Multi-objective optimization of distributed generation in electrical grids using an adaptive chaotic salp swarm algorithm

This research introduces an Adaptive Chaotic Salp Swarm Algorithm (AC-SSA) for optimizing the placement and sizing of Distributed Generation (DG) units in radial distribution networks. The proposed AC-SSA incorporates advanced population initialization and a stagnation-driven chaotic neighborhood search to enhance the balance between exploration and exploitation, hence averting premature convergence. The optimization concurrently reduces real and reactive power losses, voltage variation, and total energy expenditures, including investment, operational, and maintenance expenditures. The algorithm's efficacy is confirmed using the IEEE 33-bus and IEEE 69-bus test systems, utilizing the backward-forward sweep (BFS) approach for load flow analysis. The findings indicate that the proposed AC-SSA significantly reduces total power losses and voltage variations in comparison to traditional SSA and other metaheuristic algorithms. This insertion reduces the active power loss to 15 kW and 30 kW for the IEEE 33-bus and IEEE 69-bus systems, respectively, corresponding to a power-loss reduction of 92.59 % and 86.63 % when compared to the baseline networks. Moreover, the AC-SSA demonstrates expedited convergence, enhanced stability, and reduced standard deviation values, hence affirming its resilience and efficacy in addressing intricate multi-objective optimization challenges associated with DG integration. The results indicate that the AC-SSA is a viable method for smart grid planning and the optimization of power distribution based on renewable energy. Two key enhancements to the standard Salp Swarm Algorithm. • Proposes an Adaptive Chaotic Salp Swarm Algorithm (AC-SSA) for optimal placement and sizing of DG units in radial distribution networks. • Enhances classical SSA using advanced population initialization and a stagnation-driven chaotic neighborhood search to improve exploration–exploitation balance and prevent premature convergence. • Addresses multi-objective optimization, simultaneously minimizing real/reactive power losses, voltage deviations, and total energy costs (investment, operation, maintenance). • Validated on IEEE 33-bus and 69-bus systems using the backward–forward sweep method, outperforming conventional SSA and other metaheuristic techniques. • Demonstrates faster convergence, improved stability, and lower standard deviation values, confirming AC-SSA's robustness for DG integration and smart grid planning.