On the Use of Causality Inference in Designing Tariffs to Implement More Effective Behavioral Demand Response Programs

Abstract

Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers’ consumption elasticity. It determines the flexibility of each client under the considered DR program and identifies whether the tariffs offered by the DR program affect the consumers’ usual consumption or not. The aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. We identify a set of critical clients who actively participate in DR events along with the most responsive and least responsive clients for the considered DR program. We find that the percentage of DR consumers who actively participate seem to be much less than expected by retailers, indicating that not all consumers’ elasticity is effectively utilized.

Simulating Tariff Impact in Electrical Energy Consumption Profiles With Conditional Variational Autoencoders

“…However, recent research developed mathematical and statistical models for modeling price responsiveness from domestic consumers. Ganesan et al applied a causality model to the Low Carbon London data set in order to rank consumers according to their responsiveness to tariff changes, and outperformed correlation-based metrics [13]. Saez-Gallego and Morales applied inverse optimization to improve the accuracy of load forecasting when aggregating a pool of price-responsive consumers and considering the effect of calendar and weather variables [14]; Le Ray et.al.…”

“…To measure the impact of the tariff on the energy consumption, a causality model similar to the one proposed by Ganesan et al (see [13]) is considered. The finite set of available tariff is denoted by P = {Low, High, Normal} and its cardinal by |P|.…”

“…, T }, of energy consumption, tariffs, and exogenous variables, respectively, a model that gives, for the tariff p and the exogenous variables x h , a forecast of the expected consumption at h when tariff p is picked, is trained. In the original model, the authors used kernel regression and then an approach based on bootstrapping to provide an estimation of the standard deviation (see [13] for further details). In this work, for any exogenous variable x h t and tariff p h t , the random energy consumption…”

Simulating Tariff Impact in Electrical Energy Consumption Profiles with Conditional Variational Autoencoders

“…However, recent research developed mathematical and statistical models for modelling price responsiveness from domestic consumers. Ganesan et al applied a causality model to the Low Carbon London data set in order to rank consumers according to their responsiveness to tariff changes, and outperformed correlationbased metrics Ganesan et al [2019]. Saez-Gallego and Morales applied inverse optimization to improve the accuracy of load forecasting when aggregating a pool of price-responsive consumers and considering the effect of calendar and weather variables Saez-Gallego and Morales [2017]; Le Ray et.al.…”

“…To measure the impact of the tariff on the energy consumption, a causality model similar to the one proposed by Ganesan et al (see Ganesan et al [2019]) is considered. The finite set of available tariff is denoted by P = {Low, High, Normal} and its cardinal by |P|.…”