Online Optimization to Enable Sustainable Public Transport

Abstract

Electrifying transit bus networks (TBNs) has recently become a challenging problem that many public transport operators (PTOs) around the world are facing. Unlike diesel buses (DBs), battery electric buses (BEBs) need to recharge several times during the day due to their limited driving range. Hence, electric TBNs are sensitive to operational delays and uncertainty which may affect the planned charging schedule and result in operational infeasibilities. Moreover, they are only sustainable when powered by renewable energy resources, which are often subject to intermittency and uncertainty. In this work, we tackle the complicated problem of planning the charging schedule amid these various sources of uncertainty. We develop a real-time decision support system (RDSS) that uses real-time data, predictions, and mathematical optimization to develop and update the charging schedule in order to mitigate the impact of operational uncertainties. We formulate a mixed-integer linear programming problem that is solved via the RDSS to optimize the charging schedule. Our results show the benefits of online optimization compared to other charging strategies. Even conservative offline optimization might not be sufficient to guarantee reliable operation, especially under severe weather conditions with high energy consumption rates. The online strategy can maintain higher reliability and renewable energy utilization levels, while reducing the impact on the grid. The study has been carried out in cooperation with the PTO in a major European city to assist them in their TBN transition process. Based on our insights, the PTO started developing online monitoring and control system adopting some of our suggestions.

Keywords: sustainable public transport, electric transit bus networks, sustainable energy, smart charging, online optimization, real-time decision support system