Machine learning-based method to support TSO-DSO adaptive coordination with active power management for instability prevention

Coordination between power system operators can improve the power system stability and effectively deploy resources in distribution systems (DS). The research work of this paper provides a coordination method to mitigate the impact of dynamic events on transmission systems (TS). The proposed method uses a machine learning (ML)-based model to estimate the collective dynamic response of DS under varying TS dynamic properties, DS operating conditions, and share of inverter base resources (IBRs). In addition, the ML-based model enables TS operators (TSOs) to provide feedback to DS operators (DSOs) for controlling the IBRs’ active power output to prevent post-fault instabilities. The proposed TSO-DSO coordination method includes a risk-based active power setpoint optimizer for instability prevention. The proposed method uses existing measurement and IBR control platforms available in DS and estimates the post-fault DS dynamic response considering IBR active power control actions. Case studies on synthetic models of TS and DS covering the Zeeland province in The Netherlands illustrate the application of the proposed coordination and the instability risk mitigation when optimizing IBR setpoints. • TSO-DSO coordination for dynamic stability considering the DS dynamic response. • A machine learning-based approach to represent the aggregated DS dynamic response. • A machine learning model combining sequential and non-sequential features. • Aggregated DS dynamic response considering variable system properties and IBRs. • An algorithm for risk-based selection of TSO actions to prevent instability.