Advances in Microgrid Stability and Control

The field of microgrid research is moving towards the development of more advanced stability analysis and control methods. This is driven by the need for reliable and efficient operation of microgrids, particularly in the presence of inductive coupling lines and high penetration of distributed energy resources. Recent work has focused on the use of dynamic phasors, state-space averaging, and model predictive control to improve the stability and performance of microgrids. Additionally, there is a growing interest in the development of adaptive and hybrid control strategies, such as those using machine learning and information gap decision theory, to address the challenges of transient stability and frequency provision in grids with high inverter-based resource penetration. Notable papers in this area include:

  • A Generalized Stability Analysis Method with Dynamic Phasors for LV AC Microgrids, which presents a new stability analysis method for microgrids with inductive coupling lines.
  • Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning, which introduces a framework for proactive redispatch of resources to ensure transient stability in grids with high IBR penetration.

Sources

A Generalized Stability Analysis Method with Dynamic Phasors for LV AC Microgrids

Modelling and Control of a Buck Converter Using State-Space Averaging and Classical Feedback Techniques

Hardware test and validation of the angular droop control: Analysis and experiments

Integrated Switched Capacitor Array and Synchronous Charge Extraction with Adaptive Hybrid MPPT for Piezoelectric Harvesters

Model Predictive Black Start for Dynamic Formation of DER-Led Microgrids with Inrush Current Impacts

Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning

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