Energy Management for Prepaid Customers: A Linear Optimization Approach

TL;DR

This paper introduces a simple linear optimization tool to help prepaid energy customers prioritize appliance use, extend critical device operation, and avoid disconnection, using only average demand data.

Contribution

It presents a novel linear optimization approach that is easier to implement than complex models, maintaining similar effectiveness in managing household energy use.

Findings

Linear model performs comparably to complex MILP models under realistic conditions.

Simple greedy approach solves the optimization problem efficiently.

Model effectively extends critical appliance operation and prevents disconnections.

Abstract

With increasing energy prices, low income households are known to forego or minimize the use of electricity to save on energy costs. If a household is on a prepaid electricity program, it can be automatically and immediately disconnected from service if there is no balance in its prepaid account. Such households need to actively ration the amount of energy they use by deciding which appliances to use and for how long. We present a tool that helps households extend the availability of their critical appliances by limiting the use of discretionary ones, and prevent disconnections. The proposed method is based on a linear optimization problem that only uses average power demand as an input and can be solved to optimality using a simple greedy approach. We compare the model with two mixed-integer linear programming models that require more detailed demand forecasts and optimization solvers for implementation. In a numerical case study based on real household data, we assess the performance of the different models under different accuracy and granularity of demand forecasts. Our results show that our proposed linear model is much simpler to implement, while providing similar performance under realistic circumstances.