Data-Driven Predictive Flexibility Modeling of Distributed Energy Resources

Abstract

The potential of distributed energy resources (including, flexible electrical loads) in providing grid services can be maximized with the recent advancements in demand side control. Effective coordination of the flexible loads for grid services, while satisfying end-user preferences and constraints, requires the knowledge of aggregated predictive flexibility of the distributed energy resources (DERs). Recent works have shown that the aggregated predictive flexibility of DERs can be modeled as a virtual battery (VB) whose state evolution is governed by a first-order dynamics including self-dissipation rate and energy capacity. Identifying the VB model parameters for a collection of DERs, however, is challenging primarily due to the following reasons: (1) the composition of DERs is time-varying and uncertain, with the device availability determined by uncertain end-user behavior, (2) the underlying device models and parameters are mostly unknown and uncertain, and (3) lack of available behind-the-meter sensing and measurements (partly due privacy concerns). As such, data-driven deep learning based frameworks have been proposed in this work to identify aggregated predictive flexibility models of a collection of DERs, using front-of-the-meter data (such as net power consumption, etc.). The effectiveness of the proposed frameworks is demonstrated on an ensemble of residential air conditioners and electric water heaters.