Voltage Deviation Improvement in Microgrid Operation through Demand Response Using Imperialist Competitive and Genetic Algorithms

In recent decades, with the expansion of distributed energy generation technologies and the increasing need for more flexibility and efficiency in energy distribution systems, microgrids have been considered a promising innovative solution for local energy supply and enhancing resilience against net...

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Bibliographic Details
Published in:Information (Basel) Vol. 15; no. 10; p. 638
Main Authors: Ghaffari, Mahdi, Aly, Hamed H.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2024
Subjects:
Algorithms
Alternative energy sources
Complexity
Cost control
demand response
Demand side management
Distributed generation
Electric potential
Electricity
Electricity distribution
Energy consumption
Energy distribution
Energy efficiency
Energy management systems
Energy resources
Energy storage
Evolutionary algorithms
Genetic algorithms
Genetic research
ICA
Imperialism
Mathematical optimization
microgrid
Optimization
Optimization algorithms
Optimization techniques
Renewable resources
Stability
Voltage
voltage deviation
ISSN:2078-2489, 2078-2489
Online Access:Get full text
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Summary:In recent decades, with the expansion of distributed energy generation technologies and the increasing need for more flexibility and efficiency in energy distribution systems, microgrids have been considered a promising innovative solution for local energy supply and enhancing resilience against network fluctuations. One of the basic challenges in the operation of microgrids is the optimal management of voltage and frequency in the network, which has been the subject of extensive research in the field of microgrid operational optimization. The energy demand is considered a crucial element for energy management due to its fluctuating nature over the day. The use of demand response strategies for energy management is one of the most important factors in dealing with renewables. These strategies enable better energy management in microgrids, thereby improving system efficiency and stability. Given the complexity of optimization problems related to microgrid management, evolutionary optimization algorithms such as the Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) have gained great attention. These algorithms enable solving high-complexity optimization problems by considering various constraints and multiple objectives. In this paper, both ICA and GA, as well as their hybrid application, are used to significantly enhance the voltage regulation in microgrids. The integration of optimization techniques with demand response strategies improves the overall system efficiency and stability. The results proved that the hybrid method provides valuable insights for optimizing energy management systems.
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ISSN:2078-2489
2078-2489
DOI:10.3390/info15100638