Scopus Research — Teeb Basim Abbas Al-hasnawi

In this paper, a robust fuzzy multi-objective framework is performed to optimize the dispersed and hybrid renewable photovoltaic-wind energy resources in a radial distribution network considering uncertainties of renewable generation and network demand. A novel multi-objective improved gradient-based optimizer (MOIGBO) enhanced with Rosenbrock’s direct rotational technique to overcome premature convergence is proposed to determine the problem optimal decision variables. The deterministic optimization framework without uncertainty minimizes active energy loss, unmet customer energy, and renewable generation costs. The study also examines the impact of dispersed and hybrid renewable resources on solving the problem. In the robust optimization framework considering the deterministic obtained results, the focus is on determining the maximum uncertainty radius (MUR) of renewable resource generation and network demand based on the uncertainty risk. The MURs and system robustness are optimally determined using information gap decision theory (IGDT) and the MOIGBO, considering various uncertainty budgets under worst-case scenarios. The deterministic results indicate that the MOIGBO effectively balances the objectives and identifies the final solution within the Pareto front, according to fuzzy decision-making. The results also reveal that the dispersed case yields better objective values than the hybrid case. Furthermore, the MOIGBO outperforms MOGBO and multi-objective particle swarm optimization (MOPSO) in improving distribution network operations. The robust results show that maximum system robustness is achieved at 30% uncertainty risk due to forecasting errors, with MUR values of 0.54% for resource production and 12.56% for load demand. © The Author(s) 2024.

Optimizing business strategies for energy through machine learning involves using predictive analytics for accurate energy demand and price forecasting, enhancing operational efficiency through resource optimization and predictive maintenance, and optimizing renewable energy integration into the energy grid. This approach maximizes production, reduces costs, and ensures stability in energy supply. The novelty of integrating deep reinforcement learning (DRL) in energy management lies in its ability to adapt and optimize operational strategies in real-time, autonomously leveraging advanced machine learning techniques to handle dynamic and complex energy environments. The study’s outcomes demonstrate the effectiveness of DRL in optimizing energy management strategies. Statistical validity tests revealed shallow error values [MAE: 1.056 × 10(−13) and RMSE: 1.253 × 10(−13)], indicating strong predictive accuracy and model robustness. Sensitivity analysis showed that heating and cooling energy consumption variations significantly impact total energy consumption, with predicted changes ranging from 734.66 to 835.46 units. Monte Carlo simulations revealed a mean total energy consumption of 850 units with a standard deviation of 50 units, underscoring the model’s robustness under various stochastic scenarios. Another significant result of the economic impact analysis was the comparison of different operational strategies. The analysis indicated that scenario 1 (high operational costs) and scenario 2 (lower operational costs) both resulted in profits of $70,000, despite differences in operational costs and revenues. However, scenario 3 (optimized strategy) demonstrated superior financial performance with a profit of $78,500. This highlights the importance of strategic operational improvements and suggests that efficiency optimization can significantly enhance profitability. In addition, the DRL-enhanced strategies showed a marked improvement in forecasting and managing demand fluctuations, leading to better resource allocation and reduced energy wastage. Integrating DRL improves operational efficiency and supports long-term financial viability, positioning energy systems for a more sustainable future. © The Author(s), under exclusive licence to Korean Carbon Society 2024.

This study aims to synthesize Nio film doped with Sn element at a ratio (2,4,8 and 16) % by using Physical Vapour Deposition RF Sputtering with annealing at 200, 300, 400, and 500. The results of XRD appear to be crystallizing of the thin film preparation at 500 ºC, and the annealing under this temperature will be amorphous. The SEM and AFM tests of structures, roughness, and particle size found that the particle size will be decreased and surfaces will be smoother with an increase in temperature of annealing at 500 ºC. The roughness value is (22-84) nm, and the lower values of roughness are at 500 ºC. Transmittance decreases with Sn dopant concentration, its minimum occurs for 16% SNO (sample: 3.32 eV), and by increasing more Sn dopant, it gets wider optical band gap. The resistivity decreases by increasing the tin dopant. Annealing drastically lowered the band gap, as we saw in some samples, it ran below, not annealed thin films. © 2025, Semarak Ilmu Publishing. All rights reserved.

In this study, experiments are used to evaluate the effectiveness of dynamic vibration absorbers (DVAs) in minimizing vibrations in beam structures. The dynamic vibration absorbers are modest additions to a structure that employ a mass-spring system tuned to the natural frequency of the structure to lower vibration levels. These absorbers were added to a beam construction as part of the experimental investigation, and the vibration levels under various conditions were measured. Under pinned-free boundary, the dynamic behavior of a beam is experimentally investigated with various combinations of the design parameters (mass and spring) and locations of the dynamic vibration absorbers. The beam is subjected to external vibrations, and both with and without the absorbers, its amplitude is measured. According to the results, adding DVAs to the beam structure significantly reduced vibration levels, particularly closer to the natural frequency of the beam. The dynamic response is greatly reduced by mass and stiffness (from, for example, 0.018m to 0.00052m). However, depending on the DVA location, this effect can change. The minimal requirements of the DVA parameters can better reduce the dynamic response if the DVA is positioned at the point of maximum displacement for each corresponding mode. © 2025 Penerbit UTM Press. All rights reserved.

The efficiency of dynamic vibration absorbers (DVAs) in reducing vibrations in beam structures is experimentally assessed in this research. The dynamic vibration absorbers are small devices added to a structure, that use a mass-spring-damper system calibrated to the natural frequency of the structure to reduce vibration levels. The experimental examination included the addition of these absorbers to a beam structure and the measurement of the levels of vibrations under various circumstances. The dynamic behavior of a beam is examined experimentally under two boundary conditions (pinned-free, cantilever), with different configurations of the dynamic vibration absorbers' design parameters and placements along the beam. External vibrations are applied to the beam, and their amplitude is measured both with and without the absorbers. Investigations are conducted on the effects of the absorber's mass, stiffness, and locations. The findings demonstrated that adding DVAs to the beam structure greatly reduced vibration levels, especially at the beam natural frequency. Both mass and stiffness significantly reduce the dynamic response (from, for instance, 0.018m to 0.00052m). However, this effect changes based on the boundary conditions. If the DVA is situated at the point of maximum displacement, the minimal needs of the DVA parameters can better reduce the dynamic response. © 2024 Author(s).