Impact of artificial intelligence-driven digital twins and lean six sigma-assisted power system asset management on long-term investment planning

Abstract

The resilience of power grids with high share of variable renewable energy sources (VREs) is increasingly challenged by cybersecurity risks, ageing infrastructure, and inefficient asset management. These vulnerabilities have contributed to widespread blackouts, such as the Iberian Blackout (April 28, 2025), India’s blackout drills, and the Pakistan-India outages (May 10, 2025), and if left unaddressed, could erode consumer confidence and hinder overall investment in grid infrastructure. Considering this rationale, this study examines the potential of Lean Six Sigma (LSS)’s Define, Measure, Analyze, Improve, Control (DMAIC) methodology in power system asset management (PSAM), and the integration of AI-driven Digital Twins (DTs) in smart PSAM. It introduces OptimTwin, an integrated framework combining AI-driven DTs and LSS-assisted smart PSAM. Furthermore, the study evaluates the impact of OptimTwin-based PSAM on long-term investment decisions while ensuring the flexibility of emerging power grids. Using the extended IEEE-118 bus system’s region 1 as a case study, various scenarios were analyzed with the IRENA FlexTool to assess the impact of OptimTwin in investment planning. According to the results, OptimTwin-based PSAM improved investment strategies via life cycle cost (LCC) analysis, enabling 97% VRE penetration with grid flexibility and 9.8% return on investment (ROI).