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Multi-objective optimization of distributed generation in electrical grids using an adaptive chaotic salp swarm algorithm
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DOI：10.1016/j.uncres.2025.100299.png)
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Abstract
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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.
Keywords:
Distributed generation
Adaptive chaotic salp swarm algorithm
Multi-objective optimization
Load flow analysis
Electrical network
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