Indirect control of flexible demand forpower system applications.

This paper describes the application of a grey-box modelling methodology to the system identification of a domestic refrigeration unit utilizing measurements from an instrumented commercially available freezer. The proposed models are formulated using stochastic differential equations (SDEs), estimated from the measurements applying maximum likelihood estimation (MLE), validated through the model residuals analysis and cross-validated to detect model overfitting. We show that including a nonlinear description of the coefficient of performance (COP) of the refrigeration cycle improves the ability of the models to explain the dynamics observed from the measurements. As an application, we show how the proposed models can be utilized to predict and optimize the electricity consumption of a freezer in a smart grid scenario utilizing model predictive control (MPC). The proposed methodology and models can be adopted and adapted to identify other kinds of refrigeration units and for real-time online estimation of the parameters.

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