Peak load minimization of an e-bus depot: impacts of user-set conditions in optimization algorithms

Enrico Toniato,Stevan Marinkovic,Verena Tiefenbeck,Prakhar Mehta

doi:10.1186/s42162-021-00174-4

Abstract

The transport sector is responsible for 25% of global CO2 emissions. To reduce emissions in the EU, a shift from the currently 745,000 operating public buses to electric buses (EBs) is expected in the coming years. Large-scale deployments of EBs and the electrification of bus depots will have a considerable impact on the local electric grid, potentially creating network congestion problems and spikes in the local energy load. In this work, we implement an exact, offline, modular multi-variable mixed-integer linear optimization algorithm to minimize the daily power load profile peak and optimally plan an electric bus depot. The algorithm accepts a bus depot schedule as input, and depending on the user input on optimization conditions, accounts for varying time granularity, preemption of the charging phase, vehicle-to-grid (V2G) charging capabilities and varying fleet size. The primary objective of this work is the analysis of the impact of each of these input conditions on the resulting minimized peak load. The results show that our optimization algorithm can reduce peak load by 83% on average. Time granularity and V2G have the greatest impact on peak reduction, whereas preemption and fleet splitting have the greatest impact on the computational time but an insignificant impact on peak reduction. The results bear relevance for mobility planners to account for innovative fleet management options. Depot infrastructure costs can be minimized by optimally sizing the infrastructure needs, by relying on split-fleet management or V2G options.

Highlights

The transport sector accounts for 25% of the global CO2 emissions (IEA 2020)
Time granularity and V2G have the greatest impact on peak reduction, whereas preemption and fleet splitting have the greatest impact on the computational time but an insignificant impact on peak reduction
The modular optimization approach developed in this work takes advantage of a flexible problem formulation while maintaining the computational time reasonably low

Summary

Introduction

The transport sector accounts for 25% of the global CO2 emissions (IEA 2020). Electric vehicles (EVs), ideally powered by renewable energy sources, have the potential to drastically cut these emissions. Of which buses are the most widely used form in the EU (2050 long-term strategy — Climate Action), offer a massive electrification opportunity. About 745,000 buses operate in the EU, accounting for 55.7% of all public transport journeys and ferrying up to 32.1 billion passengers per year Toniato et al Energy Informatics 2021, 4(Suppl 3): long-term strategy — Climate Action). As of 2019, only 4,500 electric buses operated in the EU, with a doubling in new registrations from 2018 to 2019 (Global EV outlook). The use of electric buses (EBs) is expected to increase considerably in the coming years, spurred by greener policies and government incentives (Buses — ACEA European Automobile Manufacturers’ Association)

Objectives

Results

Conclusion