Chinese Journal of Ship Research, Volume. 19, Issue 4, 139(2024)

Optimization method for island and reef hybrid power generation systems' power capacity based on adaptive ant colony algorithm

Weibo LI1, Zhiming PENG1, Hao ZHANG1, Maojie ZHANG1, and Hualiang FANG2
Author Affiliations
  • 1School of Automation, Wuhan University of Technology, Wuhan 430070, China
  • 2School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
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    Objectives

    Aiming to address the existing challenges in the power capacity configuration of island and reef hybrid power generation systems, this paper proposes an optimization method based on the adaptive ant colony algorithm (ACA).

    Methods

    An ACA is used as the core optimization tool to configure the power capacity of an island and reef hybrid power generation system. The process of ants foraging is simulated by employing the ACA and using the power generation of renewable energy as dynamic pheromones in the search space. The optimal solution is then found through global search, achieving the full utilization of renewable energy. Taking Wai Lingding Island as the target island, a 'wind-solar-diesel-storage' microgrid hybrid power generation system model is constructed, and the ACA is used to optimize its capacity configuration.

    Results

    The simulation results of the algorithm indicate that, compared to the improved Grey Wolf algorithm and Artificial Bee Colony algorithm, the ACA can effectively reduce the operational costs and environmental pollution of the microgrid hybrid power generation system, while ensuring the stability of the power supply.

    Conclusions

    The results of this study can effectively increase the power supply stability of the microgrid hybrid power generation system, reduce operating costs and environmental pollution, and thus achieve efficient utilization of energy resources.

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    Weibo LI, Zhiming PENG, Hao ZHANG, Maojie ZHANG, Hualiang FANG. Optimization method for island and reef hybrid power generation systems' power capacity based on adaptive ant colony algorithm[J]. Chinese Journal of Ship Research, 2024, 19(4): 139

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    Paper Information

    Category: Technology and Equipment for Development and Utilization of Marine New Energy

    Received: Aug. 11, 2023

    Accepted: --

    Published Online: Mar. 14, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.03505

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