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.

Escalating energy demands and climate change challenges necessitate the adaptation of renewable-based microgrid systems in the energy sector. The proposed work employs a robust Multi Agent System (MAS) technique to achieve efficient and automated control of the hybrid microgrid operation. The hybrid microgrid system incorporates Renewable Energy Sources (RES), a diesel generator, and a battery storage system. The operation of the hybrid microgrid consists of three distinct modes: islanded, transition to grid, and grid-oriented mode. The system’s performance is optimized by considering factors like climatic patterns, energy costs, connected source characteristics, and load demand. Different climatic scenarios are assessed for each mode of operation, where the best, extreme sunny, extreme cloudy, and worst climate conditions are considered for islanded mode; sunny and cloudy scenarios are considered for transition to grid mode as well as grid-feed and grid-tied modes are considered for grid-oriented operation of the microgrid. The simulation studies are performed using the MATLAB/Simulink R2021a environment. Furthermore, Particle Swarm Optimization (PSO) is implemented to optimize power allocation within the microgrid and enhance its cost-effectiveness. The optimization results demonstrate efficient utilization of available energy sources along with effective energy management facilitated by the MAS control system. The results emphasize the importance of adopting a MAS approach for achieving smart energy management through comprehensive analysis and integrating decentralized energy management techniques for optimal accommodation of distributed energy resources in hybrid microgrids.

The planning and operation of radial distribution networks face increasing challenges such as active power losses and voltage instability, prompting a focus on integrating renewable energy resources to mitigate these issues. This study presents a techno‐economic optimization framework leveraging the cheetah optimizer, a recently introduced metaheuristic technique, to optimize the accommodation of distributed energy resource units within the IEEE 33‐bus radial distribution networks utilizing MATLAB environment. Both single and multi‐objective perspectives are explored, demonstrating significant reductions in active power losses, minimized voltage deviation, improved stability, and maximized economic benefits. The cheetah optimizer efficacy is showcased through notable achievements, including a 94.20% reduction in active power losses and annual savings of up to $77,933 for optimal power factor mode in multi‐objective optimization, surpassing existing literature. Additionally, reliability analysis conducted with ETAP software underscores the effectiveness of distributed energy resource integration, particularly with wind turbine systems, in enhancing network reliability.

The planning and operation of radial distribution networks (RDN) face increasing challenges such as active power losses and voltage instability, prompting a focus on integrating renewable energy resources (RER) to mitigate these issues. This study presents a techno-economic optimization framework leveraging the Cheetah optimizer (CO), a recently introduced metaheuristic technique, to optimize the accommodation of distributed energy resource (DER) units within the IEEE 33-bus radial distribution networks (RDN) utilizing MATLAB environment. Both single and multi-objective perspectives are explored, demonstrating significant reductions in active power losses, minimized voltage deviation, improved stability, and maximized economic benefits. The Cheetah optimizer (CO) efficacy is showcased through notable achievements, including a 94.20% reduction in active power losses and annual savings of up to $77,933 for optimal power factor (OPF) mode in multi-objective optimization, surpassing existing literature. Additionally, reliability analysis conducted with ETAP software underscores the effectiveness of DER integration, particularly with wind turbine systems, in enhancing network reliability.