# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
## 6 Discussion  
This paper analyzes the charging scheduling problem on large-scale electric bus depots and proposes two algorithms for the load peak minimization. Real data from the bus depot in Alsterdorf  
Figure 11: Losses in transformers and cables resulting from the original, greedy and heuristic schedule.  
Figure 12: Load profile resulting from the original, heuristic and greedy schedule in the summer months, with no preconditioning and the ambient temperature of 28 \({}^{\circ}\)C.  
in Hamburg were used for the simulation. The proposed algorithms were able to reduce the peak demand and flatten the peak load at the bus depot. Greedy schedule resulted in a slightly higher peak demand but an evenly distributed load during very high load periods. It managed to reduce the load peak for 24.4% compared to the original load profile. Nevertheless, it did not manage to reduce the cumulative energy losses within the depot. For the analyzed period of 36 h, it resulted in 2 kWh additional losses compared to the original load profile. Heuristic schedule in this case achieved an even smaller peak load and reduced it for 27.1% compared to the original load peak. It also reduced the energy losses for 23 kWh. The heuristic algorithm managed to distribute the average load better during the night as well as during the day, in the time from 09:00 to 12:00. However, it resulted in a load profile with more small amplitude fluctuations compared to greedy.  
For the summer scenario, all algorithms showed smaller load peaks. This is due to the lower demand for preconditioning compared to the winter months. Greedy schedule managed to reduce the peak load for 37.7% compared to the original load profile. Heuristic schedule on the other hand reduced the load peak for 42.6%. In this case, the greedy algorithm resulted in a relatively flat load profile in time from 20:00 to 02:00. In the same time, the heuristic load profile once again showed many small amplitude load changes.  
The charging process was considered as a non-preemptive job for the purposes of this paper. This means that once a bus has started charging, it continues until it reaches its goal SoC. The process can also be observed in a preemptive manner, allowing the bus to charge several times within its available charging interval. Additional interesting variable that could be included in the scheduling algorithms is the load forecast from the grid operator. The load profile at the bus depot would in that case support the overall load profile in the grid. Such scenarios, including online scheduling, are the topic of our future research.  
[PERSON] wrote the article and programmed the proposed algorithms. [PERSON] and [PERSON] developed the proposed algorithms. [PERSON] supervised the work and provided helpful feedback regarding both the algorithms and the article.  
This work is a part of the project Accompanying Research for Charging Infrastructure on Bus Depots. It is supported by the German Federal Ministry of Transport and Digital Infrastructure (AKZ G20/[PERSON]/3).  
The authors would like to thank HOCHBAHN AG for the good cooperation and the provided data.  
The authors declare no conflict of interest.