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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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TZID:Asia/Hong_Kong
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DTSTART:20250101T000000
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DTSTART;TZID=Asia/Hong_Kong:20260129T110000
DTEND;TZID=Asia/Hong_Kong:20260129T120000
DTSTAMP:20260510T191823
CREATED:20260119T015543Z
LAST-MODIFIED:20260119T015543Z
UID:114612-1769684400-1769688000@ece.hku.hk
SUMMARY:Seminar on Distributed Optimisation Frameworks for Large-Scale Nonlinear Programming in Power Systems
DESCRIPTION:Abstract\nThe ongoing energy transition is challenging centralised power system paradigms by rapidly integrating distributed energy resources (DERs)\, which introduce significant supply-demand variability. This variability complicates grid management and necessitates enhanced coordination among operators. Centralised data aggregation further exacerbates privacy risks and strains the communication infrastructure as the proliferation of controllable devices increases.\nTo address these challenges\, this presentation introduces advances in distributed frameworks for nonconvex nonlinear programming (NLP). The first approach refines a distributed Sequential Quadratic Programming (SQP) framework that integrates the barrier method and Schur-complement-based communication reduction\, enabling efficient parallelisation through graph decomposition. Large-scale AC optimal power flow (OPF) benchmarks demonstrate its superiority over the centralised solver IPOPT. The framework solves problems with over 500\,000 variables at speeds 2–8 times faster than IPOPT on standard workstations while maintaining numerical robustness. The second approach leverages the hierarchical structure of integrated transmission–distribution (ITD) systems and casts coordination as a non-iterative\, two-layer optimisation scheme. By communicating aggregated distribution-level flexibility to the transmission layer\, the method eliminates the need for detailed distribution-network models in system-level coordination. Simulations under severe weather conditions in Germany demonstrate robustness to prediction errors and underscore the scalability and privacy-preserving properties of the proposed strategy. \nSpeaker\nDr. Xinliang DAI\nPostdoctoral Research Associate\,\nPrinceton University \nSpeaker’s Biography\nDr. Xinliang DAI received the B.Sc. degree from Jilin University\, China\, and the M.Sc. and Ph.D. degrees from the Karlsruhe Institute of Technology (KIT)\, Germany. He is currently a Postdoctoral Research Associate with the Zero-carbon Energy Systems Research and Optimisation Laboratory (ZERO Lab) at Princeton University\, USA. His research interests include graph-based distributed optimisation\, flexibility aggregation\, and GPU acceleration for large-scale optimisation. \nOrganiser\nProfessor Tao LIU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20260129-1/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2026/01/1280-3.jpg
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