International Research Journal of Education and Technology

The increasing penetration of renewable energy sources in modern power grids introduces significant challenges in voltage stability, frequency regulation, and power oscillation damping. This paper presents a Hybrid Energy Storage System (HESS) integrating Supercapacitors and Battery Energy Storage Systems (BESS), controlled by an Adaptive Neuro-Fuzzy Inference System (ANFIS), to enhance transient stability in the Modified Finnish Grid. Load flow and transient stability analyses were conducted using ETAP, simulating disturbances such as three-phase faults, sudden load increases, and renewable energy variability. Results indicate that HESS significantly improves system stability compared to standalone BESS and STATCOM solutions. Voltage recovery time was reduced by 57.1%, stabilizing within 1.8 seconds, while frequency deviation was maintained within ±0.5 Hz, achieving a 49.1% faster recovery (2.7 seconds). Power oscillation damping improved by 74.4%, with oscillations subsiding within 3.2 seconds compared to 7.8 seconds without HESS. Additionally, the maximum stable power export increased by 44.4%, allowing a higher 650 MW transfer without compromising grid stability. The integration of ANFIS-based control enables real-time optimization, dynamically adjusting power dispatch between supercapacitors and BESS. The results confirm that HESS is a viable solution for improving transient stability, enhancing power export capacity, and managing renewable energy fluctuations. Future research will focus on real-time hardware implementation and AI-driven predictive control strategies to further optimize energy storage performance.

Keywords—Hybrid Energy Storage System, HESS, Adaptive Neuro-Fuzzy Inference System, ANFIS, Supercapacitors, BESS, Transient Stability, Renewable Energy Integration, Load Flow Analysis, Frequency Regulation.