164 citations · 213 across the 13 of their papers we have counts for
3 papers · 1 filter
Physics-Informed Neural Networks for AC Optimal Power Flow
Rahul Nellikkath, Spyros Chatzivasileiadis
This paper introduces, for the first time to our knowledge, physics-informed neural networks to accurately estimate the AC-OPF result and delivers rigorous guarantees about their p…
Network-Aware Flexibility Requests for Distribution-Level Flexibility Markets
Eléa Prat, Irena Dukovska, Lars Herre +3
This paper proposes a method to design network-aware flexibility requests for local flexibility markets. These markets are becoming increasingly important for distribution system o…
Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow
Rahul Nellikkath, Spyros Chatzivasileiadis
Physics-informed neural networks exploit the existing models of the underlying physical systems to generate higher accuracy results with fewer data. Such approaches can help drasti…