There are reliable solutions for overcoming the mismanagements and inefficiencies in the microgrid, which have been discussed, in the following proposed study. It focuses on the utilization of Renewable Energy Sources (RES) for operating the microgrid in a smart way such that the supply demand ratio is balanced profiting both the utility user and the end user. Power sources are scheduled as per requirement based on their availability and per unit cost. Centralized Multi Agent System (MAS) technique is adopted in which a central controller controls the operation of the whole microgrid system. Load agents attached to the system are of two types, i.e., critical load and non-critical load. The power to the critical load is to be maintained as a result of which in case of any emergency situation the power supplied to the non-critical load is shed off in order to make the critical load running. Different techniques are utilized for load management. Demand Side Management (DSM) is one of those techniques in which the load shifts from peak to off-peak hours and vice versa. Further, on the simulation of the proposed study has been performed in MATLAB/Simulink software and its hardware implementation has been done as well. The output results achieved indicates the supply to the load agents depending upon the availability of the power sources.

The integration of distributed generation (DG) into distribution networks introduces uncertainties that can substantially affect network reliability. It is crucial to implement appropriate measures to maintain reliability parameters within acceptable limits and ensure a stable power supply for consumers. This paper aims to optimize the location, size, and number of DG units to minimize active power losses and improve distribution System (DS) reliability while considering system operational constraints. To achieve this objective, multiple tests are conducted, and the particle swarm optimization (PSO) technique is implemented. The simulation studies are performed using the ETAP software 19.0.1 version, while the PSO algorithm is implemented in MATLAB R2018a. ETAP enables a comprehensive evaluation of the DG system’s performance, providing valuable insights into its effectiveness in reducing power losses and enhancing system reliability. The PSO algorithm in MATLAB ensures accurate optimization, facilitating the identification of the optimal DG unit location and size. This study uses a modified IEEE-13 bus unbalanced radial DS as the test system, assessing the effects of photovoltaic (PV) and wind DG units under various scenarios and penetration levels. The results demonstrate that the optimal DG unit location and size of either a single PV or wind DG unit significantly reduce power losses, improve DS reliability, and enable effective load sharing with the substation. Moreover, this study analyzes the impact of DG unit uncertainty on system performance. The findings underscore the potential of optimized DG integration to enhance DS efficiency and reliability in the presence of renewable energy sources.