A model-free method for learning flexibility capacity of loads providing grid support

TL;DR

This paper introduces a data-driven, model-free method to determine the capacity of flexible loads for grid support by analyzing feasible spectral densities, applicable to nonlinear load dynamics without requiring detailed models.

Contribution

It extends previous spectral density-based capacity characterization methods to nonlinear load dynamics, removing the need for explicit models or parameters.

Findings

The method accurately characterizes load capacity using only simulation data.

It generalizes previous linear methods to nonlinear systems.

Numerical results show good agreement with model-based solutions.

Abstract

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.