Virtual Inertia Support in Power Systems for High Penetration of Renewables—Overview of Categorization, Comparison, and Evaluation of Control Techniques

Abstract

By replacing conventional generation units with renewable energy sources (RESs), the power system gains an alternate source of future power generation and a better environmental platform. RESs, on the other hand, are unable to provide the required power demand due to poor inertia responses and low-frequency stability. As a result, multiple inertia augmentation control strategies were developed to increase frequency stability and maximize power usage in the grid-integrated renewable energy systems. Accordingly, this study thoroughly reviews existing virtual inertia control (VIC) strategies for improving inertia response and frequency stability. This study investigates 51 VIC approaches regarding required parameters, configurations, key contributions, sources, controllers, and simulation platforms. Furthermore, to emphasize the most promising ones, the VIC approaches are classified as intelligent, adaptive, derivative, coordinated control, and other VIC techniques. The classification approach is followed by the system configuration and the mode of operation of each control scheme. Integrating intelligent methods, such as fuzzy logic, genetic algorithm, non-convex optimization, and heuristic optimization, signify intelligent control methods. In contrast, adaptive control schemes emphasize the adaptation of control operations. These studies include both the standalone and grid-connected RESs frequency and power control approaches with necessary mathematical modelling and equations, which are rarely available in the recent existing works. The current state of research on improving frequency stability and inertia response in the grid-integrated RESs is discussed. Finally, this literature review reflects the present status of VIC technique research paths, and the categorization and analysis of these approaches demonstrate an extensive insight into the research field.

Keywords

• Renewable energy
• virtual inertia control techniques
• synthetic inertia control
• frequency stability
• intelligent control
• adaptive control