Integrating artificial intelligence in energy transition: A comprehensive review - Qiang Wang; Yuanfan Li; Rongrong Li (Energy Strategy Reviews)
The global energy transition, driven by the imperative to mitigate climate change, demands innovative solutions to address the technical, economic, and social challenges of decarbonization. Artificial intelligence (AI) has emerged as a transformative technology in this domain, offering tools to enhance each link in the energy system. This comprehensive review examines the current state of AI applications across key energy transition domains, including renewable energy deployment, energy efficiency, grid stability, and smart grid integration. The study identifies the pivotal role of AI in accelerating the adoption of intermittent renewable energy sources like solar and wind, managing demand-side dynamics with advanced forecasting and optimization, and enabling energy storage and distribution innovations such as vehicle-to-grid systems and hybrid energy solutions. It also highlights the potential of AI to advance energy system stability, address cybersecurity risks, and promote equitable and sustainable energy systems. Despite these advancements, challenges remain, including data quality and accessibility, system interoperability, scalability, and concerns regarding privacy and ethics. By synthesizing recent research and practical case studies, this paper provides insights into the opportunities and limitations of AI-driven energy transformation and offers strategic recommendations to guide future research, development, and policy-making. This review highlights that AI is not just a tool but a transformative catalyst, reshaping global energy systems into equitable, resilient, and sustainable frameworks, essential for achieving a net-zero future.