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

TL;DR

This paper introduces a CVAE-based method to simulate household electricity consumption under different tariffs, capturing rebound effects and consumer segmentation for better demand response planning.

Contribution

The novel approach uses CVAE to generate consumption profiles that include rebound effects and segments consumers by their response to tariffs, improving demand response simulations.

Findings

Comparable performance to semi-parametric models in average data generation

Capable of reproducing rebound and side effects in consumption profiles

Effective consumer segmentation based on elasticity to tariffs

Abstract

The implementation of efficient demand response (DR) programs for household electricity consumption would benefit from data-driven methods capable of simulating the impact of different tariffs schemes. This paper proposes a novel method based on conditional variational autoencoders (CVAE) to generate, from an electricity tariff profile combined with exogenous weather and calendar variables, daily consumption profiles of consumers segmented in different clusters. First, a large set of consumers is gathered into clusters according to their consumption behavior and price-responsiveness. The clustering method is based on a causality model that measures the effect of a specific tariff on the consumption level. Then, daily electrical energy consumption profiles are generated for each cluster with CVAE. This non-parametric approach is compared to a semi-parametric data generator based on generalized additive models and that uses prior knowledge of energy consumption. Experiments in a publicly available data set show that, the proposed method presents comparable performance to the semi-parametric one when it comes to generating the average value of the original data. The main contribution from this new method is the capacity to reproduce rebound and side effects in the generated consumption profiles. Indeed, the application of a special electricity tariff over a time window may also affect consumption outside this time window. Another contribution is that the clustering approach segments consumers according to their daily consumption profile and elasticity to tariff changes. These two results combined are very relevant for an ex-ante testing of future DR policies by system operators, retailers and energy regulators.