Application of Quantum Computing in Power System Optimization: A New Method to Improve Grid Efficiency

The quick development of power systems and the growing need for grid stability and energy efficiency have placed a lot of strain on conventional optimization techniques. Quantum Computing (QC) is increasingly recognized as a powerful tool for advancing power system optimization by addressing complex, large-scale challenges that traditional methods struggle to solve efficiently. The goal of the study is to optimize power systems using QC to increase grid efficiency. This study introduces a novel Refined Tunicate Swarm Optimized Malleable Random Forest (RTSO-MRF) designed to improve grid efficiency by leveraging QC. The RTSO-optimized MRF, which is essential to this strategy, is designed for crucial grid management functions such as defect detection, load balancing and real-time power distribution modifications. Historical power grid operation datasets are collected from a variety of sources, this information can include fault records, load profiles, generating statistics and power flows, with preprocessing steps such as data cleaning, managing missing data and outliers by using Z score normalization. Feature extraction is carried out using power spectral density to enhance fault detection and operational stability. The QC-based RTSO-MRF model demonstrates marked improvements in accuracy (98.4%), precision (97.5%), recall (98.33%), F1 score (98%), MAE (0.98), MSE (2.51), RMSE (1.23). This QC-based method presents a significant step forward in power system optimization, paving the way for robust and efficient future grid systems.