چکیده مقاله
The accelerating pace of climate change has rendered traditional energy infrastructure planning models obsolete, particularly in high risk sectors such as oil, gas, and petrochemicals This study introduces a novel resilience oriented framework that leverages Stochastic Multi Agent Reinforcement Learning SMARL to enable autonomous, real time microgrid reconfiguration and adaptive grid islanding in response to climate induced disruptions The model integrates probabilistic climate forecasts RCP 8 5 , equipment failure rates, load criticality hierarchies, and distributed energy resource DER availability into a unified decision making architecture Validated using operational data from an offshore platform cluster in the Persian Gulf and an onshore refinery along the U S Gulf Coast, the framework demonstrates a 47% reduction in Mean Time To Recovery MTTR , 31% decrease in production losses due to blackouts, and 29% lower emergency diesel consumption during extreme weather events Unlike rule based or single agent systems, the proposed SMARL approach enables coordinated, risk aware responses across multiple microgrid zones, transforming passive redundancy into active, learning based resilience This research provides a scalable blueprint for climate adaptive energy management in critical industrial infrastructure
کلیدواژهها
نویسندگان
Siamand Salimi Baneh
Kurdistan Provincial Gas Company
شیوه ارجاع
Salimi Baneh, Siamand,1404,Resilience-Oriented Energy Infrastructure Planning in Oil & Gas Value Chains under Climate-Induced Disruptions: A Stochastic Multi-Agent Reinforcement Learning Framework for Adaptive Grid-Islanding and Microgrid Reconfiguration,Ninth International Conference on Management, Optimization and Development of Energy Infrastructures,Tehran
ارائهشده در
مجموعه مقالات نهمین کنفرانس بین المللی مدیریت، بهینه سازی و توسعه زیرساخت های انرژی29 آبان 1404 · تهران