Particle Swarm Optimization and Improved Beetle Antenna Search-based Sparrow Search Algorithm for Optimal Planning Distributed Photo Voltaic Access

In recent years, Integration of Photovoltaic (PV) cells with distributed networks play a crucial role due to their capability to enhance the sustainability of renewable natural energy resources. Even though there is advantage still this system generates significant challenges for grid planning such as incorporating multiple distributed energy resources, and ensuring scalability for larger networks and thus it is essential to develop a grid planning system using hybrid optimization techniques. However, the intermittency and variability of PV generation pose significant challenges for grid planning, requiring advanced optimization techniques to ensure stability and efficiency. This paper suggest a novel hybrid approach using combination of optimization techniques such as Particle Swarm Optimization (PSO) and Improved Beetle Antenna Search-Based Sparrow Search Algorithm (IBASSA) for grid planning with distributed photovoltaic access which is validated through bus (33) of IEEE Radial Distribution System (RDS). Here, PSO is incorporated because of its capability of fast convergence and IBASSA is imposed due to its characteristic behavior enhance global search function and avoid local optima. The proposed hybrid approach is evaluated using the metrics power loss and min voltage values that gave spectacular results when compared with existing models such as genetic algorithm.