Probabilistic characterization of electricity consumer responsiveness to economic incentives

Abstract

Within the framework of research of this thesis, which is the feasibility and the efficient implementation of demand response as a flexibility resource for a more active and efficient operation of distribution networks, the aim of this paper is to present an empirical methodology to obtain a full characterization of residential consumers’ flexibility in response to economic incentives. The aim of the proposed approach is to assist a hypothetical demand response provider in the task of quantifying flexibility of a real population of consumers during a supposed trial that would precede a large-scale implementation of a demand response program. For this purpose, mere average values of predictable responsiveness do not provide meaningful information about the uncertainties associated to human behaviour so a probabilistic characterization of this flexibility based on Quantile Regression (QR) is suggested. The proposed use of QR to model individual observed flexibility, provides a concise parametric representation of consumers that allows a straight application of classification methods to classify the sample of consumers into categories of similar flexibility. The proposed modelling approach also depicts a full picture of uncertainty and variability of the expected flexibility and enables the definition of two specific risk measures for the context of demand response that have been denominated flexibility at risk (FaR) and conditional flexibility at risk (CFaR). The application of the methodology to a case study based on a real demand response experience illustrates the potential of the method to capture the complexity and variability of consumer responsiveness.