Enhancing Smart Grid Efficiency Through Big Data Analytics: A TOE-Based Framework for Renewable Energy Integration

The increasing penetration of variable renewable energy sources, such as solar and wind, presents challenges in real-time data processing and system coordination within smart grids. This paper addresses the need for a systematic approach to integrate big data analytics (BDA) into renewable energy management. Anchored in the Technology–Organisation–Environment (TOE) framework, the research contributes a structured model to support digital transformation in the energy sector. The research contribution is a validated conceptual framework that defines the enablers and constraints of BDA adoption in smart grid systems. The study applied a four-phase qualitative methodology: (i) a narrative review of 65 peer-reviewed publications to extract relevant constructs; (ii) thematic synthesis to identify technological, organizational, and environmental dimensions; (iii) development of a TOE-aligned conceptual model; and (iv) expert validation involving seven industry specialists. The results indicate the framework’s robustness in supporting: (a) scalable data ingestion from distributed sources; (b) seamless integration with existing energy information systems; (c) enhanced compliance with sectoral data regulations; and (d) organisational readiness through skill and infrastructure alignment. Key refinements from expert feedback led to clearer indicators and architecture pathways. The final model offers practical guidance for energy utilities and policymakers to deploy BDA capabilities that improve resilience, operational efficiency, and sustainability. This study advances conceptual understanding and supports future empirical research on smart energy data ecosystems.