Optimal scheduling of distributed generation in smart microgrids: A comprehensive model and efficient algorithm

This study proposes an optimal scheduling model for distributed generation (DG) within smart microgrids, incorporating various distributed energy resources (DERs) such as photovoltaic panels, wind turbines, biomass generators, and energy storage systems. To address the complexities of the scheduling...

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Published in:Journal of physics. Conference series Vol. 2876; no. 1; pp. 12032 - 12036
Main Author: Zhang, Fan
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.11.2024
Subjects:
Biomass energy production
Design optimization
Distributed generation
Energy costs
Energy sources
Energy utilization
Genetic algorithms
Particle swarm optimization
Renewable energy
Resource scheduling
Smart grid
System reliability
Wind turbines
ISSN:1742-6588, 1742-6596
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Abstract This study proposes an optimal scheduling model for distributed generation (DG) within smart microgrids, incorporating various distributed energy resources (DERs) such as photovoltaic panels, wind turbines, biomass generators, and energy storage systems. To address the complexities of the scheduling problem, we design a hybrid optimization algorithm combining Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). This hybrid algorithm leverages the global search capabilities of GA and the local search efficiency of PSO to achieve robust and efficient convergence to near-optimal solutions. A comprehensive case study based on a real-world smart microgrid system demonstrates the effectiveness of the proposed model and algorithm. The results indicate significant reductions in total operational costs, enhanced renewable energy utilization, reduced grid dependency, and improved system reliability. This research highlights the potential for broader implementation of the model, contributing to the advancement of smart grid technologies and the transition towards sustainable energy systems.
AbstractList This study proposes an optimal scheduling model for distributed generation (DG) within smart microgrids, incorporating various distributed energy resources (DERs) such as photovoltaic panels, wind turbines, biomass generators, and energy storage systems. To address the complexities of the scheduling problem, we design a hybrid optimization algorithm combining Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). This hybrid algorithm leverages the global search capabilities of GA and the local search efficiency of PSO to achieve robust and efficient convergence to near-optimal solutions. A comprehensive case study based on a real-world smart microgrid system demonstrates the effectiveness of the proposed model and algorithm. The results indicate significant reductions in total operational costs, enhanced renewable energy utilization, reduced grid dependency, and improved system reliability. This research highlights the potential for broader implementation of the model, contributing to the advancement of smart grid technologies and the transition towards sustainable energy systems.
Author Zhang, Fan
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Cites_doi 10.1016/j.egyr.2023.05.067
10.3390/en17071760
10.3390/en9121031
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Vignesh Babu (JPCS_2876_1_012032bib6) 2023; 9
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SubjectTerms Biomass energy production
Design optimization
Distributed generation
Energy costs
Energy sources
Energy utilization
Genetic algorithms
Particle swarm optimization
Renewable energy
Resource scheduling
Smart grid
System reliability
Wind turbines
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