Time and Supply Fairness in Electricity Distribution using $k$-times bin packing

2026-05-12

AI Summary

This study addresses the challenge of fairly allocating household electricity usage under supply constraints according to egalitarian principles. The authors introduce the $k$-bin packing problem ($k$BP), which assigns each electricity demand to $k$ time slots, and apply it for the first time to equitable electricity distribution. They demonstrate that temporal fairness in electricity access can be modeled as a $k$BP with bounded $k$, and develop generalized algorithms based on First-Fit and its variants. For energy-based fairness, they propose four novel heuristic algorithms and establish a comprehensive evaluation benchmark. Experimental results on real-world data show that the proposed methods significantly outperform existing heuristics, achieving superior performance in connection-time fairness and setting a new state-of-the-art benchmark for energy fairness allocation.

Abstract

Given items of different sizes and a fixed bin capacity, the bin-packing problem is to pack these items into the minimum number of bins such that the sum of the item sizes in each bin does not exceed the capacity. We define a new variant, k-times bin-packing (kBP), in which the goal is to pack the items so that each item appears exactly k times in k different bins. We generalize existing approximation algorithms for bin-packing to solve kBP and analyze their performance ratios. The fair electricity division problem motivates the study of kBP. The goal is to allocate the available supply among households using some fairness criteria, such as the egalitarian principle. We prove that every electricity division problem can be solved by k-times bin-packing for some finite k, which depends only on the number of households. We implement generalizations of the First-Fit and First-Fit Decreasing bin-packing algorithms to solve kBP and apply them to real electricity demand data. We show that our generalizations outperform existing heuristic solutions to the same problem in terms of the egalitarian allocation of connection time. We study another variant of the egalitarian allocation problem, in which the goal is to maximize the minimum number of watts allocated to a household. For this variant, we prove an impossibility result: there does not exist such a k that depends only on the number of agents. This impossibility result motivates us to develop four different heuristic algorithms to solve the egalitarian allocation of watts problem. We evaluate the heuristics by summing the minimum watts allocated to any household in each hour, yielding a fairness metric that reflects the lowest watt allocation across all hours. A higher total minimum of watts indicates a more equitable distribution. Thus, we establish new benchmarks for fair allocation of watts.

Problem

• fair electricity allocation
• egalitarian fairness
• k-times bin packing
• minimum watts maximization
• resource distribution

Innovation

• k-times bin packing
• fair electricity allocation
• egalitarian fairness
• approximation algorithms
• heuristic algorithms