 Απέρασμα στο βιβλικό της εμπειρικής ευρώπης. Η τόπιξη είναι πώς οι πρόσφυρες νομίκες ευρώπης υποσταθούνται με τη δημότητα της ευρώπης ευρώπης σε πρόσφυρες νομίκες ευρώπης. Συντάξει, ο νομίκος Νικολόπης από Σερθ θα προσφέρει το σχέδιο της ευρώπης από τις ευρώπης ευρώπης της ευρώπης σε πρόσφυρες νομίκες. Νικο και τους συμβήλικους θα δημιουθούν σε χlama πάντα ευρώπης για πεταιία της ευρώπης πράγματαубες των ευρώπης αυ insecurity. Συντάξει, ο νομίims Κόμπα Πtill θα προτελεί πώς η δύση ποτέ είναι subject to such problems. Σο εύρώπης της ευρώπης sociale Θα έχουμε χρόνια για παιδίες και συγκρατία. Θα δώσω τη φλόγραφη για Νίκος με την πρώτη παράδειγμα. Ευχαριστώ πολύ, Παναγιώτης. Φυσικά πολύ καλή μήνες, Νίκος, από μου. Ευχαριστώ. Φυσικά. Λοιπόν, για να σας δώσουμε ένα σύγχωρο επίπεδο, Σερθ είναι σχετικός κορδινότητας σε αυτές οι δύο πρόσεις. Σερθ είναι σχετικός, είναι υποστηρική. Και πιο δύο η ιδέα είναι να προσπαθήσουμε να σας δώσουμε, να ξέρουμε και να ξέρουμε πώς και οι ιδέες έχουμε, όταν σχετικά με την ευκαιρία της ευκαιρικής δύο πρόσυγης. Όπως ξέρουμε, η σχετική σχετική πρόσυγης σχετικά με κάποιες σχετικές πρόσυγες, που δημιουργούνται στους σωλούς, να μιλήσουν πιο σχετικό, πιο εννοείσια και καλύτερα. Και υπάρχει μια κλπτά της Φελουσίδης, που προσπαθούν να επαφήρουν κάποιες αυτές τις αδύσσεις στη δύο της ευκαιρικής, να επιβάζουν πάντα. Στην σχετική, στους ευκαιρικές της ευκαιρικής δύο πρόσυγης, Αλεξανδρόπολη είναι ένας της Φελουσίδης, να έχουν νησκοταζού, ουτρεκτ και ογκοδερβούκας, στους ευκαιρικές της Φελουσίδης. Και η σχετική είναι να δείξει, πώς κάποιες αυτές τις σωλούσεις, να δημιουργήσουν στους ευκαιρικές της Φελουσίδης, μπορεί να επαφήρουν στην ευκαιρική της Αλεξανδρόπολης. Στην σχετική, ας δείξω, να πω, ότι μπορεί να είμαι η πιο σχετική, αλλά αυτή η δύο της ευκαιρικής δύο πρόσυγης, δεν είχα δημιουργήσει με πολλές ανθρώπες, λοιπόν, είναι η δύο της πολιτιγμικής ευκαιρικής δυο πρόσυγης της Αλεξανδρόπολης, η οποία κάνω μια αργανή, μια συμβουλία με ο Αλεξανδρόπολης εικοσύστη, που δημιουργούν τη μηραίκαινη και η Ενωγιγιγφία, η οποία είναι η κυρυγή του Αλέξανδρούς. Οπότε θα τους πω, μεταγράψω, και την πρόσφυγη που δημιουργήσω, για τον οποίο θα θέλω να ευχαριστήσω. Σήμερα θα υπάρχει μια παράδειγμαση από κ. Καν, από EDF, συγκεκριμένοι με το σοφτουργείο που χρησιμοποιήσουν within EDF για να υπάρξουν δυνατότητα σε δυνατότητα δυνατότητα για δυνατότητα σε δυνατότητα σε δυνατότητα. Οπότε να ξεκινήσουμε... Γιατί, ευχαριστώ για τη δυνατότητα. Μπορείτε να κάνετε τη δυνατότητα από τη δυνατότητα. Είναι καλύτερο για το σοφτουργείο. Ευχαριστώ. Μπορείτε να δείτε αυτό. Ναι, ναι, είναι καλύτερο. Ευχαριστώ. Λοιπόν, as I told you, I briefly described the objectives of this presentation and webinar. We will start with a short description of the replication activities for Alexandroupoulis, which fall under the transition track number one, which in our case deals with transforming current existing building stock into energy efficient or even energy positive. We will have one slide dealing with software selection, so available tools in the open literature that can be used for supporting replication activities, so dimensioning in reality of systems and feasibility studies. We will proceed with a detailed description and presentation of the case studies analysis, ending with the valuation of the numerical results and some lessons learned. And as I told you, we will end this webinar with the case of a better sizing by Christian Keim, mainly to have a look also on the commercial tools that big companies like EDF use for studying such cases. So in order to speed up the process, a key question always deals with what does a replication activity include. And from our perspective, monolacity is a feasibility study. That means that we need to have in the end some dimensions, some design specifications that can be mature enough in order to lead as well to tendering. And at least for the case of Alexandroupoulis, this is considered to be very important because it will help them in the near future to make public procurements aiming at actually implementing these solutions. And in that respect, we decided, generally, to use some available software tools that aim towards this direction. And first we had, let's say, a screen of available tools in the Open Literature that can support such type of activities. The axis upon which this evaluation is made is, first of all, an energetic one. But we should consider as well environmental impacts and as well financial aspects. So for example, what can be the payback period of the solutions to be included, considering that someone will invest on that. So just a short description of Alexandroupoulis is a city located in north and eastern Greece, close to the border with Turkey. They have already validated SEAP and SEACAP is member of the Covenant of Mayors and is a founding member of GreenCity's network. And as part of their plans is to become sustainable and innovation have considered as well the TAP, which is the pipeline dealing with natural gas to fit as well the whole Europe. And their aim as part of their SEAP is to reduce the emissions by 20% with a vision up to this year, which as you may know will need to be updated to all European countries with a vision up to 2030 or even 2015. Just to give you the short background, in Irish project we have defined actually five transition tracks and the transition track number one, which deals with energy retrofit of buildings. We have two solutions oriented to the case of building level. We have to do with the retrofit solutions of individual buildings to become energy positive, to become energy efficient, even energy positive. And another reduced case has to do with the setup of a new built neighborhood. While a solution 1.2 under the same transition track deals and goes up to the level of the district. So from the building level to the district level. The buildings that have been selected to act as guidance for the replication activities include let's say nursery, free nursery schools. The senior citizen community center and some office buildings. As you can see, we don't have in this case any residential building, but mainly a tertiary one. And their location can be depicted, is depicted in the let's say map that you see here. So they are quite spread, so they cannot be considered to be part of a district, but as individual assets. So costas, liberóbulos, if you can also present some details about the selection of the districts and buildings for the case of Alexandrupolis. Yes, of course. Good morning to everyone. This is costas liberóbulos. I am the site manager for IRIS project regarding the replication activities in Alexandrupolis City. I would like to start by saying that as Nico said we have signed the covenant of mayors and our target was 20% until 2020. I'm happy to say that there was a recent municipal decision to update this target for 40% until 2030. So we signed the new covenant of mayors, the updated covenant of mayors. So to begin to start with the buildings, you saw the previous slide that Nico's presented is about seven buildings that are owned by the municipality. These buildings have already been renovated regarding energy, meaning that they have installed renewable energy systems from another project. This was the base case in order to make them positive. That's why we chose these seven buildings. Regarding the second slide that you see now on the screen is a new area that was introduced in the city. It's not built yet. So that's why we decided to investigate the feasibility of developing a new district with positive energy buildings. This is what we studied here. Nico, can you please forward the slide? Nico? Yes, of course, I'm trying. Okay. The replication of the second solution of Irish project regards the retrofitting of an existing district of buildings. For this solution, we chose a social housing district. These were buildings developed for social housing now are privately owned from citizens of Alexandrupoli, but it is important to say that these buildings can be found very often in Greece regarding the current situation of these buildings. All buildings are built between 1970 and 1980. This means that they are unisolated because there was any regulation at that time in Greece. That's why we selected these buildings. It's a district that can be or we're trying to see how can be a near zero energy district replicating solutions that have been demonstrated or will be demonstrated within Irish project. Next slide, please. Here you can see the technologies that we selected to replicate, to investigate their feasibility. In these three case studies as described, you can see that we have the insulation, of course, photovoltaics, batteries for electrical storage, district heating and cooling using ground source heat pumps and other technologies for achieving the target of either positive energy buildings either near zero energy districts. In that respect, considering that, as you may see, there are varying solutions that should fit different use cases, the main problem we needed in the beginning to face was the selection of the various tools that we will use from conducting this type of feasibility studies. In that respect, we set a range of selection criteria upon which we made our final decisions. The most important of which were, let's say, whatever tool is selected to be in position to model as many as possible technologies from those that we have selected. Another thing has to do with the cost effectiveness. That means that we prefer to have tools that more or less are not accompanied by any licensee fee, so they are open software. A very key factor is related to the easiness of use. That means that in this type of replication studies, we should not expect that always these are conducted by experienced users with a deep background in modeling tools. In that sense, we need to select tools that can be easy, user-friendly, and can be, at least on a first level, be used by non-experienced and non-experts. In that sense, we made a little survey, and in the next slide we will present what we have identified in the Open Literature, while the next range of criteria deal with the ability of these tools to provide simplified outputs and require at the same time simplified inputs. So the level of detail is not very deep, unless someone needs for any specific reason to have this requirement, while the tool needs to provide as many as possible performance evaluation parameters like technical, financial, environmental, as we told you. So in this slide, you may see variable tools that exist and are used for this type of analysis, either on a building or in a district level, dealing mainly with energy aspects. Most of them are well known, and we try to categorize them with presenting their key features. In our case, we have selected actually red screen and energy plan. Red screen is a tool that has already some databases across at least Europe, which can provide data about environmental conditions, so temperature, humidity levels, solar irradiance. So this is quite a good tool, easy to use, but mainly orients the case of single building levels. So with this type of tool, one can examine as well scenarios like those of the districts, but from our perspective does not meet this type of requirements, as in the case of building level simulations, and for that reason we have selected energy plan. A key issue for this type of software solutions deals with the interoperability of these tools and how the output of one tool can be used as an input in the other tool. So keeping that as well as a selection criteria, we selected these two tools since the output of red screen from the building level can be used as an input to the energy plan for the district levels. And the good thing for both tools is that they combine several renewable technologies like photovoltaics or batteries, but also heating and cooling systems. So to start with we will give a short overview of the steady-stage simulations representing all three main energy vectors, electricity, heating and cooling. While we will, as a next step, we will proceed with the case of non-steady simulations. And this mainly deals with electricity and heating, electricity flexibility, voltage and frequency control, mainly on the electricity level, but also each integration with the other rest of networks like heating or cooling. So a typical example deals with the case of a power-to-heat concept where any excess of electricity can be used through the use of heat pumps for providing as well heating or cooling. So, Vasilis, may you start giving a short overview of red screen. Yes, hello, thank you Nikos. Hello everyone, my name is Vasilis and I work with Serge Perri. Yeah, as Nikos mentioned, we used red screen for the building level analysis and then the input from that was used in energy plan for the district level analysis. So red screen is a quite well known software for pre-feasibility studies. It provides simplified technical modeling for a range of technologies for power heating and cooling. It is a simulation tool but it can be used for other purposes as well for performance analysis of existing projects. And it also includes extensive databases for various technologies. So that was a great benefit in order to simplify the simulation process. It provides fast results so you can try very different configurations, different systems and then get an easy result in terms of the technical performance and the financial or environmental performance. It is user friendly and it has extensive financial sensitivity and risk analysis capabilities as well apart from the technical side. And it has, there are quite a lot of tutorials online where you can get some easy training and also tutorials included in the software so the user can train themselves. And it is cost effective so there is, at the moment you get the annual fee for 10 PCs for a price of 600 euros approximately or even free distribution for academic and research institutes. So if you would like to move to the next slide Nikos. And then for the district analysis energy plan was used. The energy plan was used to analyze energy, environmental and economic impact for large national and regional energy systems. It simulates the operation of a system, our energy system on an aerial basis and it includes the electricity heating components, cooling components, industry and transport sectors. Again it is user friendly it is deterministic in nature which means that how many times you run the model you get the same result it is not stochastic. It has fast comparison of different scenarios it allows for fast comparison of different scenarios so the analysis is done quite fast and quite an important bit is that it has a freeware distribution and again there is available online training there are available resources where the user can train themselves. Can we move to the next slide? Again it was mentioned earlier we used the simulation procedure that we used for the building level we considered we used red screen where the inputs are includes factors such as the climate the internal conditions the building fabric and systems and some renewable energy production and then the output of the software is the heating requirements on an annual basis the cooling requirements and the electricity load of a house of a building as well as the energy production from the system and then the emissions reduction and in addition the user can also do financial analysis and emissions analysis and then for the district part where we have to simulate the district we use the output of red screen which is the heating and cooling and electricity requirements we used as an aggregate for all the buildings of the district we used this input in the energy plan software which provided the aggregated energy balances for the whole district So a great deal of information a great deal of information was required to collect before the analysis was conducted the vast majority of that information is information collected on site in order to map the local condition and the context and the core information required on the preliminary analysis for this project and then this was used to develop the model based on the local context and then some information about the systems and the buildings was collected from relative literature from the software databases which were really helpful in the analysis there were information from partners we get feedback from partners adjusted to the site localities as well as expert feedback from previous project and local partners in the area of Alexandropolis Yeah, thank you Vasiliis in that respect what we need to emphasize is that the role of the local municipality is a key aspect in this type of replication activity we need to be satisfied that needs to be a strong collaboration almost day to day between the partner who develops this replication activity on the level of design and they mention it with the local stakeholders who need to provide relatively extensive data for conducting them so Kostas can you share with us your experience Yes Nikos, thank you What we did in Alexandropolis regarding transition track 1 as we say in Iris and regarding the collection of the required data is that we followed two stage approach one, the first stage is a broad approach that includes the assessment as the city needs the challenges policy context analysis Kostas, excuse me Kostas, sorry to interrupt you please everyone that can Jult his microphone if he is not speaking or she is not speaking because there is a lot of noise I think it's Vasiliis So, as I said we followed the two stage approach the first stage is a broader approach includes the assessment of the city needs and challenges the policy context and the stakeholder analysis we investigated the sustainable energy action plan of the city, the technical program the operational program we discussed with the stakeholders that we found that are relative to these solutions that we are investigating Of course, we did a very good and thorough read of the solutions to be replicated from the partners, the lighthouse cities from the other partners of Iris and we then concluded to the ours as we say in Iris the replication what we are interested regarding the demonstrated solutions and then having in mind the local context we decided some indicative case studies for replicating these solutions Nikos, please the next slide and the second stage is more specific and it refers to the actual collection of the data that are required to make the feasibility these studies that is to say the data that are required from the two software tools that we selected with SERF to be used for their feasibility studies so in collaboration with SERF we started collecting the required data and this data were collected through collaboration with the technical department of the municipality I have to say that really important and helpful was the collaboration with local technical experts actually from our partner in Iris the energy high cluster which is a cluster of engineering companies here in Aleksandrupolis so they have really good information regarding the feasibility of several solutions in energy retrofitting and renewable energy integration on buildings so this information was available in order to assess the replicability of the solutions that are demonstrated in other lighthouse cities and also some information was collected from the local organization which is the University of Thrace regarding some efficiency numbers and some more scientific questions and curious that we have on these solutions Nikos, I think you may proceed Yeah So more or less as we told you we needed to gather various types of data either from experienced local partners there and a key aspect that we needed to include as well in collaboration also with the lighthouse cities at least for the innovative systems is to have some cost estimations of the various systems in order to proceed with the business models while as we told you online databases and little data are always helped over this scope and the third pillar of availability of data deals with what already this type of tools have in their databases So as we told you we already presented a red screen mainly compares to different scenarios One day is entitled as base case so more or less reflects the current status of the building and another one entitled as proposed case has to do with the inclusion of the various systems and their comparison Towards this aim we will in the next slides present you more or less the actual and some snapshots from this tool in order to have a more elaborated view on what it offers and how it operates We will start with presenting you the type of input data we use how we include them within the software how we define the use cases and we will end up with some indicative results So Vasilis If you can start Yes Hello As mentioned earlier there are several information that we need to include We start off with some basic information required for the analysis So this is the weather data and the internal conditions inside the building The climate data that we used is for the city of Alexanderpolis Red screen includes extensive climate databases which you can use Also alternatively the user may also include their own climate data if they find them more appropriate and the data is in terms of temperature, humidity, solar radiation level and hitting a cooling degree days In order to define the internal conditions again the input is quite simple We consider the schedule of the building that is used the occupancy schedule we consider the internal conditions the thermostat setting for heating and cooling and in this case we use some reasonable temperatures of 21 degrees for space heating and 25 degrees for the cooling season and again another information that is used is the change over temperature So this is the external temperature when the software considers that heating is no longer required and then above the temperature the cooling is required So a change over temperature from heating to cooling to 18 degrees we use in this case So we start off with quite simplified input for the software So if we move on the next slide and then having defined this basic information the next step is to define the equipment characteristics for providing heating and cooling The input required again is quite simplified So the user defines the fuel that is going to be used and the seasonal efficiency of the equipment and that is used for when calculating the emissions analysis and the financial analysis In this case we consider the district heating So this case studies about the building of the positive energy district that we saw earlier which was a district of 100 houses that has a district cooling network heating and cooling network with geothermal heat pump So in this case we consider the district heating and cooling and this information that we saw here just indicative the analysis was done in energy plan later on for the district heating and cooling So if we move on the next slide So having defined the basic information for the simulation and then the information on the heating and cooling system then this is the biggest part of the rescue analysis where we define the energy consumption components and the energy production components So in building the energy consumption components the losses due to the building fabric and then there is the the energy consumption from appliances that are used from lighting and hot water and we start off with defining the parameters of the building in order to calculate the building envelope energy requirements So to do that we use some information on the building geometry the orientation the thermo-physical properties of the building elements such as the u-values for walls, windows roof and floor the solar heat gain coefficient of windows the shading factor of the windows and the infiltration of the building envelope and then the output of the software considered in the climate as well is the fabric heating and cooling load as well as the energy saved if we were considering a retrofit scenario which is not the case here because we are just looking at a new built dwelling where basically the base case and the proposed case are the same So the information required for the building fabric analysis for the building fabric energy requirements is quite common used in such calculations and what we did we used the information from the National Technical Guidelines on Energy Assessments but then this should be readily available in every country on the guidelines Alternatively for in order to do a fast analysis or if this information is not provided then RedScreen Databases RedScreen has databases and recommended values for most of these parameters and then having defined the energy requirements of the building fabric we then proceed with calculating the energy requirements for lighting and cooling Again it's quite simplified information that's required In the case of lights what we need to input is the energy required the load for lights which is the lighting load per unit area the floor area of the building and the number of hours that the lights are used and then with this simple information the software will calculate the annual electricity requirements for lighting but on top of the electricity requirements we also calculate the effect of these heat gains on the heating and cooling load and the same applies for the electrical appliances So the user here enters some information on the typical appliances that are used in a house such as computer, dishwasher, TV etc. the number of hours and the nominal these appliances are used and the nominal power of these appliances and then the software will calculate the annual electricity consumption and also the effect of these appliances as heat gains on the space heating and cooling load If we can move on to the next slide please Thank you And then the next step is to calculate the solar hot water there is a hot water consumption and to do so the software has an embedded algorithm where the required input is the daily hot water used in liters per day and then the desired temperature of water of the hot water as well as the supplied temperature throughout the year as a minimum and maximum and then with the algorithm we will calculate the energy consumption the annual energy consumption for hot water and again the information here was provided from National Technical Guidelines but then if these are not available Red Screen Wood has several recommended values so the user can do a good approximation analysis to move on to the next slide please For the basic energy consumption components and then the software gives the option of including some energy production technologies as well in terms of electricity has the option of building integrated PV or wind turbines or even generic green energy sources you can also consider some solar thermal for hot water or solar air heaters for space heating and this would then consider in the energy balance and how they would reduce the energy requirements of the building and in our case PV system was considered where the level of information is the capacity of the PV system and then the typical losses from the from the cables and the system as well as the inverter efficiency and stuff like that so if we can move on the next slide Red Screen Wood then take into account all this information that we used for the energy consumption and the energy production and provide the results of for the energy balance for the building and the results are presented for the space heating requirements space cooling requirements as well as the electricity consumption for lights and the electrical appliances and the consumption for hot water it also has the option to provide a simple payback period for each measure so as was discussed earlier when we when we model in Red Screen a retrofit scenario which was not the case here the user had the option to include a retrofit measure and then there was the option to include the cost on that and based on that cost the software can calculate a simple payback period for each one of these measures and then the user in this page here you can click or you can select or diselect a measure and see how it changes the simple payback period of the whole investment or even what's the payback period for that particular measure so that provides the fastest estimation of the contribution of its measure on the whole strategy and that concludes basically the analysis for the building level and then for the district level we use we use the energy plan which we in general it is used to model much larger systems so it's for national and regional energy systems but we found it useful with minor data manipulation and structuring to conduct a technical evaluation of the district scale which is a smaller one so again the procedure is follows the same structure so we first define the energy demands of the district then we define the energy supply system and in the end we consider the system balancing storage to get the results so the inputs are for the heating for the energy demand the inputs are in the form of an annual value and an hourly distribution profile as mentioned earlier we did the analysis in red screen we get the annual values for heating, cooling and electricity and then this was for one building and then we multiply that times the number of buildings and we get the district level energy requirement so this is the input from red screen and in order to conduct the analysis we were then required to develop the hourly profiles for the electricity demand, heating, cooling demand and the PV production we can move on to the next slide please thank you so this is the layout for the electricity demand all we need to do is to include the electricity demand which was from red screen and then the distribution profile which is a TXT file that we developed and the electricity demand profile was developed considering the electricity, the appliances schedule and the lighting schedule that we considered in red screen we created a typical profile, daily profile for winter and summer and that was then that was then constructed for the whole year based on winter and summer scenarios right so that was the electricity demand distribution profile we also had to do the heating and cooling demand profiles and for that all we had was the annual the annual value from red screen in order to do so we used the heating and cooling degree days method so we found whether data in hourly format and choose an appropriate base temperature for calculating the heating degree days and cooling degree days and that provided the distribution and an appropriate base temperature was required to do the calculation of heating degree days and cooling degree days so base temperature of 18°C for heating and 24°C cooling was considered suitable for the climate based on previous studies and whether data in hourly format were obtained from a website that provides this type of files for building simulations these are typical meteorological year files and they are constructed for building simulation purposes and they are basically considered typical meteorological year from a 20 year dataset from weather stations and another source of TMY with the data is also from the GRC TMY generator thank you where you can get the TMY file for a given longitudinal attitude so having started the heating and cooling distribution profiles we were then able to proceed with the analysis as we said it was a district heating network where the heating and cooling was provided so heating from the district in order to account we had the heating demand for the whole district from the red screen analysis and we got the aggregate for the whole district and we had the hourly distribution profile of heating demand so what was required then was to account for the losses of the district heating and due to the fact that there was no available information from local district heating networks we had to find that information from literature and from feedback from experts so the value of 7% losses was considered appropriate quite conservative actually so the heating demand was then increased by 7% to account that part of the heating losses of the district heating network and the district and the heating demand profile was ready made so we were ready we were that was ready then and the similar approach was was followed for the cooling network we had the cooling distribution the hourly distribution for cooling demand and then we had the cooling demand of the whole district and then we had to account for the heating for the cooling losses of the district cooling network and this was found to be 4% based on literature and the size of the network we considered and therefore the cooling demand was then increased by 4% so we defined the energy requirements then the next step was to define the energy supply systems in this case we used the the energy was 500kW PV system that is 5kW per house so we considered installing a 5kW PV system on the roof of each house and then again we had to consider the hourly distribution profile of the PV system in order to get the temporal electricity production from the system so the PV production the hourly profile was developed with the formulas from again the national directives on climatic conditions and that this formulas considered the effect of solar radiation levels and panel temperatures if that is if that is too difficult to do for a simplified analysis there are a simpler approximation method would be to assess the PV production in Rexrin and then create the distribution profile using the hourly solar irradiance data from the TNY file or alternatively there are even ready made PV and another renewable energy profiles that were developed within the framework of the heat roadmap for project and these are available for 14 EU countries however these are available for country level and not site specific level so here should be taken there or even if there were if there was available data from an existing PV system that would be again an easy way to develop this profile So in order to speed up a little bit the process more or less the idea is that the structure will follow the energy plan is the same with that of the Rexrin so after having defined details about the demand side we make more or less the same exercise for the power production and any storage solutions in that case it was the geothermal heat pump for heating and batteries for electricity we have considered some let's say dedicated values in terms of dimensions able to support the half a day or even two days autonomy of the system all this data are inserted within energy plan and let's say the output of all this range of input data derive profiles on an order basis of the heating and cooling demands the power production from different renewable resources including as well the case of imports from available from existing grids and to go on with the educative results except from technical evaluation which can be depicted in the sense of some profiles and let's say time series on a day or on a month basis not less discretization these type of tools can also support the financial business models and provide input for that also providing values about the expected payback period net present value and IRR so you can shortly present some dedicated values to save some more time we did the assessment of the zero energy scenario against the business as usual scenario for the whole district the business as usual scenario has the buildings constructed on an insulation according to building regulation there is no energy production and heating and cooling was provided by heating oil boilers and conditioning units while the zero energy scenario here is buildings with improved insulation there is the district PV for 500 kilowatts a battery with one day's autonomy and the geothermal heat pump with the district heating and cooling network and for these two cases we calculated the heating and cooling requirements as well as the electricity production from the PV and the electricity exports and imports and then the CO2 emissions and the costs for running and constructing these cases if we move on to the next slide please so we were able to calculate some basic the KPIs from the project which is considered the degree of energetic cell supply basically the ratio of the produced energy divided by the energy consumption for the thermal and electrical energy so in this case in the zero energy scenario that we considered all thermal energy is geothermal so the degree of energetic cell supply was 100% and then for the electricity the locally produced energy is the production from the PV system and what was consumed in the case was the energy consumed directly from the PV at the time it was produced it was plus the energy that was the electricity that was stored and used later on by the by the district and finally the amount of electricity imported and in order to convert that into primary energy terms we multiplied the electricity import by 2.9 which is the conversion factor for primary energy for degree electricity mix and then this gave us the result of 100.4% of degree of energetic self supply so the criterion here was made for both the thermal and electrical energy and then the next KPI we used for the environmental analysis was the emissions reduction which is basically the emissions of the positive energy scenario minus the emissions of the businesses usual scenario and then resulted in significant CO2 reduction as well so we can move on the next slide thank you and now for the financial evaluation we considered the cost for the geothermal heat pumps the district heating and cool network cost of the PV system and battery as well as the conventional equipment used in the business for the heating oil boilers and AC units as well as the electricity prices and the insulation cost and we consider a project lifetime of 25 years and then an annual energy price increase of 2% with a 0.5% annual performance reduction of the PV system and that gave us a single payback period of 14.7 years a discounted payback period of 11.6 years then at present value was positive and the internal rate of return was 6% higher than the 5% which is usually considered for assessing such potential investments which gives us the indication that this is an investment which is both technically and financially feasible and then we conducted this analysis for a parametric analysis by changing the insulation levels so we consider three different insulation levels and then different size for the PV system and different size for the storage for the considering different autonomous for the district and identified those configurations where these were technically and financially feasible Thank you Thank you All these type of simulations deal mainly with steady state conditions while as you know from real life conditions one should decline as well after instabilities on the grid and especially when some integration of renewables and their injection into the grid additional aspects should be considered in that respect SIRTH develops well house built models as that of Intema in order to investigate this type of instabilities and mitigation plans towards the design of these systems we call this software Intema and the added value is that compared to commercial products it is an open access so there is no any license free while someone can actually integrate all three main energy vectors on a common platform and examine different scenarios this is intended mainly to support district level energy systems including many different technologies we are in position to modern more or less every type of system standard or even innovative ones and the intention is also to support this type of replication activities in the sense to consider any instabilities mainly on the electricity grid network especially when operates in a synergetic way with the heating or cooling ones So I will give the floor to my colleague Makis Hello Dimali I will try to retrieve Intema in fact is not a single software it's a framework because it contains quite many tools the core of the Intema is the Intema library which is developed in Modenka and includes energy production consumption and storage models and control components Intema also includes some forecasting modules for energy demand production demand response module optimal power flow modules these the last three are developing python what we can do with Intema we can assess the electrical grid performance we can examine solutions that offer ancillary services we can estimate the response of the electrical grid and these are what we call short scenario simulations for specific technical challenges but we can also do long-term simulations to evaluate and optimisation annual and life-side systems performance such as power flow studies and we can also identify roles and scenarios in energy management systems and we can propose economic dispatch strategies this is an example we have contacted Intema it is similar with the previous examples that Placilis presents it is about electrification of heating on the left side you can see the Intema main workspace the graphical representation of the system as you can see there is a district heating system some new energy sources new energy system for production and we have a boundary that communicates with the outer grid what is the general idea the general idea is to have to install a district heating system operating with heat pumps and try to use the excess electricity from the new energy sources to provide heat to the district heating network ok this is a more detailed slide on the left we can see the district in detail there are lines, houses, loads also some local renewable energy sources and on the left on the right side it is the district heating detail that also contains a solar heater for the district heating network a storage tank the heat pump and of course a controller that the controller would decide when the system is going to operate of course the system we have implemented some control strategies for example to operate when excess energy is available from the renewable energy sources but of course if you don't have renewable energy sources heat is a priority so the heat pump will operate we will operate for the heat pump we have created a detailed model it will estimate the coefficient of performances that we have implemented this module inside the heat pump the results of the model in order to be fastest ok some results we have on the left side we have the simulation data for approximately had trend houses as was the previous example what is important here is that we have found that the electric the excess electricity that goes to the network and the electricity for heat after electrification is lower but that means that we have to buy from the grid 70 megawatts this is a nice result from the enterma because you can see that these synergies have also some negative effects we can see it in the next slide also which is more detailed here are the various energy flows from the wind from the TV production we can see the power to grid line but the bottom there is some small times that we have to buy power from grid this is why this is close to near zero energy systems not zero because sometimes we have to buy from the grid this is what we have to optimize by using electrical batteries that we don't have in this case next so so in summary we would say that the approach we followed is from a bottom to top considering the level of detail so we started with the building level simulations and then we used that as an input for the district level ones we conducted several parametric analysis in order to investigate what is the best suited configuration to minimize as much as possible the financial aspects mainly of the investment so in these slides you could see the final configuration but the good thing with these tools is that they can support any parametric study in a simplified way so if someone wants to go into more deep of course then we will need other type of tools more detailed like for example that of Intema this type of tools that we implemented for the case of Iris project we have vision to do the same work for at least the Greek fellow city in the positive is that with let's say a series of input data in collaboration with the local municipalities even non-experts can have the first estimate of expected impacts in terms of technical, economic and environmental aspects through the use of this simple to use and user friendly software and through this type of tools one can calculate the expected KPI which is a requirement and this type of research can be further used for tendering processes from the municipalities in order to transform the theoretical study into practical one and in that respect always the collaboration with the local municipalities, party and local stakeholders experts as well is very important at least for providing necessary input data that should be used I'm not going into more details other than those so I give the floor to Christian from EDF to present let's say more commercial oriented viewpoint in terms of sizing of systems so Christian you may have the word Williammi? Yes, Christian I will present that Okay Wait the headphones Yes You want to you want for me to charge Can you do you have it integrated in your powerpoint the presentation Hello Do you have the powerpoint integrated into my presentation to your powerpoint Maybe it's easy Okay Yeah For sure Christian Panos Yes Perfect Thank you, sorry So I try to be really brief I think we don't have much time So just to give you a small insight in how we used our software for sizing of the battery of the building in red which maybe you know if you can go to the next slide please because can you go to the next slide please Yes So can you push forward so everything appears and exactly So what we are talking about is the in red building so for them who don't remember it's one of the partners in iris and they have a building which actually the educational building which will be delivered beginning of next year and it's an individual self consumption of PV so the PV will be used to run whatever electrical system behind within the building and what's interesting is that we have 175 kW peak of PV then we get to the battery storage you have heard you have the result but I give you how we get there and then we have integrated also the fact that we have electrical vehicles within the building and they will be charging certain points within the premise of this building and also use the PV to recharge the batteries Next slide please So we can go run through these three next slides because actually it's just to show you that we use different type of input data so we use the building load which we had a half an hour level and then we split down to a 10 minute level and we also isolated the fact that we have electrical vehicles charging and there might be possible to integrate that into the management of the battery and the forecast and we have a PV production which you see here what you have maybe to get back home is that we have enough capacity to absorb at the PV during usual working days but that you see in the right side in the weekends we have huge amounts of surplus PV which does has no use and just goes into the grid So next slide please I don't go into details otherwise we close the time just to say that we took information we had to, so as you see we had to on one side to know how we manage the battery we have to know how much PV will be produced and then how much load we have on the other side and how much we can control by the electrical charging, by the battery or by modeling the PV production So we go quite into detail and how we assess the production side and we construct the historical data and what is maybe important to see at this point is that we use these input data to train the forecast algorithm within the management system So we integrate in the sizing the fact that we have a risk of uncertainty in the in the forecast of the production and of the demand So we already integrate in operational as we were in real operation the fact that we don't have some risks in this type of forecast So we took satellite images and other type of information coming from the meteorological station in France on the site of the IMRAID and with that we get all the parameters and be able to reconstruct a reference scenario let's say for the PV next slide please and here just to show you we did the same with the load which we had at a half an hour time and as you might see there are little movements in the graph and the upper graph in which we have actually introduced a little bit of noise to just not be too static and let's say flat line of load because it was very simplistic and you see that also in the graph below which you see that we have in the middle somewhere a huge moment which you have no load which is mostly in the holiday times because it's a university so you have big periods of holidays and so PV can just go into the grid Next slide please So just to show you the next slides will be to show you how increasingly we increase the scenarios of complexity for the management of the battery and what I didn't say so far is that we integrated into the let's say valuation all economic and technical parameters of the battery, of the feed-in tariff of the electrical tariffs of the building the carpets of the PV the carpets of the battery same in the OPEC so the operational expenses and this will give us later the maximum integrated all this into the management of the battery and how they should operate Here we have the reference scenario so it's just the PV production and the load one with the other, we have no battery yet and then we have our reference values which will be more time afterwards we'll have the presentation we will be able to see in how far this reference value so self-consumption rates of production rate PV surplus and the profitability evolve with different type of operational strategies So this is our reference scenario Can you move forward please and here we integrated just as simple simple strategy which is to say with the battery we try to absorb as much as possible of solar energy but we try to release as much as possible into the grid as you see in the curve they're pretty simple I will just show you an attention in the right side of the graph you see that the blue line has a huge peak and stops and you have a huge surplus which is in the upper lower part in red it's the PV surplus which we will never be able to absorb so practically by just doing self-consumption you actually go almost until infinity the battery to absorb all the PV surplus so you have no really an optimum for that it's just how much budget you have as investment which will decide where you stop your investment the size of the battery let's say next slide please so I don't want to go into the details of the graph is that to show you different operation so in this case it's a tariff optimization and peak shaving and how already this creates a lot of complexity into the operation of the battery management of the charging and discharging cycle and so it's actually the most realistic way we would operate and use the battery when we go online so move forward please so once we had these results we actually did different sensibility analysis so we run different scenarios variating different aspects like the size of the battery the feeding tariff or for the PV surplus or some grid tariffs to see how this the whole business model reacts so what we can say in our French case it's the left side what you see in the left side it's actually how the battery sizing is impacted by the grid tariff so the fixed prices so the subscription cost to the grid which in France are very very low so the grid tariffing system in France actually doesn't give any reward in reducing subscribed power so to make big shaving pure big shaving and this you see in the left side on the right side what you see is that actually these services if you do peak shaving peak shifting and try to reduce as much the maximum output power you never get really working business model let's say instead if the input how you say the PV tariff is too high so we always have kind of a negative business model for this type of approach next slide please so this is re-aligned in this graph what you see here and it's basically saying you have the variation here is by the the tariff the tariff for the feeding tariff for the PV system was just basically to have working business model for a battery with a PV system you shouldn't have an injection with today France type of regulation and economics let's say set up and parameters so actually we have almost positive net present value for a battery and a PV system in operation for 15 years in case we have no retribution for the PV surplus next slide please so we did a lot of analysis and actually the choice at the end to be really frank wasn't a mathematical perfect type of choice we really tried to focus on what could lead us to a battery sizing which was interesting for the building and less not so let's say negative in terms of business model overall business model in the case we have name read so actually looked at let's say the inflection point were the rate let's say the PV surplus that gets absorbed by the battery becomes flat by invested euros so in the point where for each invested euro we have less absorbed PV it's the point of research and the reform goes about a battery of 100 kW by 150 kWh so it's a rather arbitrary choice but what is that we have taken is this next slide please and then we looked for the point if we take now the battery and instead of just focusing on what you could do for the self-consumption as such we looked what we could do in case we do grid services so grid services to the grid so tertiary and primary services and so to see if a service which includes the PV, a battery or operation system it is able to sell flexibly to the grid how this whole together works and what are the good news is that it seems in this case we have done it for the tertiary services and you see we have taken different high hypothesis of the availability of this type of service of the battery and you see that the net present value what you see in this graph the left side is in this case you always have a net positive or your case so it means that once if we just use the battery to absorb PV and work as a self-consumption optimization of the self-consumption mean it's not really having an economical profit instead if you now open it up and put some availability of this battery and service to the grid you get possibly potentially to a positive business model so next slide please and the same in this case for primary reserves but we don't want to delete this but the point here is that also with primary reserves which actually is a frequency control you also have an increase of the net present value of the battery but in a different way than tertiary services because you operate completely differently your battery system and since you don't get out as much as you would imagine because primary reserves are actually the most valuable type of of services so why did this give you different work we have done and I think it was the last slide for me yes so it was just give you an insight of the work we have done and how we the type of results we got by making a simulation really down to let's say as we would do it in a real case and this give us some insight in how we will have to operate the battery in a few months and in that building and next city so wow that was for me, thank you very much thank you very much Christian and with this slide from our side we are let's say finished concerning the presentation we prepared so should you have any questions please feel free to make them and thank you for your attention one question if no one else has one I was thinking about the dimensioning and the pricing of the PV and the storage comparing the case in Alexander Poulis and now the immediate case the pricing for storage has usually been the problem in the cases that we have had in Finland and of course it's quite dark here when we need the power but anyway we have had hard times getting like a profitable model where we use storage for PV but in the case in Alexander Poulis it seemed to be quite easy to make a feasible model with storage but again in the immediate case it seems to be a little bit more difficult so where do you take the dimensioning and the pricing for the PV and the storage in Alexander Poulis for the simulation that you did so first of all for sure the context is a little bit different in the sense that the buildings in the case of Alexander Poulis are much smaller than that of Inbred and also they have with different profiles so Inbred is a university and as Christian said it's not operable during summer so this thing changes the configuration of the whole system in terms of batteries to answer a more explicit question Vasilius do we have the values for the batteries that we assumed Yeah, I think it was around 400 kWh and this was taken as an estimation so we had from previous project we had some offers on different systems to be fair we had some quite different values there and we took an average so we had some quite more expensive than that and some things that was cheaper than that so we took like a basic average of that price and it worked around as an average of these two prices you can tell with a few experts and we got this average price So for the case of Alexander Poulis who was 400 years per kWh while for Christian for the case of Inbred I don't have the values for megawatt hour in my mind I can search for it if you want to but we took actually the real price we get from the tendering from the Inbred so we took actually the price tool cost to have the delivery of the batteries as it will be on the day I think but I can search for it if you need it We had quite diverse prices so we took an average So Maurici the answer is that this is dependent first on the profiling the typology of the building and then the price that one assumes which is deviating from country to country from case to case Yeah, that's a good answer and it will be interesting to compare and see how our cases add up Okay, thank you Any other question? I'm fine, thank you guys Okay, so that means that the presentation will be uploaded on the M-desk, right? No on the M-desk the presentation and the video will be uploaded on the Irish website Alright Yes Thanks in our YouTube channel Okay, thank you One short question again from Maurits if it's okay Regarding the red screen software presentation when you did the simulation for the buildings themselves I think maybe you didn't really follow very closely Did you do it for like one building and then or kind of content that for all 100 buildings or did you somehow do it for all 100 buildings at once, I was just thinking for the areas towards south and the glazing and the windows and so on So did you do it for one individual house or did you do it for all of the 100 at once? No, we started first of all the red screen is oriented to the case of buildings So you can see in this slide Mainly that we have started for at least the case 1.1 5 different buildings While of course when we go on the level of the district we aggregate this information from the building to the district level So in the energy plan which is oriented to the district level simulations for sure we do not include the individual characteristics of each building so one had different buildings but actually we select typologies and then we multiply the results with this representative typologist Okay, yeah Any other question? It's Charlotte from Denmark here I was just wondering if the presentation will also be available in the positive team will it be uploaded there? Yes, yes, I will upload it also to positive teams Super Thanks From my side, if it is to say one key message is that for conducting replication activities you can have many approaches but from my point of view whatever is done should be in the end to some representative KAPIs and this KAPIs should be technical environmental, social whatever and to do this theoretical study at least for the case of fellow cities it's very important that first a very concrete ecosystem or its city is built so you need to have the experts within this ecosystem who have a general how of the situation, either energy or transport or whatever and this is very important in order to have the necessary input data for conducting whatever type of analysis one wants so at least from our perspective I would like to thank Alexander Rufels and especially Kostas for having this role this is a key important role and it requires as I said almost day to day interaction in order to engage the local ecosystem to have the necessary data so that you can do the best that one can do for preparing replication activities which in the end will lead to investments and achieve in the short term the goal of self-sustainable cities on a European level. The selection of tools is made upon an area that one puts in our case we select with those that can be user friendly does not require very extent know how and deep know how on the analysis of energy systems because this is also a lesson for the local municipality people who in most of the case do not have the necessary expertise to go into very deep technical details so you need to present them something that is as easy to understand as possible without many technical requirements so this is one key lesson learned from this exercise so unless you don't have any other question I hope that it was useful at least to give some insight of how we deal that in Aries and how we envision to do that at least for positive for the case of Ioannina municipality and thank you very much for your time Thank you Nicos