Managing Communication Delays and Model Error in Demand Response for Frequency Regulation

Abstract

This study develops and compares several networked control and estimation algorithms to manipulate the power consumption of a population of residential thermostatically controlled loads (TCLs) to fulfill PJM frequency regulation requests given an imperfect communication network and modeling error. The algorithms rely on a model of the plant to reduce the effects of communication delays, and include a stochastic, predictive controller and two Kalman filter-based state estimation techniques. The first estimator uses a set of independent Kalman filters that run in parallel, and the second incorporates individual TCL models that rely on identified thermal parameters. We use simulations to examine 1) the algorithms’ ability to adequately provide frequency regulation under a range of delay severities, and 2) the effect of increased modeling error. We find that both estimator–controller combinations provide acceptable frequency regulation with average delays of 20 s and minor modeling error. When we increase the modeling error by using a higher order model to represent the TCLs within the plant, the first estimator provides acceptable frequency regulation, whereas the second estimator provides poor frequency regulation.