# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
## 5 Results  
The two algorithms described in Section 4 were used to create charging schedules for the Bus Depot Simulator. The schedules were created for five working days but only a 36 h period was simulated. This is because there is no difference between the load profiles on different working days. The algorithms were observed in two separate scenarios. An additional third scenario, with no charging schedule was also observed. This scenario will further be referred to as the \"original schedule\". Figure 8 shows the total load profile at the point of connection using the original schedule. The dark blue area shows the load resulting only from preconditioning. In this scenario, the buses are charged on demand as soon as they arrive to the depot. This leads to a high load at approximately 21:00 in the evening. The peak load in this case is 7.5 MW. The preconditioning has its peak at approximately 05:00 in the morning, with up to 4.1 MW. The preconditioning load cannot be rescheduled and it is considered to be the base load. Figure 9 shows the load profiles resulting from the schedules created with the greedy and heuristic algorithm, compared to the original schedule. Greedy algorithm leads to a load peak of 5.67 MW, which corresponds to the maximum of 37 buses charging simultaneously. Heuristic schedule results in a peak load of 5.47 MW, corresponding to 35 buses charging in the same time. Compared to the original schedule, the load peak resulting from the greedy algorithm is 24.4% smaller and the load peak from the heuristic algorithm 27.1%. The heuristic schedule also shows deviation from the original schedule during the morning hours, between 9:00 and 12:00. Greedy algorithm did not reschedule any charging tasks in this period and shows no deviation from the original. The load profile from the heuristic algorithm shows more small amplitude fluctuations during the night but it is generally better distributed. The greedy schedule resulted in a load profile, which was closer to constant in the time period from 20:00 to 23:00, but then showed a significant load dip at approximately 04:00. Figure 8: Load profile resulting from the original charging schedule together with the preconditioning load. The difference in load distribution between the three compared scenarios is shown in Figure 10. The greedy algorithm minimized the peak demand but in the area of lower load it does not show any difference to the original schedule. Heuristic algorithm instead managed not only to reduce the peak demand but also to flatten the average load. The flattening occurred two times, once in the range from 5.0 to 5.47 MW, and once more in the range of 2.0 to 2.3 MW. The active power losses in the transformers and cables within the bus depot for the three compared algorithms are shown in Figure 11. The original schedule resulted in energy losses of 2634 kWh for the analyzed period of 36 h. The greedy schedule leads to energy losses of 2636 kWh and the heuristic algorithm to 2611 kWh. Although the greedy schedule shows smaller load peak compared to the original, it resulted in slightly bigger energy losses with the increase of 2 kWh. The heuristic algorithm on the other hand shows smaller load peak and smaller energy losses. Compared to the original schedule, it reduced the energy losses for 23 kWh. Figure 10: Load distribution from the three compared algorithms during the simulated time. Figure 9: Load profiles resulting from the original, greedy, and heuristic schedule. The preconditioning in winter months leads to a high load peak at the depot. However, during summer months, when there is no (or less) preconditioning, the peak load can be further reduced. Figure 12 shows the load profile from the original, heuristic and greedy schedule in summer months, assuming there is no preconditioning and the ambient temperature is 28 \({}^{\circ}\)C. The energy consumption of buses is smaller in the summer months.