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

Abstract The transport sector is responsible for 25% of global CO 2 emissions. To reduce emissions in the EU, a shift from currently 745,000 operating public buses to electric (EBs) expected coming years. Large-scale deployments EBs and electrification bus depots will have considerable impact on local grid, potentially creating network congestion problems spikes energy load. In this work, we implement an exact, offline, modular multi-variable mixed-integer linear optimization algorithm minimize daily power load profile peak optimally plan depot. accepts depot schedule as input, depending user input conditions, accounts varying time granularity, preemption charging phase, vehicle-to-grid (V2G) capabilities fleet size. primary objective work analysis each these conditions resulting minimized results show that our can by 83% average. Time granularity V2G greatest reduction, whereas splitting computational but insignificant reduction. bear relevance mobility planners account innovative management options. Depot infrastructure costs be sizing needs, relying split-fleet or