Curative or remedial actions are the set of immediate actions intended to bring the power grid to a safe operating point after a contingency. The effectiveness of these actions is essential to guarantee curative N-1 security. Nowadays, curative actions are derived ahead of time, based on the anticipated future grid state. Due to the shift from steady to volatile energy resources, the grid state will frequently change and the curative actions would need to be pre-planned increasingly often. Furthermore, with the shift from large bulk production to many small decentralized energy sources more devices need to be actuated simultaneously to achieve the same outcome. Instead of pre-planning, we propose to calculate these complex curative actions in real-time after the occurrence of a contingency. We show how the method of Online Feedback Optimization (OFO) is well suited for this task. As a preliminary demonstration of these capabilities, we use an (OFO) controller, that after a fault, reduces the voltage difference over a breaker to enable the operators to reclose it. This test case is inspired by the 2003 Swiss-Italian blackout, which was caused by a relatively minor incident followed by ineffective curative actions.
Finally, we identify and discuss some open questions, including closed-loop stability and robustness to model mismatch.
Adaptive Tuning of Online Feedback Optimization for Process Control Applications
TU Delft（代尔夫特理工大学）；Eastern Switzerland University of Applied Sciences（东瑞士应用科学大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Norwegian University of Science and Technology（挪威科技大学）
PB-OEL: A Performance-Bounded Online Ensemble Learning Framework With Mixed Feedback for Real-Time Safety Assessment
Department of Automation（自动化系）；Institute for Embodied Intelligence and Robotics（具身智能与机器人研究院）；Tsinghua University（清华大学）；China Ship Research and Development Academy（中国船舶研究发展academy）
