 So welcome to this event. Today is the session two of our webinar titled Electric Vehicle Total Cost of Ownership and Great Integration Tools, which is a workshop under the GEF-funded Immobility Support and Investment Platform for Central and Eastern Europe, West Asia and Middle East. This event, as you will have seen in all of the invitations, is co-hosted by the EBRD, from which we have here today Victor Vonella to join us and give some operating remarks, and also from our side as the IAEA, International Energy Agents. Before the beginning, I would like to give notice that this event, the same as the last session, will be recorded and both the presentation of today's slides and the recording will be available in our websites in the end page for this specific session. Before we begin, I would like to thank EBRD for their great collaboration. We have been working with Victor for a long time already to make sure this event is as best as possible and as possible for all of you attending today. So now I will give the floor to Victor Vonella to give some operating remarks from the EBRD side. Thank you, Javier. Can you see my screen now? Yes, yes we do. Okay, so thank you. Good morning, good afternoon everyone. My name is Victor Vonella and I work at the EBRD. Thank you very much to the IAEA for co-hosting with us this event, which is the second part of a workshop in which we are sharing the work they have done as part of the Global Electric Mobility Program for elaborating certain tools related to immobility. In the case of today, it will be an EB charging and grid integration tool. But before we enter into that, I would like to just introduce very briefly what is this regional investment platform about and the overall global program. So this program is funded by the Jeff7 and it's a global electric mobility program implemented with several partners. It's the EBRD and the IAEA of course, but there are also several UN agencies like UNEP, UNIDO, UNDP and other partners like the ADB and others. So this program aims at increasing the capacity in the preparation of electric mobility on different parts of the world and also in fostering investment in the sector. It is composed by two main components. The first component has four working groups elaborating knowledge and tools. These four working groups cover cars, another one on heavy duty vehicles like buses and trucks, a third on two and three wheelers and a fourth on battery and EB grids and EB charging in general. The second component is composed by four regional platforms. These platforms cover four continents. So there is one for Asia and the Pacific, another one for Africa, a third for Latin America and a fourth one covering Eurasia, which is the one being implemented by the EBRD and the official name is Regional Support and Investment Platform for Central and Eastern Europe, West Asia and the Middle East. This whole program supports a big number of projects around 40 to 50 different parts of the world and in our case it supports six national projects in our region funded by the Jeff plus others that may join in the coming months. Among the main outcomes that we expect to achieve with this platform is to develop a community of practice, a network of practitioners in the mobility in the region. We also aim at increasing the capacity for project development and to improve the the the ties with EB suppliers in the region and we also expect that through the platform there will be conditions created for market expansion and increased investment in the sector. To give a few more details we have structured the platform across five components, a community of practice as I mentioned in which we meet from time to time with different with the different project managers to share the experiences in the in in their projects and to understand what are the main challenges that they're being faced. I help this web space in which we share the the work we are doing and we try to also sort some of the issues that the projects may be maybe facing. Then we also have an annual event. This year it was in Izmir and it was focused on trainings and on electric buses but next year we'll likely be in March in London and in which we try to create some networking. We do some presentations and probably some technical visits as well. The fourth component is probably the most important one which is about training and capacity building and today's seminar is part of the dissemination activities that we organize among others and I will explain those later. And finally we have a fifth component called a marketplace and it comes a bit later in the program when the projects have already progressed a bit more and it consists on supporting them to to find finance and how to scale up their demonstration projects into a bigger scopes. In terms of upcoming events, which this is my last slide, of course we are we have been delivering these workshops with the IEA today and also the first session that happened last week. I also wanted to mention that we are delivering a set of training in in electric mobility with interpretation in Russian and Arabic. The first session happened in October but the second session is in this week in 16 November with a focus on EV policy. The third one will be mid-November mid-December sorry probably on free electrification and there will be three more sessions on EV charging and battery recycling early next year. And finally I just want to remind as well that we will have this annual in-person workshop in the first quarter of 2024. I may share more information about that and yeah that's that's my lead for my side. Thank you very much. If you want to have more information just drop me an email and I will be happy to have a chat with you. Thank you. And with this I think that it's time to go back to the IEA. Thank you. Thank you Victor. We're really happy to work with you and collaborate for this event. Now I will give the floor to our colleague Jack Poichet. Jack is the IEA's Jeff Immobility working group core coordinator and also is our system transformation analyst of our unit which is the Renewable Integration and Security Presidency Unit. Jack the floor is yours. Thank you Javier. Thank you very much Victor. It's a pleasure to co-host this event with the EBRD and to see the progress of the platform. It's really nice to see that the platform is taking life and that a lot of events are scheduled and are going to take place. I wish also to thank my colleagues Per Adder-Svidel and Shane McDonough who presented during the previous event on Thursday about the total cost of ownership tool and I hope you you were able to join that event too. If not the recording will be made available online soon. So I just remind about the role of the IEA in this Global Electric Mobility program. We lead the work on two specific working groups. The first one about light duty vehicles. The second one about charging and grid integration. It is a pleasure to be part of this program and to be delivering knowledge products and to also track data about countries, policies and markets. It is a great value for us to be part of this network because we work with the number of countries around the world. The members of the IEA family but being part of this network allows us also to reach beyond those countries and it has a value for us to work with a number of agencies like the EBRD and all the countries from which you belong. It's a two-way collaboration. We deliver knowledge products and we share this information like doing these events as the one today but in return we also get a better understanding of the challenges of all the countries beyond those we are used to work with. The work of IEA in electric mobility goes beyond looking only at the vehicles and the technology. Indeed transport electrification will play a major role in the clean energy transitions and we also look very carefully at the integration of EVs in the electricity system. In our unit, the RISE unit, Renewable Integration Secure Electricity Unit, we are specialized in looking at how to ensure secure yet affordable and clean electricity supply and we do analysis on electricity grids, electricity security and the integration of renewables as well as new electricity uses like electric vehicles. There are a growing number of studies that look at the potential consequences for the power system of the growth of EVs and I will just give a couple of examples of the United States of America for example where EVs are booming. If two out of three cars in the USA were electric by 2050 that is more or less 190 million electric vehicles, the peak demand of electricity could grow by as much as 32% compared to business as usual. If we look at the distribution system, a service area in California would need to upgrade five times more feeders than the original plans to accommodate EVs by 2030. So just considering this couple of examples we see that neglecting the electricity grid could be a real challenge for the growth of electric mobility. I will just briefly introduce the charging and grid integration tool before heading over to Xavier. He will speak much more in details but of course we are really proud and excited about this tool because when we looked around what existed we found a number of tools but none of them was actually meeting the needs especially of emerging markets where the availability of data is very limited. So we wanted a tool that allows anyone around the world to assess the simulate and assess actually the consequences of feeds that would be specific to their own country. So we developed this tool with the intent to be pedagogical but also with the ability to be used by professionals like planners and policymakers in a more advanced modeling work. This tool is actually a companion to a policy manual that we released about grid integration of EVs and that we released last year in December. It is also available on the IEA website so feel free to to download it. I will hand over now back to Xavier who will introduce more in detail the the tool and I will also add that you have in the zoom window you have a Q&A box and you are free to post any question at any time during the presentation so that we can address them afterwards. Thank you very much and have a pleasure. Thank you very much Jack. So now I will continue with the presentation but let me also say what Jack said. We have a Q&A box that you can use to put your questions that we will address at the end of my presentation but if it happens that you have a very pressing question that you really need to get answered in order to understand the rest of the presentation then feel free to raise your hand and we will get back to you as soon as we can see. So now I will share my screen and begin with the presentation. Can you see my slide? Perfect. Okay so as explained today I will be presenting the EV charging and grid integration tool and just for clarity when I say EV I refer to electric vehicles and I will show first talk about a bit about grid integration of EVs and why it's important to talk about this topic and what kind of frameworks we can have to integrate EVs more efficiently to the grid then I will go right away into doing a live demo of the EV charging and grid integration tool that as Peter said it was developed under the collaboration of the IA and the JET program and then at the end I will have a post a Q&A to answer all of the questions you may have about the tool. So let's begin. First an important message is that the EV charging demand and faster charging will grow substantially. What we see is that EV charging demand is already substantial today accounting from more or less 110 terawatt hours annually which is in 2022 the equivalent of the demand of the whole country of the Netherlands and it in our scenarios could grow between 950 terawatt hours or even reach 1700 terawatt hours by 2030 in our case in the scenario which could be in line with the net zero emissions pathways and the commitments set in the Paris agreement. So this shows that in our basis the EV charging demand will be growing substantially and that's one reason why we should care about this topic but also a second aspect that why it's important to care about the growth in EV charging demand is not only because it will grow on an animal basis but also its structure will change and this is because larger vehicle sizes for example buses or trucks which will require a faster charging in many cases because of their larger battery size will have a bigger role in the system and therefore this could put increasing stress on the power systems that we need to address and optimize in order to avoid significant issues for the power systems. So what we see is that in road transport electrification we can have several impacts. What are the impacts of EV charging? We can have for example impacts on depending on three variables. One is the time meaning when and also for how long the charging takes place. The second one is the location. The location for example is if it's in a small distribution grid if it's in a highway if it's in a residential area for example all of these will impact how much of an effect the EV charging of that particular vehicle or vehicle fleet will have on the system. And third also the charging capacity namely how much power that charging is drawing from the system. All of these three aspects will have a very important big importance on the definition to understand how much impact we will see from the EV charging of several different fleets. So based on that we can see different impacts. For example we could see low impacts such as in workplace charging. This is because that that can take place over a longer period of time which means that to get the same amount of energy the vehicles can be charged with less power. However in contrast we for example we think of unload charging for example in a highway normally the drivers would like to stop as little time as possible that means they would prefer to use a higher charging power and this could have higher impacts on the vehicle. At the same time we can also think of going from challenges to opportunities and these opportunities are unlocked by smart charging. For example taking into account that the long time of the charging windows meaning how much time the vehicle is stationed there and able to recharge we would have the highest opportunities for example in work-based charging or home-based charging. This is because the vehicles are stationed for a long time and this means that if the technology is available the charging can be rearranged to ensure that the energy required is met on time but the charging can be distributed among time to when it's most beneficial to the power system. Therefore the home charging for example and work-based charging can have larger opportunities in terms of flexibility and manageability of the charging than for example in unload charging which as I was saying before drivers normally tend to arrive and want to live as soon as possible. Going further into flexibility what we see is that charging flexibility is really essential and needed to lower system costs and emissions. For example what we see is that flexible demand in terms of EV charging can mean system cost savings for example in terms of peak power system costs also average operational costs and also therefore emissions. This is because if we have the ability to shift the demand for example what we see here on the left to where the renewables are mostly available which are cheaper and also they are low emissions sources of electricity that can mean that we can both avoid emissions but also avoid operational costs if we have the proper technologies in place to enable that smarter charging or what we call managed charging. Now I would like to give a few examples and some ideas out of our policy manual that Jack already mentioned which is the policy manual for grid integration of electric vehicles and my key message here is that effective and coordinated action is really essential to integrate EV successfully at scale. This is not only a matter for policy makers of course because all of the stakeholders of electric transport can be involved. This includes for example pilot project developers, industry leaders, academics, all of the stakeholders will be under participation will be essential to ensure an efficient integration of EVs. So what we see is mainly for key steps for policy makers to successfully integrate EVs. The first one is to prepare institutions for the electric mobility transition. This is a policy maker should engage with the electric mobility stakeholders as I was saying not only for example vias with the Ministry of Energy of each country but also include in the discussions the industry associations for example pilot project developers and so on and so on and this is important to get the most insights possible to plan for the best transition but not only it's important to engage with them bilaterally but also to break silos in planning and policy making for example building joint offices of energy transportation such as what happens in the US. Second what we recommend is to assess the power system impacts then a very important step would be to define an electric mobility strategy for example defining certain goals understanding how the electric mobility transition could evolve over the years for example to understand if a certain country there could be a larger fleet of trucks of buses or for example two wheelers that of course depends on the country and then some very important steps include to gather data and develop insights and then with all of those insights to assess the grid impacts on the mobility scenarios to prepare and upgrade the grids and the power systems whenever it's necessary. The third aspect is directly going into deploying measures for grid integration this includes for example that all charging solutions could be accommodated but we encourage if possible to have smart or what we call managed charging in place. The second aspect would be to facilitate aggregation by enforcing standards and interoperability. The third one would be to find ways to value the flexibility of EVs for example to through time of use types or other market mechanisms to ensure that the flexibility provided by EV charging is incentivized in the market schemes or in some way. The fourth one would be to coordinate EV charging with renoms and then the fifth one would be to incentivize smart readiness meaning that the technologies that are deployed for EV charging infrastructure ideally should have smart technologies already available for example communication protocols. The last step would be to improve planning practices and this refers to in conducting proactive grid planning to really prepare and try to upgrade the grids whenever needed trying to anticipate when the needs will arise and also to fully reflect the full value of EV charging for example considering what benefits flexible charging can bring to the system when planning the power system as most countries normally do guarantee. I will now go into two examples and then I will go right away to the two. So to assess the power system impacts our key recommendations are first to develop mobility scenarios as I was saying before. So one example of this is what the transmission system operator at Bay of France is doing but also for example in the US what the national laboratory and rail is doing in terms of developing scenarios to understand how electric mobility could evolve. Second one is to develop travel service to understand the traveling patterns and also by vehicle for example to understand where vehicles drive and at what times of the day and for how long this is can done by for example doing travel service such as going to Chile and Thailand and also for example what is being developed in terms of EV charging patterns in the case of us. The third one would be to deploy digital technologies for example that's the case of what they're doing for GPS systems in light duty vehicles and in trucks in many places for example in the US and in Europe and the last step what we recommend for example is to get more data and that way develop more insights that can be useful for the planning ideally to record charging sessions and make those that data open access for example what is being done in Germany in a public tender that was developed recently. Now I will focus on some measures that we see for grid integration so based on that what we have is a framework that we developed for this report called grid integration of electric vehicles that is not meant to be a step by step guide that is mandatory to follow but more as a reference to guide policymakers depending on what level of development the electric mobility transition has in their car so for example we have this ordered in four phases phase one would be if the country is not seeing any noticeable impact of electric mobility in their power system and there what we would recommend for example would be to encourage higher electric vehicle uptake through incentives and also by developing public EV charging infrastructure. This is the case of most countries today. Now a second phase would be when the electric vehicle charging demand begins to be noticeable but at the same time the power system overall doesn't have a big need for flexibility for example this would be the case of Norway that despite Norway having a high uptake of electric vehicles the fact that they have also a lot of hydro power which is flexible in its operation means that there is not that extra flexibility gap to meet and thus this country would be allocated in this phase of our framework and in this case what we would recommend is to begin looking at measures that are passive for example time of use types that normally have different prices of electricity throughout the day to encourage changes in behavior of the charging of the users and that is one example of a measure that can be useful in this phase of the kind. A third one would be when flexible electric vehicle load is significant meaning that there is some flexibility already on the EV charging side and at the same time the system needs more and more flexibility to be provided so this can be the case for example France Netherlands and the US and in this case we would also recommend to look into some other measures that we call active for example unidirectional vehicle to meet which means that in this case there is the ability of the system to control the charging of the vehicles directly instead of the case of the time of use types where it's more an incentive to to modify the charging based on the user's preferences and then the last one will be a case in which both a flexible load is highly available meaning the EV charging demand because of the television plays and the participation of the public is quite flexible in its behavior and at the same time the system needs a high amount of flexibility to ensure to meet the demand at all times so this for example is what we see in some island power systems so for example in the case of the Azores Islands in Portugal and in Hawaii and in those cases there are already some active measures deployed as pilots for example what we call B2G, B2G in this case would be B directional meaning that not only there is infrastructure for the grid to control the charging but at the same time the whenever it's needed the electric vehicle can provide back power to the system thus providing even more benefits to the power system than in previous cases so with that I finished my introduction and I will go to the tool I will leave here the QR code a bit in case you want to go right away and open the tool and follow what I'm doing in my presentation yeah and also Jason kindly left the link to the tool in the chat so you can also use that to access it all right and let's go into the tool so the tool here has three main motivations and for those motivations three aspects of the tool will develop the first one is as she previously said to assess the impact of EV charging on the power system the second one is more specifically assess the effect of measures for mitigating mitigating EV charging impacts I already discussed some of those measures for example time of use tariff and unit directional charging which is B1G and the third motivation of the tool is not only to assess the effects of the measures of the charging in terms of demand for the system we would also like to estimate what are the CO2 emissions directly because of EV charging and of course this will depend directly on the power system that we are modeling so for the first motivation we developed the model of simulating the EV charging behavior which output is a weekly EV charging demand profile the second one follows the same logic so it's also the weekly EV charging demand profile but in this case it's a variation that has some managed or we would call smart charging measures in days and then the third model would be based on a simplified representation of the electricity mix meaning without complex modeling of the grid just a simplified representation of the electricity mix the tool gives as an output the calculation of the weekly and also annual CO2 emissions for the user to compare different measures and assess their impacts not only in terms of demand but also in terms of emissions so let me first show you what the tool can do and I will then show you how to get there to this results so the main output as I was saying of the tool is a weekly demand profile on a five-minute resolution basis so what you can get with this tool is a simulation of the EV charging demand based on either one single fleet or even up to 10 different fleets that you can define and with that you can assess the results of the EV charging profiles and the emissions profiles both by fleet meaning that you can overlap the different fleets and see what impact each other needs has at every moment of the week but also by charging location this means for example what's the charging profile in over a week of an enroute charging or what have as workplace charging or for example what we call home charging or default charging and people would be whatever the for example the buses or the trucks are stored so with this this is a very useful output because it can be used for a variety of ways for example you could export this demand curve for a planning exercise if you're doing a power system modeling for example it's capacity expansion planning if you are let's say a pilot project developer and you would need to know for example if you would like to test 10 or 100 buses in a pilot you can make a first assessment of what could be the transformer needs meaning based on the peak demand simulated with how much grid capacity you would need for your project and also it can be useful to simulate different policies for example if we simulate different types of use drives and drive design we could see what impacts this has on the grid and on the charging profile so as i'm explaining this can have utility for a variety of stakeholders and that was our main aim when developing this so i will go to my first example so this is a tool and just before going to the tabs i will show a bit what we have here so first here's an explanation of the tool and here i will not show you at the moment but you can check later that there is a technical note and a guide to using the tool that has a lot of detail explaining what are some of the assumptions behind how does the modeling work and some example cases that you can do with this tool and then going to the tool right away what we see are several tabs the first one is where you define the feed so what you have here first is a label that you can modify for example if i want to do buses i can just write buses but you can name this as whatever however you want then you can select the vehicle type this is done for example in this part where you can select two wheelers three wheelers ldbs with which would be like utility vehicles or in a similar way cars and vans a bus or a truck and then you can select for the the type of vehicle on the left what the stock is meaning how much how many vehicles of that type do you want to simulate then the tool also for the fleet definition has some predefined and technical characteristics that you can modify for example the average battery capacity how much energy consumes per kilometer and also some different behavioral patterns for example the average weekday driving and the weekend driving and this is different because in many of these cases the driving behavior changes between the week and the weekend lastly you can define here what behavioral traffic you want properly you want to have for example if it's a car normally we would select private driving but if we have a fleet then normally we would select that fleet based driving to reflect better the characteristics of that fleet and then here you can have one more segment and you can in tools that have up to 12 different segments where you can modify the characteristics of the size of the fleet but also this behavioral or technical characteristics that I was showing before so let's do an example on 100 buses just for illustration so as you see here this already changed automatically so going to the default values but you can modify this however you wish so having already defined the fleet I will go to show what is here in the behavior profile stuff so here you can define different behavior profiles for all the different fleets you define so here the segment we have is buses it's the only one because we only have defined one segment in the fleet tab and here you will begin to see a lot of different options each of which has a question mark here in case you want to read a bit more about what that means so basically what we have here at first is the charging availability what this means is that by its location meaning home depot workplace roadside charging destination and routes you have different technical characteristics for example the charging power and the availability here will depend both on the availability of infrastructure meaning if there exists any type of for example home charging in the area that we are modeling but also what level of access do drivers of this fleet have so for example what we could think is that on the workplace normally in the case of the people that go to the work and there is a let's say 60 percent of people that have access to a workplace charging during the week but because they don't go normally to the weekends or to the office then the weekend availability of that same type of infrastructure is lower because of that access thing so it's not only a matter of if the charging infrastructure exists but also how or what share of the people or the drivers of that specific fleet have access actually on the week or on the weekend that level of to that infrastructure I will just leave this default and then here we have other parameters for example you have to here select some preference and you can modify the preference for example if you want to simulate what happens if the drivers in some location for example they prefer to charge more at work more in highways for example and routes or more simply in their home depot by default in the case of buses 95 percent of preference goes to home depot but this could be modified as you wish for example for the case of buses but also for other types of vehicles such as cars and trucks and then here all we have what we have is you can select different options in terms of behavior with regards to the arrival and staying times so for example normally what we have by default here is that buses tend to arrive at 8 p.m and tend to stay for 12 hours but of course you can change this however you you you wish to try to reflect better the arrival times and stay times of the vehicles in your own specific context so now let's go to the results so as I was saying here you have the first step this is what you can see for example if you select just one simple fleet as I did before so this for example allows you several things you can check the maximum power demand over the week which the highest point is almost 1,700 kilowatts then you also have an indicator here of the average EV power demand over the week and you also have calculations of the energy consumption over the week and an estimation based on the weekly value of the annual EV charging demand because of this fleet you defy so here you have here the option to see all of this but on the on the chart itself and it's an interactive chart but you can also download this if you want to process the data for example or if you want to load this data to another tool for example for simulating the power system or other kind of simulation you can download all of these results as csv files here below so this would be the example here of the case of the buses where we see that for example the peak charging would be happening at midnight which is usually when the buses are stationed in their depot before they most of them go out on the next day to continue driving now we'll continue and i will do some comparisons based on so i can example with cars so for that i will do a clean start of the tool just to show you again in practice how this can be used to modify whatever you want and see when you want to simulate so as i was saying i will do an example with cars i will then go to ldbs which as i was saying is stands for cars and vans so this is the closest one to cars i will use that i will keep the stock at one thousand and i will show you now the results in terms of the car right so in this case as you see normally because the profiles of the cars are defined in such a way that reflecting the real-life situations normally people arrive to their homes around six or seven p.m and normally without any managed charging measures in place that is when they connect the vehicle to charge so what we see here is normally peaks around the time peaks around seven seven and a half or seven p.m and this is following as i was saying the behavior that we think by default is what happens with the car plates of private drivers so with this you can assess in the total demand by the whole segment but also you can check it by location this is also something that could be interesting let's check out these results for example here as i was saying and maybe it's even easier to see here the light blue color reflects the home charging so this means drivers connect and the peak demand because of the car fleet is seen whenever the drivers come back to their house but also there is some level of workplace charging which is here the back blue one this is because also in this simulation the car drivers have some level of availability of charging in their workplace and those some of the charging needs in this case of the fleet are met directly in the workplace and that also decreases a bit the let's say the pressure on the home charging in the late afternoon or evening so here as i was i'm showing you can hear by selecting my location you can check similar indicators here on the top but in the chart itself you can see different location values and the charging profiles that they are observing by each different location now a different example i will show is just to simulate some of these variations in the scenarios or the cases that we are modeling what happens if because of any reason for example because of a subsidy or an incentive by a government what happens if there is a strong incentive towards workplace charging to try to for example have more synergies with solar PV availability during the day so with that i will go again to reflect the tool i will quickly put this as cars and then to modify the availability between home charging and workplace charging i have to go here to the behavior profile step as i was saying before so for example here i can put this to 100 just as an illustration and then increase this both to 70 percent and here when going to the result step you will see a strong shift to the case of workplace charging so as you see here before the strongest peak because of higher availability and also the preference of car drivers was in the evening in terms of home charging but now because we modified the availability of the workplace charging then what we have is a stronger peak in the workplace charging that happens also in the early morning and thus we can also find ways to reduce the weekly profile and the impacts of the grid of the charging if we have options to shift the charging in terms of location from more home base charging to now more workplace charging now one last example i will show you before going to any different part of the of the presentation is what happens if we simulate more than one feet so i will just replicate now what we had before for cars but now i will add a second feet which in this case will be buses so going back to my first example here 100 buses and i will set this as fleet driving and based on that you will see here the combined results and now that i have actually what i want to show you that in this tab called bike segment you can see both the demand curve by bus or by cars here like to and you can even check them separately if you would like if you prefer just a line chart instead of a stack area chart which is here okay just a second now we'll go to explain a bit the different modules which is a motivation behind implementing smarter or managed charging so this is basically directly dependent on the flexibility available that will enable flexible charging so this is for example the tool what it does is that it checks the flexibility availability which means that it checks if in the charging window that we have meaning the state time that the tool has that the simulation has is it possible to shift some of that charging to a different place in time for example or also to modulate a bit the charging power in order to decrease the impact to the system then if there is some margin when we call here flexibility in that charging window the question the tool asks itself is about the participation rate is the public available to participate and also is the infrastructure adapted if that answer is yes at least to a certain share of the fleet then the tool applies a managed charging measure this can be a for example what we call balance charging in this case you have to make the contrast what we call unmanaged it's basically just the driver arriving to the charging spot connecting and the charger in the unmanaged case would be at full power until the battery is completely full and they will stop even if it has more time to charge it will charge at full power instead what we call the balance charging or more generally unmanaged way of charging is that the charging can use sort of that time window in order to shift the charging for example maybe to stop charging in some moments but then charge again or in the case of balance charging to charge on an average lower power but for longer time and those ensuring that the same energy is provided but as the power namely in kilowatts for example is lower that has a lower impact on the grid and then some other examples of managed charging include time of use times as I was saying but in the case of b1g it's the same logic so for example we have a reference profile in the case of time of use times it's a reference price that changes over the day and thus it can influence when the vehicles charge but at the same time in the case of b1g or unidirectional active charging this means that it's a signal that points to the current power system demand so it will mean that in the case of b1g the charging will try to take place whenever the system demand is lowest in order to arrive the total demand meaning system base demand plus eb demand to be the minimal as possible at every time so now I will show first the case of balance charging so let's go now to the example of cars I will do it again just for again showing how this works and then if we want to apply these measures then we have to go to the advanced options tab so here we let all of that other those other parameters by default and here we can select by every feed in this case we'll select it for the feed for cars what charging strategy they have so in this case I mentioned balance so I will use the balance so the in this one so in this case let's look at the results so here what you see is that for that fleet of 1000 cars we have a maximum eb power demand of around 500 kilowatts and the peak even though it's larger than the lower levels of the week during the the middle of the day is a lot less pronounced that if we have unmanaged charging this is because with the strategy the system tries to take advantage of the long stay times of the vehicles after arriving from work until leaving the house in the next morning and that allows the system to see less impacts because there is a lower average and also a lower peak the demand in terms of charging power than in the unmanaged case and just to show that remember that here in the balance case we had around 500 kilowatts but in the case of unmanaged we'll have a higher so as you see here the unmanaged case which is basically as I was saying before drivers just arriving connecting the charger turning on full power until the battery is full this leads itself to a higher peak and therefore in this case we would see that the balance charging could be an effective way to decrease the impact of the system now let me go to another example which is as I was saying before the time of your time of your start so in the same tab that is called advanced options then we can go here below and we actually can modify you have a default value and you can modify the daily type schedule that we have implemented so as you can see here the tariff is at its lowest between 11 p.m and until more or less 8 9 p.m and then it begins to increase and reaches its highest point in the evening which is normally when the power system demand peak happens so let's have a look at how this impacts the charging of the charge so this is also an interesting example and also illustrates why it's important to take into account unexpected secondary effects when designing policies or in this case the tariff what it shows is that with the tariff scheme that we set which was a higher tariff in the evening and then a strong decrease right away at around 11 p.m or midnight is that in this case most of the drivers would be connecting their electric vehicle right when the price drops significantly this would be a midnight so what this can cause is actually depending on the fleet size in some power systems it could cause a secondary peak in the power system demand and this would be in many cases not something that the power system operators would wish because for example if the country has a lot of solar PV resource at midnight that's not available so that wouldn't be something idea for the system to deal with and therefore it's really important to when designing such tariffs to take into account and try to do some simulations to understand what possible unintended consequences the tariffs could have in the charging behavior of the drivers in this case cars so now I will go to the V1G example as I was saying V1G is a case in which infrastructure is in place to allow automatic control of the charging meaning that in some of your tariffs it's normally based on a decision that the driver takes but in this case if the customer is already participating in the V1G scheme then there is automatic control happening in order to minimize the impacts on the grid so here what we have for the active control with unidirectional charging is basically the input as I was saying before is a reference occur that as opposed to the time of use tariffs that is based on a reference price in the case of V1G is based on reference demand because as I was saying the idea of V1G is to minimize the total demand which is equal to EV charging demand plus all of the rest of the sources of demand of the power system so in this case we can go to the power grid tab and here we can go to a bit below and we see the reference curve what we see here for example is that the demand normally tends to peak around 6 7 p.m every day and weekdays and that the demand is at its lowest point in the weekend which is normally the case in most power systems so just to illustrate what happens and and just to show you that this V1G scheme is working in the tool I will do some modification here to make sure that the demand is at its lowest point on the weekend okay so it's always low okay yeah so I'll go now to the results so as you see here I just artificially created an incentive for the charging to take place in a higher sphere in the weekend so as you see here the charging over the weekend tends to be normally stable with some changes there here and there but as I artificially just some illustrative example for this presentation puts the demand at its lowest point over the weekend then what we see is that that influences the charging to be highest in the weekend this is because as I was saying the V1G algorithm tries to minimize the total demand with total demand being a EV charging demand plus the base system demand so as I put the system demand in a lowest point over the weekend then the EV charging will try to be shifted to be done whatever the system demand is as its lowest point which in this case as I artificially created that would be over the weekend okay okay so those were some examples of the advanced measures of the tool and now we'll go back to the presentation to explain a bit how the last part works which is the CO2 emissions due to EV charging so the way this works is they make how do we estimate the emissions that are directly because of EV charging first what we calculate is a net curve meaning that we for the total demand that we simulate we subtract the for example wind and solar PV in order to get what is the residual demand that has to be met with thermal power plants so we calculate that net load curve without and also with EV charging and this is done in order to have a comparison of the demand that the thermal power plants would need to meet with or without EV charging then what we do is that with those two different net load curves the tool simulates an operation of the power system as I was saying at the beginning it's a simplified version based on the simplified power mix so there is no detail with modeling or other more advanced constraints it's just a simplified dispatch simulation so with that there is an operational schedule for the plants meaning which plant is running and how much does it emit and what's and based on the minimum price for emitting the demand and then by knowing that operational schedule of the plants that were run to meet the net load without and also with EV charging the emissions can be calculated for both cases from the emissions factors of those power plants and thus by having the CO2 emissions with EV charging and without we can estimate directly the EV charging rated emissions by comparing those and specifically by subtracting the emissions with charging to the emissions without with this method what we can do is we can have an approximation to have an idea of which emissions are directly cost in the power system specifically because of EV charging and that way we can have an idea for example as I was saying before by comparing different measures how all of these measures are affecting the emissions of the generation because of EV charging and this can be useful to compare different policies are also scenarios on various aspects such as policies such as time of use or other things so now I will show the same example before but I will show how this works so again I will create a blank version I will go leaving it again as cars I will keep the stock in 1000 and here just to show you and here you can also define your power system so this is basically a similar to v1g if you want to have a specific modeling you should input here your demand curve of the power system and here we have already some plants in place by default but you can also modify this generation capacity and basically for example modify the prices for generation emissions factors and the style capacity to better reflect what you have in your own jurisdiction or in your area of modeling of choice so for example here in this test and like default power system we have just to simulate the electricity mix we have some plants on coal some plants of oil which is less capacity than coal in this case some gas capacity here some solar PV some wind on shore and some hydro but here you can also add new sources or you can also delete some of this or modify depending on how you would prefer to simulate your power system which of course would depend on the bilabidity of generation capacity and the prices of those generation assets which depend on your specific context so here in the power grid tab you would modify the generation capacity if you would need to modify that you can also modify the eb curve the sorry the demand curve that is without eb charging and you want to either do it here for example and also you can upload a csv file if you so prefer so with that here just before going to the other tool sorry to the other tab here we have a simulation and here you can actually see how that power system dispatches is running so for example here we have during the day we have some contribution of solar PV as expected so you can for example press all of this if you want to single out one of these generation sources you can check actually the profile of solar PV and you can also deselect or select to show or omit some of the other aspects to in order to have an idea of how this hourly simulation is running and also with that way have an idea of how the power system is operating okay so with that as I was saying here you can modify the electricity mix basically to to get another demand to have as an input for your power system definition and with that you can calculate and see the emissions here so in this case based on the algorithm I explained also the default electricity mix and demand that I show this is the profile and also at the time with a interval resolution showing what would be the emissions with because directly of eb's so here we have indicators such as the weekly marginal eb emissions the annual which is an extrapolation and then what's the eb share of total emissions and here you can also show the also the non eb emissions which in this case are much bigger than the emissions because of eb's and those if we also included it's not shown but of course this would depend on the on the simulation of the system and also the size of the fleets and the characteristics of those fleets so with that I will go back to give some inclusive remarks on my presentation and then I will be happy to take questions that you may have so as shown today the electrification of road transport as I showed in my first slide of the presentation is ongoing and it will exert it as it contributes to the carbonization and helps to reduce dependency on positive fuels the electrification will contribute to a growth in electricity demand but at the same time it is an opportunity for the power system as the new end users of electricity such as transport can have some embedded flexibility as we discussed today in the presentation the power sector can accommodate a wide range of driving solutions but as I as we were saying in the grid integration manual encouraging a smarter or what we call managed charging can bring gains in terms of avoiding peak and also general operational costs and also emissions and also if it's possible to allocate that demand when it's more linked to renewable output it can support faster growth of renewables such as wind and solar panels what we also see is that we believe the flexibility of new electricity end users such as in this case transport needs to be supported and incentivizes early on and lastly what we would like to remark is that our EV charging grid integration tool as I showed today with various examples can be very useful for a wide range of users for example well for developers who would like to assess the grid capacity needs policymakers who would like to see what impacts a large fleet or a medium fleet would have in their system or also compare different policy outcomes for example and also system operators in their planning exercises and also including utilities and academics who may do different types of types so with that I thank you again for your attention and I will open the floor for questions in the Q&A and answer them as soon as we can yeah there is no question yet in the in the text box I hope our audience will start asking questions but I can start with one question for you let's assume I am a planner from a city or from a distribution system operator your tool allows me to look at a fleet in its globality but cars go from city to city and actually the boundaries of my responsibility might be limited so how do I do that if I am specifically looking at the impacts of EV charging on my own city or on my own grid can I do that with your tool thank you for the question yeah so what you can do is basically and I will go again to the tool to explain a bit my thinking here so basically the way to make this flexible in terms of the jurisdiction or area that you are modeling is inputting the proper parameters for example in terms of fleet or also if you want to see the power system in terms of the power system capacity and demand so for example if you were to model the whole country and let's say you know that in the country either today or you expect tomorrow at some point to have let's say 100,000 or 10,000 cars then you can also for the whole country you can input that and also input the corresponding capacity of the power system and the national level but if you want to model just a single city or an area then you would need to take the parameters and the values that correspond directly to that area for example you would need to know how many cars you expect to have or you have today in your city you need to know and input the proper value for demand in that city and also if you want you can as an approximation input the duration capacity values of all of the plants that are connected to that substation of the city so with that you can have an approximation of what the impacts would be in your specific city instead of the total country you can also even take this further down and you can also think not of a city but like of a distribution area let's say a residential neighborhood so for that you would need to know how many cars are there you will need to know for example if they have in that area workplace charging or not if there's no offices then probably not so those are the the ways in which you could adapt these two different sized areas in order to model them as best as possible I see a question from Victor yeah thank you Javier just to follow up on on these issues that I just introduced so first at national level is there a limit about the number of vehicles that you can introduce into the model because I understand that you can have different segments so you could model the whole fleet of the country and I just understand if we are we talking if there is a limit on the device you know you do to the capacity of the software or whatever just to understand if there is a million or a hundred thousand and and if it can help you to model the whole electrification of a fleet thank you yeah well there is no hard set limit however because it's a web based tool there is some limit that we have found in practice that we are working to try to address in in the future so I think that limit is set up more or less around 100,000 vehicles more or less so there what happens is that because it's a web based tool even if you have a very strong computer a very powerful computer then the browsers are normally designed to cap the resources to a certain limit so what what you can do there is for example try to go for a smaller fleet size and based on that try to extrapolate a bit the results ideally you would have the proper fleet size but as I was saying based on this a programming solution where we are an issue we are facing at the moment that's what we would recommend if you were to model a very large sample of the okay so follow up with this now let's take the example of a bus company that is has a project to electrify their fleet the bus fleet so he wants to design a new depot and to understand the charging profile one this comes from a real example of a project where we did in in in one country central asia so just understand the limit of the the tool we you don't do you have ways to input a maximum supply that comes from the capacity of the transformer in that area you know that is covering so because I would like to know if there is possible to impose a maximum power supply and then adapt these manage and manage profiles of the of the charging strategy to that would that be possible or who would you try to address this this thing it's a it's a very good point at the moment the tool only models demand so it's not considering any type of grid constraint at least if you model the simplified uh of course like in some cases you would the tool if for example there's not enough capacity because of for example not enough available charging infrastructure in some cases the tool will show you a percentage of unmet demand so you would use this to check for example if it could be your case but what I would recommend as would you cannot pre-select a cap on the demand you can just simulate various scenarios and see what's the victim and the other way you can already have an idea even though you cannot model before like as an input the limit by simulating these scenarios and inputting correctly the fit size you want to have you can already have an idea of what's the peak that you could see in a week and with that decide for example if you would need to in any case implement some of your style or different scheme or just to maybe lose the fit size or just simply go for a higher transformer size because one of the thoughts that we had at the moment as well is that if we can have the peak demand of the different strategies from the tool for instance you can cross check that with the capacity of the local you know substation or transformer that can be done and then you could reassess even if it's worth it to upgrade the transformer or if you could try instead to invest on solar pv or like as a local supply of gener of electricity that will decrease the needs on the local transformer and then you know that's an alternative so I think and you could with that information you could also estimate the profile of emissions with the tool right if you have okay thank you yeah I mean also one thing we're working on is because we only have a default profile of solar pv and wind we're also working at some point hopefully in the soon in the future to deploy the the ability for the user to modify those profiles to better reflect their local conditions because of course there are some countries that have excellent solar resource and some that have less excellent solar resource but also have wind so it depends on it so as you saw before I showed them you can defend the capacity and for all of the types of plants but we're trying to hopefully soon develop the ability for the user to modify the generation purpose of parallel numbers as well so that's one thing you can and final question about the use of the tool when you develop all these segments on these profiles you have to extract the results in that same session right it's not way to like save the profile or save the the the the whole system that you have developed it's for each session you have to download the results if you want to then use them afterwards yeah that is correct yeah okay no the implementation that you have to every time you open or you refresh the web page then you have to import all of their parameters accordingly and then you can go download the results as I was saying you can download the results in terms of location in terms of segment if you are more than one segment and also in terms of emissions makes sense for our best tool so thank you okay so do we have any more questions okay I don't see any more questions is there any more questions here from my audience thanks Javier and thanks Victor for the very interesting question so Javier this tool is designed to give you the possibility to assess the impact of so to visualize the the charging load curve of a fleet so piggybacking on the question of Victor so how what is actually the the thought process that the user should have if he wants to understand the impact on the grid so can you briefly remind that or clarify as a user maybe I'm just interested in knowing what is the energy required the electric energy required to charge a fleet but many of the users will actually be interested into understanding better the limitations in the grid or like Victor raised the question of a fleet operator who has constraints in the grid that he can affect and he will then have limitations on how much charging he can do so can you briefly explain how the user will integrate the use of the tool into a project or an assessment that he may have on on the interactions between the electric vehicles and the system sure of course of course this will depend on the user so for example let's just go back again to that example and then give more going back to the pilot project for example a developer's example so in that case if a certain company wants to test for example the operation of 10 or 100 buses or any different type of a electric vehicle in this case the thought process would be first they would need to understand decide how many vehicles they can actually afford and put into test and in their case probably the most important aspects that they can get of the tool are two things one would be the big capacity which can inform the need for a grid hosting capacity so this would mean for example that with this they can do a preliminary assessment of whether the location where they are planning to put their depot and the charging infrastructure is adequate for the charging power that they will demand and also with the weekly profile they can also assess what's the hourly expected hourly profile of charging and with that for example they can also see if it's the best based on the local power prices so these are two aspects in which for example this tool could help a pilot project developer if you think of a system planner for example a system operator many times also in collaboration with policy makers and the ministries for example energy mining companies in those cases there the question would be how could this impact the power system expansion in terms of the need to upgrade the network infrastructure for example transmission and are also often for example even distribution if it's a lower level case so in that case for example you can create different demand curves based on the expected fleet size for every city as I discussed before or even for the whole country if you want and then based on those demand curves you can input those demand curves to your modeling exercise and by that you can see how much extra capacity is needed for example in terms of generation in terms of grids or other things and with that you can use that to inform the planning of the power system based on those demand inputs that you will have also that could in many cases influence and also try to help in creating the planning some other logic of trying to understand the flexibility and to what value the flexibility of the EV charging so as we said before in the fourth step of the policy manual it's also important to value the flexibility provided by EVs so in this case if you input for example to this planning exercise unmanaged demand curve with for example a comparison with the balanced charging scheme or with the time of new stars then you can also see oh maybe these schemes mean less demand for that location and with that we could save some money because of not needing to invest as much in more grids or more batteries or stuff like this so this is also I think one of the things in which this work would help a stakeholder in which in this case would be a policymaker in collaboration with a system upgrade so those would be two examples uses you wouldn't have for this since there is no no other question yet from the audience I have one for you also an additional one which which I received a lot of times so there is a lot of talk about the future vehicle to grid so what what does the tool allow to do if someone wants to assess the potential of vehicle to grid in a system thank you so as I was showing here in this tool and because we don't have a complex modeling of the power system which will need extra work in each particular area or country context what we have here is an implementation of what we call V1G which is active unidirectional charging so as I was saying what this means is that this charging scheme is aimed at using the power system demand as a reference point to try to allocate the EV charging whenever that power system demand is its lowest in order to minimize the stress of the system so with that as I showed in one of the examples today with the tool we can actually simulate the V1G a charging scheme and for that there are several other things that need to be in place so one of course you need to define correctly the fleet size and the fleet characteristics and the behavioral patterns that fit and also for V1G the reference curve is the demand so you will need to define the demand and as I showed you can either use the standard demand that we have in the tool you can modify it with the sliders there or also you can upload your own CSV file that has your own demand and based on that and based on all of the modeling and all of the input data that the tool has the tool will give you as an output the resulting demand charging patterns with the V1G scheme implemented it's also important and I also mentioned the tool and the representation that all of this also needs participation rates so it's not only a question of having infrastructure but also how many users are actually available to participate in that so the tool also allows you to modify the parameter to account for for example what happens is there is V1G but only with a very few select users or what happens if V1G is rolled out massively and most or all the users have a willingness to participate in that scheme. Thank you Javier so I see no other question from the audience I hope this was instructive and that the introduction was satisfying for everyone who participated of course the tool remains available online there is also a manual on the tool itself which gives more descriptions so the feedback is always welcome there is a contact address also provided in the on the manual so please feel free to contact us afterwards if you have questions or comments or feedback about the use of the tool I think Javier we can slightly conclude this webinar thank you very much everyone and in particular to EBRD and to all the colleagues who participated to the organization so I think we can now close this webinar and thanks everyone. Yeah thank you everyone and just as a last reminder we will upload this recording to our event website so you can check this and we hope to have it published hopefully by the end of this week and that way you can we will be able to both check the slides represented today and also to watch again the video in case you would like to check a specific section of our presentation today so many thanks again and many thanks as Jack was saying to the European Bank for reconstruction and development EBRDs and also Victor to you and we hope to see you again in some future events. Thank you Javier, thank you Jack, bye. Bye.