Adaptive Fuzzy Particle Swarm Optimization for Hybrid Renewable Energy Systems in Direct Current Electrical Distribution System

The integration of renewable energy sources into modern power systems is essential for reducing carbon emissions and improving energy sustainability. Hybrid renewable energy systems, especially photovoltaics (PVs) and wind turbines (WTs), present an attractive solution due to their complementary generation profiles. However, their optimal placement and sizing in electrical distribution networks remain challenging due to nonlinear constraints and multiple objectives such as minimizing power losses, improving voltage profiles, and reducing operational costs. Advanced optimization techniques are therefore critical to achieving these goals.

This work presents an Adaptive Fuzzy Particle Swarm Optimization (AFPSO) algorithm applied for optimal integration of hybrid PV and wind energy systems in Direct Current electrical distribution networks. The proposed method, tested on a multi-objective function that adressed three objectives, minimization of active power losses, improvement of voltage deviation, and reduction of cost, is applied to the IEEE 69 bus system. AFPSO incorporates fuzzy logic to adaptively regulate PSO parameters, enhancing exploration and exploitation capabilities.

Comparative numerical comparison with Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) algorithms is performed; the comparison reveals that AFPSO achieves the best results in terms of power loss reduction, voltage profile enhancement, and economic performance. The approach demonstrates strong potential for supporting efficient and reliable renewable integration in DC distribution systems.