Quantum-Inspired Particle Swarm Optimization for Smart Grid Energy Distribution

Abstract

The increasing integration of renewable energy sources and advanced communication technologies is transforming conventional power systems into smart grids. However, the stochastic and dynamic nature of energy generation and demand introduces significant challenges in achieving efficient, reliable, and cost-effective energy distribution. Traditional optimization techniques, such as classical Particle Swarm Optimization (PSO), often suffer from premature convergence and limited exploration capability in complex, non-linear environments. This paper proposes a Quantum-Inspired Particle Swarm Optimization (QPSO) approach for smart grid energy distribution. The method leverages probabilistic position updates and mean best guidance to enhance global search capability and avoid local optima. A multi-objective optimization framework is developed to minimize operational cost, reduce transmission losses, and improve load balancing while satisfying system constraints. The proposed model is evaluated on standard test systems and compared with classical PSO. Results demonstrate improved convergence speed, better solution quality, and enhanced robustness under dynamic grid conditions. The findings highlight the effectiveness of QPSO for real-time smart grid optimization and its potential for largescale energy management applications.