Based on improved crayfish optimization algorithm cooperative optimal scheduling of multi-microgrid system

In order to solve the influence of the complex interaction relationships among subjects on the system solution accuracy and speed of the Multi-Microgrid system under the high penetration rate of new energy. Firstly, the paper establishes the bi-level optimal scheduling Stackelberg game model based o...

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Veröffentlicht in:Scientific reports Jg. 14; H. 1; S. 24871 - 18
Hauptverfasser: Yan, Dongmei, Wang, Hongkun, Gao, Yujie, Tian, Shiji, Zhang, Hong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 22.10.2024
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/166/987
639/4077/4079
639/4077/909
Algorithms
Crayfish optimization algorithm
Emissions
Energy storage
Humanities and Social Sciences
Multi-microgrid system
multidisciplinary
Optimal scheduling
Optimization algorithms
Pollutants
Pollution control
Science
Science (multidisciplinary)
Shared energy storage
Stackelberg game
Shared energy storage
Stackelberg game
Crayfish optimization algorithm
Multi-microgrid system
Optimal scheduling
ISSN:2045-2322, 2045-2322
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Zusammenfassung:In order to solve the influence of the complex interaction relationships among subjects on the system solution accuracy and speed of the Multi-Microgrid system under the high penetration rate of new energy. Firstly, the paper establishes the bi-level optimal scheduling Stackelberg game model based on shared energy storage, considering the inter-subject interaction in MMG. Subsequently, based on the four improvement methods of Chaotic Map, Quantum Behavior, Gaussian Distribution, and Nonlinear Control Strategy, the Chaotic Gaussian Quantum Crayfish Optimization Algorithm is proposed to solve the optimization scheduling model. The improved algorithm exhibits superior initial solutions and enhanced search capability. In comparison to the original algorithm, the relative errors of the CGQCOA optimization outcomes are 98%, 20.96%, 98.74% and 16.55%, respectively, enhancing the model-solving accuracy and the speed of convergence to the optimal solution. Finally, the simulation demonstrates that the revenue of Microgrid 1, Microgrid 2, and Microgrid 3 have increased by 0.73%, 1.17%, and 1.04%, respectively. Concurrently, the penalty cost of pollutant emission has decreased by 5.9%, 11.5%, and 12.68%, respectively. Furthermore, the revenue of the shared storage have increased by 1.91%. These findings validate the efficacy of the methodology proposed in enhancing the revenue of the various subjects and reducing pollutant gas emission.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-76041-5