Large language models in sustainable energy systems : a systematic review on modeling, optimization, governance, and alignment to sustainable development goals

Abstract

Sustainable energy systems (SESs) support intelligent modeling, automation, and governance that enable energy access, infrastructure innovation, and climate resilience. Despite their potential, their integration with large language models (LLMs) raises concerns regarding energy intensity, transparency, equity, and regulation. This study adopts a mixed-methods review combining a BERTopic-based thematic analysis and case-based synthesis to examine applications of LLMs in energy modeling, optimization, etc., and to assess their alignment with the United Nations Sustainable Development Goals. These applications support SDG 7 (Affordable and Clean Energy) by improving access to energy knowledge and decision support, SDG 9 (Industry, Innovation and Infrastructure) through intelligent and scalable digital infrastructure, and SDG 13 (Climate Action) by climate-responsive planning and operational efficiency. The findings reveal that modular, agent-based LLM workflows enhance energy modeling and regulatory compliance. However, sustainability trade-offs necessitate responsible Artificial Intelligence (AI) governance emphasizing transparency, ethical design, and inclusivity. This review informs policy and practice by suggesting that LLMs offer potential value for sustainable energy application deployment within responsible AI governance frameworks that emphasize ethical design, accountability, and equitable access. The study provides future research directions using the ADO (antecedents–decisions–outcomes) framework, emphasizing regulatory readiness, ethical design, and inclusive governance aligned with SDGs 7, 9, and 13, among others.