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This work mainly focused on the development of the controller for performing the automatic voltage regulation (AVR) action in the most optimal way. In this regard, the tilt-integral-derivative-filter (TIDF) controller is suggested in this work. Moreover, the controller TIDF requires soft computing methodologies for its optimal functioning, and hence the donkey and smuggler optimization algorithm (DSOA) is conceived. However, the efficacy of the TIDF is demonstrated with the other controllers that emerged from the background of intelligence and fractional calculus. Further, the dominance of DSOA is also established with other soft computing techniques. The optimization of TIDF using DSOA is performed under the integral square error (ISE) index, and a comparative analysis is made among the several time domain indices to establish its dominance. Finally, the simulation analysis revealed the overall efficacy of the DSOA-tuned TIDF controller under the ISE criteria and confirmed the optimal AVR performance.

This research examines the way to a carbon-neutral future by looking at the integration of renewable vitality sources and vitality capacity advances in feasible energy arrangements. Through a multidisciplinary approach, enveloping information collection, numerical modelling, and scenario investigations, the study investigates the flow of transitioning vitality frameworks. The optimization of energy capacity capacities is educated by scientific models, uncovering that as renewable infiltration increments (20%, 40%, and 60%), optimal capacity capacities rise correspondingly (300 MWh, 700 MWh, and 1200 MWh). Situation investigations illustrate that higher renewable entrance and appropriately measured energy capacity capacities lead to significant diminishments in CO2 outflows (25%, 45%, and 65%) while keeping up positive financial reasonability. Sensitivity investigations confirm the vigour of the models, showing the versatility to varieties in key parameters such as renewable asset accessibility, energy capacity productivity, and capital costs. A comparative investigation against related work underscores the competitiveness and uniqueness of the proposed approach, emphasizing the noteworthiness of numerical modelling in optimizing energy frameworks. This research contributes profitable insights for policymakers, industry partners, and analysts committed to exploring the complexities of accomplishing a carbon-neutral future. The discoveries displayed here, together with those from different studies crossing worldwide vitality exchange, green hydrogen production, and urban arranging, collectively contribute to the broader discourse on economic energy move.

This study focuses on the possible transformative effects that green nanotechnology has in the healthy energy applications and the repression of nature`s negative impacts. By means of environmentallyfriendly production of these nanomaterials, including silver nanoparticles (AgNPs) and quantum dots, the current research produced the marked increase in efficiency of solar cells, with a jump to 53% in efficiency compared to traditional solar cells. Moreover, the dopping of lithium-ion cells with silicon nanoparticles brought about a great enhancement, exhibiting a 50% specific capacity progress contrasted with the batteries that continued traditional anodes. Hence, these excellent findings demonstrate the effectiveness of nanotechnology in transforming energy that gathers and holds technology. The paper did an LCA that entailed a detailed analysis of the life cycle of green nanomaterials in order to measure the impact on the environment. The analysis presented by the LCA indicated the application of alternative materials that resulted in a significant reduction of GHG’s (32%), energy of 33%, and In TP of 37%) in comparison with conventional ones. This highlights the sustainability gains that will be obvious by the use of green nanotechnology as a technology of choice, in energy applications. The findings provide numerous beneficial principles for development of green technologies.