An overview of distributed microgrid state estimation and control for smart grids.

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Control Theory

Background:

• Growing concerns over fossil fuel emissions, global warming, and energy crises necessitate the integration of renewable distributed energy resources (DERs) into smart grids.
• Smart grids enable intelligent energy distribution and control, facilitating accurate state estimation (SE) and real-time monitoring of intermittent renewable energy sources.

Purpose of the Study:

• To propose a novel accuracy-dependent Kalman filter (KF) based microgrid SE for smart grids.
• To develop a discrete-time linear quadratic regulation for controlling state deviations in microgrids with multiple DERs.
• To integrate these approaches for novel contributions in green energy and control research.

Main Methods:

• Development of an accuracy-dependent Kalman filter (KF) for microgrid state estimation (SE) in smart grids.
• Implementation of a discrete-time linear quadratic regulation (LQR) for state deviation control in microgrids with multiple DERs.
• Integration of KF-based SE and LQR control for smart grid applications.

Main Results:

• The proposed KF-based microgrid SE algorithm demonstrates accurate state estimation.
• The integrated control algorithm effectively manages state deviations in microgrids with multiple DERs.
• Simulation results confirm the proposed method's superior accuracy and control performance compared to existing approaches.

Conclusions:

• The developed KF-based microgrid SE and control algorithm offers accurate and reliable performance for smart grids.
• This research contributes novel methods for integrating and managing renewable DERs in smart grids.
• The findings support the advancement of green energy technologies and control systems.