 my name is Javier Jorquera and I'm very honored to be here with all of you in the second session of the event titled Electric Vehicle Total Costs of Ownership and Grid Integration Tools, which we are very happy to co-organize with the Asia Development Bank. So this session, as I mentioned previously, will focus on EV charging. So after some opening remarks by both games later from the ADP and then Bermudel from the IA, I will continue to present some work done at the IA on the topic of EV charging followed by a Q&A to answer all of the questions you may have on the tool it will present or also on the other sites I will present. So just as a notice before we begin, this session is being recorded and it will be made available in the IA website later in the event page we have created for this session. And also please, if you have any question, type it in the Q&A, but if it's a case that during my presentation you are completely lost and you feel like you need to ask a question to continue understanding the rest of the presentation, please feel free to raise your hand and we will get back to you as soon as we see it. So with that I will continue to pass the floor to our dear guests, which will give the opening remarks today. So first I would like to hand the floor over to James Leather. James is a director of the transport in the transport sector office at the Asia Development Bank. Please say James the floor is yours. Thank you very much and welcome everybody. It's great to be collaborating between ADB and the International Energy Agency, IEA. What the purpose of these workshops is to showcase the practical and insightful tools for supporting e-mobility deployment. There is a lot of interest around this area, there's a lot of complexity around this area and it's particularly important in the Asian region given the dominance of particularly the two three-wheelers vehicles, but we need to look at the whole remit of transport. So it's with that background that we are very happy to collaborate with IEA on this. This workshop is presented under the e-mobility support and innovation investment platform, which is financed by the JET, the Global Environmental Facility. The purpose of the platform is to bring together and coordinate governments, development organisations, private sector financiers and experts in the field to understand exactly what are the requirements and we'll hear much more about that. This is the second week of these workshops. In last week's sessions we focused on the total cost of ownership of e-vehicles and today we'll hear more on the impact of EV charging on the energy grid. So looking at that complexity between transport and energy and the requirements within those. Again it's a complex area but we must see joint collaboration to look at how we do this and any policies that set it be that in climate change or in energy and transport they must all be joined up and the kind of tools that will be presented today and were presented last week greatly enhance our ability to have dialogue with governments across the region to make sure we are speaking with one voice in a joint and coherent way across the various mine agencies. So it's very good to collaborate on these areas. It's very good to enhance the available knowledge, the expertise and the experience that we can share across there. One final point I'd like to raise, we will be putting a link in the chat box to a needs assessment survey to try and ensure we provide the best service possible in these areas. So if you do see in the chat box that will pop up. But once again thank you very much for joining us and I would now like to hand back to Per, my buddy, thank you. Thank you very much Jamie and from our side to ATIA it's a great pleasure of course to have this opportunity to organize or co-organize this event with ADP within the framework of the Jeff funded global e-mobility program. Yeah collaboration is the word building knowledge it's another important area and of course speaking to many of you taking that knowledge into practice is of course a part of the purpose of today's event but also the continued discussion we're having within Jeff funded global e-mobility program. We at ATIA we of course we're data driven we focus mainly on building scenarios for the future looking at the information data we can get on the on the electrical vehicles side we have the global EV outlook which is an annual publication coming out every year in ocean track markets technologies policies etc. This is also accompanied with the database we can find data on both the policies and the data we have as well all for free but this work also goes into other analysis such that the things we do on supply chains that clinic manufacturing the different road map we do for seeing how the world can lead go to net seromissions. So our analysis is usually important for us when it comes to EVs in the transport sector and that's why also we see great value to be part of this program and this discussion here today with you because many of you might have information and data from countries in areas where we don't have outreach today so that's why our role now for the the Jeff program is certainly helpful for us to build those relationships and we think it's a two-side win we get the chance to build help build global knowledge but it's an opportunity for you as well to get access to some of the analysis that hopefully can also help you in your internal processes in your countries. So if you want to be on our kind of ongoing mailing list don't hesitate I'll send you our contact email in the chat later but they're coming to a very important topic of today which is indeed charting infrastructure and that angle as you said Jerry may link to also the ensuring grid capacity and planning and this is not a challenge only for a few selected countries this is one of the challenges we see in many countries of how to get it right both when it comes to the planning side how to ensure that you from a government side have a world government approach to make sure that different departments responsible different part of the value chain can also talk to each other but it's also important for ensuring that grid capacity as I said so when we talk to members of the electrical vehicle initiative they also many of them stress this as a challenge they might have different approaches to it if it's how to find the best approach to find which the right locations for the charging stations both when it comes to meeting consumer needs but also of course related to as I said these grid connections and we know that if there's no grid connection in place it can take many years and yes this is a challenge of course a big challenge for many governments as well of course the lack of staff personnel to work on the EV ecosystem challenges related to what to do and how to strengthen non-public charging the business cases for EV charging is also not clear in all countries today of course payment nothing charging speeds reliability and accessibility so it's a very important topic which we we see that we should use any opportunity to have to also discuss and facilitate that knowledge and experience exchange with between different stakeholders so we'll head over now to Javier and again I want to thank him our colleagues at ADB and also Jay Sun for the preparations of this work and I hope you get a chance to ask many questions today but also see as a stepwise approach where we happy to follow up later as well to have further discussions and again we are again very keen to hear from you as well what challenges you see in different countries in a specific region so thank you very much again and I hand back now to you Javier for taking us through this great presentation thank you very much Ben and thank you very much James for those of you who maybe were not here last week Per Anders Bidel is the IA's Jeff 7 program coordinator and he's also part of the energy technology policy division at the IA so also before going to my presentation I'd like to thank all of the ADB colleagues that have been organizing this with us including of course James but also Johanna and Pamela who is I think not here in joining the webinar today so with that I will now continue with my presentation and as I was saying please if you have any question that is really making you lose the the flow of the presentation and you feel very lost feel free to raise your hand and we're going to get back to you as soon as we as we okay so now I will begin to share my slides okay do you see my slides yes yes okay thank you and let's begin so as I was saying previously today the focus is on specifically on a tool and on the topic of electric vehicle charging and I will use my presentation the acronym EV for electric vehicles for your information so today as I present and prepare this here in the outline of the presentation and I will first talk in a more general way about grid integration of electric vehicles or EVs then I will do a special focus on the EV charging and grid integration tool that we developed at the IA under the Jeff program and then I will hold the Q&A session with all of you to answer all of the questions you may have on this lecture presented or also in the tool that I will do a live demo so allow me to begin setting a scene by talking about the electricity demand prospects we have in our IA analysis but we see here is that electricity demand and also the faster charging in particular are set to grow substantially at least out of 2030 and of course this would extend beyond 2030 but we see here is that today according to the global electric vehicle outlook that already mentioned the electricity electric vehicle charging demand account for 110 terawatt hours today which is roughly to give you an idea the power demand of the red lights on an annual basis this is important highlight because this is already becoming significant at a global level and according to our scenario analysis this demand by 2030 could grow to reach up to 1700 terawatt hours in 2030 in our net zero scenario which is that that that level would be almost three times the demand of Korea today so it would be a really massive increase and a massive level of electricity demand at a global level by 2030 in that specific scenario this growth is explained by several factors such as lower cost of ownership of the electric vehicles versus internal combustion engine vehicles but also because of several other aspects such as government policies including for example subsidies or tax reliefs what does this mean for the power system this means on one hand increased needs for electricity supply for the power system not only due to higher demand overall on an annual basis but also because compared to what we see today the structure of that demand will change this is because a larger vehicles such as for example buses and trucks they are expected to be deployed at a larger scale than what that we see today and this means that in most cases these vehicles will use faster charging so this will change in a fundamental way the structure of the electricity demand used for EV charging these vehicles as I was saying buses and trucks could account for over a third of EV charging demand by 2030 and could put additional stress in the power system due to their higher charging power preference so what is in practice the impacts that this growth in demand could cause to the power system we see that the impacts relate to several factors this is mainly for example a first point in the time meaning when the charging takes place and for how long second the location if it's in an execution grid that is maybe already saturated and has little hosting capacity or maybe alternatively in a grid that has a lot of extra capacity that can host new electric vehicles pretty well and also it depends on the charging power that is being utilized for example if it's a slow charger with for example five kilowatts instead of maybe superfast charging which in the case of cracks would reach almost up to four megawatts so the difference can vary significantly so depending on the all of these three factors we see that different charging locations and charging schemes can have different impacts on the power system for example we see the chart on the left a low impact would be work charging because normally the people there leave their car for several hours so there is a flexibility opportunity there because the what we call the charging window maybe the time that the need to spend there in the charging location is normally long so that means that they can charge at a lower power than other parts and thus the impact on the power system can be lower than in other parts but in contrast for example if we think of annual charging for example if you think of a highway where the cars just stop for as little time as possible there normally the power tends to be higher that could cause a higher impact on the power bit first because the charging power could be higher and this could be meaning more stress for the power system at the same time we really would like to highlight that these quick integration challenges can be turned into opportunities for the power system is if what we call managed or smarter more flexible charging is available so for example in the case of output charging because the charging window is not too long it's not too much time in which the user wants to stay there as he or he would like to leave normally as soon as possible but in the case of for example home charging where normally users tend to leave their car there up to 12 hours there the flexibility opportunity because of the duration of stay and it's really present that it can be utilized to ensure that the written integration of electric vehicles happens in the best way possible so what we see is that charging flexibility is really essential to lower system costs and emissions and this is why we in our reports we will really push in that the government should if possible try to find a way to implement smarter ways of charging to make the most out of the benefits of connected to charging schemes so how does this help essentially because smart charging can reduce system costs and integrate more variable levels by shifting part of the easy charging load in time for example here on the left side chart we see that's an assimilation we did for our report in 2021 for the Korean scenario in 2035 where we see that if you see the dark blue line which is related to unmanaged charging and if we use smart charging which could be the line on green the charging pattern can follow the normal output more closely and therefore this can enable significant reductions in both big costs of our system operating costs in total but also on emissions because the load can be shifted to where the normal output is at its highest. Further B2G what is called a bi-directional B2G charging which means that not only the out the the charging that is taken out from the grid can be modulated but also in some cases the B2G can provide power back to the grid this can provide further benefits in terms of uh flexibility and also in terms of normal integration for example if that extra electricity is stored during the daytime in case of the high solar pd output then that extra and that a battery can work as a grid asset and give back some of that power when the system in some cases more mostly needs it demand hours for example at 7 p.m. So now I will discuss a bit on policies and our work on that topic. A key message I would like to give first is that we believe that effective but also coordinated action is needed to integrate eds successfully at scale and I would like to emphasize the coordinated because although normally the IA we focus on giving policy recommendations so and mostly policy makers this is such a big challenge but it really needs to have not only policy makers involved but every stakeholder in the whole value chain of the ed ecosystem involved. This includes a policy makers as I was saying but also of course it includes the utilities, the transport sector, people, pilot project developers, academics, everyone should be involved to ensure that this large transition to electric mobility is done in the best way possible. So for that we developed a in a report called breathing integration of electric vehicles that was published last year in December four key steps for policy makers to successfully integrate electric vehicles. The first one would be to prepare institutions for the electric mobility transition. At first this means that policy makers should engage with electric mobility stakeholders on all of the possible aspects as I was saying this includes academics, pilot project developers, utilities and so on but also not only should they engage separately with them but also they should break silence in planning and policy making. For example by creating joint offices of the transportation and the urban planning and the energy sectors so that because of this ecosystem we need input from all of them it's important to ensure that the coordination across all of these sectors is done in the best way possible. Second we recommend the policy makers to really carry out a deep assessment of the power system impacts this right so first defining an electric mobility strategy where for example you think of setting some targets or at least projecting some scenarios of what electric mobility mobility uptake you would see in the country. The second point would be to gather data and develop insights for example by then collecting data on the e-decharging sessions tracking the movement of vehicles for example to understand the driving patterns and their arrival times and departure times from the normal resting spots. All of this data can really be helpful to develop insights and just make the assessment of power system impacts better. And the third point would be to combining all of that targets you may have set in the strategy and also the data you have collected and the insights you have gained to assess the great impacts on their serial mobility scenarios to plan for the most robust power system possible and also to coordinate the e-decharging infrastructure appropriately. The third point would be to deploy measures for grid integration. At first the first point would be here to that we recommend to accommodate all targeting solutions meaning that it's better to have a charging infrastructure than none at all. But whenever possible we recommend policy makers to encourage managed charging through several options of policies for example tariff or incentives subsidies and many others. The second one would be to facilitate aggregation by enforcing standards and interoperability to ensure that for example the vehicles can charge in different funding spots. The third one would be to value the flexibility of electric vehicles for example through private design for example time of use tariffs that can give more value to the flexibility provided by electric vehicles. The fourth one would be to coordinate e-becharging with renewables this can be done for example by incentivizing data and charging in the workplace as normally that correlates quite well with the solar output so that way you can coordinate as I would say the e-becharging with their local output and Facebook would be to incentivize smart readiness for example this can be done through ensuring minimal communication protocols that the infrastructure has to have available in order for the smart charging to be carried out on some phone day. The last point we recommend is to improve planning practices this has to do for example with carrying out practically planning so try to reassess where the extra capacity upgrades in the network would be needed and do that practically. And also the second point would be to reflect the full value of e-becharging this relates for example considering how e-becharging can help your expansion plan for example in the case of transmission and distribution networks. So today because of the focus of the session I will mostly focus on talking on the power system impacts but also on how to make this integration better. I will now go with this point. So what is really important is to really assess and plan for the minimization of the power system impacts and to unlock in the best way possible the opportunities provided by e-becharging it's really important to assess two aspects. One is the current status of the e-be-charging in your respective countries but also then assess how it could evolve over time. Here I we have a chart that shows the estimated stock share of all vehicles on the left for example we see that in Vietnam a large share of the vehicles are two wheelers whereas in Japan the share is lower and on the right hand side we see a chart that provides same information but only for the electric vehicle fleet of vehicles so excluding one of the gasoline and diesel e-beach for example. What can you take out of this slide? So on the right side you can observe policymakers can have an idea of what is the current status of the electric vehicle fleet so with that information can be used to design the best policies and the measures for today if they are needed for example they are having already some issues with some the transformers in some distribution and network areas for example but also looking at the complete vehicle fleet you can have an idea of how the the electric vehicle fleet could evolve over time so this both pieces of information both the current today's e-be fleet but also the outlook that we expect to have for the electric vehicles over time are both really essential pieces of information to plan for the best e-becharging strategies and infrastructure development possible. So we have some recommendations on this aspect for assessing power system impacts the first one it would be to develop mobility scenarios this includes for example in some cases the transmission system operators such as APPE in France develop mobility scenarios to try to assess how their infrastructure upgrade needs could evolve over time because of the e-be deployment but also by national laboratory for example on a more research oriented focus such as in the case of the United States directly linked to when I was seeing about gathering data and insights we see at least three aspects one would be to develop travel surveys for example those that are occurring down in Chile and Thailand this allows policymakers and whoever is interested on the topic to understand what are the driving patterns for example from where and to where drivers move by type of vehicle namely for example trucks motorbikes and cars and so on this can be really useful to assess where for example the charging needs may arise and then to plan accordingly for that but also it could be really useful for example to understand the arrival and departure times and this can be beneficial to design policies that accurately reflect the driving patterns and behaviors of the users in this in every jurisdiction the second aspect would be it's also possible to go beyond service and also deploy ticket at the moment such as GPS systems to really follow a monitor with a high time resolution where the vehicles are moving and thus to understand their driving patterns better and then the third point but you also can do is to record charging sessions and make a data open access such as what they have done in Germany in the public centers now the other aspect I will focus on is which measures can be deployed for grid integration so for that we provide a framework for grid integration of electric vehicles that is not sort of a completely linear path but it's thought of more of an idea on how to support policymakers to assess which measures could be more of a priority to deploy depending on the level of update and the impacts that the EVs are causing in their own power system in their jurisdiction so we begin with phase one phase one is a phase in which the power system the power system would not see a noticeable impact because of EV charging so in this case this is the case of most countries today and in this case what we recommend is to encourage higher LPP to update uptake through incentives and through the deployment of public charging infrastructure for example a measure of this could be to practically coordinate for charging station deployment in areas that could be beneficial to the grid a second phase would be when a power system is already beginning to see a noticeable EV charging load but at the same time the power system may not yet have a high demand for power system flexibility so this is the case for example in Norway and you may be surprised at this because Norway has a very high electrical uptake but the thing is that in the case of Norway in this particular example because of the high availability of hydropower electricity in Norway they don't have very big flexibility needs so although the electric vehicle load can already be significant then because the flexibility demand is not too high and this doesn't yet require that many advanced measures that we could maybe see in different systems which do have a higher need for flexibility so in such a case such as what we find in Norway we recommend to implement some passive measures such as for example time of use styles which are deployed in that country and that would be a very effective way to ensure that the the electric vehicle charging is integrated smoothly into the power system the third phase would be when the flexible electricity to load is significant and at the same time there is high flexibility demand so for example this is what we find in some cases such as in France in Netherlands and in the US where the electric vehicle and uptake is significant at the same time the power system is also in need of flexibility so in this case as more flexibility could be needed we recommend a bit more advanced measures such as what we call active measures one of the examples is unidirectional V1P or V2P which means that depending on a signal that the power system can send to the charging infrastructure which is directly tied to the demand of the power system in that moment then the charging of the electric vehicles can be modified to try to minimize the impact of their charging demand in the power system to avoid the power system impacts that I was discussing and the last phase would be a phase in which flexible EV load demand is high available and also at the same time the flexibility demand is high so this is a case that is normally particular of island power systems and for example you would think of the Asoides Islands in Portugal or in Hawaii in the islands of Hawaii and in these cases because for example certain technologies for smart charging have been in place such as bidirectional V2P which is when not only the charging can be modulated but also the vehicles themselves can provide power back to the grid this phase means that the extra availability of flexibility plus the option to give back power to the grid from electric vehicles can really ensure that the system costs and the emissions remain as low as possible if the measures such as bidirectional charging V2P are deployed in time. Okay so now I will continue with the tool here you can see both the QR code and the name for the tool I will present but also here I will send this presentation later and we will post it in the IA's website event page you can also check in more detail the reports I was talking about which is the report that is about policy manual for the integration of a little bit so I see Jason can already put the link I will continue as I was saying please if you have any question please raise your hand and we'll get back to you shortly okay so now we'll focus on presenting the IA's EV charging and grid integration so this tool has three main motivations the first one is to assess the impact of EV charging on the power system the second is to assess the effect of measures for mitigating EV charging impacts and here it's mostly about what we call managed or smart charging and third it's so estimate the CO2 emissions related to EV charging so that policy makers utility professionals academics and so on can for example compare different measures and see whether outcomes could be on the emissions of the power system that can be directly related to electric EV charging so the first model to assess the impact of EV charging on the power system it provides a simulation of EV charging behavior and its main output is the weekly EV charging demand profile on a five-minute resolution basis the second one would be the output would be essentially similar so it's also a weekly EV charging demand profile but here you can have implemented some managed charging measures and then you can compare with the unmatched states what are the differences and what are the maybe the benefits of certain charging schemes such as time of your stars and me one me the third one based on a significant representation of electricity needs of your own jurisdiction which you can modify the tool it provides some calculation of yearly CO2 emissions for you to be able to assess differences across different scenarios and measures which can help decide which measure could be the best to implement depending on the local context of the jurisdiction so this is and I will begin by showing the main output so let you know where I'm heading and then I will show you how to create such simulations with the tool so the main output of this tool as I was saying is a weekly electricity demand profile which is showing which electricity demand is posed directly by EV charging the charging power can be analyzed in five-minute intervals so it has a high-term resolution and the user can also check the results by different turning locations and also for each of the fleets that you can define for the simulation this information that we provide and I will do a live demo to show more in detail all of these options you can assessment the tool it can be useful to several stakeholders and I would really emphasize this so it's not only for policymakers but also for many other stakeholders in the EV ecosystem for example the first talking about policymakers and utilities in those cases these stakeholders could benefit from the tool so if they for example would like to know whether the grids for generation capacity need any aggregate to accommodate more EVs and policymakers and academics for example can also study trade-offs in between different charging schemes and between for example different charging infrastructure availability and also if you think for example of pilot project developers the pilot project developers can use it to provide a preliminary assessment of the electricity demand perks that could be associated with the test system and with that they can design and check if the hosting capacity in where they plan to determine the project of the pilot project of EVs is enough or if maybe they should move elsewhere where the grid has more hosting capacity to have their test done so I will now go with the motivation number one and I will show some examples. Just one mention if I'm going to the tool so this tool provides several options for charging for example as you will know there are several charging locations that can be utilized for example you can have home charging and group charging which is in our group targeting the gaze of the highways which is normally higher powered you can also have workplace charging the fourth one you can also see is called depot targeting which is for example where you store commercial vehicles or for example bus tracks this is what we call a depot you can also have for example roadside charging which is normally what you see in a parking spot by the street or you can also have what we call destination charging and this mostly refers to for example when you are going to a place for a specific activity for example if you are going to the gym or you're going to the cinema or to the mall um this is what we would call destination charging because you're basically going there for a specific activity and then as soon as you finish that activity you go and leave the place so I will now begin with a flight demo and showing a basic example of the affiliate of parking and buses okay do you see my screen okay so before I'm going to the example of the buses I would like to note that here in the tool which we already send the link for you have here a full description of the technique and note and a guide so here you can read more in detail for example some ideas on how to use the tool and how the tool works behind the scenes so this can be really useful if you want to understand a bit more and how this tool works and what you can do with it so as I would say my first example would be about 100 buses so here I go to the first step here which is called speed so here for example I can put a label to this uh so I will just name it buses for specific reasons then you can select the vehicle type here you have various options such as two wheelers three wheelers light duty vehicles buses or trucks so I will select bus in this moment and then you can select the stock which is how many of those vehicles are in this so I will just leave it at 100 so in this case you see several other parameters for example the average of the capacity the energy consumption which is how much electricity consumes per kilometer driven and also some driving behaviors and for example what's the average one day driving with some variation that can be defined both for the weekdays and also for the weekends because we know that um the driving behaviors can vary significantly in some fleets depending on if it's a Monday to Friday or if it's a weekend day so with that you can modify this according to your specific context of your jurisdiction and here we provide some default values but if you have the information that is representing adequately your local context you can modify this as you please to make it more suitable for your own needs so that's a flip top the second tab I will show is related to the charging availability so here for example um you can see many different options you can modify and all of them have here this question mark where you can read what this parameter represents so for example here you can have charging infrastructure availability of for example a home depot charging workplace charging roadside destination and route if you wonder why this is different for weekdays and weekends it's also because this can vary significantly depending on the day of the week so I think the easiest example to explain this is in the workplace so if we have cars for example or two winners and as people don't love to work on the weekends normally you would expect a much lower availability for those vehicles in the workplace on the weekend which is here when we put in our default values so with this you can modify the availability for example to have more or less charging available for the users at the home level at the workplace level and and so on and so forth and with that you can test various scenarios but for now we'll just leave it there and I will just continue to show the the basics of the results yes I see there's a question there please go ahead hi Javier I just had a doubt actually regarding the home depot I had like would it be different for like single homes and apartments like how we set these numbers based on the availability how would we differ how would that differ and one I had another doubt about the destination so can you be more specific on what sort of destinations like because based on where the people are like the person is driving to so if it's driving to a mall or suppose a someplace else so the network very eventually right so I just added out on yeah I will begin with your second question thanks for the question so the destination can be defined as for example as we can see here with another definition so the formal definition we put here is charging at a place of interest for example in shopping mall restaurant public institution the gym some of these places are examples of what we call destination charging and here do we differentiate it from the other places so this is a destination of a journey that is not work and not home so why do we differentiate this because the this normally these places tend to have different timing powers and characteristics so for example normally at home you leave the car much longer but in this case depending on the destination you tend to stay for two three hours at most so that is why we made a separate category for this type of charging infrastructure in our on the second question I believe it was about how to define home charging based on the home and apartment right was it the question yes like a single home a bungalow sort of or an apartment like where there are multiple homes yeah so basically as here we're kind of defining the the the parameters on kind of a fleet thinking basis so here we kind of define okay this specific home has a charger of five kilowatts this other one has of them like you have to think of kind of an average or a representative and a way to simulate the feet so in this case to make it more adapted to the local context you would have to think of maybe what's the most typical charging power for example so many places have like you can put a custom one for example type type is a I think a rather common a case for home charging that is not high power but it also depends if you're for example doing a like a single home charging case or if you're looking at like maybe a common charging place in a residential neighborhood so all of these factors have to be accounted for and in the end what you modify is both the charging power and also potentially you may modify this availability if I don't know for example people are not there on the weekend and yeah then that would change is it okay thank you thank you sure okay so I just like already said it quickly to go back to the in the phone by us and set your bus the rest is the same so here I just jump right into the results so here's what we see as the basic sample so you can see here the results by segment and by location here I only have one segment so basically one fleet which is the basis so that's why you don't see more but if you have more fleets defining the thick tab you can see up to I think 10 fleets and see how each of them impacts the whole the amount of the simulated system so with this one you can download the data as a csv file which you can analyze in excel or your prepared data analysis analysis software or language program language for example here then you can assess with the cursor for example what's the charging power from this fleet on let's say on tuesday at 9 25 throughout the whole week and you also can see several different other aspects for just just for example the maximum eb power demand in the whole week for example this can be used to assess the hosting capacity needs of the test fleet you are simulating or so you can get the average demand you can also learn in terms of energy the estimated weekly total and also that can be extrapolated by the tool for the annual eb energy so as you can see here with this simulation you can already see some interesting patterns and examples that can give you an idea of for example how the fleet you are simulating would impact the local power system that you are defined okay now I will do another example I will do an example of cars so I will always try to start from scratch to always show you how you can do it what I would say at first you go by default to the fleet tab you go to cars which is what we call here light duty vehicles and I said I was going to do an example of 1000 so okay here's a 1000 value by default I will leave it there and I will not modify any of these parameters so okay so as you can see here like just to give you an example I changed here the segment to cars and already then the charging the charging power decreased by default and this is because first I will show in buses and buses tend to have faster charging times in order to avoid being charged like for very long time but because the batteries in cars are smaller than normally the charging power that we can have for example in a home place is much lower for example here's 25 kilowatts instead of the 22 that we have by default on the depot charging of the bus just to give that measure here so here I will update the results and as you see here this is the profile of 1000 cars for charging so for example here you can see that normally the cars related demand tends to peak when people come back from work so around 630 or 7 you can see normally the peak there which in this case of 1000 cars can reach almost one minute of charging demand so it becomes significant then normally the profile decreases after they have charged during the the night and they in some cases increase later on during the day if they for example have workplace-based charging so now I will show just with this way that by location the charging of this example here for example as I was mentioning here the light blue color relates to home charging in this case of cars so as we could normally expect at least this tends to be higher when the cars come back to their homes but also here on the more dark blue color we see that there is some level of workplace charging so in the case of cars with the base default values we have for this simulation we see that the electricity that they need is provided in a video shared by home deeper charging at least home charging sorry which is carried out after they come back from work to their homes but then also while they are working if they bring their cars to the work and they charge them there this can also cause to have some level of charging in the case of the workplace and I will also show later that that possibly this could be increased depending on the incentives and availability of infrastructure that you may define so now let me just do a change and show you what happens if you for example think of a case in which for example utilities or policy makers deploy certain measures that incentivize workplace charging and those make maybe home base charging less attractive so if we think of that okay we can go to behavioral profiles what we can do to simulate that in a certain jurisdiction there's less home charging and more workplace charging is to basically play here with this values of the charging availability which is somehow directly related to the infrastructure availability of that specific charging type so for example if we were to have a policy that cost the local jurisdiction to have less home charging and more workplace charging we would then decrease this so we would hear for example just as an arbitrary value we can lower this to 30 percent and then for example we can increase here the availability to 90 percent so let's see how this impacts the problem this was the best case and as you can see now the workplace charging takes a much higher role a bigger role in the charging so previously we had the patterns such as the light blue color so the home charging had the biggest peak when the people came back from the home but now the biggest peak is because of the settings we defined in the case of the workplace charging so this would be the simulation of an example where there is for any reason less availability of home charging and because there is more instead of that there is more workplace charging then the charging demand in the end will shift as a system level to the workplace and this can be also sometimes beneficial for example as I was showing in one of my first slides in many jurisdictions you can see that the solar PD output can be significant so for example if you were to think of a way to assess and try to implement that a way to coordinate the charging with solar PD output for example then possibly encouraging workplaces to have more charging infrastructure could be a good way to do it because as you can see here in the simulation this could be more linked to the solar PD profiles in that case so this can be as I was showing an effective way to simulate these trade-offs between for example what happens the charging demand if you have predominantly home charging or what happens if you have predominantly workplace charging to give you as an example of how this works now we'll go back to my slides and then continue with some other examples okay so what I showed before was just some examples of what we call unmanaged charging so what that says mean basically unmanaged means that you just connect the charger to the vehicle and the charger is working at full power since the moment it's connected until the battery is full and then it stops so it's basically like not really flexible or not really modulated it's just like you connect it charges at full power and then when the battery cannot take any more power then it stops so what we call managed charging is basically to modulate that in various ways and of course this will depend both on how much power you need to charge how much energy you need to sort of refill in the battery and also how much time you have in what we call the charging window or how long the vehicle takes so for example one form of what we call managed charging is what you see here on the right is what we call balanced so basically if you know for certain how long the charging window will be and if you know how much energy you need to to refill so to say to the battery then you can define the charging power meaning the kilowatts or kilowatts so the power unit to be lower to using all of that charging window to charge the same energy and I'm sure that the the vehicle driver is happy with the battery level when he or she comes back to take the vehicle back so for example here this case in this chart we see that the energy charged in most cases would be the same so the driver would see no difference when they go back to get their car or a bus for example but for the power system this can be beneficial because the charging power can be modified with managed charging to reduce the impacts on the power grid so the question that the tool asks itself to see if managed charging is possible and can bring benefits is okay do we have some flexibility so is my driving window long enough to maybe move a bit of that turning a meter to decrease it a bit and spreading over time and then if there is some flexibility on that aspect then the tool checks if about the participation rate so is the infrastructure adapted so you can define some parameters in the tool and also the drivers are interested in willing to participate in these things because it is so obvious that some drivers will be interested to participate and then if all of this is a yes then the tool for each specific vehicle in the simulation it applies a much charging measure and then you can assess that so I mentioned that one example of managed measure is balanced basically just spreading out the charging for longer time with a lower but constant power as you can see here in green but also some other managed or what we call smarter driving measures can be a time of use types which basically is a varying tariff of the electricity price depending on the time of the day and so for example here in this aspect you could see that the way that this tariff could influence the charging is that normally the vehicles would try to minimize total cost of the power right so this means that they would try to follow and charge the most whenever the tariff is lower so with that basically if the tariff is well designed it can be really useful to shift the charging loads of the vehicles and those so try to decrease the impact on the power system so now I will go back to the same example I was doing before so 1000 cars but I would apply balanced charging to show you the difference in maximum power time so now reset it to scratch cars here 1000 I would just leave it there so if you want to modify the charging scheme you have to go to the ship called advanced options so here you can see okay I will omit in these other parameters here so balanced so as I was saying I will make now an example with balance charging so I would if I want to do that I have to do it here in under the managed and opportunity charging section specifically when I select this balance the charging strategy here so I will just leave all of the other values by default I'm sure that the difference so here you see that the peaks compared to the previous case are much less pronounced so here the for this 1000 vehicles we see that the maximum e-power demand over the week is little above 500 kilowatts whereas for example if I go back and just reapply the unmanaged which is like the default case the peak goes almost to double it's almost 1000 so this shows you that at least this example of measure can be really useful to decrease the impact on the power system in terms of the maximum peak power and in that aspect it can be really useful to as a measure to to reduce the impact of our power system through the use of the smarter charging what we call managed charging now going to the same part I will show for example the impact of time of use times so for example you can go here to select the same part here time of use and here you can also modify in the tool itself what is the credit tariff schedule that you want to simulate so for example the base case that you will see here is okay like a very low tariff during the nighttime then it means increase a bit during the day and the default tariff is at its highest in the late afternoon or evening which is normally when the peak power system demand as a complete system happens so here this is what for example if we look there so now let's look at the impact of that on the simulation okay so here is something I wanted to show because it can be interesting and like it can also show that sometimes the trade-offs have to be carefully assessed what we see here is actually a bigger peak than before as you remember well I showed the the peak of like the unmanaged phase for the 1000 cars was about 950 kilowatts but now the peak is higher even if the energy on the whole week is the same so here why is this important to show so I just wanted to make a point here that the tariff design is very important why does its peak happen if you see here um so there is a very strong here a strong decline here like around 10 and then 11 p.m so this means that normally as if the the vehicles follow the economic rationale they will try to charge whenever it's the best kit for them but also considering the cost of charging which is directly in this case linked to the time of use tariff so that's causing that because the the because it's lower in that moment and it's also when they are in the home to to charge that means that that is causing a peak there so it's important to if time of use tariff is inside it's important to to really try to simulate all of these effects to to avoid for example when we put for here a secondary peak of demand of the power system so this is something like that it's really important to to simulate because of course if the time of use tariff in the end could cause an issue because of the way it's defined it's not something that probably policymakers would be having so this is something that I wanted to show and it's also something that you can simulate with the pistol and lastly before going to my last example um I will quickly show a v1g and I will go a bit quickly so to leave more space for questions but please if something is not here you can ask me later so as I will say v1g is basically one directional vehicle to read but it means is that the grid can set signals to the turning infrastructure depending on the demand it's experiencing at the whole grid level and with that it can try to accommodate the charging demand of the electric vehicles in particular to try to minimize the whole system demand and those minimize the stress of the power system so here again we have those options I select active control with any directional turning or v1g and here in power grid you can select you can see here the base power system the banter so why is this important it's because as I was saying the v1g is directly connecting the power system and using the power system demand to influence when and how much the evs charge so for that it's essential to have an idea of what is the base the electricity demand of the of the power system without the evs so here you can either upload your own file and also maybe download if you want to have an example file of how the file looks but you also can for example modify this here in the sliders so you also make an example like if I force the power system to have a very low demand weekends then you will see that the charging with this scheme is lowest on the power systems on the weekends here yeah so sorry I think I explained the problem so basically as v1g is following the demand patterns of the power system and it's trying to minimize the total meaning power system on its own plus evs charging as I was showing here if the demand is lowest of the power system during the weekends this is why the v1g scheme would try to accommodate more evs demand on the weekends because that way if the total evs sorry the total power system demand is lowest on weekends but then and then that leaves more space to shift some of the charging of the week to the weekend and thus that's the optimal place to have the charging because from the grid point of view to have more ev charging on the weekend is optimal because there the power system demand is is lower so as I was saying here in the power grid tab you can modify this as you please and you can also upload the file to try to accommodate it to your local context and the local data you have for your own your station so I will now quickly introduce my presentation with the last example of emissions and then I will open the floor for questions okay as I was saying the last part of this motivation of this tool was to estimate the CO2 emissions to ev charging so basically how it works is that the estimate of emissions is produced in the following way first the simulation computes what is the net load meaning the total demand minus the belongs with and without ev charging to see what is the demand that would need to be met with thermal power plants for example so then with the calculation of the net load without and with the ev charging the simulation runs a simplified power system to run the power plants and because of these power plants have emissions factors then we can calculate the emissions cost in both cases basically the case without and with evs and by comparing the emissions related to the power system with and without ev demand we can assess the emissions that are directly the cost of ev charging so here's basically you can kind of see an hourly simulation so this is a simplified dispatch meaning that the algorithm is designed to minimize the cost of the marginal cost of every hour simulated and by that it computes the optimal dispatch plan which then by knowing the emissions factors of each simulated plant it can calculate the emissions of both with and without ev charging and by that calculate the emissions that can be directly caused by ev charging so now i'm going to show you how it works um i went for the last time reset this and go from scratch so here let's do the card example again and here and just to mention it briefly here you can define various parameters of the electricity list because of course the power system is really important in to define properly to assess properly the emissions so here you can define like for example the plant type if it's cold or oil or gas what's like the total capacity of the system um and also here you can see a simulation of the generation units so with that like if you want to check the emissions values you just have to in the results and it's available for every charging steam and every other simulation possibility you will always have these emissions that are available to you so here how do you see basically you see a a chart which you can also download the data for which is a five minutes interval the chart for the emissions that are directly the cost of ev so marginal emissions from evs and you also can have uh an estimate based on that weekly profile of the weekly emissions and also on the annual emissions so with this if you compare the other measures i was showing you can also compare not only the charging demand between measures but also the emissions that can be caused by all of these different measures and maybe have then a different metric to assess how attractive for you is a certain measure versus another one by using the emissions method but also the as i was saying before the tracking method okay so with that let me finish my presentation with some final remarks and then open the floor for questions so the distribution of load transport is ongoing and it will accelerate as soon as contributing to the carbonization and helps reducing dependence on positive tools as i was saying this will contribute to a higher power demand but at the same time it can give an opportunity for the power system in terms of flexibility the power sector can accommodate a wide range of charging solutions but we think encouraging much recharging is the best way forward the flexibility of new electricity and uses such as cv charging needs to be incentivized from early stages and as i showed for a long amount of time our tool can be a very useful resource for a wide range of stakeholders for example public developers, police and maker system operators and also utilities and academics so with that let me finish my presentation and i open the floor for questions in the Q&A many next so if you will defer you can also raise your hand and you can ask the question directly using the microphone yes um sorry i hope you i can answer it correctly uh okay yeah thank you thank you very much for the interesting and very helpful the presentation uh actually there are strong demand of the how to deploy the electric vehicles specifically the deployment of the charging stations but my question is about the standard of the charging stations and i recognize that there are the several world standards including the the test slides and test slides of charging standard and also the europe the convo the japanese china more and maybe the china also have something and as far as i observe this year the test slides standard is likely the market defect but in that sense can we recommend the test slides standards as a standard design if we are requested to design the developing member countries the charging station infrastructure okay thank you for the question so in our case this is not my particular area of expertise but to the best of my knowledge we don't at the ia at least have a specific recommendation on which standard is the best of course as i was saying in my presentation there are several benefits of having common standards which because this facilitates interoperability but as i was saying to be very specific with you as we don't have analysis for our conclusion of if this is the best standard available then i will not be in a position to recommend to make this standard the most like the usual one over the other ones that for example you mentioned correctly that are available in europe so in this sense that what we can suggest to the developing countries is please first to conduct their own assessment of the best of the standard for your country yeah i think of course i think i think every country has specific conditions so i think every country i think well maybe the best way forward would be to carry out their own assessment of which standard would be most adequate for their own context and based on that assessment decide which standard to use okay thank you very much thank you okay um i have a question written internally i will read it now so the question is whether we can conclude that this tool could be used by the policymaker to determine where's the appropriate location to put the ebitrading station based on demand and great availability so i think you can use this tool as an approximation for that purpose as i was saying i will repeat this this tool doesn't have a very complex and detailed power system simulation underneath because of course every power system has its own complexities its own design for example in terms of transmission capacity congestion risks for example and stuff like this so what we do here is just to provide a simple kind of what you would call in the academic literature the economic dispatch algorithm without accounting for grid constraints or stuff like this because this would require a deeper study um but with this you can see for example some trade-offs are like some impacts we would have in terms of supply and demand and mismatch what you can do um for example i didn't mention it too much but i can also mention it now what you can do if you if you want to tailor this for a specific sub area in your country is that you have to define the parameters properly so for example if you're thinking of the whole country to make this assessment valid for that whole country area you will need to have information for okay for example the total leaflet in the country the total duration capacity in the country and with that you can have a simulation that is closer and more adequate to the reality of the country context but for example if you want to assess just a city you would kind of find approximations with for example by inputting the fleet size and the behavior of just the vehicles in that city and also try to input the values for example for generation capacity that this like kind of directly linked to that city so with that um you can make an assessment of for example okay what's like the demand you can see for this specific vehicles in this city or this region um and with that you can be closer to a simulation of the specific local context i have to remark as i was saying that this is useful as a preliminary like a complex enough a simulation in in most cases but if you really need a like a detailed study where for example you also need to account for a brief congestion or like some more complex constraints for the power system or then it would be needed to to complement this tool with other more specific studies that the for obvious reasons would go beyond the capabilities of this tool because this tool is aimed to help to simulate and understand the trade-offs and like maybe to get the dimension of for example the charging demand but to make it reflect um with a very specific precision the local context of distribution with you would need maybe something extra to complement this tool um to have an idea so what you could do with this tool as an input for the later studies you could for example as i showed you can download the data so you can take the data for the charging profile for example in the unmanaged space and use it as an input for your power system simulation to see how this would impact and you would for example simulate several cases with the tool to get different demand curves and with that simulate the power system impact so that's i think with that i should have given you various ideas of maybe how to use these tools to try to simulate a more specific case in your own use station yeah but thank you for the question i see here a question or a comment that that person found interesting that we projected a high capability vehicles and buses will make up about one third of the charging demand and i apologize if i didn't mention it but the chart i showed is on the global level so not only the the case of asia but it also includes other regions of the world so maybe this is one reason why you would see that the projection i showed in our scenario is different from the measure or the projection you mentioned from the asian transport observatory yes also there's a question right here yes we will exactly thank you jason we will upload the slides after the workshop okay is there any other open question which you would like me to answer okay i think we don't have any more questions so i will now take the liberty to grab up the webinar so i would like to first thank everyone for the participation and your interest and your questions we're really happy at the ia first to have this opportunity for a big collaboration with the adb but also to have the opportunity to totally present to you our tools but also to interact with you and give your your views and your feelings on what we are presenting as we mentioned before um johanna and james shared a survey that they would like to to please fill in the link that they sent at the beginning as jason kindly put the email there if you have any question and you have any feedback or if you would like to let us know of any important development we should be aware of in your specific jurisdiction and you can email us at the address there which is jeff.mobility.wg4 at ia.org um and yeah with that i would love to to close this i'm really happy with the outcome of this webinar and i'm looking forward to to having more opportunities to collaborate with all of you thank you very much