Novel Quantum-Inspired Control Algorithm for Optimal Resource Allocation in Smart Grids.

Abstract

This study addresses the inefficiencies in traditional power distribution systems by adopting a quantum-inspired control algorithm, specifically the Quantum Particle Swarm Optimization (QPSO) algorithm, for optimal resource allocation in smart grids. The QPSO algorithm utilizes principles from quantum computing to optimize complexities in smart grids. Key performance metrics, including resource allocation costs, power allocation, energy demand, energy supply, and voltage and frequency constraints, were evaluated. The results demonstrate significant improvements in resource allocation efficiency, with optimized total cost values and closely aligned energy demand and supply. Energy efficiency remained ideal, and voltage and frequency were maintained within set constraints. This study contributes to smart grid optimization by showcasing the potential of QPSO quantum-inspired control in enhancing resource allocation efficiency. The findings suggest that this approach can improve responsiveness and sustainability in energy systems, providing a foundation for future exploration of quantum techniques in smart grid technologies. The results of this study have important implications for the development of efficient and reliable smart grid systems.