Akademik Veri Yönetim Sistemi
Ain Shams Engineering Journal, cilt.17, sa.7, 2026 (SCI-Expanded, Scopus)
The significant integration of renewable energy sources (RESs) in contemporary power systems presents a challenge to stability due to their intermittent and low inertia/damping characteristics. Frequency stability is crucial for the reliable and consistent operation of energy systems, especially given the power framework’s essential role in meeting supply and demand. The use of communication networks to control and coordinate distributed energy sources introduces inevitable communication time delays (TDs) that negatively affect the frequency stability of power systems. Furthermore, the stability issue is a complex challenge in conjunction with TDs due to uncertainty in system characteristics and RES production. Superconducting magnetic energy storage (SMES) represents a method for energy storage that minimizes stability issues arising from numerous disturbances that disrupt the operation of the energy system. The SMES retains surplus energy and reintroduces it to the grid, thereby mitigating power and frequency fluctuations and enhancing stability and reliability. This paper initially proposes the incorporation of the SMES into a microgrid (MG) system to improve frequency dynamics, followed by a novel application of Kharitonov’s Theorem and Rekasius substitution utilizing Resultant Theory to calculate robust stability delay margins (SDMs) of the MG system augmented with the SMES, referred to as the MG-SMES system. The comprehensive robust SDMs for various controller gains and the percentage uncertainty in system parameters are calculated for the MG-SMES system to evaluate the effects of SMES and parametric uncertainties on robust SDMs. Results demonstrate that robust SDM markedly improves with the incorporation of the SMES control loop; however, it diminishes as the proportion of uncertainty escalates. For example, with the integration of SMES, SDMs are increased in the range of of 44.59% − 98.38% for the case of rated parameters. Meanwhile, the percentage drop in SDMs is to be in the range of 7.377% − 44.147% and 14.455% − 98.920% for 2% and 4% uncertainties in system parameters, respectively. Finally, time-domain simulations are utilized to validate robust SDMs, supported by a quasi-polynomial mapping-based root-finding technique (QPmR).