Enhanced Stability and Optimization of SMES-Based Deregulated Power Systems Using the Repulsive Firefly Algorithm

Mohanty A., Mohanty S., Mohanty P. P., Soudagar M. E. M., Ramesh S., Bhutto J. K., ...Daha Fazla

PHYSICA C: SUPERCONDUCTIVITY AND ITS APPLICATIONS, cilt.632, ss.1354692, 2025 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 632
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.physc.2025.1354692
  • Dergi Adı: PHYSICA C: SUPERCONDUCTIVITY AND ITS APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Chemical Abstracts Core, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1354692
  • Recep Tayyip Erdoğan Üniversitesi Adresli: Evet

Özet

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.