Risk Assessment and Reliability-Centered Optimization for Asset Management and Maintenance Strategies in Renewable-Pentrated Power Systems

Dear Colleagues,

As global power systems rapidly integrate renewable energy as a primary source, the grid is experiencing fundamental shifts in its operational landscape. The intermittency, volatility, and uncertainty of renewable generation, together with the increasing reliance on power electronics and advanced control devices, introduce new challenges for asset management and maintenance strategies. Traditional maintenance practices are no longer sufficient; instead, utilities require risk-informed and reliability-centered frameworks that ensure both resilience and cost-effectiveness.

The “dual challenge” of renewable penetration and aging infrastructure has significantly altered equipment performance profiles and failure risks. It is now critical to develop quantitative risk assessment models, optimize maintenance scheduling under uncertainty, and establish adaptive strategies that balance reliability, economics, and sustainability. Moreover, digital technologies—such as data analytics, stochastic optimization, and reinforcement learning—play a pivotal role in enabling predictive, intelligent, and resource-constrained decision-making in asset management.

This Special Issue aims to bring together state-of-the-art research and practical advancements in risk-based assessment and reliability-centered optimization for asset management and maintenance planning. By bridging theoretical development with real-world applications, this topical collection will provide guidance for ensuring the safety, reliability, and long-term sustainability of renewable-penetrated power systems.

Topics

• Risk assessment methodologies for power equipment under renewable energy variability;
• Reliability-centered optimization for asset management and maintenance planning;
• Probabilistic and stochastic modeling of equipment degradation and failure;
• Multi-objective optimization of cost, risk, and reliability in renewable-dominated grids;
• Artificial intelligence and machine learning for predictive maintenance, diagnosis, and decision support;
• Intelligent sensing technologies and long-term sensor reliability under harsh conditions;
• Multi-physics simulation, digital modeling, and digital twin technologies for power equipment;
• Full life-cycle health assessment, life extension, and risk-informed maintenance strategies;
• Intelligent transmission and substation equipment technologies for renewable-penetrated systems;
• Unmanned and autonomous inspection/maintenance technologies under complex conditions;
• Case studies and industrial applications in renewable-intensive power systems.

Dr. Nan Zhou
Dr. Lingen Luo
Dr. Lijing Zhang
Guest Editors

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