Citation :

Shun Quan Chai, Min Keng Tan, Kit Guan Lim, Ahmad Razani Haron, Kenneth Tze Kin Teo, "Advancements in Automatic Generation Control: Trends in Intelligent Optimization for Modern Power Systems," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 5, pp. 104-137, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I5P108

Abstract

Modern power systems are growing increasingly complex with high uncertainty because of the increasing utilization of Renewable Energy Sources (RES). Thus, a power system requires intelligent optimization using Artificial Intelligence (AI) to improve power grid stability. However, there is still a lack of comprehensive reviews that trace the evolution of AI-based algorithms in optimizing Automatic Generation Control (AGC), including their challenges and future directions. Therefore, this paper presents a chronological review of these methods in optimizing AGC from the 2000s to the 2020s. This analysis explains how these AI algorithms have solved challenges in AGC across different eras, each with its own distinct advantages. Generally, AI algorithms in AGC are categorized into three main groups, which are fuzzy logic, metaheuristic algorithms, and machine learning. Fuzzy logic dominated the 2000s because its rule-based control reduced the complexity of mathematical modeling in power systems. Metaheuristic algorithms have dominated AGC studies from the 2010s through the current 2020s because they can obtain optimal AGC controller parameters in high-dimensional search spaces efficiently. Recently, in the 2020s, machine learning techniques have been applied increasingly to optimize AGC. This is because they support model-free learning in power systems with complex parameters that are difficult to model. The integration of RES in the power grid remains one of the main challenges in current AGC studies, which should be focused on by future studies. Thus, this review provides valuable information for future research in AGC to enhance the stability of the power grid.

Keywords

Automatic Generation Control, Fuzzy Logic, Evolutionary Algorithm, Swarm Intelligence, Machine Learning.

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