QUANTUM-INSPIRED OPTIMIZATION OF RENEWABLE ENERGY STORAGE SYSTEMS USING AI-DRIVEN SMART GRID ANALYTICS IN PAKISTAN

Abstract

This study develops and evaluates a quantum-inspired optimization framework integrated with artificial intelligence (AI)-driven smart grid analytics for improving renewable energy storage system performance in Pakistan. The increasing penetration of renewable energy sources such as solar and wind has created significant challenges related to intermittency, load balancing, energy dispatch, and grid stability. To address these issues, the study employs a simulation-based quantitative design to analyze the effectiveness of hybrid AI–quantum optimization in enhancing storage efficiency, forecasting accuracy, and overall smart grid operational performance. The framework utilizes AI-based predictive models for energy demand forecasting and quantum-inspired optimization algorithms for optimal energy storage allocation and real-time decision-making. The results indicate that the proposed hybrid model significantly improves renewable energy storage efficiency, reduces operational costs, enhances load forecasting accuracy, and strengthens grid stability compared to conventional optimization approaches. Furthermore, energy storage efficiency is found to partially mediate the relationship between AI-driven analytics, quantum-inspired optimization, and smart grid performance, confirming its central role in system optimization. The findings demonstrate that quantum-inspired techniques outperform traditional heuristic methods in handling complex, nonlinear energy optimization problems, while AI enhances predictive intelligence in dynamic energy environments. This study contributes to the growing literature on intelligent energy systems by integrating AI and quantum-inspired computation within a unified optimization framework for renewable energy systems in emerging economies. It provides practical insights for policymakers, energy planners, and utility operators in Pakistan to improve energy sustainability, efficiency, and resilience.