Simulating Tariff Impact in Electrical Energy Consumption Profiles with Conditional Variational Autoencoders
This work addresses the need for data-driven simulation tools for energy system operators, retailers, and regulators to test demand response policies, though it is incremental as it builds on existing methods like CVAEs and clustering.
The paper tackled the problem of simulating household electricity consumption under different tariff schemes for demand response programs, proposing a conditional variational autoencoder method that generates daily profiles and clusters consumers by behavior and price-responsiveness, with results showing comparable performance to a semi-parametric baseline in average value reproduction and the ability to capture rebound effects.
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.