Review of machine learning techniques for optimal power flow

Abstract

The Optimal Power Flow (OPF) problem is the cornerstone of power systems operations, providing generators’ most economical dispatch for power demands by fulfilling technical and physical constraints across the power network. To ensure safe and reliable operation of power systems, grid operators must steadily solve the nonconvex nonlinear OPF problem for immense power networks in (near) real-time, which poses tremendous computational challenges. The enormous amount of available data created by power systems digitalization and recent breakthroughs in machine learning have opened up new opportunities for grid operators to build shortcuts to predict or solve the OPF problem close to real-time. This survey overviews recent attempts at leveraging machine learning algorithms to solve the transmission-level OPF problem. On this basis, the groundwork is laid for commonly employed machine learning approaches leveraged to address the OPF problem. Subsequently, the frequently used performance evaluation metrics in learning-based OPFs are delineated to judge efficiency from diverse aspects (e.g., optimality in terms of the dispatched cost, feasibility concerning technical constraints, and computational efficiency) compared to conventional approaches. Next, the trend and progress of recently developed algorithms are discussed. Finally, the challenges and open problems at the interface of machine learning and OPF problems are highlighted.