Designing a Robust and Cost-Efficient Electrified Bus Network with Sparse Energy Consumption Data

TL;DR

This paper develops two mathematical models to optimize charging infrastructure for electric buses under uncertain and sparse energy data, improving reliability and reducing costs compared to traditional methods.

Contribution

It introduces a novel distributionally robust optimization model that effectively handles data sparsity and uncertainty in energy consumption for bus network design.

Findings

Ignoring consumption variability leads to 55% infeasible trips.

Worst-case design increases costs by 67%.

Data-driven model reduces costs by 28% while maintaining reliability.

Abstract

This paper addresses the challenges of charging infrastructure design (CID) for electrified public transport networks using Battery Electric Buses (BEBs) under conditions of sparse energy consumption data. Accurate energy consumption estimation is critical for cost-effective and reliable electrification but often requires costly field experiments, resulting in limited data. To address this issue, we propose two mathematical models designed to handle uncertainty and data sparsity in energy consumption. The first is a robust optimization model with box uncertainty, addressing variability in energy consumption. The second is a data-driven distributionally robust optimization model that leverages observed data to provide more flexible and informed solutions. To evaluate these models, we apply them to the Rotterdam bus network. Our analysis reveals three key insights: (1) Ignoring variations in energy consumption can result in operational unreliability, with up to 55\% of scenarios leading to infeasible trips. (2) Designing infrastructure based on worst-case energy consumption increases costs by 67\% compared to using average estimates. (3) The data-driven distributionally robust optimization model reduces costs by 28\% compared to the box uncertainty model while maintaining reliability, especially in scenarios where extreme energy consumption values are rare and data exhibit skewness. In addition to cost savings, this approach provides robust protection against uncertainty, ensuring reliable operation under diverse conditions.