Estimation and Control of Quality of Service in Demand Dispatch

TL;DR

This paper develops statistical methods to estimate and control the quality of service in demand dispatch, ensuring reliable grid support while maintaining acceptable service levels for individual loads.

Contribution

It introduces a Gaussian approximation for QoS distribution and proposes local control techniques to truncate QoS, balancing grid performance and service quality.

Findings

QoS histogram is approximately Gaussian.

Local control can truncate QoS to guarantee bounds.

Tradeoff exists between tracking performance and QoS bounds.

Abstract

It is now well known that flexibility of energy consumption can be harnessed for the purposes of grid-level ancillary services. In particular, through distributed control of a collection of loads, a balancing authority regulation signal can be tracked accurately, while ensuring that the quality of service (QoS) for each load is acceptable {\it on average}. In this paper it is argued that a histogram of QoS is approximately Gaussian, and consequently each load will eventually receive poor service. Statistical techniques are developed to estimate the mean and variance of QoS as a function of the power spectral density of the regulation signal. It is also shown that additional local control can eliminate risk: The histogram of QoS is {\it truncated} through this local control, so that strict bounds on service quality are guaranteed. While there is a tradeoff between the grid-level tracking performance (capacity and accuracy) and the bounds imposed on QoS, it is found that the loss of capacity is minor in typical cases.