A Nonparametric Bayesian Methodology for Synthesizing Residential Solar Generation and Demand Data

TL;DR

This paper introduces a nonparametric Bayesian approach that synthesizes realistic demand and solar generation profiles for prosumers, aiding planning and analysis in low-voltage distribution networks.

Contribution

It develops a hierarchical clustering and Markov chain-based model to generate synthetic profiles from limited data, enhancing probabilistic analysis of distributed energy resources.

Findings

Synthetic profiles closely match observed data statistically

Behavioral differences significantly affect demand profiles

Varying solar penetration impacts demand and generation patterns

Abstract

The uptake of behind-the-meter distributed energy resources in low-voltage distribution networks has reached a level where network issues have started to emerge, which requires new tools for operation and planning. In this paper, we propose a methodology for synthesizing stochastic demand and generation profiles for unobserved customers with rooftop PV, called prosumers. The proposed model bridges the gap between the limited available empirical data, and the large amount of high-quality, stochastic demand and generation data required for probabilistic analysis. The approach employs clustering analysis and a Dirichlet-categorical hierarchical model of the features of unobserved prosumers. Based on the data of clusters of prosumers, Markov chain models of demand and generation profiles are constructed from empirical data, and synthetic demand profiles are subsequently sampled from these. The sampled traces are cross-validated and show a good statistical fit to the observed data. Two case studies are considered to confirm the validity of the proposed methodology. The first studies the impact of behavioral differences on the synthetic demand profiles, while the second looks at the impact of varying solar generation penetration on demand profiles.