Along with industrialization and rapid urbanization, environmental remediation is globally a perpetual concept to deliver a sustainable environment. Various organic and inorganic wastes from industries and domestic homes are released into water systems. These wastes carry contaminants with detrimental effects on the environment. Consequently, there is an urgent need for an appropriate wastewater treatment technology for the effective decontamination of our water systems. One promising approach is employing nanoparticles of metal oxides as photocatalysts for the degradation of these water pollutants. Transition metal oxides and their composites exhibit excellent photocatalytic activities and along show favorable characteristics like non-toxicity and stability that also make them useful in a wide range of applications. This study discusses some characteristics of metal oxides and briefly outlined their various applications. It focuses on the metal oxides TiO₂, ZnO, WO₃, CuO, and Cu₂O, which are the most common and recognized to be cost-effective, stable, efficient, and most of all, environmentally friendly for a sustainable approach for environmental remediation. Meanwhile, this study highlights the photocatalytic activities of these metal oxides, recent developments, challenges, and modifications made on these metal oxides to overcome their limitations and maximize their performance in the photodegradation of pollutants.

The rapid growth of renewable energies specifically wind and solar with their intermittent phenomena has recently become an exciting and important topic among researchers worldwide. The integration of such devices in distribution networks comprising of battery energy storage systems (BESSs) is considered as efficient and complementation approach which their well-managed configuration and scheduling via optimal arrangement yield not only productive and reliable power delivery to end-users, but cost-saving opportunity for utility managers. When dealing with photovoltaic (PV), reactive power capability of PV inverter is another aspect of interest that can contribute in enhancing effective penetration of wind, solar, and storage system into distribution networks. In this paper, a robust and novel optimization technique epsilon multiobjective genetic algorithm $\epsilon$ -MOGA is proposed and applied to a real 162-bus distribution network. A three-objective function optimization encompassing environmental, technical, and economic aspect is developed to size the centralized wind-farm and BESS parameters, distributed rooftop PV, and PV inverter reactive power control in an optimal manner. The proposed arrangement is simulated in MATLAB and compared with several cases to show its superior implementation concerning carbon emission reduction, better technical aspect, annual saving, and grid power alleviation.

Photovoltaic (PV) systems encounter substantial losses throughout their lifespan due to the different derating factors of PV modules. Those factors mainly vary according to the geographical location and PV panel characteristics. However, the available literature does not explicitly concentrate on the technical and economic impact of the derating factors within the PV system. Owing to that necessity, this study performs a comprehensive analysis of various PV loss parameters followed by a techno-economic assessment of derating factors using the average value on a grid-connected and optimally tilted PV system located in Hatiya, Bangladesh. Some criteria linked to the derating factors such as PV degradation and ambient temperature are further explored to analyze their impact on the aforementioned power system. Simulation results show that PV power generation would vary around 12% annually, subject to a 10% variation in the derating factor. Again, a 10% difference in the derating factor changes the net present cost (NPC) by around 3% to 4%. The system provides the best technical performance concerning annual PV production, power trade with the grid, and the renewable fraction at a higher value of the derating factor since it represents a lower impact of the loss parameters. Similarly, the financial performance in terms of the NPC, levelized cost of energy (LCOE), and grid power exchange cost is found to be lower when the derating factor value is higher.

Renewable energy resources (RERs) such as wind and solar are said to be considerable promising of the power system worldwide, and Afghanistan is evaluated for abundant and feasible electricity generation capacity from these resources. It fortifies merging of RER to the electric power system of Afghanistan where power quality issue sums up with scheduled and unscheduled load shedding due to the shortage of electricity. This research study presents an optimal solution comprising of rooftop solar photovoltaic (PV) as distributed generation to a real and substantial 162‐bus electric distribution network (EDN) in Kabul, the capital of Afghanistan. Genetic algorithm (GA) based on Newton–Raphson power flow with the objective of power loss minimisation is put forward for sizing and placement of the solar PV at practically available locations or candidate buses of the network. This approach tends to reduce the dependency on the import power and at the same time improves the performance of the current system through minimisation of the total power loss and voltage deviation. The proposed method is simulated by MATLAB ^® software to compare and demonstrate the performance of the system under different scenarios of the PV allocations.