AI in power systems: a systematic review of key matters of concern

Abstract

Recent advances in Artificial Intelligence (AI) have generated both excitement and concern within the power sector. While AI holds significant promise, enabling improved forecasting of renewable energy generation, enhanced grid resilience, and better supply-demand balancing, it also raises critical issues around transparency, data privacy, accountability, and fairness in power distribution. Despite the growing body of research on AI applications in power systems, there is a lack of structured understanding of the key socio-technical matters of concern (MCs) surrounding its integration. This paper addresses this gap by conducting a systematic literature review combined with qualitative text analysis to identify and synthesize the most prominent socio-technical concerns in the academic discourse. We analyzed a curated sample of peer-reviewed papers published between 1987 and 2024, focusing on high-impact journals in the field. Our analysis reveals four major categories of concern: (1) Operational Concerns-relating to AI’s reliability, efficiency, and integration with existing grid systems; (2) Sustainability Concerns-centered on energy consumption, environmental impact, and AI’s role in the energy transition; (3) Trust Concerns-including transparency, explainability, cybersecurity, and ethics; and (4) Regulatory and Economic Concerns-covering issues of accountability, regulatory compliance, and cost-effectiveness. By mapping these concerns into a cohesive analytical framework, this study contributes to the literature by offering a clearer understanding of AI’s sociotechnical challenges in the power sector. The framework also informs future research and policymaking efforts aimed at the responsible and sustainable deployment of AI in power systems.