Risk Management and Performance Optimization in Solar Smart Grids with AI and IoT

Abstract

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is revolutionizing the management of solar smart grids by enhancing risk management and performance optimization. AI-driven predictive maintenance utilizes real-time data from IoT sensors to anticipate equipment failures, thereby reducing energy losses and minimizing carbon footprints. In the United States, the Department of Energy's Artificial Intelligence for Interconnection (AI4IX) program has allocated $30 million to expedite the integration of renewable energy projects into the power grid using AI, aiming to address the existing backlog of 2,600 gigawatts awaiting connection. However, the increasing reliance on interconnected devices introduces cybersecurity vulnerabilities. Experts have raised concerns about potential cyberattacks on solar batteries and smart home devices, emphasizing the need for robust security measures to protect critical energy infrastructure. This paper explores the dual role of AI and IoT in enhancing operational efficiency and addressing emerging security challenges within solar smart grids, thereby contributing to sustainable and resilient energy systems.