Enhanced stability and optimization of SMES-based deregulated power systems using the repulsive firefly algorithm

IF 1.3 3区物理与天体物理 Q4 PHYSICS, APPLIED

Physica C-superconductivity and Its Applications Pub Date : 2025-04-07 DOI:10.1016/j.physc.2025.1354692

Asit Mohanty , Sthitapragyan Mohanty , Pragyan P Mohanty , Manzoore Elahi M. Soudagar , S. Ramesh , Javed Khan Bhutto , Abdulwasa Bakr Barnawi , Erdem Cuce

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引用次数: 0

Abstract

The growing incorporation of renewable energy into deregulated power systems requires advanced hardware solutions, such as Superconducting Magnetic Energy Storage (SMES), as well as more complex control algorithms that surpass traditional Automatic Governor Control (AGC) methods based on PID or IPC techniques.

This paper introduces an innovative method utilizing the Repulsive Firefly Algorithm (RFA) for the dynamic management and optimization of a two-agent deregulated power system. The RFA, augmented with a repulsion mechanism, markedly enhances exploratory capabilities and reduces premature convergence, hence providing strong performance in extremely dynamic and uncertain grid settings. The proposed RFA-based method efficiently mitigates frequency fluctuations and optimizes power distribution across independent market entities by dynamically adjusting the control settings of the SMES and other system components. The fast response and exceptional efficiency of SMES are vital for stabilizing the power grid during fluctuations caused by renewable energy sources. Simulation results indicate that RFA surpasses traditional methods, providing enhanced control accuracy, diminished frequency fluctuations, and increased power flow stability. This study highlights the capability of RFA as a sophisticated optimization instrument for improving the resilience and efficiency of contemporary deregulated power systems that incorporate renewable energy sources.

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利用排斥性萤火虫算法增强基于中小企业的解除管制电力系统的稳定性和优化

可再生能源越来越多地纳入解除管制的电力系统，需要先进的硬件解决方案，如超导磁能存储（sme），以及更复杂的控制算法，超越传统的基于PID或IPC技术的自动调速器控制（AGC）方法。本文介绍了一种利用排斥性萤火虫算法（RFA）对双智能体解除管制电力系统进行动态管理和优化的创新方法。RFA与排斥机制相结合，显著增强了探索能力，减少了过早收敛，因此在极度动态和不确定的网格设置中提供了强大的性能。提出的基于rfa的方法通过动态调整中小企业和其他系统组件的控制设置，有效地减轻了频率波动，并优化了独立市场实体之间的功率分配。在可再生能源引起的波动期间，中小企业的快速响应和卓越的效率对于稳定电网至关重要。仿真结果表明，RFA优于传统方法，提高了控制精度，减小了频率波动，提高了潮流稳定性。这项研究强调了RFA作为一种复杂的优化工具的能力，可以提高纳入可再生能源的当代解除管制电力系统的弹性和效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physica C-superconductivity and Its Applications 物理-物理：应用

CiteScore

2.70

自引率

11.80%

发文量

102

审稿时长

66 days

期刊介绍： Physica C (Superconductivity and its Applications) publishes peer-reviewed papers on novel developments in the field of superconductivity. Topics include discovery of new superconducting materials and elucidation of their mechanisms, physics of vortex matter, enhancement of critical properties of superconductors, identification of novel properties and processing methods that improve their performance and promote new routes to applications of superconductivity. The main goal of the journal is to publish: 1. Papers that substantially increase the understanding of the fundamental aspects and mechanisms of superconductivity and vortex matter through theoretical and experimental methods. 2. Papers that report on novel physical properties and processing of materials that substantially enhance their critical performance. 3. Papers that promote new or improved routes to applications of superconductivity and/or superconducting materials, and proof-of-concept novel proto-type superconducting devices. The editors of the journal will select papers that are well written and based on thorough research that provide truly novel insights.