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<p>This research presents a novel framework that integrates renewable energy, smart grids, quantum computing, artificial intelligence (AI) and the Internet of Things (IoT) to create sustainable, resilient and intelligent power systems. The study addresses critical challenges in renewable energy integration, including variable generation, power quality issues and grid stability. The core innovation lies in applying quantum computing algorithms, enhanced by AI, for real-time optimization of smart grid operations. Quantum computing’s parallel processing capabilities enable rapid solutions to complex optimization problems, while AI provides predictive diagnostics and adaptive control. This synergy enhances fault detection, load balancing and power quality management. The framework incorporates Unified Power Quality Conditioners (UPQC) and microcontroller-based smart meters for real-time monitoring and compensation of voltage sags, swells and unbalanced loads. IoT-enabled data acquisition supports low-latency decision-making, ensuring efficient and stable energy distribution. Hardware implementations and simulation-based case studies validate the approach, demonstrating improved grid resilience, reduced energy wastage and enhanced operational security. Cybersecurity considerations are addressed through quantum-safe encryption and secure communication protocols. This work bridges the gap between theoretical advances in quantum-AI technologies and their practical deployment in renewable energy-driven smart grids. The proposed solution offers a scalable, secure and future-ready architecture aligned with global clean energy and sustainability goals.</p>

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Harnessing Quantum Computing and AI for Enhanced Optimization in Renewable Energy-Driven Smart Grid Systems

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • Krishna Sarker

摘要

This research presents a novel framework that integrates renewable energy, smart grids, quantum computing, artificial intelligence (AI) and the Internet of Things (IoT) to create sustainable, resilient and intelligent power systems. The study addresses critical challenges in renewable energy integration, including variable generation, power quality issues and grid stability. The core innovation lies in applying quantum computing algorithms, enhanced by AI, for real-time optimization of smart grid operations. Quantum computing’s parallel processing capabilities enable rapid solutions to complex optimization problems, while AI provides predictive diagnostics and adaptive control. This synergy enhances fault detection, load balancing and power quality management. The framework incorporates Unified Power Quality Conditioners (UPQC) and microcontroller-based smart meters for real-time monitoring and compensation of voltage sags, swells and unbalanced loads. IoT-enabled data acquisition supports low-latency decision-making, ensuring efficient and stable energy distribution. Hardware implementations and simulation-based case studies validate the approach, demonstrating improved grid resilience, reduced energy wastage and enhanced operational security. Cybersecurity considerations are addressed through quantum-safe encryption and secure communication protocols. This work bridges the gap between theoretical advances in quantum-AI technologies and their practical deployment in renewable energy-driven smart grids. The proposed solution offers a scalable, secure and future-ready architecture aligned with global clean energy and sustainability goals.