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Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

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  • Łukasz Rokicki

    (Faculty of Electrical Engineering, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, Poland)

Abstract

The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is connected. Suboptimal microgrid configuration may cause problems in networks managed by distribution system operators, as well as for electricity consumers and owners of microsources and energy storage systems connected to the microgrid. Structures particularly sensitive to incorrect determination of the operating states of individual devices are hybrid microgrids that combine an alternating current and direct current networks with the use of a bidirectional power electronic converter. An analysis of available literature shows that evolutionary and swarm optimization algorithms are the most frequently chosen for the optimization of power systems. The research presented in this article concerns the assessment of the possibilities of using artificial immune systems, operating on the basis of the CLONALG algorithm, as tools enabling the effective optimization of low voltage hybrid microgrids. In his research, the author developed a model of a hybrid low voltage microgrid, formulated three optimization tasks, and implemented an algorithm for solving the formulated tasks based on an artificial immune system using the CLONALG algorithm. The conducted research consisted of performing a 24 h simulation of microgrid operation for each of the formulated optimization tasks (divided into 10 min independent optimization periods). A novelty in the conducted research was the modification of the hypermutation operator, which is the key mechanism for the functioning of the CLONALG algorithm. In order to verify the changes introduced in the CLONALG algorithm and to assess the effectiveness of the artificial immune system in solving optimization tasks, optimization was also carried out with the use of an evolutionary algorithm, commonly used in solving such tasks. Based on the analysis of the obtained results of optimization calculations, it can be concluded that the artificial immune system proposed in this article, operating on the basis of the CLONALG algorithm with a modified hypermutation operator, in most of the analyzed cases obtained better results than the evolutionary algorithm. In several cases, both algorithms obtained identical results, which also proves that the CLONALG algorithm can be considered as an effective tool for optimizing modern power structures, such as low voltage microgrids, including hybrid AC/DC microgrids.

Suggested Citation

  • Łukasz Rokicki, 2021. "Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator," Energies, MDPI, vol. 14(19), pages 1-24, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6351-:d:650013
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    References listed on IDEAS

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    1. Oussama Ouramdane & Elhoussin Elbouchikhi & Yassine Amirat & Ehsan Sedgh Gooya, 2021. "Optimal Sizing and Energy Management of Microgrids with Vehicle-to-Grid Technology: A Critical Review and Future Trends," Energies, MDPI, vol. 14(14), pages 1-45, July.
    2. Dwi Riana Aryani & Hwachang Song, 2016. "Coordination Control Strategy for AC/DC Hybrid Microgrids in Stand-Alone Mode," Energies, MDPI, vol. 9(6), pages 1-20, June.
    3. Xiong Wu & Zhao Wang & Tao Ding & Zhiyi Li, 2019. "Hybrid AC/DC Microgrid Planning with Optimal Placement of DC Feeders," Energies, MDPI, vol. 12(9), pages 1-23, May.
    4. Jing Li & Hongda Cai & Pengcheng Yang & Wei Wei, 2021. "A Bus-Sectionalized Hybrid AC/DC Microgrid: Concept, Control Paradigm, and Implementation," Energies, MDPI, vol. 14(12), pages 1-24, June.
    5. Shahriar Rahman Fahim & Subrata K. Sarker & S. M. Muyeen & Md. Rafiqul Islam Sheikh & Sajal K. Das, 2020. "Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews," Energies, MDPI, vol. 13(13), pages 1-22, July.
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    Cited by:

    1. Bo Hu & Nan Wang & Zaiming Yu & Yunqing Cao & Dongsheng Yang & Li Sun, 2021. "Optimal Operation of Multiple Energy System Based on Multi-Objective Theory and Grey Theory," Energies, MDPI, vol. 15(1), pages 1-21, December.
    2. Grzegorz Dudek & Paweł Piotrowski & Dariusz Baczyński, 2023. "Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications," Energies, MDPI, vol. 16(7), pages 1-11, March.

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