Chaotic PSO-based optimization for power loss in grids with renewables and new loads

Abstract

The increasing integration of renewable distributed generation (DG) systems, particularly wind turbines (WT) and photovoltaic (PV) units, has reduced dependence on conventional power plants. However, the simultaneous connection of new loads introduces significant challenges for grid efficiency, especially regarding power loss minimization. Existing optimization techniques often face limitations in convergence speed and solution accuracy when addressing such multi-variable, dynamic problems. To address this gap, this paper introduces a Chaotic Particle Swarm Optimization (CPSO)-based approach for the optimal placement and sizing of WT-PV-DG systems in distribution networks with new loads (NL). The proposed method improves the balance between exploration and exploitation in the search process, thereby enhancing convergence and solution robustness. The methodology is applied to the IEEE 14-bus test system under both constant and variable load and climate scenarios. Results show that the proposed CPSO achieves a 6.5% reduction in power losses compared to the base case before optimization, and up to 14% compared to cases with new optimized loads only, highlighting its effectiveness and practical significance for improving distribution system performance.