Sustainable Energy and Artificial Intelligence

Abstract This research paper aims to introduce a novel approach for evaluating the reliability of smart distribution system in the presence of microgrids. Microgrids, predominantly utilizing wind turbines and solar cells, pose a unique challenge to assessing distribution system reliability. To tackle this, the study employs the Monte Carlo method to calculate the availability of wind and solar energy. Subsequently, the presented method in this article is utilized to obtain reliability indices for the distribution system with integrated microgrids. Importantly, the proposed method takes into account the influence of renewable resources during system recovery. By incorporating this method, the assessment of distributed generation resources within microgrids becomes more straightforward for evaluating the reliability of the distribution system. The effectiveness of this method was assessed by modeling the desired indices on a distribution feeder comprising 13 distribution posts. It is assumed that each of these 13 points incorporates a microgrid dedicated to supplying local customers. Wind turbines and solar cells are installed as backup power sources for each customer.

Abstract The continuous fluctuations in electricity demand across different locations in the distribution network led to variations in power flow in the lines and, consequently, changes in power losses and voltage drop. With technological advancements and restructuring of the electricity industry, these changes have become more serious. So, maintaining a single network configuration at all hours may not be optimal. To address this, researchers have proposed and implemented dynamic reconfiguration of distribution networks as an effective solution. This study investigates the dynamic reconfiguration of radial distribution systems, using reinforcement learning to minimize the number of switches. The primary objectives of dynamic reconfiguration are to minimize power losses and enhance bus voltage profiles. A case study is conducted on the IEEE 33-bus standard network. Initially, it is assumed that remotely controllable switches are installed on all lines, allowing full participation in the reconfiguration process. Subsequently, the proposed algorithm for reducing the number of switches identifies critical switches that should be eliminated at each stage, followed by another round of dynamic reconfiguration. For each stage of switch reduction, an economic analysis over 20 years is performed, considering installation and annual maintenance costs alongside power loss costs. Finally, a sensitivity analysis determines the optimal number of switches based on switch costs and electricity tariff rates. The results indicate that lower electricity tariffs and higher switch costs improve the effectiveness of the proposed switch reduction method.

Abstract Abstract
Welding is a critical and sensitive technique for joining parts in industry. It has numerous applications and, in some cases, is irreplaceable. Therefore, it's essential to examine components produced by welding to determine the tolerable strength of welded sections for design and manufacturing purposes.
This study utilized thermo-elasto-plastic finite element analysis to examine the thermomechanical behavior that emerges from the arc welding of overlapping 37St steel sheets. The welding process was simulated in two stages using ANSYS software. The weld strength was measured through two independent analyses: thermal and mechanical. To simulate the heat source during welding, the element "birth and death" feature was utilized. Due to the high temperature gradient in the weld zone, the material's thermophysical and mechanical properties were considered temperature-dependent.
The calculation of weld strength is of great importance. Given that experimental methods are time-consuming and costly, the provision of fast and economical methods is significant. In this article, weld strength has been calculated using two methods: simulation with ANSYS software and artificial neural networks with MATLAB software. Finally, the results obtained from these two methods (numerical simulation and artificial neural networks) have been compared with experimental results.

Abstract The transition to clean energy demands advanced thermal energy storage (TES) solutions, especially for high-temperature applications like concentrated solar power (CSP) and industrial processes. Molten salt nanofluids—formed by dispersing nanoparticles in molten salts—offer a promising pathway to enhance thermal properties while maintaining high thermal stability and cost-effectiveness. This review summarizes the recent progress in the development, properties, preparation, and potential applications of these materials. Key focus areas include enhancements in specific heat capacity and thermal conductivity, which are critical for efficient heat storage and transfer. Notably, experimental studies report up to 100% increases in specific heat—defying classical predictions—possibly due to interfacial nanolayers, ionic rearrangement, or secondary nanostructures. Thermal conductivity improvements vary depending on nanoparticle type, morphology, and dispersion quality. The review also covers common base salts (nitrates, carbonates, chlorides) and a wide range of nanoparticle additives. Preparation methods such as ultrasonication and in-situ synthesis are discussed, along with challenges related to nanoparticle agglomeration, sedimentation, and long-term stability. Viscosity, corrosion behavior, and thermal cycling stability are also examined, as they critically affect system efficiency, pumping power, and material compatibility. Molten salt nanofluids hold strong potential for CSP, geothermal energy, enhanced oil recovery, and next-generation nuclear systems. However, commercialization is hindered by uncertainties in scalability, lifecycle impacts, and regulatory readiness. The review highlights the need for standardized methodologies, cross-disciplinary collaboration, and integrated performance-sustainability assessments to advance these materials toward practical deployment.

Abstract High temperature of hydrogen desorption from MgH2 (about 350 °C) and its slow kinetics are the main challenges in using this material for solid-state hydrogen storage. This research aims to study the effect of adding Nb2O5 on reducing the temperature of hydrogen desorption and using polymethylmethacrylate (PMMA) coating to prevent the oxidation of MgH2 particles and improve the kinetics of hydrogen desorption. For this purpose, three samples include a: as-received MgH2, b: MgH2 milled for 4 h and coated with PMMA, and c: milled mixture of MgH2 – 5wt. % Nb2O5 and coated with PMMA were produced. The prepared samples were analyzed using TG-DSC, XRD, EDS, and FESEM. The results showed that adding Nb2O5 and coating with PMMA reduces the hydrogen desorption temperature (about 40 °C) compared to the as-received MgH2. The amount of hydrogen desorption up to 310 °C for samples a, b, and c was measured as 2.2, 1.6, and 1.7 wt. %, respectively. The amount of hydrogen desorption after 39 minutes at 310 °C was 3.5, 1, and 1 wt. % for the mentioned samples, respectively. Studying the hydrogen desorption properties of the samples after 45 days showed the positive effect of the PMMA coating in preventing the oxidation of MgH2.

Abstract In this work, the effect of inlet air temperature and volume flow rate is studied on the moisture content of epoxy-based coating powder and energy consumption of a fluidized bed dryer system. The inlet air temperature range was determined to be 45-60 oC based on the glass transition temperature of epoxy-based coating powder. The results showed that by increasing the inlet air temperature in the fluidized bed dryer, the drying rate of the samples increased, where the highest drying rate was obtained at 60 oC. In the temperature range of 55 to 60 oC, an increase in inlet air volume flow rate led to an increase in the drying rate. An increase in the inlet air temperature significantly led to an increase in the energy consumption for drying the samples. The drying rate of the samples did not have a constant trend because of an increase in the inlet air volume flow rate. The results showed the drying rate of samples increased by an increase in volume flow rate from 1.0 to 1.4 m3/s, and its value decreased from 1.4 to 1.6 m3/s. However, the amount of energy consumed by the dried samples increases with the increase in the inlet air volume flow rate. The results of this study can be effective in achieving the optimal amount for drying the powder and reaching the optimum value of less than 2% by spending the minimum amount of necessary energy and the maximum drying rate of the powder.