On Data-Driven Modeling and Control in Modern Power Grids Stability: Survey and Perspective

TL;DR

This survey reviews recent data-driven modeling and control methods for modern power grids, addressing challenges from renewable integration, uncertainty, and nonlinearity, and discusses future trends in the field.

Contribution

It provides a comprehensive overview of emerging data-driven techniques and their applications in power grid stability and control, highlighting future research directions.

Findings

Data-driven methods enhance grid stability amid renewable variability.

Control strategies improve efficiency and security of power systems.

Future trends include advanced data analytics and adaptive control.

Abstract

Modern power grids are fast evolving with the increasing volatile renewable generation, distributed energy resources (DERs) and time-varying operating conditions. The DERs include rooftop photovoltaic (PV), small wind turbines, energy storages, flexible loads, electric vehicles (EVs), etc. The grid control is confronted with low inertia, uncertainty and nonlinearity that challenge the operation security, efficacy and efficiency. The ongoing digitization of power grids provides opportunities to address the challenges with data-driven and control. This paper provides a comprehensive review of emerging data-driven dynamical modeling and control methods and their various applications in power grid. Future trends are also discussed based on advances in data-driven control.