The integration of high-penetration renewable energy is significant for environmental improvement and the transformation and upgrading of energy systems. However, the uncertainty of renewable energy generation and component failures pose significant challenges to system stability. In this study, we focus on the optimal scheduling and reliability assessment of regional integrated energy systems (RIESs), and we propose a sensor physics data hybrid-driven dynamic reliability assessment and risk mapping framework for power systems with high renewable energy penetration. The proposed framework integrates quantitative reliability assessment and an uncertainty mapping strategy for high-penetration renewable energy systems, as well as a sensor physics data hybrid-driven dynamic fault rate perception strategy. This enables the precise assessment of system reliability and real-time monitoring and the dynamic perception of component failure risks. When gas turbines fail, the operating costs of integrated energy systems (IESs) rise significantly. For example, the cost of IES1 surges from 16791.27$ to 33299.5$. After equipment recovery, IES1’s energy procurement cost decreases by approximately 8.5%, highlighting the value of the proposed framework and algorithm in enhancing the operational efficiency, economy, and reliability of RIESs. In this work, we provide new insights for the optimized design and operational management of IESs.