Enhancing grid efficiency in Bangladesh via AI-optimized hybrid renewable energy systems: An integrated ISM-MICMAC-DEMATEL framework

Integrating artificial intelligence (AI) into hybrid renewable energy systems (HRES) can improve grid efficiency, reliability, sustainability, and renewable penetration. However, few studies have examined AI adoption opportunities in developing countries, such as Bangladesh, using integrated structural modeling. Bangladesh faces major challenges, including grid unreliability, rural electrification gaps, and renewable energy intermittency. This paper fills that research gap. It identifies fifteen key opportunities for AI adoption in HRES through literature review and expert validation. Using insights from 30 domain experts, it analyzes the relationships among these opportunities with an integrated ISM-MICMAC-DEMATEL framework. The results show that Cost Optimization through AI (COA) and Rural Electrification Access (REA) are the main driving factors. These two opportunities strongly influence others and create cascading benefits across the system. MICMAC and DEMATEL analyses also highlight Regulatory Alignment for Renewables (RAR), Strengthened Grid Networks (SGN), and Improved Data Security in Grids (IDSG) as important leverage points. These findings provide actionable insights for policymakers, grid operators, and stakeholders. These findings can help accelerate AI adoption in HRES, support Bangladesh's 40% renewable energy target by 2041, and provide a strategic framework for decision-making in energy transitions in developing countries.