Behavioral and Population Data Driven Distribution System Load Modeling

TL;DR

This paper presents a novel methodology for modeling residential distribution system loads by incorporating human behavioral patterns, census data, and geographic variations to improve grid service participation analysis.

Contribution

It introduces a behavior-based load profile model using appliance simulations and census data, highlighting the impact of human behavior and demographics on load variability.

Findings

Load profiles vary significantly by income levels.

Behavioral factors influence daily load patterns.

Geographic differences affect aggregate load characteristics.

Abstract

Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.