## 3 Calculation of emissions due to the operation of the electricity distribution network.
This indicates that shifting all charging to off-peak hours may not be the best option, as it will also result in congestion and, therefore, the need for investment in the network. * Distributing loads throughout the day (reducing the simultaneity factor) reduces the need for investment in the distribution network to 28.6% of the value with simultaneity factor 1 (51% EVs scenario). However, this option implies investments in infrastructure and telecommunications network and data processing and automation and control systems, which were not accounted for the study. * Joule losses in the distribution network will increase by around 13% in peak hours and by around 26% in off-peak hours for 51% EVs but will be 40% for 62% EVs. This increase in losses is due to the 16% increase in load during off-peak hours for a scenario of 62% EVs. According to [5], the increase in load could be even greater: \"Adding one or two EVs to every household with level 2 charging in a wealthy neighborhood could effectively increase the demand and load for electricity by 150%\". To calculate CO\({}_{2}\) emissions resulting from the increase in losses in the distribution network resulting from EV consumption, data on electricity consumption in the city of Vienna (on which the previous calculations were made) and average losses in the distribution network in the EU were considered. In this study, it was considered that EES infrastructures in the EU have similar characteristics. In this way, it is possible to use Vienna and EU data as guidance for the order of magnitude of losses and respective emissions, although it is recognized that it is not an exact calculation, since the exact value depends from country to country, and from network to network. According to [40], the majority of Joule losses in EES cables occur in the distribution network and are responsible for 97% of global CO\({}_{2}\) emissions from EES activities. There are also emissions due to the manufacture of cables and other infrastructures; however, they only correspond to around 3% of total emissions, which is why they will be ignored in this study. Considering the study by [41], on average, a distribution network will have 5.54% technical losses. This value is compatible with that mentioned in the study by [42], which states that \"most utilities (\(\ldots\)), which ranges from 4 to 13%\" of losses depending on total power. In the study by [43], the power losses in the studied network are around 11% for peak hours. According to [44], in 2014, the EU average of electrical energy losses in transport and distribution networks was around 6.5% of total power. Portugal was one of the countries with the highest losses, which amounted to 10%. The overall electrical energy consumption of the city of Vienna in 2016 [45] was 8.162 TWh. Using the EU average loss value (6.5%), the additional value of losses due to EVs for the city of Vienna was calculated, taking into account the 50% EVs scenario (10% of which in V2G mode). For this, the average increase in losses for off-peak and peak hours was used according to [15], which resulted in an annual value (using 2016 as a base case) of over 103.45 GWh of losses for 1 year. Using the expected number of EVs (scenario with 50% EVs) we obtained 199.611 kWh/EV. Using 9000 km as the average annual circulation results in 2.22 \(\times\) 10\({}^{-2}\) kWh/km. Applying the EU mix, we obtain emissions of 13.22 gCO2e/km. It was considered that this value of additional losses would also apply to the G2V case, since in the study [15] that was used as a reference, only 10% of the EVs were in V2G mode. Taking into account the results obtained, Table 5 presents the summary of all partial calculations and the total calculation of emissions depending on the EV integration mode.