An advanced meta metrics-based approach to assess an appropriate optimization method for Wind/PV/Battery based hybrid AC-DC microgrid

• Published in: e-Prime Vol. 9; p. 100640
• Main Authors: Papari, Behnaz, Timilsina, Laxman, Moghassemi, Ali, Khan, Asif Ahmed, Arsalan, Ali, Ozkan, Gokhan, Edrington, Christopher S.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-09-2024; Elsevier
• Subjects: Control; DC Loads; Distributed Generations; Energy Storage Devices; Management System; Microgrids; Optimization; Uncertainty; Distributed Generations; Uncertainty; Control; Management System; DC Loads; Microgrids; Energy Storage Devices; Optimization
• ISSN: 2772-6711; 2772-6711
• DOI: 10.1016/j.prime.2024.100640

•A hybrid energy system associated with renewable energy sources (RESs) has been introduced that makes larger strides towards green and reliable power networks compared to conventional AC/DC networks•A deep analysis of heuristic evolutionary optimization approaches for this hybrid energy system is conducted•The review outlines the present research work and helps provide insight into future control requirements and management systems for modern power networks•Different algorithms like the heuristic approaches, Crow Search Algorithm, Modified Crow Search Algorithm, Particle Swarm Optimization, and Genetic Algorithms are compared depending on their application in MGs•Finally, a meta-metrics comparison yields results showing that a Modified Crow Search Algorithm outperforms others across a spread of test criteria.
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.