12月2日 IEEE TNSE杰出讲座系列活动 | Steven Low教授谈当可再生能源持续激增，智能电网如何保持“智能”

Learning and Control in Power Distribution Grids

Our energy system is undergoing a historic transformation to become more sustainable, dynamic, and open. The power distribution system, where most smart grid innovations will happen, is not well modeled, with the topology and line parameters poorly documented, inaccurate, or missing. This makes maintaining voltage stability challenging as renewable generation continues to proliferate. We present three results to address this challenge. The first result is a method to exactly identify the topology and line admittances of a radial network from voltage and current measurements even when measurements are available only at a subset of the nodes, provided every hidden node has a degree at least 3. The second result is a learning-augmented feedback controller that can leverage real-time measurements to stabilize voltages without explicit knowledge of the network model. We provide convergence guarantee for the proposed method. Finally, we describe the design and deployment of a large-scale EV charging system and an open-source research facility built upon it.