## 6 Discussion  
In this section, performances of all the scenarios are summarized in Table 5 and the results are discussed focusing on the impacts on the utility grid in terms of energy and power with/without considering the presence of PV panels. First of all, the charging of EBs shows that the determined average bus consumption in terms of kWh/km is consistent with the literature data [4] which validates the presented model. However, it should be noted that there is a potential for further improvement of the presented model, through an analysis of the GTFS and Open Street Map data, by defining classes of roads according to the number of curves, intersections, and traffic lights. Additionally, the obtained energy consumption is 36 kWh (9.4%) less in scenario 3 (where a 70 kWh battery weighs 467 kg) compared to scenarios 1 and 2 (where a 422 kWh battery weighs 2.8 t). Therefore, the total energy consumption of the two buses during their daily operation decreases based on the capacity of on-board battery. Secondly, analysing the minimal SoC of the on-board battery allows assessing the adequacy between the capacity of the batteries and the charging scenario. For instance, in scenario 2, the minimal battery SoC of bus n\({}^{\circ}\)2 is determined at 73.3% which indicates that the battery is oversized or that it can be charged less frequently and/or at a lower power. On the other hand, in scenario 3 in which buses operate with a lower battery capacity, the SoC of bus n\({}^{\circ}\)2 decreases until it reaches 22.2%, which might increase charging time of the battery for next trip and cause delays in case of higher consumption. Lastly, the maximal charging power of the bus fleet is analysed for each scenario. The highest total power is determined at 1200 kW in scenario 3 ter where the two buses charge their battery simultaneously on route. Although the peak power only lasts a few seconds/minutes, it can jeopardize the reliability of the utility grid due to severe changes in grid consumption in the case of a massive deployment of EBs. Utilization of PV energy can be considered low, which occurs because it is not able to charge the EBs' batteries on the road. Therefore, the utilization of the hybrid energy system which is formed by PV panels and stationary storage is essential for increasing clean energy utilization for EBs. On the other hand, the PV panels might be integrated into the roof of the EBs in order to use produced PV power directly while moving on the road. However, in that case, the surrounding buildings around the road which EBs use to perform their services are required to be modelled in detail to consider the impact of the buildings' shadows over the PV panels of the EB bus. Concerning the utilization of local PV energy, simulation results show that only a small fraction of PV energy is directly supplied to EBs and the rest of the energy is injected back into the grid. The obtained self-consumption of the PV energy varies according to the scenarios as seen in Table 5. The highest utilization of the PV energy is obtained in scenario 2 (charging at terminals/depot) with 81 kWh, while the minimum is determined in scenario 1 (charging at depot) due to charging only at night. Moreover, it can be seen that the total energy production on January the 15 th is lower than the buses' energy consumption. On the  
Figure 14: Total charging power and PV production over time—scenario 3.  
other hand, the produced PV energy in July is sufficient to supply all buses' consumption during the day if an additional stationary storage system is deployed. Overall, this paper presents a methodology for modelling and analysing the consumption power of the EB fleet for various scenarios. The potential impacts of the EBs charging on the utility grid are discussed considering a local PV production without trying to minimize them via an optimization. However, results show that optimal planning and management are required for minimizing the grid impacts while maximizing the potential benefits.