Functional Model of Residential Consumption Elasticity under Dynamic Tariffs

TL;DR

This paper introduces a Bayesian probabilistic model to accurately predict individual consumer demand elasticity under dynamic tariffs, aiding utilities in optimizing demand response programs.

Contribution

It presents a novel functional model using Bayesian methods to quantify individual consumer load adjustments in response to dynamic pricing, enhancing demand response strategies.

Findings

The model accurately predicts consumer load adjustments at various price levels.

It quantifies the likelihood of consumer reactions to demand response signals.

The framework improves utility control and reliability in demand response programs.

Abstract

One of the major barriers for the retailers is to understand the consumption elasticity they can expect from their contracted demand response (DR) clients. The current trend of DR products provided by retailers are not consumer-specific, which poses additional barriers for the active engagement of consumers in these programs. The elasticity of consumers demand behavior varies from individual to individual. The utility will benefit from knowing more accurately how changes in its prices will modify the consumption pattern of its clients. This work proposes a functional model for the consumption elasticity of the DR contracted consumers. The model aims to determine the load adjustment the DR consumers can provide to the retailers or utilities for different price levels. The proposed model uses a Bayesian probabilistic approach to identify the actual load adjustment an individual contracted client can provide for different price levels it can experience. The developed framework provides the retailers or utilities with a tool to obtain crucial information on how an individual consumer will respond to different price levels. This approach is able to quantify the likelihood with which the consumer reacts to a DR signal and identify the actual load adjustment an individual contracted DR client provides for different price levels they can experience. This information can be used to maximize the control and reliability of the services the retailer or utility can offer to the System Operators.