Assessing Electricity Service Unfairness with Transfer Counterfactual Learning

TL;DR

This paper introduces a transfer learning-based method to evaluate systematic unfairness in power outages, revealing that disadvantaged communities face longer outages, especially under severe conditions.

Contribution

It presents a novel transfer learning approach for estimating counterfactual unfairness in energy systems, addressing data scarcity and heterogeneity issues.

Findings

Low-income areas experience longer outages.

Elderly populations face increased outage durations.

Discrimination worsens under severe conditions.

Abstract

Energy justice is a growing area of interest in interdisciplinary energy research. However, identifying systematic biases in the energy sector remains challenging due to confounding variables, intricate heterogeneity in counterfactual effects, and limited data availability. First, this paper demonstrates how one can evaluate counterfactual unfairness in a power system by analyzing the average causal effect of a specific protected attribute. Subsequently, we use subgroup analysis to handle model heterogeneity and introduce a novel method for estimating counterfactual unfairness based on transfer learning, which helps to alleviate the data scarcity in each subgroup. In our numerical analysis, we apply our method to a unique large-scale customer-level power outage data set and investigate the counterfactual effect of demographic factors, such as income and age of the population, on power outage durations. Our results indicate that low-income and elderly-populated areas consistently experience longer power outages under both daily and post-disaster operations, and such discrimination is exacerbated under severe conditions. These findings suggest a widespread, systematic issue of injustice in the power service systems and emphasize the necessity for focused interventions in disadvantaged communities.