Data-Driven Security Rules for Real-time System Prediction and Control

The integration of renewable energy into the power system requires us to rethink the prevailing paradigms of system operation. In the future, operations will be subject to more uncertainty and volatility than in the past. Novel operating decision-making tools are needed to consider these new dynamics, otherwise, investments in redundant grid infrastructure to maintain the current levels of security of supply will increase. In this project, machine learning is investigated as a means for operating the power system and ensuring that system security is maintained. The focus of this project is on static and dynamic security assessment (DSA) and control.

Authors: Jochen Cremer, Federica Bellizio, Ioannis Konstantelos, Goran Strbac, Pauline Gambier-Morel, Patrick Panciatici

Read the report here.