Generating Multivariate Load States Using a Conditional Variational Autoencoder

TL;DR

This paper introduces a novel conditional variational autoencoder model for generating realistic multivariate power load scenarios, improving statistical accuracy and tail distribution representation for power system planning.

Contribution

It proposes a CVAE-based generative model with stochastic output variation and co-optimized parameters, enhancing multivariate load data generation quality.

Findings

Outperforms existing data generators in accuracy.

Produces realistic tail distributions in load scenarios.

Improves statistical properties of generated data.

Abstract

For planning of power systems and for the calibration of operational tools, it is essential to analyse system performance in a large range of representative scenarios. When the available historical data is limited, generative models are a promising solution, but modelling high-dimensional dependencies is challenging. In this paper, a multivariate load state generating model on the basis of a conditional variational autoencoder (CVAE) neural network is proposed. Going beyond common CVAE implementations, the model includes stochastic variation of output samples under given latent vectors and co-optimizes the parameters for this output variability. It is shown that this improves statistical properties of the generated data. The quality of generated multivariate loads is evaluated using univariate and multivariate performance metrics. A generation adequacy case study on the European network is used to illustrate model's ability to generate realistic tail distributions. The experiments demonstrate that the proposed generator outperforms other data generating mechanisms.