphically in the form of a transition graph, which describes the logic of the operation of the SGN. Based on the graph of states and transitions (graphical model) according to the Kolmogorov-Chapman algorithm, an analytical model of the reliability behavior of the surveying-geodetic network was built. A system of linear Kolmogorov-Chapman differential equations was compiled and solved. The distribution of probabilities of being in each state of the surveying-geodetic network has been obtained. Originality. For the first time in surveying practice, a reasonable choice of a discrete-continuous stochastic model of the functioning of a surveying-geodetic network based on the application of the state space method has been made. This model most fully describes the process of functioning (behavior) of the dynamic system. Dependencies between reliability indicators and safety indicators are established. It is recommended to use a model with ongoing network recovery, which allows you to maintain a given level of reliability through timely maintenance (recovery). Practical value. The most expedient time for restoration of geodetic points with certain failure intensity parameters has been determined. It has been done in order to maintain the SGN in an operational state with a given level of reliability. Current network recovery makes it possible to maintain reliability at the desired level. In the case of complete restoration of the SGN, the readiness factor will be lower, but such a system will be significantly cheaper.

The present work aims to obtain a ranking of hydro generating units using the analytic hierarchy process (AHP) method, based on the maintenance indicators of the Santo Antônio Hydroelectric Plant (SAHP). The purpose of the generated classification is to provide subsidies to assist maintenance planning, determining the most critical turbines for performing preventive maintenance, in order to increase the availability of equipment, in addition to reducing the probabilities of forced shutdowns for corrective maintenance, impacting factors the plant’s availability factor. Thus, the main contribution of this work is the application of AHP to classify the SAHP generating units, using parameters and actual historical data from the plant and hydraulic turbines.

At the SMARTY-22 conference, a review of the regenerative methods development was presented, including its application to the study of a non-renewable k-out-of-n system. This paper develops the previous study for the renewable k-out-of-n system, including an investigation different preventive maintenance strategies based on the system state observation. We also include the review of Smith’s regeneration idea development. Some new results are presented that form the basis for an algorithm for comparing preventing maintenance strategies with respect to the maximization of the availability factor. A numerical study was conducted for the 4-out-of-6 and 4-out-of-8 models. The study demonstrates the sensitivity of decision making to the shape of the repair time distribution.

Contributing to achieving sustainability in South Africa’s energy sector, this study probes financial sustainability and its relationship to the environmental sustainability of Eskom. This is because, over the past three financial years (FY2018–2019 to FY2020–2021) of Eskom’s generating plants’ performance, the energy availability factor (EAF) has taken a deep dive, reaching an extremely low generation availability year-end performance of 64.2%, translating to approximately an average of 29,800 MW available generation capacity out of a nominal generation capacity of 46,466 MW in FY2020–2021. Therefore, the study employed a quantitative research methodology, where the relevant financial records were analysed, and the necessary energy calculations made using descriptive analysis in Microsoft Excel. The findings show that the volumes (GWh) produced by the OCGTs during this period far exceed the regulatory approved volumes, thus attracting substantial costs, amounting to ZAR 25.9 bn instead of ZAR 8.9 bn, that could have been spent on the OCGTs if the level of efficiency achieved in FY2016–2017 and FY2017–2018 was maintained. The analysis also revealed that the OCGTs’ long-term financial and environmental sustainability could be achieved through switching from diesel to natural gas, thus resulting in lower fuel costs and lower emissions. Further, potential savings of approximately ZAR 27 bn (excluding capital expenditure) at a 10% load factor can be realised over a ten-year period when the natural gas price is sitting at ZAR 85/GJ (minimum). Finally, in order to attain financial and environmental sustainability, it is recommended that both Eskom’s and the independent power producers’ (IPPs) OCGTs must switch fuel from diesel to natural gas and be run at a 10% load factor, allowing the OCGTs to be run as mid-merit generators.

Water resources have uneven distributions over time, space, and source; thus, potential impacts related to water use should be evaluated by determining the differences in water resources rather than by simply summing water use. We propose a model for weighting renewable water resources and present a case study assessing water scarcity footprints as indicators of the potential impacts of water use based on a life cycle impact assessment (LCIA). We assumed that the potential impact of a unit amount of water used is proportional to the land area or time required to obtain a unit of water from each water source. The water unavailability factor (fwua) was defined using a global hydrological modeling system with a global resolution of 0.5 × 0.5 degrees. This model can address the differences in water sources using an adjustable reference volume and temporal and spatial resolutions based on the flexible demands of users. The global virtual water flows were characterized using the fwua for each water source. Although nonrenewable and nonlocal blue water constituted only 3.8% of the total flow of the water footprint inventory, this increased to 29.7% of the total flow of the water scarcity footprint. We can estimate the potential impacts of water use that can be instinctively understood using fwua.

In this paper a novel method for reliability prediction and validation of nuclear power units in service is proposed. The equivalent availability factor is used to measure the reliability, and the equivalent availability factor deducting planed outage hours from period hours and maintenance factor are used for the measurement of inherent reliability. By statistical analysis of historical reliability data, the statistical maintenance factor and the undetermined parameter in its numerical model can be determined. The numerical model based on the main- tenance factor predicts the equivalent availability factor deducting planed outage hours from period hours, and the planed outage factor can be obtained by using the planned maintenance days. Using these factors, the equivalent availability factor of nuclear power units in the following 3 years can be obtained. Besides, the equivalent availability factor can be predicted by using the historical statistics of planed outage factor and the predicted equivalent avail- ability factor deducting planed outage hours from period hours. The accuracy of the reliability prediction can be evaluated according to the comparison between the predicted and statistical equivalent availability factors. Furthermore, the reliability prediction method is validated using the nuclear power units in North American Electric Reliability Council （NERC） and China. It is found that the relative errors of the predicted equivalent availability factors for nuclear power units of NERC and China are in the range of-2.16% to 5.23% and -2.15% to 3.71%, respectively. The method proposed can effectively predict the reliability index in the following 3 years, thus providing effective reliability management and mainte- nance optimization methods for nuclear power units.