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Optimizing renewable based generations in AC/DC microgrid system using hybrid Nelder-Mead – Cuckoo Search algorithm

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  • Senthil Kumar, J.
  • Charles Raja, S.
  • Jeslin Drusila Nesamalar, J.
  • Venkatesh, P.

Abstract

This paper proposes a Hybrid Nelder-Mead and Cuckoo Search (HNMCS) algorithm to minimize the power loss in hybrid AC/DC microgrid systems by optimizing the output power of Renewable Energy Distributed Generators (REDG). The non-linear power loss minimization problem is solved by the proposed HNMCS to optimize the size of REDG. So far, the REDG sizing is determined by considering generator output as variable whereas in the proposed technique, the area required for the operation of REDG in hybrid AC/DC microgrid is taken as variable. The microgrids are developed by categorizing the existing distribution system to multiple zones. A hybrid AC/DC microgrid is developed with AC grids supported by substation and DC grids operated by their individual REDG units. The suitable location for REDG units including the combination of solar-photovoltaic modules and fuel cells in DC grid is identified by Loss Reduction Sensitivity Factor (LRSF). A standard 33-bus and 69-bus radial distribution system is modeled as a hybrid AC/DC microgrid system. The system is analyzed for its performance in stand-alone system and extended to zone cataloging as residential, industrial and commercial zones. The proposed HNMCS algorithm identifies the optimal solution for REDG sizing with improved convergence rate and reduced simulation time.

Suggested Citation

  • Senthil Kumar, J. & Charles Raja, S. & Jeslin Drusila Nesamalar, J. & Venkatesh, P., 2018. "Optimizing renewable based generations in AC/DC microgrid system using hybrid Nelder-Mead – Cuckoo Search algorithm," Energy, Elsevier, vol. 158(C), pages 204-215.
    • Handle: RePEc:eee:energy:v:158:y:2018:i:c:p:204-215
    DOI: 10.1016/j.energy.2018.06.029
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    Cited by:

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    2. Chen, Xiaoyuan & Zhang, Mingshun & Jiang, Shan & Gou, Huayu & Zhou, Pang & Yang, Ruohuan & Shen, Boyang, 2023. "Energy reliability enhancement of a data center/wind hybrid DC network using superconducting magnetic energy storage," Energy, Elsevier, vol. 263(PA).
    3. Peddakapu, K. & Mohamed, M.R. & Srinivasarao, P. & Licari, J., 2024. "Optimized controllers for stabilizing the frequency changes in hybrid wind-photovoltaic-wave energy-based maritime microgrid systems," Applied Energy, Elsevier, vol. 361(C).
    4. Ehab S. Ali & Sahar. M. Abd Elazim & Sultan H. Hakmi & Mohamed I. Mosaad, 2023. "Optimal Allocation and Size of Renewable Energy Sources as Distributed Generations Using Shark Optimization Algorithm in Radial Distribution Systems," Energies, MDPI, vol. 16(10), pages 1-27, May.
    5. Oruc, Ridvan & Baklacioglu, Tolga, 2022. "Modeling of aircraft performance parameters with metaheuristic methods to achieve specific excess power contours using energy maneuverability method," Energy, Elsevier, vol. 259(C).
    6. Lee, Jui-Yuan & Aviso, Kathleen B. & Tan, Raymond R., 2019. "Multi-objective optimisation of hybrid power systems under uncertainties," Energy, Elsevier, vol. 175(C), pages 1271-1282.
    7. Belqasem Aljafari & Subramanian Vasantharaj & Vairavasundaram Indragandhi & Rhanganath Vaibhav, 2022. "Optimization of DC, AC, and Hybrid AC/DC Microgrid-Based IoT Systems: A Review," Energies, MDPI, vol. 15(18), pages 1-30, September.
    8. Ghiasi, Mohammad, 2019. "Detailed study, multi-objective optimization, and design of an AC-DC smart microgrid with hybrid renewable energy resources," Energy, Elsevier, vol. 169(C), pages 496-507.

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