Performance Assessment for Automatic Generation Control via Data Selection and Model Identification

Automatic generation control (AGC) of power generation units aims at providing satisfactory responses of generated active powers to desired active powers dispatched from a power grid center. This paper proposes a method to estimate two frequently-used AGC performance metrics of response rapidity and accuracy. The proposed method is composed of two main  parts . The first part selects step-like data segments as those being similar to a designed step-change time sequence, based on consecutive piece-wise linear representations of the desired active power. The second part estimates performance metrics based on a dynamic model identified from the selected data segments. Uncertainties of the estimates are measured by their confidence intervals, which are obtained through step responses of the identified dynamic model with surrogated parameters.  The proposed method resolves two practical challenges: one challenge is that the performance metrics are defined for step responses, but the desired active power in practice changes in various forms, many of which are not suitable for performance assessment. The other challenge is that severe noise often contaminates data samples of the desired active power and generated active power, which may result in large estimation errors. Numerical and industrial examples are provided to support the proposed method.