Data-Centric Modeling of Inverter-Based Resources using Systems Behavioral Theories

Incorporation of inverter-based resources (IBRs)—including renewable energy technologies and energy storage systems into smart grid can provide cost savings, reduce CO
2
emissions, and enhance grid resilience. However, traditional approaches for the control of IBRs rely heavily on pre-established models and systems identification tools, which are often difficult to obtain and might not be able to capture parameter changes or uncertainties. This paper proposes a new data-centric model identification approach for grid-connected IBRs using emerging systems behavioral theories. It is shown that by utilizing the fundamental principles of persistence of excitation, the required data to identify the IBR dynamics can be minimized. In addition, utilizing available trajectory of input/output data organized into the matrix form, we show that Hankel matrix of input/output data can be utilized to find a model-free data-based representation of IBR dynamics. Compared with existing model-free control designs that heavily rely on machine learning methods, require a large amount of data, and cannot provide stability guarantees, the proposed method requires minimum data, is generalizable to various IBR operating conditions, and provides convergence guarantees given the persistence of excitation condition is met. Time-domain simulations validate the effectiveness of the proposed data-centered IBR control approach.