  Flexible loads are a resource for the Balancing Authority (BA) of the future
to aid in the balance of power supply and demand. In order to be used as a
resource, the BA must know the capacity of the flexible loads to vary their
power demand over a baseline without violating consumers' quality of service
(QoS). Existing work on capacity characterization is model-based: They need
models relating power consumption to variables that dictate QoS, such as
temperature in case of an air conditioning system. However, in many cases the
model parameters are not known or difficult to obtain. In this work, we pose a
data driven capacity characterization method that does not require model
information, it only needs access to a simulator. The capacity is characterized
as the set of feasible spectral densities (SDs) of the demand deviation. The
proposed method is an extension of our recent work on SD-based capacity
characterization that was limited to linear time invariant (LTI) dynamics of
loads. The method proposed here is applicable to nonlinear dynamics. Numerical
evaluation of the method is provided, including a comparison with the
model-based solution for the LTI case.
