Abstract
This paper discusses the growing influence of renewable energy and distributed generation, emphasizing the need for smart control systems to maximize benefits and optimize network performance. However, the absence of a standardized evaluation framework makes it challenging to compare different control systems effectively, especially in large-scale hybrid networks with both AC and DC components. While hybrid energy systems show promise for greener and more reliable power networks, they introduce complexity to control methods. Researchers are exploring innovative approaches, including linear and nonlinear techniques, to leverage renewable energy sources effectively in hybrid grids. The paper provides an overview of heuristic evolutionary optimization methods for microgrids (AC, DC, and hybrid AC-DC), highlighting promising techniques such as Crow Search Algorithm, Modified Crow Search Algorithm, Particle Swarm Optimization, and Genetic Algorithms. Comparative analysis suggests that the Modified Crow Search Algorithm performs best across various evaluation criteria, indicating its potential for optimizing microgrids effectively.
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