A survey on load frequency control using reinforcement learning-based data-driven controller

Abstract

Load frequency control (LFC) is a significant control problem in the operation of interconnected power systems. It keeps the change in system frequency within specific limits by maintaining the balance between power generation and load demand. In modern interconnected power systems, various control strategies, including conventional control techniques and other data-driven approaches, have been adopted to improve the effectiveness of LFC. The control technique based on reinforcement learning (RL) is one of the contemporary data-driven control strategies for LFC. Recently, the attention of researchers has surged towards RL-based control strategies for LFC. Several survey literature has been published in the field of LFC concerning the various control strategies for the effective operation of the power system. However, these surveys have not considered a complete systematic review of RL-driven LFC. An exhaustive review is essential to demonstrate the current status and identify future advancements in this field. This paper presents a comprehensive review of LFC based on the RL-driven control strategy. This study begins by presenting a mathematical and conceptual understanding of reinforcement learning. Finally, a broad classification of RL algorithms and the algorithm-wise literature survey on LFC are provided extensively. This comprehensive and insightful literature survey may serve as a valuable resource for the researchers, addressing the gaps between recent advances, implementation difficulties, and future developments in LFC using the RL-driven control strategy. © 2024 Elsevier B.V.