Advanced Fast Computing Methods for Uncertain Power System Operation
* 21PESGM0118, TBD: R. DAI, GEIRINA * 21PESGM0119, TBD: X. ZHANG, University of Birmingham * 21PESGM0120, TBD: J. LIU, Sichun University * 21PESGM0121, Learning sequential distribution system restoration via Graph-reinforcement learning: J. WANG, Southern Methodist University * 21PESGM0122, BCU method for fast stability assessment of power systems with renewables: H-D CHIANG, Cornell University * 21PESGM0831, Propagation of post-fault frequency effects into the GB Distribution network under low-inertia conditions: P. IMRIS, Brunel University London, M. BRADLEY, Brunel University London, G. TAYLOR, Brunel University London, Y. LI, National Grid Electricity System Operator, L. YANG, National Grid Electricity System Operator
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Members: $5.00PES
IEEE Members: $10.00
Non-members: $20.00
26 Jul 2021
The aim of the panel is to share the advanced computation methods including artificial intelligence technologies for power system operation with renewable generation uncertainty, topology uncertainty (outage), and load uncertainty. The panel is to build date-driven and model driven hybrid model and develop parallel computing to solve the hybrid model on time to meet the uncertain and fast change operational conditions presenting to operators in control room. Participants will get a chance to interact with experienced speakers and professionals from independent system operators, utilities, universities, and vendors.
Chairs:
G Liu, Envision Digital
Sponsor Committees:
(PSOPE) Technologies &, Innovation Subcommittee
