Hi there, we are waiting just one or two minutes to fill up the audience a bit and then start the webinar. One more minute and then we'll start. Good morning everyone and welcome to this webinar by LCP Delta about the German Battery index. Thanks there for joining and I'm really interested in this webinar. This will be about the German Web battery index, LCP Delta designed, which is an independent index and hopefully not just another index you will see coming up these days. There have been recently some indices published and this is another one which shows our capabilities or where we transfer LCP Delta's capabilities from the UK market to the continental European market that the first market is Germany. We show some more depth in the intraday modelling as well as we will present customization tools to adapt the index according to the asset. Just maybe some things beforehand before we start the actual meeting. All slides will be shared afterwards, including the recording access to the index and also the QA session. It's depending on how far we will come regarding the agenda. We'll have a short intro about the battery, German battery market and why it's from a qualitative perspective interesting and how we see it developing up to 20-30. Then my colleague Ed will show our power modelling and how we set up the power modelling, the long term revenue forecasts. And then we dive into the battery index and how we did it in UK with our leaderboard by my colleague Schiff and adapted it to the German market. And then Dom will show how we the methodology of how we modelled the German battery index including the customization and how we did the intraday and or we planned the intraday index, this up to come by Dom. Then we have a external panel where our friends from Suena, Entrex and Opton will answer questions especially regarding the index. Looking forward to this discussion. So very briefly who's speaking today I mentioned joining us is Stefan from Entrex CEO and Co founder, Moritz from Suena as Head of Strategy Trading Strategy and Martin Senior Investment Manager from Opton. With me today is Schiff, head of Powertragon and the tech and data team, as well as Ed Smith, who's responsible for the power modelling and Dom who is on the short term trading side responsible for the index and the methodology of the index. My name is Stefan. I'm responsible at LCP Delta for the German speaking market where we also set up this month our office in in Berlin and I'm happy to host this webinar here. So please enjoy very briefly. As I mentioned, we're just getting our foot on the ground on the German market, who's LCP Delta. We are helping our clients to invest and steer through the energy transition with different solutions on the market. 1 is our subscription research out of date, our data-driven portals and individual consultancy. What we did a lot in the UK especially on the power modelling side where we calculated long term revenues for different assets, mainly batteries in in UK. Our approach is a data-driven bottom up data research we are doing. So this being said, I'm coming now to the trends we see. I will be very quick on this because we have a few slides we have to go through. So very briefly, the German market is one of the three top markets in Europe next to UK and Italy. When you consider would also consider the residential market in addition, it would be the top one market on the front of the Metre side, it's one of the top three and this is up to 20, 30. What we see, we saw just last year quite some price spreads on the market that way. What makes this market interest and where we see also a lot of interest coming into this market. Looking into projects on the mid term, we see a trend that the battery duration and the battery project size are increasing. So higher duration of the batteries as well as the overall projects are becoming more on the regulatory frame. We see some things on the on the on the sky or let's say in the in the future on the horizon, the power market design, how it will be designed in 2028, will it be a capacity market That's something up to come. And of course, quite important, the federal election in Germany coming up. And how will the energy system as a whole be impacted by this? One point always to mention quite special on the German market, this so-called Barkos sushis construction surplus, I would say, which needs to be considered when taking an investment decision. As in addition, a few figures here, we see quite a high growth or quite a growth on the German market, 1 giga Watt and more per power capacity. And overall also on the battery capacity, we see a strong growth on the short term. A lot of these projects are mainly driven by the innovation tenders and the grid boosters, which means these projects are in the market but they are not really on the trading market. They are for system stability or asset stability. After this part is decreasing and really trading projects will become live. The data is to be taken out of our report from the German battery market as well as well as of our data portal Star Trek. With this being said that Germany is an interesting market. I think Schiff will dive into this and to the market prices and market analysis a bit deeper. I will first now hand over to Ed who will give a view on the power market power price design. It's. Please add. Thank you very much, Stefan and good morning everyone. As Shipman Donald discussing, one of the real challenges with developing a kind of reliable and detailed German Bears index is the requirement for sophisticated asset specific modeling. With that in mind, I'm going to give a short background on our modeling approach that we've been using for a number of years. So LCP Delta has over 15 years of experience in developing fundamental space models of European power markets with a historic focus in GP, but an increasing focus in other markets, in particular Germany. No approach has always been from the bottom up. That's individual assets responding to fundamental market signals, and this approach has been essential in a rapidly progressing energy transition where no year is like anything we've seen before. That could be through skyrocketing numbers and negative prices, short term market volatility, or some of the larger macroeconomic changes that we've seen in the last few years. This has made us a trusted partner for investors, developers and governments navigating the opportunity to energy transition. We think combine that with sophisticated optimization of storage operation across different markets, different revenue opportunities and we offer asset specific forecasts for best revenues across major European markets including Germany, which we're discussing today. So where do indices and data fit into all of this? Well, our data platform and that has been used by traders on the ground across Europe since 2017. And this gives us insights into how these assets are traded that we can use to inform our modelling and make sure they're grounded in reality. And indices give us a grounding of current revenue opportunities in different markets and tracks how these are changing over time to make sure that we're kind of reflecting ongoing trends in our modelling. And more generally, across LCB Delta, we produce deep research into all key topics of the energy transition across Europe, increasingly finding that having expertise in one particular area doesn't provide, doesn't provide accurate picture of opportunities for different assets. And that's particularly true for bears. This reset does feed directly into our understanding of markets and the assumptions that that drive our modelling. And I've highlighted 2 really good examples here. The first is our energy storage research service, which covers how best markets are evolving, new opportunities in different countries and attracts best build through store track, which Stephane already showed my previous slide. We also cover a range of topics in residential energy, which are crucial to developing consistent and credible market scenarios across Europe. These include heat EV markets and soda PV. We also stay on top of developments in flexibility markets and these could provide competition for bears, an important part of forecasting bears revenues. If you'd like to hear more about that's forecasting, then please get in touch with me afterwards. But to avoid taking too much time away from the index, which is why people have come along today, I'm gonna hand over quickly to Shiv. Yeah. Hi, everyone. Thanks, Ed. I'm Shiv and I head up power trading at LCP Delta. For the last six years, as Ed has mentioned, we've been working with a mixture of traders, optimizers, asset owners and investors in the GB market who use our kind of real time analytics and broadcasting tool Enact. So an obvious question is why we're looking at Germany next. We've obviously been working kind of in the GB space with indices for quite a while now. I think it's to answer that question crucially, we need to answer why is all of Europe looking at Germany next. So I've pulled together this table from market report that we're releasing soon and it really stood out to me. We've analyzed several countries here, Germany, the Netherlands, Belgium, France, Spain, GB and Italy on some very simple metrics just to highlight the attractiveness across the day ahead market intraday and ancillary markets. There's a lot of information on screen, but just to summarize some takeaways. I guess firstly, day ahead spreads in Germany continue to rates from 20/23/24 with an average daily price, it's almost 170 euros mega hours last year, that's compared to 140 hours in GB. If we jump to the intraday side, which we're going to delve into in a bit more detail in a second, Germany also sees some very high intraday spreads at an average of €122 per mega hour per day. And again, that's increasing from 2023. But I think what really makes Germany stand out is that 4th column there, market depth with an average of 500 GW hours traded a day. Now if you compare that to the second highest country, which is GB only at 100, it's quite a stark difference. And while it's been overtaken in 2024, Germany still remains a key contender in the ancillary markets with both high availability prices on average and a relatively deep market. Now obviously, FCR and AFR are key, opportunity points are best, and Dom's going to highlight how we've chosen to really focus here on our modeling. But decrease in 2023, they're still quite high, but Dom's going to delve into the ancillary market. But I thought I'd just quickly analyze the intraday market in Germany a bit more. Here. We're looking at the intraday continuous market. I think this is one of the key areas which a lot of indices currently lack sufficiently KTML modeling and something which conversations with yourselves as asset owners or optimizers have highlighted should really be a focal area for us. Dom's going to dive into our methodology here later. But just at a high level, we can see on this graph really clearly that Germany has the greatest market depth, trading 145% more volume than GB in 2024, which is the second deepest market behind Germany. And Germany has also seen a volume growth of almost 50% over the last four years now, as well as depth price in Germany remain interesting. On the top chart on the right, we're kind of seeing the average ID full price across multiple intraday continuous markets, which is a weighted average price across all trades. And then the trading period now countries very similarly in their pricing, very consistent. You dive into daily price spreads, which you can see in that Germany again stands apart almost the risk of market. It's a massive opportunity for best in teaching. These came up time and time again in conversations with optimizers is the value of capturing churn in the intraday market in Germany. Now we're defining churn here as buying and selling within a single trading block, arbitraging the price. This can be both asset backed, changing your position or purely financial. So to do some really simple cross country analysis, we simulated executing a single buy sell in each trading block across every period in 2024, locking in some churn revenue. So on the very right side of the chart you see the effects of being an unachievably good trader, capturing the highest and lowest price in each. That's that kind of P-100 percentile and P0 percentile. And if you capture that, Germany could earn you almost double the revenue of any other country. But that trend doesn't hold true for lower percentiles as we drop down to slightly less price captures. So for example, if you bought the 20th price percentile and then sold power at the 80th price percentile in each trading bloc, all markets start to represent very similar opportunities. And what that could have quite stocked different highlights is that the opportunity in Germany all exists in the margins of the market. And using something like an average price in the index, whether it's ID one, ID 3 or ID 4 isn't always going to be a sensible choice. And you need a more sophisticated approach which deals with churn. Now just before I hand over to Dom to kind of go into the part everyone's kind of, I guess, looking forward to, which is the index itself, I thought I'd turn back to our kind of GB index and identify what some of the key learnings we took away and then apply to our German index. So in GB, we've produced leaderboards for more than half a decade now and we've recently bolstered that with indices also. But one key learning in GB is that standard market curves aren't really good enough for real world use. If you take an average one hour 20 megawatts index, that's not going to give you the same results as your particular one hour 20 MW assets. So that's true for indices, as with the GB index, which I'm displaying from an act on screen here, but it's also true for our longer term modelling where we can work with store casts on revenue forecasts and for battery assets. So in GB, we use a create your own approach where you select your own variables and in that will generate your bespoke indices. So for Germany, that's no different. So some key takeaways we've applied to the German index, I guess take away #1 as mentioned, an average index isn't enough. On screen, you can see in blue an average GB one hour best index over the last year and we can see just how much that varies from the other two lines, which are two real world sample assets in orange and grey. So that's quite a substantial difference over time, but also a substantial difference within individual days. And that's especially true for December for example, with really volatile GB pricing. And that's something we see in Germany also kind of custom indices can behave drastically different day-to-day. So what's our learning from that? Our German index needs to reflect individual asset variability and not just an average take away #2 for us was that cycling makes a huge difference. Now that might seem obvious, but on screen I've charted every GB best asset that we have visibility of and I've got their normalized profit on the left pound per kilowatt per annum against the number of cycles that asset made. And we can see, you know, drastically large variations in revenue. So a learning for our German index is that you need to be able to change the daily cycling assumption and that needs to be configurable by yourselves. And then our final take away is that trading strategies matter. So again, on screen you've got 2 sample GB one hour assets with real world data. On the top, that asset is sat in kind of being in wholesale markets, whereas on that bottom, the assets mainly sat in ancillary markets. So the optimization approach between those two assets varies significantly and they're assassin completely different markets. So again, what's the learning? Our German index needs to let you choose which markets you're optimizing over rather than us making blanket assumptions. So keeping all of that in mind, I'm going to hand over to Dom now for the part that probably everybody's waiting on. It's kind of how our index works and how we kind of adopted that kind of similar create your own approach that we have in GB. 51 and thanks Chef. So my name is Dom and I lead on the development of our battery modelling here at LCBLSA. And so I'm going to give you some details around the functionality and the modeling approach or our index. So as she mentioned, taking average index isn't always enough, which is why we'll soon be releasing ours as a creator, an index index where you have the control to customize your asset. So first, you might choose from the assets physical parameters such as a capacity, duration and efficiency. You can then choose which markets that you want to operate in. And then finally, you can set some trading parameters. So currently this is just a number of cycles, but this will expand when we improve on today continuous modelling, which I'm going to discuss in a bit more detail in the coming minutes. So on to the actual modelling. First, we have the capacity markets, so SCR and AFRR capacity. On the right here you can see a screenshot from our flex prep product showing the average availability prices over 2024. And you can see the prices are generally higher in summer, likely because high seller output is pushing spectral fossil generation out of the merit order in the wholesale markets. And this is putting a premium on flexibility. And we'll see this come through when we see some of the end results of the index. In terms of modeling, our PREACH is pretty simple and probably quite similar to some of the other indexes you may have seen. The asset can access proportion of the total market which is more relevant for FCR as it has much more volumes. In terms of prices, FCR is pretty easy as we we just take the clearing price and for AFR capacity we assume the asset can achieve the average availability price precommunication requirements to take into account for bid sizes. So for example, FCR we know can't fully bid in as it needs to reserve some power capacity for state of charge management. And finally, we're assuming that we we bid in the same sort of waste blocks across the day in terms of the wholesale markets and we're currently modelling day ahead and intraday continuous. Both of these use an optimization model to arbitrage every different delivery periods, an approach which I'm sure you're all familiar with if you've looked at other indices. For the intraday continuous, we're currently using the ID one index as a proxy and post some volume restrictions on it to reflect the lower liquidity in these markets. And while I think this is not too bad approximation, we're developing a more sophisticated model for each day continuous, which will help us capture the value of churn as well, which I'm going to discuss in a couple of minutes. Now AFR Energy is where I think our index starts looking at different from the others out there. We're again using optimization approach to arbitrage over the day on top of the existing dispatch from wholesale markets with the assets achieving the mean activation price. And where I think it's interesting is the betting rules and the state of charge management. So we start by estimating the maximum potential volume that our asset could capture if it was activated by taking our total activated volume in that delivery window and multiplying by capture rate. And you can imagine if this volume is very low, then our asset is likely to are likely to be activated much regardless of how much it bids. Whereas if there's a lot of volume in the market, then the assets choice of bid is going to affect fiscal dispatch a lot more. We also assume the asset can basically opt out of being activated by bidding especially high at maximum bids, which gives the assets of flexibility. However, if the asset does intend to get activated, then it must be affecting any AFRR capacity obligations that it has. So if for example the battery has a 20 MW capacity contract, then it must bid at least 20 megawatts if it wants to get activated. This bid is then going to impact how it manage its state of charge. So the operating assumption in the model is that the asset would be fully activated for this bids. So in our example, this would be kind of charging or discharging 5 MW hours over the 15 minute delivery periods. And because of this the asset must be sure it has sufficient headroom and footprint in a state of charge. To me it's pre qualification requirements and the actual activation asset does achieve is estimated by the lower of either half of the assets bids or the maximum potential activation volume that we calculated earlier. So to continue an example, if there is a high volume overall activate in the market, the actual activation here will be 2.5 MW hours. Whereas if the total volume is low, then maybe an asset already activated for some fraction of a MW hour regardless of this bid size. And so the reason for this complexity is to try and limit the foresight. The model has of course an optimization model with perfect foresight. So we impose this state of charge management that assumes for activation the asset to reflect the uncertainty inherent in AFR energy activation, but also acknowledging that reality and asset is unlikely to be fully activated for the whole delivery. So I appreciate that is quite complicated explanation. So please do be aware that we'll be releasing a document outlining a methodology very soon on the index web page. So now I've described the individual markets and how we model them. But where it's really interesting is in our cross market optimization. And so for a single simulation, the capacity market participation is calculated first, then the energy markets are optimized sequentially taking this in the previous market dispatch into account. And let's say this because of 1 simulation. And of course, we know one of the challenges of optimizing in multiple markets is how you allocate the battery capacity between these markets. And so we've run multiple simulations over range scenarios, varying proportion of a power we bid into the capacity markets as well as testing different allocations of cycles between the energy markets. We then calculate the percentile that represents the 75th percentile with respect to total revenues to be our chosen simulation for the index. And by doing this, we're trying to represent the uncertainty and having to inherent and allocating capacity between the markets and limit this effect of perfect foresight in the modeling. So the child on the right shows an example day from a few weeks ago with some capacity blocked out for FCR and AR capacity and then the energy trading happening with the remaining state of charge that isn't allocated to these markets. So I mentioned earlier about some of the more sophisticated intraday continuous modeling that we're currently developing. So Ship discussed how much opportunity there is in this market for intelligent traders to capitalize on. And we think optimizing over the ID 1 is OK as a proxy, but it misses out from the key dynamics of the intraday continuous market. So while our current modelling does take volumes into account, simply looking at a single figure from the depth of market doesn't represent the fact that market liquidity is going to be changing over time and accessing this liquidity is huge to go involve a large number of trades given that most offers are for less than a MW hour. And of course, you can't seriously talk about modelling the intraday continuous without considering share. Smart optimizers are able to take advantage of price movements over time to take profits by trading the same delivery. Multiple times with the ability to keep these trades back by the asset to minimize risk. These are dynamics that we need to capture when we release our updated intraday continuous modelling. So that covers modelling. After the webinar on Monday, we'll be sharing a link where you can go to the index page and play around with a few of our premade indices lessons here. The functionality I've described to create your own index will be released very soon. I would like to say next week, but I think the development team might kill me. So I will say very soon. But you can, as you said, you can set off, set up your assets to run and when your asset practice is finished, you can choose a time range. You can normalize your results if you wish soon the breakdown by markets, explore the results if you want. Currently all the markets I've listed are available, the intraday options and are more advanced in today continuous models to come soon. On the right hand, you can see a screenshot from tool and you can see the margins across the whole of 2024 for about default assets. So I think I have time for just one more quick comparison about how to illustrate how different assets impact their margins. And so these default assets will run over the and I'll apologize for my pronunciation advance that Dinkleflatter periods in December last year where there were high prices across the energy markets, particularly in intraday continuous asset volumes. Asset limited, the larger 50 MW asset cannot achieve the same margins as a smaller 20 MW assets on a per kilowatt basis. And being able to bat test multiple assets would allow users to understand these dynamics and how different asset configurations affect revenues. As I said before, we'll share the link to the index on Monday, although any inactive. Users would already. Have access. And now I will pass over to Stefan and our guest speakers for the panel discussion. Thank you. Thanks, Tom Stiff Annette for this. So it was a quick run through. We will share the slides for if you would like to do a deep dive later also. Yeah. And sorry for some technical issues on our side regarding the audio connection. Yeah. With this being said, I'm happy to start now the discussion with our panelists first with a short introduction round starting with Martin from Opton, Just presenting briefly Opton as investment company. Yeah, hello everyone. I'm, I'm Martin from Opton and I will just very briefly introduce Opton to to people who don't know it yet. Option is a Danish asset manager and developer within especially PV, but since 2021 also within this, our core markets are we're in Europe, but we do act on a global base. On a global scale. We've been active since 2009 and now I have a portfolio of above 2 giga. Within this, we start in 2021 with two one hour systems medicines grown and we now have above 300 which are in operation on a construction or already spilled, which has spread over 7 different projects. So far. All our projects are located in Germany, but we do have development activities in other countries such as Denmark and and Italy. And we also looking in general to expand our our portfolio to our European markets and that's that's also from my side. Yeah, then hand you over to to Morris from Serena, please. Yeah. Good day to all the listeners. Thanks, Stefan. I'm Morris, I'm Head of Training Strategy. It's Serena Energy and we have built the energy trader for battery storages and renewables, which is a fully automated multi market optimization across all the ancillary services and spot markets. For example, in Germany, we have 8 different markets which results in 570 training products per day and in our case to about 50,000 training decisions daily in real time. So it's quite a complex trading algorithm we built there and we use it to Realise the business case of various customers for stand alone storages, collocation projects and also for direct marketing of renewables. And with our multi market approach and especially the automation of trading, we really maximize revenues across those markets and minimize risks. We take the, the human error out of it and we, we can also diversify the, the income streams across the different markets. And we, we pride ourselves on always incorporating battery diagnostics into our trading. So with every decision we evaluate how our trading affects the aging of the battery. And we are also very proud to incorporate various AI models across our trading processes, from forecasting to trading signals in the continuous integrity market to modeling the the health parameters of the battery. And this could have only be possible with a great team. So our founders started in 2015 with researching battery optimization. So by now we have 10 years of really in depth knowledge on how various can can support the electricity grid. And since 2021 we use those insights and this this knowledge in power trading. We have now a team of 29 employees, so really a great group and of experts in in software development, data science and a lot of knowledge and power trading and energy markets. And with that we grew our portfolio to about 300 megawatts under management. So yeah, room to grow, but it has been a fun ride for us. And yeah, we want to thank our over 60 operators, developers and investment investors who who trust in Serena and very happy to participate today in the discussion. We feel like we had our or played our role in, in bringing sort of the transparency in the sector to the front and then the very indices as we publish our results since May on LinkedIn. And yeah, happy to to give a bit that the traders perspective today and looking forward to the discussion. Thank you Mullet for this. And last but not least, Stefan from Ntrix, please go ahead with your company presentation. Good. Yeah. My name is Stefan Schutzen, found and CEO of NTRIX. Pleasure to be here. We as NTRIX, we are an optimizer as well for batteries. So do something similar as small as just described. So in the end it's about trading batteries across all the different revenue streams that are out there and keeping the asset specifics degradation and those aspects in mind. We are live with assets since quite some time. We took the first batteries live in 2022 and we have right now a bit over 900 megawatts on a management for megawatts of projects we'll go live in this year. And we work with broad variety of customers and so companies like Aquila Energy and Carvers and that professional investors in that space, as well As for example, municipal utilities. And we also, for example, run the largest residential VPP and virtual power plant in Europe for empire. And maybe basically, yeah, connect 90,000 households and to take care of the entire value chain from connecting to the households, aggregating them up and then trying them on the market and then of course feeding all of this back again to the households. So that's also the end customer can benefit from the energy transition and and the value of flexibility. Thanks Stefan for this maybe yeah, starting with the first question, as you know, the development of this index was pretty close to the to the webinar. So quite live some of the things you you just learned now in this where we now we didn't have a large time beforehand to to discuss it. My first question to you would be how do you see such an index for your business? Does it help or where does IT support your business? And how do you see the LCP delta index and where you see the gaps also to reality? So quite a broad range of questions and I would start with probably staying in the order with Martin to start answering this question. Thank you. Very there was a lot of questions in one goal to see if I can at least get there, get one of them. So I think from a business perspective it's, it's quite interesting. I'm not sure if anybody really has a 2 hour system that's live in Germany right now. So having some more visibility on what a 2 hour system would actually make. And I think this is a bit different from from forecast that we're seeing. So actually having something live will be a good benchmark to have a better feel of the opportunity in the market. Looking a bit further ahead when when we start having more, more systems going online when also being able to benchmark, you optimise against this will will add some value. And I think especially the effect that you'll be able to to make your own index that will also allow you to to make sensitivities. OK, that feel of what does it mean if I increase cycling? What does it mean if I increase duration of assets? I think, I think that that would add a lot of value to to the business. So basically some of the stuff we plan to put in there for the for the customization, but it maybe, yeah, it was a lot of questions in, in one where, where do you see the gaps towards the reality of the assets you're operating and and the revenues created there? Well, I think you're already discussed at the main point doing the representation of this. Of course, the return revenue from from finance trades that's currently not included getting ready and wet there. I think we'll get quite close to reality especially also again with being able to customize your index to to your asset. Maybe one more point is this, this restriction that when bidding into AFR and FCR markets capacity will be split evenly over day. When we added to that, there will be some somewhat different, I'm not sure how big of an impact, but better something else. But I will be what I would probably if I if I were to pinpoint something I would like to see improve, that would probably be that one. OK, thanks. Thanks. Martin Moritz, what is maybe starting with one questions on this? What is your view on on this index and the way you probably see gaps to reality? Yeah. So I see 2 main benefits of the general topics of indices, both from a investor but also from a trader's perspective. And for both, I see quite similar challenges. So just from a trader perspective, we use a lot of internal benchmarks to monitor trading. So we have lower and upper bound benchmarks to show us what's sort of the minimum amount we should achieve in a certain market situation, but also what's like the maximum you could possible reach. And we work with a lot of different internal benchmarks to give us a lot of. Yeah. Range of market situations and and insights into the market. And there always is the problem between simplicity of the index and how realistic is it is for the actual trading. So of course, you don't want to rebuild a complex trading algorithm for benchmark, but still it has to be realistic. And especially from a trader's perspective, I would be more focused on that balance, on the realistic side than the simplifications. And with this index, I see pros and cons. So I really like that it's configurable so that you can put in different assets, different market combinations, different training parameters. I also like that you calculate a lot of different training scenarios with the different capacity shares per market, the different splits of cycles. And I would, I would also think that it would be very valuable to, to publish the, the bandwidth for or range of, of results from those scenarios just to get like a, a view view on how easy it is to make money in certain situations, How, how big the range is between different settings, how important it is to choose a certain market. And on this other side where I see some problems, this of course with simplifications. And I think there's some easy improvements you could make to just make the index a bit more realistic. So I think some things like mean prices in AFR capacity or perfect foresights in, in all markets those are, I would say these improvements you could implement that would make the index more realistic. But in general, I think that's that's sort of the the problem of of all the indices we see right now, but also easy fixes to implement in my opinion. OK. Thanks Moritz for this And now to to Stefan. How what's your general thought about the index and also the gaps and and pros and cons as lot more it's mentioned them? Stefan. Hi, Stefan. Can you hear me? Oh, I think Stefan, you got in. You can't hear me. Is it? Working now? Yeah. Is it working now? Yes, it's working now. Sorry. I had to reconnect the microphone. OK, sorry for that. No problem, right. So I think a bit so directly I do agree with what has been said before and maybe a bit more broadly speaking. So I think what such an index can do is that it can provide a direction and and the gut feel kind of, well, not a gut feel net can provide a good estimate, but one shouldn't take this as as something that you can take at face value with regards to how much money you can make with battery. I think what you're doing very well here is that you can indeed control for some assets specifics. Yeah. And you know, you even go to things like how many cycles can you go? What is the impact of round trip efficiency and so on, which I think it's already very advanced and sometimes overlooked because 2 two or three percentage points of difference on on round trip efficiency impact the top line already quite a bit. So I think it's cool and that these aspects are being are being considered, but still in the day-to-day the optimization obviously looks quite different. Yeah. So we do have, for example, for example, a dynamic distribution across the capacity markets. I think you make some efforts on estimating churn, but in reality, the churn will look very different on AFI Energy. And we have, we have an entire team working on, on improving those algorithms. So it's not a surprise that your solution yields different results than than what we as traders and can get out of this because this is in the end our core business. Yeah. And I think directly if you look over the course of a year and so on, probably you will be somewhat in the right ballpark, but it will be a couple of percentage points off. And I think that is just important to keep in mind that there is a data and that it's fine to have that data in there. And you just have to understand for what kind of decisions you want to use such an index and what things are maybe not perfect perfect applications and use cases for this. OK, thanks. Yeah, of course there needs to be simplifications and yeah, we can't do the same sophisticated algo trilogies implement in a simple index as you mentioned. Maybe one question also from the audience which fits quite good and also leads to the first question, how do you think the introduction of the index will impact the revenue sharing models of optimizers? So basically, yeah, would would you see this as a contractual addition to to your contracts with the investors or the battery owners maybe starting this time the other way around asking Stefan first if this is OK with you and. Now, now that my mic is working, we can do that. So in the end, I think there is potential in in adjusting this. And yeah, I think it's also a good question to to ask Martin. Obviously we like to be, we like to be remunerated based on the performance and that we bring into the market. Yeah. And that does overall the the, the reasoning for why optimizers are often times working with a with a profit share. This already aligns incentives very well. But then again, I somewhat stick to the answer that I gave before. If you really want to say, you know, now you basically have, let's say a dynamic revenue share that depends on the outperformance of the benchmark. Then you have to be very mindful to which extent you can really use that because the margins that you can use for outperformance. It's it's hard probably in advance when you, when you structure the contracts to exactly define the range that you would like to see for what kind of outperformance. So from a negotiation and contract structuring perspective, it might not be as trivial to simply we say this is the metrics where we say, OK, 20 percentage out performance of the SCP index gives us X percentage points extra profit share. And also that might be a bit complicated. But I think that is something that there's an over direction that would be in principle feasible. And maybe after a bit of learning over time and one would be able to to apply this a bit more, a bit more specifically right now I would have the impression that it might be a bit of a too rough estimate. And then the normal profit share already aligns and centers fairly well. Yeah. So the extra benefit that you would get from this, I would say is is mediocre. OK, OK. Would it OK for the for the yeah or further future, would a comparison between an index and your performance, portfolio performance increase the trust in you as an optimizer? Stefan, to continue this question. Yeah, I think it's So I think then I said the most important bit is that you see that overall things are moving in line. Yeah. So if I see the index improving, let's say year over year by 40% and I as an optimizer only improve my performance percent by 10%, then I have a problem. And then the customer should point this out and say what is happening. Yeah. And maybe there are good reasons for why that is happening. But overall I think they should be moving somewhat in line. But I look at it a bit as I don't know financial stocks now you, so you can take the MSCI World as a benchmark and then you compare it with another index. And obviously the absolute value of quote, UN quote that you will be seeing will be different, but the percentage movement will be somewhat telling. And if the MSCI World moved up by 25% and your portfolio manager managed to achieve -10% and then you should ask the portfolio manager what they did. Whereas if MSCI World went down by 25% and your portfolio manager achieved 0%, they probably did a pretty good job. And I think that is more or less how you can how you can use it in the end. OK. Thanks for this. Moritz, what is your view on this different view or similar to Stefan? Yeah, I see it in a similar way. I would say if we use a benchmark for sort of a derivative derivative for, for a revenue share or a contract negotiation, I think it would be would have to be rather conservative because I mean, for example, a simplification such as mean price of a for energy. There are days where the bits are really, you have a distribution with a really long tail. So even the mean price of the bits is very optimistic as you would would have to have a great insight on the on the merit order to achieve this price. So on those days, if you have such simplifications, it's not really a fair comparison to to a train strategy that uses data in real life and builds forecast malls and market and has different risk parameters and so on. So I would see a benchmark there as a basis for for a contract and has to be read a conservative. And then I don't know how much value it actually brings there. But I think the the general topic is, is just starting right now with the indices. I think there's a lot more improvements to come. So maybe, maybe it's a valuable idea in in in the future. OK, OK. And the improvements you mentioned already, Martin, how is your view on on such an index and probably agreements with your optimizers you're working together with? That's a that's a good question. I'm, I'm rather a fan of, of keeping things simple and having a straight up split. Though what what could make this interesting is trying to force the optimizer to take a view on their own performance, which is of course a lot different from asking to do a back test or whatever mighty material they're willing to show you. But in fact, you have to put the money on the line when you might be able to get a better feel of, of of own belief, belief in themselves. And I think another way you could use this in a, in a contract would be for, for early termination rights. In general, for merchant model, this is less needed as contracts are usually short. But in case you do have a long contract or if you have a Linda who, who's not familiar with, with with that optimizer, this which could make it easier to actually demonstrate on the performance. And I will probably be the index with that with a haircut or something like that. But I think for for us to consider using this in a contract, you will need that much more need a lot of faith in that index will stable in the future and that will be stable. So we're already talking about ways it could be improved. And of course, if you now start including churn revenue into this model or or changes your assumption of if our bidding well then that that could have impact for for a contract. And that's not something I that party would like. We need to have this predictable and understand what what do these difference means. It's even more complicated by the fact, well, what happens if there's a capacity market or if we get all the revenue sources for, for for the best, how should that then be be handling the index or in the contract so that at the very least it's any such changes would have to be very predictable, OK. Thanks. So this may be also related to this, how is your view on the German market? So maybe also bring up now two questions again, 1 is how you see the discussion in Germany with the splitted price zones. Do you see a realistic chance coming up for this in the in the OR in the next energy system in in Germany coming 2028 or is this nothing you can you consider for the future? Martin, I have you now on the screen. Maybe you can Give your opinion options for you on this. I mean, we, we, we don't have a, a firm view on on which way it will go. I think even the fact that there's a discussion around it tells you something about that. There's a chance that it could be split, but I would tend to our say that our base cases is that it will stay together. OK, OK. Then again, Moritz, how is your view on this? I'm I'm very unsure about the potential timeline. I would rather assume it would take a while, so I don't want to give an estimate there. I think it's very interesting. Interesting both from an intellectual challenge for Australia's and also it makes economic sense. So I I would be approval of it, but I I don't have an estimate on the on the timeline. OK. Stefan, do you take this in in consideration for for ENTREX potential price split? Yeah, yes and no, it's all in our product development obviously right now doesn't play a role because nobody knows how exactly it would look like and but of course this is something that we closely monitor. Yeah. And I think there are a couple of things that to some extent go in that direction, right. So we don't have local price zones, but I think what is becoming pretty apparent is that we are having local constraints that have to be that have to be accounted for. And, and I think that is in the end the, the interesting, the interesting bit about how the regulatory landscape will continue and developing here. And because in the end, I'm I'm pretty convinced that over the upcoming years, we will be seeing more and more situations where we understand that effect or a single price across the country doesn't really serve and serve us very well and that we dispatch costs and will will continue to increase. So at some point we have to have a system that can factor in local specifics. And obviously now as traders and we would love this to be reflected in a price and to be something that we can work with in real time and manage it based on on market signals. And probably there are also other approaches. And but yeah, I think we have to see a bit what the new government and what the respective regulators and come up with. And I don't expect things to change very fast on this one unfortunately I guess. OK. Yeah, As it would reflect more the market itself and not in Germany to have two price zones at least or probably split up price zones. I agree on this, Stephen. Then probably one last question to you also as we are going towards the the one hour, are you looking also in other market and which are the markets next to Germany which look interesting for you and probably give a short reasoning for that. And Stefan, we would start this time again with you. So back and forth. It's a bit of a ping pong. Yeah. I think, yeah, you, you alluded to some of those markets beforehand already. I think that you had a nice summary of at least some of the fundamental drivers. Yeah. So I think countries like Italy, Spain and Poland possibly, and I think they're important to keep in mind right now. They're probably also a couple of other markets where the flexibility needs aside that are maybe not as interesting from a trader's perspective, but where you will see flexibility regardless. Yeah, but I think in the end, it will always boil down to how does the energy mix in those respective countries look like? How does how's the market structured and can you actually build a business case around battery stamp? Yeah, and I think the countries that I just mentioned, those are from my perspective for the obvious ones right now and that they will be attractive going forward, still a bit with an Asterix to it and subject to how regulation and continues developing. But those are the ones that that I would be seeing right now. Yeah. Me personally I see always an issue with the accessibility of especially the ancillary markets when you go into certain markets. And this is also probably in hurdle for external not, not from the country coming companies to enter these markets or you work with local partners. Yeah, Moritz and Suena's next businesses. Where are you looking into? Yeah, I mean Schiff said. I think you had a good, good overview. I went in general see Eastern Europe as a as a potential very interesting markets. I mean the the markets structures in terms of which markets are available combatted there especially in the spot market side. And as you alluded to, spot markets are always more scalable for training operation than than ancillaries. So you need less adaptations, adaptations for a new market there. So yeah, I would just add Eastern Europe for for this list. But other than that, I think it's the the usual new markets that are interesting. Yeah. OK, OK. Eastern European. Yeah, I think it's quite interesting Martin and your countries here looking into into investing mainly for the battery as this is a battery webinar. Yeah, yeah. But in Germany is our is our main market. We had, we do have some development activities in other markets, especially Italy. I think when it comes to development that is taking a view of where the market could be at in a few years. But we do also look at opportunities in all European markets as they present themselves, they'll be more ready to build assets. I think that was also an intrics. I look very much to the revenue side of things. But but something to to add to that is of course also the bankability where at least some places needs to Europe struggle small when we also have the question of quick fees. I think the Netherlands is a very interesting market, but they they have this quick fee issue where which can be mitigated, but still they they are a significant amount of of your cost in in that market. But nevertheless, I would add vanilla lands to a list as well. OK, Netherlands. OK, Yeah, thank you. We are now reaching the end of our webinar. I would like to say thank the participants Martin, Morris and Stefan for this. Please feel free to reach out to us afterwards. We will answer especially also the technical questions which popped up in the chat after the webinar and we sent this out to all attendees. So you will have get an answer also to your questions you put in the chat here, which were not answered yet. Next week is E World. Schiff and myself would be there. And I'm pretty sure Moritz and Stefan will be also there. So please reach out to to I'm not sure if Martin, if you will be coming to Germany, but probably also I don't know, reach out to us. We put all the contact details in the slides also. And thank you for your participation. And yeah, have a good day and a nice weekend. Thank you. Bye. Bye. Thank you. _1739891898449