 Good morning. Good afternoon. Good evening, Per. I propose that we start the webinar. Okay, very good. Thanks, Rizak. So, yeah, good morning, everyone, from Paris and IA headquarters. My name is Per Anders Videl. I'm the program manager at IA's Energy Technology Policy Division. I'm part of the coordination team for the SEMS Electrical Vehicle Initiative, and most of the contact point within the Jeff Global Mobility Program. It's my great pleasure to kick off this webinar and this very exciting presentation of IA's new tool on EV charging and grid integration. So, EV charging and grid integration has become a focus area of the agency. Almost a year ago, we launched a policy brief on public charging infrastructure, and this is very much a comprehensive overview of the EV ecosystem of public charging infrastructure, as well as some key recommendations for how to ensure an efficient deployment. This was then followed up in December where we launched a policymaker's manual aimed for policymakers for how effectively integrate electrical vehicles into the grid. Today, as a companion to those two knowledge products, we launched an interactive tool where you, a policymaker, a network planner, a utility staff member, a researcher, or whatever function you have in the EV ecosystem can take a look at how EV charging impacts the power system. I would like to help you analyze whether the grid needs to be strengthened to accommodate charging a wide variety of circumstances tailored to your needs. So, we, of course, are very pleased to have this chance to present this work, very much thanks to the support from the Jeff and all the partners of the Jeff Global E-Mobility Program, which we are a happy partner to. This particular deliverable is an output of Working Group 4, which looks very much at charging infrastructure and grid integration, but also batteries. And we're very much looking forward to working with colleagues in ADB, in UNEP, in EBRD, and Centrum Auto Molina for dissemination of these tools in various regions of the world. And let me also take a moment to thank the members of the Electable Vehicle Initiative, 16 leading countries when it comes to EV deployment under the Clean Energy Ministerial. EV charging and grid integration has certainly been a topic of the key priorities for all these governments to might have different policy challenges, but it seems to one of them that also brings them all together is when it comes to effective policies, when it comes to both the deployment of EV charging, but also figuring out what's the most effective way for the integration of EVs into the grid and ensuring enough grid capacity. So we, and in the spirit of the work of international collaboration, we hope to bring together not only energy ministries, but also ministries responsible for transport policies under this work, under the Jeff work, under the EVI work, but also, of course, under the work of the IA. With those words, it's my great pleasure to hand over to my dear colleagues, Jacques Varichet and Andreas Bong for the presentation. But before that, of course, just to say a few words on housekeeping. So as you might have figured out, this will be a recorded event. And if you have any comments or questions, we ask you to use the Q&A option here in the Zoom. So with those words, over to you, Jacques and Andreas to take us through this very exciting presentation. Thank you, Pierre. Good morning. Good afternoon. Good evening, everyone. Thank you for joining. My name is Jacques Varichet. I am an analyst in the Renewable Integration and Secure Electricity Unit of the IA. And I am here together with my colleague Andreas Bong to present this tool that we are launching today. The presentation will give first an overview of why we developed this tool, in which context it's false. We will present the features of the tool, and Andreas will give you a live demonstration to follow. And there should be time for 20 minutes approximately of questions and answers at the end of our presentation. So feel free to use the Q&A box at the bottom of your Zoom window, and we will address your questions after our presentation. Thank you very much. So this tool has been developed in the framework of the Global Programme to Support Countries in the Shifts to Electric Mobility. This program has been launched by the UN Environment Programme and is supported also by the Global Environment Facility, Jeff. It contains four thematic working groups. One of them is dedicated to charging grid integration power supply and batteries, and I am the coordinator of this working group. These four working groups are delivering knowledge products on specific thematic areas corresponding to the working group scope. And they are also collaborating with regional platforms, which try to disseminate the work done under the working groups, create communities of practice and networking in their respective regional scope. The program has been designed with focus on low to middle income countries. And therefore, most of these regions are located in areas where there is a need for these countries to be supported. In addition, of course, there are country projects that are over 30 today. Most of them are already in progress while they will do onsite demonstration and benefit from the knowledge developed in all the rest of the program. So the motivation to shift towards electric mobility for countries can find a lot of different meanings. For some, it's to improve our quality, reduce the dependence to imported fossil fuels, or to contribute to climate mitigation. If we look at the greenhouse gases from road transport from well to real, including the construction, the use, the recycling of all the materials, and of course, the working operation of the vehicles. We see that EVs already today bring some greenhouse gas emission reductions, but these will increase in the future. In 2021, we estimate that 40 million tons of CO2 emissions have been avoided thanks to use of EVs instead of combustion cars. And in all scenarios, by 2030, we estimate between 460 million tons and 740 million tons of the avoided emissions. But there is a strong dependence during the operation of the vehicles on the electricity mix. That means what the power sector generation is. And this is why the decarbonization of the power sector will be also one way to accelerate the decarbonization of transport. If we look a bit more in detail on the power sector, the electrification of transport and other energy uses will have quite an impact. We see first of all that there will be a strong need for cross-sector collaboration at the institutional level. And this is why we also want to bring attention on the power sector needs to mobility people and vice versa. But we can see, notably, that the electric vehicles will bring an increase in the demand for electricity. And for example, in advanced economies, the electricity demand has been stagnating for the last decade. But now with electrification of road transport among others, but also with heat pumps, this will increase. In other economies, of course, electrification will contribute to the strong growth of electricity demand in the next decades. So let's look at some numbers here. In 2021, the demand of electricity for EV charging was 55 terawatt hours. That is more or less 0.5% of the global electricity demand or the equivalent of the electricity demand of a country like Switzerland. But already in 2022, I can anticipate the numbers that will be released soon. This number has already doubled. We have estimated to 110 terawatt hours the demand of electricity for EV charging. If we look ahead towards 2030 in our, sorry, announced pledges scenario, which is actually the scenario that corresponds to the climate pledges made by all the countries together. We see that already the demand will be 1100 terawatt hours in 2030. That means 10 times more than in 2022, 20 times more than in 2021. And that will be more or less 4% of the electricity demand. The demand for this electricity will be very different according to the countries. And you can see in red, for example, that China is already quite a significant chunk of that demand since they are way ahead of the other countries in terms of electrification of road transport. The different countries have actually a very different fit of the current or fuels combined roads transport means. So that means that every segment will also have its own need for charging solutions. And as a policymaker, there is a need to identify the priorities and develop the necessary charging infrastructure and look at the impacts on the grids of these different approaches that will be taken. So we wanted to support this exercise because we believe planning for the uptake of electricity vehicles would be a necessity. And that needs to be done together between mobility people and core sector people. So this is the reason why we developed a tool which is interactive and available will be available later today on our website. We had three main motivations. The first one was to tell or to be able to look at the impact of charging on the power system. The second one was to also look at the possible measures for mitigating these impacts and to help assess these charging impacts. And finally also try to give an overview or an estimate of what the CO2 emissions would be when we charge electric vehicles. So to achieve the first goal, we have developed a simulation of the charging behavior that releases a weekly profile of the charging. The second module does the same thing but with the possibility to implement some managed charging solution. We could manage charging any strategy that shifts the moment or modulates the power of charging in order to make the charging more grid friendly and we will see that more in detail in a minute. And finally, we also implemented simplified electricity mix representation to enable the estimation of the CO2 emissions. So let's look now more in detail on how the tool works before we will go to the demonstration. So the charging of electric vehicle from the perspective of the grid doesn't only look at energy. So if we need a certain amount of energy to charge our battery. This is useful information but to be able to assess a bit more in detail the impact on the system, we need to look at the power which is withdrawn from the grid at different moments in time. This is why we needed to look at much more in detail on the different charging solutions which are implemented and actually every charging solution has its own separate impacts. Depending on which segment of vehicles we are talking about what charges are used and at which moment of the day the power is withdrawn from the grid. If we look at the different possibilities there are quite a few as you can see on this picture. Some of them have a higher impact while others have a lower impact and this depends not only on the characteristics are just cited but also on the characteristics of the grid. So if we look for example at a simple case where we have 1000 private cars charging preferably at home, we can see the blue peaks which corresponds every day at evening charging when people come back from work and recharge their car as soon as they come home. And if on top of that we give the possibility or we have chargers on the highway or on the roads where the power is much higher, but the price might be higher so the preference to charge there is lower. We can also have the green small peaks, which are actually the events where people charge during the day at different moments or at different locations. So, we did that through, so how to put that into the tool. I will then let Andreas explain how to use the tool to represent this simple case. Hello and also welcome from my side. I will explain the tool based on on an example and give also some background information and modulation and the modulation to see jobs of the tool so first to the example and we have a fleet of 1000 private cars. And the tool there's one section to enter the fleet. So you could enter a label to make the fleet unique. Then you could also enter the stock of the of the of the fleet so the number of vehicles. And the battery capacity and the energy consumption that could be different in different countries and for different types of electric cars. And also and that's the first important and more advanced thing you could also enter the driving behavior of the driving pattern. And here you could also make a different difference between the weekday and the weekend because normally the driving behavior at weekdays and weekends is different. And so that's the fleet and the driving pattern of the fleet and that's the first input. And now one step back more to the modulation so as explained that was the driving patterns and these driving patterns corresponds to the to the charging needs. So in the tool they are on the ones on the on the one side they are charging needs. On the one side they are charging opportunities and these charging opportunities are characterized by different charging location types. So typically the driver could charge at home but also at work. The location charging a road side charging and route is also possible. And for buses there could be also opportunity charging. And you know you see the two sides of charging needs and also charging opportunities. So the task for the tool is now to find out where the charging needs are covered so at which charging location or with which charging opportunities the needs are supplied. And that is the main idea of the of the tool that's a charging preference is the connection between the charging needs and the charging opportunities. And then in addition to the tool is it's weekly based that that means as my colleague explains that you get a weekly based profile. And for that we defined all the tools based on different input for weekdays and weekends I show that before for the for the driving patterns where we similar to the child for the charging behavior. And that is why typically the behavior is different for example weekdays. There's more home and work charging and weekend there could be also destination charging and something like that. And one more thing so the tool is also fleet based that means that the input are based on free and also the output so the idea is not that to provide single and unique individual charging profiles. For a single vehicles. It's for the whole fleet. But in the modulation procedure there are some steps where individual checks for each vehicle happens. And that's, for example, to check flexible if there's any flexibility for man charging. And now let's go ahead with with the tool and the charging behavior. So we already have that we have already defined the fleet. And now we have to define the charging behavior and here first availability. The availability means here how many we could could charge at a certain of the certain location type. And we have these five location types. And for each location type, and you could enter charging power so the available charging power for these location type and availability for for weekday and weekend. So availability is should be entered based on the on the charging infrastructure, but also on the accessibility doing the entire times. And the next, and the next slide so. And besides availability, there's also the preference and the preference decides at which location types the driver will charge. And for each location type as the user could choose the preference the total preference has to be 100% because the driver has exactly one preferred location type. Typically, there's a high high preferences for for home charging. And the background for the for the preference is often the costs of electricity, and also what we are charging is comfortable for it's easier to charge at home than charging to an annual location and charge there. And so that's the preference. And as I explained before, they're also typically typical charging times, and they are linked to the availability. So the availability means that at these times, and there is, there are available charging slots for the drivers. And, and for each location to type the tool user could enter different values so for weekday and also for weekend. And there's also the vital time, and there's also the variants and then there's by normal distribution, there's the calculation by normal distribution, and thereby each vehicle gets our own survival time. And there's also a typical stay time, and thereby the charging events could be could be calculated and there's a determination of possible charging windows. So here we see the results for these for these inputs. I entered before and here you see where we high home charging that was because we have a high home availability and preference, and the charging take place in the early and during this time there's also the non EV electricity demand peak because of cooking lighting in some countries also heating, and that could lead to an overload of the grid. And there's also that the power system of that expensive term units have to run because there's a high peak from the EV and also from the non electricity and non EV electricity demand. And so now let's change the limits inputs of the tool. So, as I explained, and before the availability of of home charging was really high. And it was 90%. So 90% of the EV drivers was available was able to charge at home. 60% could charge at work and 50% at public so here destination charging. So let's imagine we the investigation is now in the city, and that typically are not so many parking stage, since next to the home. And that's why here, the charging infrastructure is characterized by more destination charging, and the city so a lot of lots and ledger activities. And that's why we have known availability of 80% for destination charging. And here is the base scenario and now you see the change to the to the new inputs and you'll see that's a home charging decrease and the public charging increase. And yeah, that shows that the EV load could be really various for different scenarios, and that the power system and also in the network must be able to manage these different situations. And they could be also a real high ramp up of the of the charge of the charging processes and events and now let's go to the preference before there was a really high preference for charging at home, and of 80%. And now, let's imagine that the government said some incentives for work charging because they are a notice or recognize that it could be helpful for power system to charge during the daytime. So the cost for for work charging would be would decrease. And that's why the people would charge at workplace for the preference for workplace charging is much higher now. And the way scenario again and now the the changed inputs. And here you see us expected that the peak of the home charging decrease and the work charging increase. And here you could see that that there could be a better use of solar energy during the daytime. And that could be all it could be also interesting to look at different behaviors of the of the EV drivers. So it could be the case that EV drivers wait for charging until the state of state of charge of the battery is equal to 50%. So in this example, 60% of the EV drivers wait for the charging. So that means that they are not directly recharge the vehicle, for example, when the when the last trip was really short. And here you see the impact or as I explained the base scenario and now the the new scenario with the with the EV drivers which wait for charging. And now you see that the charging events are less intense. That means that the people are not so high, but the charging events becomes longer. And then makes of course sense, because the, they are not so many charging events but the when you have a charge the charging is more time. So what we have looked at until now is at the charging of electric vehicles without any measures to shift or modify the charging so this is what we call unmanaged charging. To increase the penetration of EVs and particularly the integration in the grid. There are a lot of opportunities to do something different. So we call that managed charging strategies. And actually, the opportunities for managed charging will depend also on another factor which is the duration during which the vehicle will stay at the charger location. And in that case, if the vehicle stays long and the user doesn't need the charging to be very fast and immediate, there's the possibility to postpone or to modulate the power. And that means that there is an opportunity for the system for an increased flexibility. In the on this graph, now you can see that for example, homework charging are providing quite a high flexibility, because that's places where usually the power is relatively low but especially the duration of the state is the longest. So what happens if we use one of these strategies for managed charging. In this case, what we have done is to apply what we would call balance charging which is trying to spread the charging energy over the full duration of the state time of the vehicle. And what we see is that the peak is much less pronounced. And therefore the requirement on having peak units to provide the electricity supply in the early evening is much more reduced. So these managed charging strategies, especially this one that spreads the charging overnight instead of having an evening peak have a lot of benefits for the power system. It reduces the peak demand. It may reduce grid congestion in local grids. And since we can also benefit more from, in the case of data and charging of renewables, especially solar, we can also accelerate the carbonization with that. So the second module of our tool was implemented, implementing these charging strategies. And that's what we will now details more. Before we really apply the load management as a managed charging to the discussed example, I will give a little bit more background to the modulation of the managed charging in the tool. So, first to the main question in the tool is managed charging possible for different charging events. And so that is the main framework for applying the managed charging. So there are three steps. First, there's a check of flexibility. So for each charging event in the tool, there's energy required to charge. So the energy, the EV driver want to charge a certain amount of energy. On the other side, there's a amount of energy which could be charged during this charging window. So for example, when at home overnight, there's a really high available energy because the vehicle stays long at home. And the difference between these required energy and these available energy is flexibility because the EV driver of the vehicle is able to shift the charging and it will not make a difference because at the departure time the vehicle would be fully charged. And that is the first question. So is there flexibility left for managed charging. The next question is, or the next aspect is the participation rate, because it's there's a question if all the infrastructure is the infrastructure is able to participate in and manage charging and also other EV drivers, if they want to participate in managed charging, and that's why we include a participation rate for each location type so the EV, the tool user could enter different participation rates for managed charging. And then the third step is to apply the real managed, managed charging measure. Now to the three managed charging measures we have implemented. So first to balance charging. And here the ideas as my colleague explained that you use the whole staying time for for charging and reduce the maximum charging power by by use these whole staying time from arrival to departure. And so that's the first measure and the measure to and three is about the time of use and smart charging so this both measures are related to charging depending on some reference profile for the time of use is depending on some on some energy prices and terrorists and for the smart charging depending on the on the non EV electricity reference demand and and the idea is to shift the energy to the time steps where the reference profile is really low. So for example, here for the time of use, we postpone the charging to the time steps at the end of the of the charging window where the tariff is really low and we only need a short interval at the beginning because for the lowest for the lowest price it's not possible to charge the whole vehicle. So that's the idea to shift the energy depending on some reference cost. And yeah, so that's how the three, three measures and now back to our example. And let's imagine that the tool user notice that the peak power is really really high. And now the idea is to apply a balance charging to decrease the peak power and use the flexibility of the of the situation. And here, the user has to activate the balance charging from unmanaged to balance and also as I explained the different participation rates. In the example the participation rate for for home would be 70% year assumption could be that there is some some of the charging infrastructure is not able to participate because of because of technical reasons. For the workplace, there could be a more newer charger which are, which are able to participate and for destination and in general public charging the participation rate is, is low, because there's in general also not so many, so much flexibility. And so, and now, as a, as this work here you see the base scenario, so the unmanaged charging, and now the balance charging. And here where you could see that there's some significant smoothing effect on demand so the peak power is reduced. And so it could be easier to integrate the EVs now to the grid, and so there would be a lower impact on the electricity supply capacity, and that shows the potential of flexibility in the grid. There is also a third module, which allows to estimate the emissions of their carbon dioxide, but this module is only available through an API, like some other advanced charging solutions. And these API will be available by the end of the week, meaning that one user can put in input data and dialogue directly with the Python code which will give us the results, but that is offline and not in the interactive tool. So we conclude with a few key messages on the electric mobility integration in the grid and how this tool supports it. So the electrification of road transport is something that we cannot stop now, but it will also accelerate as it contributes to decarbonizing our economies and reduce the dependency to fossil fuels. EVs and electrification of transport will increase the need for electricity, but it's also an opportunity for the power sector thanks to its flexibility. The power sector needs to accommodate a lot of different charging solutions, but it should be encouraged to have managed charging to increase its penetration and grid integration. And it will at the same time support a higher rate of growth of renewable. Finally, flexibility needs to be incentivized from the early stages of EV growth. And we have for that a few recommendations in the policy manual for EV integration that you published in December. And we acknowledge also the needs that grids will be expanded to deploy the needed charging infrastructure, but it requires that the different sectors collaborate together. Before we go to the demonstration, I wish to thank all the people who have contributed from close or a bit further to this tool and the environment in which we are. The developers are under us here with me, but also Luis Juha and Juanjo. The tool specifications were developed by myself with my colleague Luis Lopez. And finally, we received a lot of support from the team of communication to deploy the interactive tool but also from a lot of colleagues who reviewed and supported the development of the tool to review and advice. So thank you very much for everyone. And now we will have a brief demonstration by address before we go to the questions that were posted in the Q&A box and thank you very much for posting questions, you can still continue doing so. Okay, now you should see my screen and web tool. So first of all, you see here four different tabs so the fleet, the behavior profiles, some advanced options and a tab to see the results. Let's start with an example. I already have entered some values so the label would be the ideas that we now simulate an intra-city bus fleet in Chile, so that's why I put a label here. So the fleet include 25 buses, the battery capacity and energy consumption you could enter here, and they're also the weekday and weekend driving, so that's the first tab. And then for the behavior, you could enter here different values. So for the bus fleet, the idea of the charging approach is that the depot location where the buses charge overnight, that's why there's availability of 100% at depot. And there's also the charging power, which is here 150 kilowatt. Here you could also enter the different preferences. In this example, it's really easy because the preference would be 100% for home charging because it's the only charging location type. So these are the different time values. So in this example, we assume that the buses enter the arrive at the depot at 11pm with the variance of one hour and stay six hours. And then you could see the results here. Of course, because of some probabilistic effects, you have some difference between the different days when the arrive, so that's a normal distribution, so there's some probabilistic effects. Let's imagine we recognize that the peak power is really high, and that's why we want to apply and know the balance charging. So now we can go to advanced options. And here we could change from unmanaged to balanced charging, and then go back to the results. And then you'll see that the axis is changed, so you'll see different shape as the peak power is decreased, as the peak power decreases, and the charging event takes longer because of using the whole flexibility. Now let's go ahead with this example, but with the unmanaged case, and let's assume that the fleet operator gets some new buses and a second fleet with also 25 buses with the same specifications of the vehicles. The different types are the same. And the idea is to use the charging, the same charging infrastructure as in the example before. And that's why we need two different behavioral profiles for the first fleet and for the second fleet. For the first fleet, we have to change now the, so go to Group 1. And for the first fleet, the idea is that the buses arrival at 11pm, and there's only three hours to charge because the other three hours, we are needed for the different, for the second group. And so that's important that the arrival time and also stay time, it's related to the connection times is great. And for the, for the second fleet, you can go to Group 2 here, and here you see that the arrival time and that's when the, when the charging starts, it's now at 2am, so three hours later. You can use these six hours staying time of the buses in two different groups. And, and you could see the charging all the results here so you see first the charging of the, of the first fleet and then the charging of the second fleet. And that's how you could use the tool to, to charge different fleets with the same charging infrastructure at different times. So it's important to see to understand that the arrival time means connection time to the grid. Let's go ahead with the second example. And here we have a two wheeler, two wheeler fleet, so personal two wheelers, 1000 vehicles. And here you could see the specifications. The driving should be now different for week days and weekend. And for the behavior profile, I already entered values. So here you could see that we have home charging with two kilowatts and that's availability of 90%. So 90% of the two wheelers could charge at home. And also there's availability of 40% 40% for roadside charging. And these both location types should cover the needs. And the preference here is 100% for home charging because it's cheaper. But some of the weekends of course cannot charge at home because the availability is only 90%. Here you could see also the time so it's here for it's different for the weekend because we assume that the two wheels will arrive earlier and with a higher variance in the weekend. And for the road, for the road side charging you see here, it's a little bit more disputed over the day. That's why the variance is, it's two hours. And then you could see the results. Yeah, here you could see the difference, the home charging and also the roadside charging it's more over the day and the home charging is in the early evening as we expect before and then the PowerPoint. And let's imagine that the tool user changed all the perspective and is interested only in the distribution analysis and they are in a really residential area. So we're only home buildings are and now the tool have an option to exclude some location types. So for example, you could exclude the roadside charging now. And that means that the modeling part is the same but only the results we see the home charging and that could be interesting, because now we see the charge need only in the investigated with the dental area. And that's also the opportunity of the possibility of the tool. So let's imagine that the government notice that it is not so good for the grid that the most of the charging is doing the early evening. And that's why there is some motivation to to increase the workplace charging at the availability so there are some subsidies to invest this get an investment in workplace charging. So let's imagine that the availability in addition to the previous was is now 30% of the weekdays of course there's no work charging at the weekend. And also because of the subsidies the workplace charging could be cheaper. And that's why the workplace preference could be now 50%. Some people also prefer prefer charging at home because it's more comfortable. And then you could see in the results now that we have more of course there's no work charging over the day or in the morning and also a decreased charging in the early evening. And the roadside charging is now only needed in the weekend because he has no is no work charging. Yeah, thank you. And that was the demonstration. Thank you. We have received a few questions already. We will start with the practical questions on the availability of the tool for example. Yes the tool will be available for free and for everyone on our website. We will post it later today. It is going through a last round of verification apologies for the small delay. And there will be an interactive version which will allow you to do exactly what Andreas just showed. But there will be also as I mentioned briefly an API so the way to access the Python codes through an advanced interface that actually enables some additional features. For example, the CO2 emissions will only be available in the through the API and the manage charging strategies of time of use and V1G. So the smart charging will only be available also through the API or the rest will be available in the interface on the web. On the website where the tool will be located you will also find a manual and the technical notes that allows you to have a better understanding of how to use the tool but also what is the logic of the monitoring behind. So in terms of clarifications Andreas we have received a question on the difference between the percentage of availability and the percentage of preference. So the preference has to be will always be a total of 100% because you either prefer to charge in a place or another but why is the availability not totaling 100% Here the idea is that the EV driver could have different charging opportunities over the day. So let's imagine an EV driver who has the opportunity to charge at home but also at work. And so the availability gives the number of possible charging windows for the user. That does not mean that all these charging windows and have to have to be used. And so that's the idea that an EV driver could have different charging opportunities over the day. And that's why the total availability of different charging location types and could be higher than 100% Thank you Andreas. So there was another question of clarification on the use of the probability of shifting charging. Can you please, we explained briefly what is this probability of shifting charging and why in your example, what was the difference between the two, I think it was on slide 16. Okay, so maybe first, short explanation to the probability of shifting so the idea is that normally if an EV driver comes home with his vehicle, the state of charge of the battery could be 90%. And then the EV driver would decide to not do not charge directly because you think okay I have 90% remaining in the battery so I could charge in the coming days or tomorrow. So that's a general idea. And the specification of the tool is that when the battery capacity is more than 50%, it is allowed to shift charging to the next day to have some security back in the battery. So the idea is here now that 60% of the EV drivers would decide to shift charging to the next day when it is, if it is possible. So that is the idea. And then the idea is now that there are less charging events because there is not a charging event every day but when the EV driver charge, the charging event is longer. And that is what you see in the next slides. So here's the normal without shifting charging profile. And then you could see there's a lower peak power. That means there's less charging simultaneously, but the charging events become longer. And that's the idea of this probability of charging and decision-making modeling of the decision-making process of the EV driver because they decide to run the charge and retry with these parameters to consider these effects. So we have a very interesting question which comes several times is why don't we have or whether we address V2G, so vehicle to grid. The reason why, so the answer is no, we don't have vehicle to grid in this tool. The reason, the main reason for that is that it's very much dependent on the rest of the system. And we don't have a full simulation of the power sector behind the engine here. So the goal here is to simulate only the EV demand and not the rest of the system. We have for the CO2 emissions a very basic version of an economic dispatch. This is quite simplified and it doesn't allow it to have really a V2G integrated in this tool. Moreover, the performance of having a V2G in an interactive tool would be very difficult. So unfortunately we don't have V2G, but we acknowledge the needs for it in the future. But since our main audience are low and middle income countries, we believe V2G is not a very short-term requirement. And other questions related to the use of the tool, I think you made a demonstration, but maybe we can briefly repeat about how can we look at a specific location in the grid, for example, a distribution grid. Because indeed, the tool simulates the whole fleet, assuming a whole system. But it is possible for a user to only look at the impact of charging on a specific location in the system, especially a distribution grid where, for example, home chargers will be located. Yeah, so let's repeat that. So we could do it with this example. So let's imagine that we have the investigations now for a distribution grid and the distribution grid contains mainly buildings with personal use. So homes from the people and the chargers for the home locations are directly connected to these buildings. And let's imagine that the work charging is in another area of the power system and should be not included in the investigation. But of course, for the EREs, it's important to simulate those. So the charging at home in the investigative area, but also the charging at different locations. But now the tool allows to exclude some charging location types. So as Jack explained, when we only want to look at home charging, we could exclude the workplace and also the roadside charging. So no use only sees a home charging for the investigated area for the investigated grid. And yeah, for example, you could use these profile or these charging profile and add it to the non EV reference electricity demand and compare it with the total capacity of the transformers or something like that. And then make some estimations of your utilization of the grid. Thank you, Andreas. There was another question related to how to use the tool and whether it can help identify weak points in the charging network. It's not designed to help you figure out where to locate charging stations, but based on an assumed charging station deployment, you can try and understand how the impact on the grid would be. If you know where the charging locations are, and you can, as Andreas showed, select the power drawn from these different locations, you can actually compare the power required for the charging to the strength of the grid. So this is one thing that you can do with the tool. Otherwise, there was another question that you may address address, for example, I will distort a bit the question that it was asking whether there was an experience somewhere with 200 city buses and what could be the impact on the grid. But I think this is something we could simulate in this tool and a user could have a look. I think your example with the 25 or 50 buses could be extended to 200 buses. Of course, so let's put here 200 buses. Of course, now the question is what is the charging strategy. Let's imagine for these examples that we have, like in the example before, also 200 chargers so that's availability would be 100%. And then, of course, when we go to the results and the peak power, we have of course we have to change it back to the unmanaged case. And then, yeah, you see, we have 15 megawatt peak power around 15 megawatt of people. Of course, that's a lot and that I think that would, that would overload the grid normally. And of course it depends on the grid configuration, but now you are able to see the load. But yeah, the tool provides the option to reduce the peak power when you apply balance charging, for example, and then you could see what we that we could house the peak power and of course it's a higher peak. Thank you, Andreas. A few more questions coming in on the use of the tool. First of all, there will be a contact email on the tool. So we will be glad to receive feedback and questions if the manual will not be sufficient. Please use that contact email. Otherwise, in terms of export, so the tool allows to export will allow to export a CSV file indeed to or an Excel sheet with the data generated with the demands managed or unmanaged and the user can then create his own curve in the format he likes. So this will be available when the tool will be put online. Now we have also a few more interesting questions on the impact of different parameters on the on the charging. Can you tell something about how the battery capacity may impact the charging behavior? Of course. So, for example, it could be that the battery capacity is too loud to come over the whole day. So let's imagine we have, we have an LDV, some personal car, and on one day that's a really huge driving range. And then the battery capacity could not large enough to cover the whole driving. And then it could be in change from location type. So it could be that from only one charge in doing the at the home location. It could be also that there's a recharge recharge event, for example, and route so that the driver decide to use the end location to recharge with a very high power and doing a small period of time to recharge the vehicle and continue driving. And that's what you see in the charging behavior. So that's why the battery and has an influence on the time behavior. I think our time is up. And we thank everyone for saying until the end I have a last, maybe seem to say is that the next steps for us will be to work with the regional platforms because we want to disseminate this tool further with the Jeff countries and their representatives. So we will have road shows with the coordinated by the regional platforms of Latin America, Africa, Southeast Asia, and Eastern Europe and Central Asia, and that we will do in coming weeks. So if you are a member of one of those countries, we will certainly have the opportunity to see each other again. And otherwise, we look forward to your feedback coming in the coming days. You will have the tool available later today with the explanation notes and the API will be posted at the end of the week. So please stay updated. And I think I will hand over back to a pair for the concluding words. Thank you very much, Jack. And also thanks to Andreas for excellent presentations. I don't think you have to add much from my side. You did a good sum up of the next step and also anyone of course interested in knowing more also looking at providing more information to us. Of course, don't hesitate to reach out to Jack and colleagues as well. So thank you very much for joining and with two minutes past the deadline, which is not too bad. Of course, wish you all a very nice rest of the day. Thank you very much.