Journal of Advances in Mathematics and Computer Science

As renewable energy systems such as wind farms, solar panels, and smart grids grow in importance, they are increasingly susceptible to sophisticated cyber threats. This paper investigates how quantum algorithms can be integrated with cloud-based Artificial Intelligence (AI) to enhance the cybersecurity of these infrastructures. Traditional AI and cloud computing solutions, while valuable, face limitations in addressing complex and evolving cyber threats, especially in the distributed environments of renewable energy systems. Quantum computing, with its ability to process data exponentially faster than classical systems, offers new capabilities for improving threat detection, encryption, and overall security resilience. This study evaluates key quantum algorithms, such as Grover’s Algorithm for faster data search and Shor’s Algorithm for breaking traditional encryption. By analyzing real-world applications, including blockchain-based peer-to-peer energy trading and AI-driven anomaly detection in wind turbines, we demonstrate the practical impact of these advancements. Furthermore, the challenges of integrating quantum-enhanced AI into existing infrastructures such as high costs, hardware limitations, and privacy concerns are explored. Case studies, including the Powerledger project’s use of Zero Trust Architecture in decentralized energy resources and Siemens' Digital Grid Solutions for smart grid protection, provide a grounded perspective on current cybersecurity practices in renewable energy. The findings suggest that while quantum-enhanced AI has the potential to transform cybersecurity in the renewable energy sector, further research is needed in areas such as quantum-resistant cryptography and scalable hybrid quantum-classical models. These technologies could play a crucial role in safeguarding energy infrastructures from increasingly complex cyber threats.