李维波, 彭智明, 张浩, 等. 基于自适应蚁群算法的岛礁混合发电系统电源容量优化方法[J]. 中国舰船研究, 2024, 19(4): 1–9. doi: 10.19693/j.issn.1673-3185.03505
引用本文: 李维波, 彭智明, 张浩, 等. 基于自适应蚁群算法的岛礁混合发电系统电源容量优化方法[J]. 中国舰船研究, 2024, 19(4): 1–9. doi: 10.19693/j.issn.1673-3185.03505
LI W B, PENG Z M, ZHANG H, et al. 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): 1–9 (in Chinese). doi: 10.19693/j.issn.1673-3185.03505
Citation: LI W B, PENG Z M, ZHANG H, et al. 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): 1–9 (in Chinese). doi: 10.19693/j.issn.1673-3185.03505

基于自适应蚁群算法的岛礁混合发电系统电源容量优化方法

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

  • 摘要:
    目的 针对岛礁混合发电系统电源容量配置存在的问题,提出一种基于自适应蚁群算法(ACA)的优化方法。
    方法 采用自适应蚁群算法作为核心优化工具,对岛礁混合发电系统的电源容量进行配置。通过采用自适应蚁群算法模拟蚁群寻食过程,在搜索空间中以可再生能源发电量作为信息素,通过全局搜索找到最优解,实现对可再生能源的充分利用。并以外伶仃岛为目标岛礁,搭建“风光柴储”微电网混合发电系统模型,采用自适应蚁群算法优化配置其容量。
    结果 算法仿真结果表明,相较于改进灰狼算法和人工蜂群算法,自适应蚁群算法能够有效降低微电网混合发电系统的运行成本和对环境的污染,确保供电稳定性。
    结论 所做研究能够有效增加微电网混合发电系统的供电稳定性,减少运行成本与环境污染,从而实现对能源的高效利用。

     

    Abstract:
    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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