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Sequential Bayesian Parameter Estimation of Stochastic Dynamic Load Models

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In this paper we focus on the parameter estimation of dynamic load models with stochastic terms—in particular, load models where protection settings are uncertain, such as in aggregated air conditioning units. We show how the uncertainty in the aggregated protection characteristics can be formulated as a stochastic differential equation with process noise. We cast the parameter inversion within a Bayesian parameter estimation framework, and we present methods to include process noise. We demonstrate the benefits of considering stochasticity in the parameter estimation and the risks of ignoring it.

Author(s):

Daniel Adrian Maldonado    
Argonne National Laboratory
United States

Vishwas Rao    
Argonne National Laboratory
United States

Mihai Anitescu    
Argonne National Laboratory
United States

Vivak Patel    
University of Wisconsin-Madison
United States

 

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