Adaptive control for microgrid frequency stability integrating battery energy storage and photovoltaic

Abstract

Abstract The integration and control of Microgrid (MG) systems remain critical challenges in the widespread adoption of renewable energy sources, especially photovoltaic (PV). An adaptive control approach is proposed in this work to improve the MG stability in the presence of PV and battery energy storage systems (BESSs). The proposed approach incorporates adaptive centralized secondary control, primary control, and local PV/BESS control. The primary control based on the droop control approach is applied to regulate voltage and frequency in a decentralized manner while ensuring balanced power-sharing among different distributed generators (DGs) in the MG. Besides that, an adaptive coordinated secondary control is implemented to alleviate the deviations of frequency and voltage caused by PV intermittent generation and load variation, which has a central controller that restores nominal setpoints for all DGs. The BESS type used in this study is a lithium-ion battery which is applied to preserve the DC bus voltage approximately constant during various events, enhance system resilience against PV power intermittency, and balance load power demand. The biggest advantage of the proposed control approach is that it dynamically regulates battery charging and discharging to compensate for variations in PV generation and load demand, ensuring stable system operation. In contrast to conventional studies that assume an ideal DC source to represent DGs, this study models PV generation with real-time fluctuations and maximum power point tracking, providing a practical and realistic simulation environment. The robustness and effectiveness of the proposed technique are validated using MATLAB Software. The results obtained signify highly efficient voltage and frequency stability, improved system resilience under dynamic conditions, and optimal power-sharing among DGs. Finally, a comparative analysis with conventional models highlights the superior adaptability and reliability of the proposed approach, making it a viable solution for real-MG applications.