This review focuses on Distributed Generation Planning within Multi-Energy Microgrids (MES), a transformative approach where various energy forms like electricity, heat, and cooling interact optimally. MES offers significant technical, economic, and environmental benefits, making it crucial to explore intricate modelling and assessment techniques for such systems, including microgrids, virtual power plants (VPPs), and energy hubs. Amid global energy challenges and environmental crises, renewable energy adoption is vital. Distributed Generation (DG) plays a pivotal role, but integrating it at scale poses challenges. Microgrids are a viable solution, and this review introduces their core concepts, emerging trends, and challenges, such as integrating more renewable energy sources, multienergy forms, complex architectures, demand-side management, and advanced storage. Furthermore, the paper delves into Multi-Energy Microgrid planning complexities, particularly in off-grid areas. It presents a stochastic investment model that minimizes costs and carbon emissions while optimizing the distributed energy resource mix, locations, and sizes. This model considers intricate energy flows and uncertainties associated with renewable generation and energy demands. Case studies confirm significant cost and emission reductions, establishing it as a promising multi-energy microgrid planning solution for the future.

Advances in renewable energy technologies are pivotal in addressing the challenges posed by the depletion of traditional energy sources and their associated environmental impacts. Among these, wind energy stands out as a promising avenue, with wind turbine farms proliferating globally. However, the unpredictable nature of wind and intricate interplay between turbines necessitate innovative solutions for efficient operation and maintenance. This paper reviews advancements in intelligent control systems, notably those proposed by Smart Wind technologies. These systems leverage a network of sensors and IoT devices to gather real-time data, such as wind speed, temperature, and humidity, to optimize turbine performance. A significant focus is on turbines employing doubly-fed induction generators, which offer benefits like adjustable speed and consistent frequency operation. Their integration into smart grids introduces challenges concerning power system dynamics’ security and reliability. This review delves into the dynamics, characteristics, and potential instabilities of such integrations, emphasizing the uncertainties in wind and nonlinear load predictions. A noteworthy finding is the rising prominence of artificial intelligence, particularly machine and deep learning, in predictive diagnostics. These methodologies offer costeffective, accurate, and efficient solutions, holding potential for enhancing power system stability and accuracy in the smart grid context.

The heat treatment-T6 offers significant benefits for Aluminum Matrix composites due to changes of structure and material properties like strength, hardness and corrosion resistance. In this study, the preparation of reinforced Molybdenum Disulfide With Al-Zn-Mg alloy composites done by stir casting methodology. Three parameters like temperature (A), Time duration (B) and quenching medium (C) are considered for Heat Treatment Process (HTP). Quenching medium, time and temperature are reserved as constant. The main aim is to compare corrosion or wear characteristics (WC) and Hardness (BHN) of prepared samples before and after HTP. POD device used to conduct test of wear rate. 12 experiments are carried out by change of MOS2 particles volume (5-20 w.t %) and Temperature (400-700º C). the experimental outcomes are discussed and compare the mechanical properties for stir-casting and T6-HTP.