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DTSTART;TZID=Europe/Stockholm:20260701T143000
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UID:submissions.pasc-conference.org_PASC26_sess117_msaSC107@linklings.com
SUMMARY:HP2C-DT: A General-Purpose Multi-Tier Digital Twin Architecture Ap
 plied to Renewable-Dominated Power Systems
DESCRIPTION:Francesc Lordan, Eduardo Iraola, Mauro Garcia-Lorenzo, and Ros
 a Badia (Barcelona Supercomputing Center)\n\nThe large-scale integration o
 f renewable energy sources is transforming power systems, introducing vari
 ability, uncertainty, and operational complexity. Traditional control appr
 oaches, designed for centralized generation, struggle to ensure stability 
 and resilience in renewable-rich grids. This talk presents HP2C-DT, a gene
 ral-purpose, three-tier digital twin architecture spanning Edge, Cloud, an
 d High-Performance Computing (HPC).\n\nAt the Edge, the digital twin inter
 faces with power electronic devices, sensors, and actuators to enable fast
  protection mechanisms and local AI-driven optimization. The Cloud tier pr
 ovides system-wide supervision, historical archives, and global optimizati
 on, while HPC supports extreme-scale computations such as probabilistic sc
 enario simulations and AI model training.\n\nAI is embedded across tiers, 
 influencing decisions that affect grid robustness, efficiency, and societa
 l reliability. To train robust AI models, HP2C-DT includes DataGen, a Mont
 e Carlo–based data generation tool using adaptive sampling guided by sensi
 tivity analysis and entropy-based exploration. By targeting high-uncertain
 ty, information-rich regions of the operational space, DataGen produces di
 verse datasets that enhance model resilience.\n\nDomain: Climate, Weather,
  and Earth Sciences, Applied Social Sciences and Humanities, Engineering, 
 Computational Methods and Applied Mathematics\n\nSession Chair: Tobias Hod
 el (University of Bern, Switzerland)\n\n
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