 Anyway, welcome to today's the Mexico SmartGrid Center webinar. It features a large group from Siemens that will be discussing some of their new software solutions for distributed energy optimization. Those of you that are not familiar with the webinar series, you can always go to our New Mexico Epscore website and sign up for our newsletter and also just pay attention to the website. We have frequent changes there and updates and we have an extensive webinar series planned over the course of the summer and also we'll be carrying it through the fall as well. The second webinar in the coming up is on the next slide and this will be led by Frank Curry from Santa Fe Community College and he'll be discussing the SmartGrid Workforce Development Program at Santa Fe Community College and this is on July 22nd. And on the next slide, just to explain how this works, at the lower portion of your screen, you should be able to move your cursor and you will see things like participants chat, share screen, and on the far right of that is something called Q&A and this is where we'll take questions at the very end of the seminar. So you can type in your questions there and then Brittany, who at least I see on my screen as well in a little picture there, will be serving as the moderator after the seminar concludes and we'll take again Q&A at that point. And the next slide, basically I just want to introduce Bill Kipnis at this point. He is the Senior Project Developer for Siemens, New Mexico. And he works in the Smart Infrastructure Division. He is importantly on our EPSCOR State Committee and serves as a major advisor to the EPSCOR SmartGrid project. His expertise is in renewable energy, microgrids, building automation, mechanical system efficiency, and water conservation. And he has degrees from the University of Chicago Graduate School of Business and Colorado College. So at this point, I'm going to transfer over control of the screen. And Bill, you can continue introducing your colleagues. Thank you. Brittany, is there any way that you can unmute Bill? Go ahead, Bill, you're on. Okay, thanks very much. Well, this is a presentation on Siemens DIAP, which is Distributed Energy Optimization, which we developed as microgrids have emerged onto the grid, and SmartGrid has become better defined. We saw the need for software for advanced monitoring of multiple microgrids and data acquisition. But I'm going to leave the details of that to Marcella and Magdalena, who are joining us from Italy. But for some intros to to our team will be available for questions after the presentation. Marcella's work includes specifications for monitoring and optimization of energy flows within SmartGrid and microgrids, and developing algorithms to optimize electricity exchanged within a microgrid, composed by molten salt storage, for example, including a large scale solar PV. He's the educated at the Polytechnic Institute in Milan. Paul Benison, which we welcome to the call today, who's business development manager for microgrids. His key solution expertise includes economic dispatch, energy arbitrage optimization, forecasting, protection, control automation, power system design, including real time load management grid synchronization power import export. Paul comes to us by way of the UK. Also joining us today is Scott Kessler, who is part of the startup of a company called LO3, which you may you may have heard about earlier. It's a Brooklyn microgrid startup focused on transactive energy markets for DERs. We're happy to have Paul prior to that he worked for a decade in EE at Connecticut Light and Power and NYSERDA. With that, Marcello, I turn it over to you. So good morning, guys. With the next 20 minutes, I will present the solution that we are using to develop SmartGrid and microgrids around the world. And within the next 20 minutes, I will present this solution with some slides. And I want to say that we will go through the introduction of the technology, how the technology is composed, and how we are applying this solution around the world. Okay. Following my presentation, I will let the stage Madalena. She is also from Italy as me, and she will introduce you a project that we are developing in UK for an important university from New Castle. So thank you, guys. This is the off. We are talking about the digital platform that on the left hand side, you can see the field side that is basically composed by a storage system, a CHP, multiple loads, whatever you want to integrate basically and connect to the off that is a cloud solution. So basically, we are connecting the local SCADA, the local controllers in order to get the data from the field devices. As soon as we get the data from the field devices, basically, as you can see, we are going from a service that is focusing on the field side and the SCADA system that is the resiliency and control. We are getting the data in order to provide a transparency and awareness of the information that we are getting from the field. As soon as we have the data, basically, we are able to interact with the information with algorithm intelligence from the users that can bring into the platform in order to basically promote monetization. In order to basically interact with the market and enable for example, demand response services or optimization algorithm to minimize the cost of the management of the energy that we are integrating from the field. This slide, basically, you can see that we are, we have in the center of our ecosystem, the op that is promoting, as I say, the monitoring and optimization service. And so the colors are representing even the use case that the op can develop. On the bottom of this slide, you can see the controllers that we are able to interact. Some names that you can see here in this slide are Siemens product, like Zigo, that is a building automation system. We can get the information from the building automation system. Exactly, you can see, for example, the MGC, the microgrid controller that is an RTU that is able to basically interact as a scatter system with a hybrid power plant, for example, CHP composing a storage system and the PB plants, they can be interacted with an RTU called the MGC that in real time will provide a resiliency service to this kind of ecosystem. At the same time, you can see that we have other controller as a block. It means that the op with standard communication protocols can interact with any scatter system that has a specific communication protocol that the op is able to communicate. So in this way, the bottom part, the devices can be interacted with the op in a quite flexible way because the op has different drivers in order to communicate with this local devices. At the same time, if we see the southbound of sorry, the northbound of this of this slide, we see other product from Siemens like Dems that is an interface with the energy market, when, for example, the op want to calculate the flexibility of the different assets that we have on the southbound and promote a trading services on the ancillary service market that create an integration with Dems could be a solution to promote a demand response service. At the same time, we can see the car or see that is the solution. Another cloud solution from Siemens to manage electric vehicle infrastructures. Again, in this way, the interaction with car or see can be a way to involve the charging units within a smart grid. For example, to modulate an electric vehicle and promote an ancillary service market and ancillary service from the electric vehicles infrastructure. At the same time, as you can see other APIs, this means that we have a rest API communication that we can involve to interact with third party cloud based system and start to create, I want to say, a custom ecosystem to our cast to our users to create a different point of communication between one one system and another one to to develop a flexible solution based on the app. I want to just go through the main the three main use cases that our solution can provide. As you already understood that we are focusing on a cloud based solution that can connect the assets in real time, getting the information and promote the transparency services. So in this way, we can navigate into our site and in a multiple size that we are monitoring. And for example, get reporting about the consumption generation and receive the alarming in real time when the threshold of consumption or generation is verified, a threshold that the user can create without any problem using our user interface and receive, for example, alarming via an email or via the push push notification in our mobile application. At the same time, as soon as the transparency value is created, we can go through the optimization one behind the meter optimization. This means that we can activate algorithm to optimize our controllable assets to minimize the cost or to respect a specific target even on the CO2 emission. And this will be the use case that Maddalena will present when we have different resources and we want to manage the different resources not to do cost optimization service, but to promote the CO2 emissions minimization. This can be another target of our optimization algorithm that we can activate, as I said, to basically schedule the controllable assets within the next hours to fulfill the target that the user can basically insert within our platform. The last use case, it's the demand response one, the virtual power plant. So we are not only able to minimize cost and minimize the CO2 emissions, but we are also able to basically create virtual power plant with a different storage system or CHP, wherever, as soon as we have a flexibility available within an asset, the app can calculate the aggregated flexibility of our virtual power plants and share this information with the TSO and promote an auxiliary service to our virtual power plant. And in this case, we can basically use the capacity of our virtual power plant and promote a frequency regulation service. At the same time, we can interact with aggregator platforms to, for example, receive a commons of flexibility and dispatch the commons in an optimal way. Again, here, involving our optimization algorithm. So, for example, we have different flexibility within our multiple sites, and we have also a different cost of each flexibility that the asset owner can share within DOP. Okay. And in this way, as soon as a command of flexibility is coming, DOP can decide which is the optimal scheduling and the optimal decision on which flexibility must be activated in order to minimize the cost of the request flexibility. And in this case, for example, we are maximizing the revenues of the aggregator that is the responsible of the management of the virtual power plant. So, again, as you understood, within these use cases, we have different, I want to call it stakeholders that will log into DOP and will interact with our solution to reach their own specific target based on the, based on the business that they develop. Okay. This is basically a sort of example of a typical microgrid that we can manage, and we are, that we are managing. And basically within this typical representation, we can see on this part of our presentation of our slide, that we have typically within our new, I want to say energy system, we can have a point of common coupling. It means a point of exchange of the electricity within the utility grid. And below our meter, that is the PCC point of common coupling, we can have a storage system that is basically to be, to be charged and discharged with the, for example, the portable type power plant that we have here, or with the CHP. Okay. In order to feed our loads that we can have here, like a building, for example, or charging units. These systems that is really common to see on our, on our city, must be managed in an optimal way. Because when we have a storage system, a PV plant and the CHP, the units must be controlled in order to reach the target that the, for example, the district eating, sorry, the district operator want to reach in term of CO2 emission, as I say, or cost minimization. So this is why we are applying DOP as a solution to get the data from the SCADA system that we can see here in order to share this information with the user and activate the algorithm in order to schedule, as you can see, the flow of information with the optimal scheduling algorithm, the program to control, like here, a storage system or a CHP in order to reach the target that we have saved before. At the same time on the left-hand part here, we can see our, not our solution, the ECAR operation center, that is a solution focused on the management of the electric vehicle infrastructure. So ECAR OC is mainly focused on contracting, defining a tariff, so it's basically a cloud solution that is not related, it's not relating with energy, it's relating with the management of the electric vehicle infrastructure. So the typical user of ECAR OC is basically the EMP, the immobility provider, or the CPO, the charging point operator. Those are the typical stakeholders that are using ECAR OC. At the same time, this solution is integrated with the OP in order to gather the information about measurements of the charging unit consumption. And the charging unit is something that you have to involve within the microgrid management, because charging units can involve really in a, can provide an issue in terms of peaks that can must be managed, because sometimes you want to install an amount of charging units that are exceeding the maximum value of the fed-out power from the grid that you can use to feed your charging unit. So you have a maximum level of consumption that you can have on your charging unit. So in this way, the charging unit must be modulated to avoid the peaks that you can reach if you don't module the charging units. This is why the charging units are another element that must be considered within the OP in order to control the microgrid in an optimal way, even considering the cost of the electricity and or the weather forecast that is a really important information to, for example, predict the renewable generation. At the same time, the electricity market is another information that you have to interact with to predict the cost for the next hours and let the algorithm to decide the best strategy. At the same time, the damage response use case is the same microgrid that we have seen before can be a part of a virtual power plant where the OP will again measure the real data in order to aggregate the real data and share the real data aggregated to the TSO and the flexibility value. The flexibility of a microgrid is something that can be calculated by the OP and or can be defined by the owner of the assets. So the owner of the assets can decide the amount of flexibility that he want to share to the to the delivery service market. So we have these two solutions but at the same time you can see you can understand that even the asset owner is an important stakeholder to be involved within the management of this kind of ecosystem. So as soon as we get this information we share the the aggregated value to the TSO and the TSO will send flexibility requests that can be dispatched in the optimal way as I have already described it before. Just to go through the architecture as I said, this solution is a cloud solution so the user will log within a specific URL username and password is even a multi-tenant so more users can log within the same instance with different rides and roles. At the same time at the bottom of this slide you can see the different interfaces that we can have from the SCADA system for charging units and inverter etc etc it doesn't matter the reality doesn't matter the physical units the important is that that physical units has a standard communication interface in fact the connectivity module is exactly the module that the user can configure the gateway that is the driver for the communication and for the translation from the local communication protocol like for example the Modbus TCP IP protocol in the native protocol of DOP that is the MQTT. So the communication must be a topic because DOP is a designer based on the IoT paradigm so first of all we have to understand how we want to get the data and this is why we have different communication gateways to interact with the field devices and get the information and produce the information in MQTT. At the same time as soon as we have the information we can basically activate the different microservices and in this in this way the platform can be composed with the different microservices that we have created we have a payable in order to allow the user to log into the into our into our platform and use it as an operator with different reporting views dashboarding that can be customized of course in order to really focus the the service on the on the user needs and as you can see here on the right hand part the different algorithm to cover optimization use case and or to cover demand response use case can be again here activated to create the service requested by the user on the right hand side here we can see that DOP again as I said is a cloud solution can be also deployed in a third-party cloud infrastructure to be evaluated of course case by case our solution by default is proposed within Microsoft Azure in Europe this is the place where DOP is is located in Amsterdam at the same time the integration is another important element because we have standard development kit to allow the user to for example integrate third-party devices so we have our MQTT specification that we can share to create by yourself your own gateway to produce the information from your own protocol in MQTT at the same time we have the development kit to allow the user to interact with REST API and for example make a communication a cloud-to-cloud communication between your own cloud system to deal with our API and another important element and is something that we want to spend a little bit of time later is the SDK for the algorithm integration and this use case is really important with the university and we are developing different projects around the world about this because we have a standard development kit for in order to allow our user to integrate their own algorithm based on the on the languages that they want to use for example Python C++ MATLAB they can develop their own algorithm and share the API again here with a framework called GRPC the information and share the data in a bi-directional way of course from DOP to the algorithm vice versa just for the communication the gateway that we have mentioned before is something that we are installing within what what it is called the nano personal computer so our really small computer and and and I want to say that there are something that you can be you know in a really easy way installed within our customer network in order to make this conversation from the local protocol to MQTT we have the other solution to to allow the communication for example via a VPN so without any gateway installation but this is the preferable way install a local IoT gateway and within this IoT gateway installed within the customer network make the the conversion from the local communication protocol in MQTT and it's really important to provide this conversion in MQTT within the customer network because MQTT is an encrypted protocol so you can go in internet without any VPN so if we install the gateway locally sharing the data within internet without a VPN it's something that it can really support the project development without respecting first of all security requirements at the same time avoid to spend too much time and effort to for example install the VPN and as I said before DOP is a platform that can be really customized in terms of communication in the in the southbound southbound via MQTT even sorry importing the data in CSV or XML via the specific importer that we have via SFTP we can import this information and or the REST API at the same time if we go into the backend we have different way to interact with the information with a specific rules engine so the user can create a rules engine for example with different modes you can activate the intelligence so you have developed your own algorithm and you can decide to activate the algorithm on a specific mode and calendars that you can create with DOP at the same time the algorithm can be even activated with triggers so for example if your consumption is exceeding the threshold that you can configure and as soon as the threshold is verified you want to activate a specific algorithm to avoid peaks this is something that you can do with DOP another module that is really important is the formula engine so with DOP you can create formulas and we have a specific UI that you can use to interact with the information and create a sort of script that can be even complex to for example create an energy model this is something that we have used with FCA in Maserati here in Italy where our energy manager from Maserati he has developed a really sophisticated formulas to for example predict the energy based on process data and this is a really important value because if you have this kind of model you can avoid meters installation that most of the time it costs money so with DOP you can create with the formula engine this kind of modeling last but not least the algorithm engines as I said before we have plug-in to interact with algorithm as I said with the GRPC framework and this is the academic package that we want to mention again you have understood that we have the basically the requirement for an academic is to interact with the data and to to use the algorithm that they that the researchers want to develop and DOP can be the interface with the local systems and the the algorithm that our users that are the researchers or the students want to develop and activate with our solution and this is what we have designed with our university and what we are doing with them and allow basically in this way the value chain to let our user to interact with the real data and this is an example of what we are doing in in our campus in South Africa where we have a micro control that is the RTU that is EcoCC that is the building automation system that the interact with the students to allow them creating logics intelligence and and promote service that is that is a unique at the end we are basically around the world and we have different projects from micro reads till demand response services in Italy but of course as I said probably at the same time I will let now if you agree I will let Magdalena introduce the project from from UK and that we can hear the project called sand and she will give you an introduction about what we are doing with also real demo about the fantastic project in UK thank you so much Hi everyone I'm Magdalena Pondini and brief introduction about myself I'm a project engineer in in the OP team and in my role I'm let's say responsible of following projects directly interfacing with the customer and let's say having a the possibility to understand what are the functionalities requested by the customers and to implement them in the configuration of the platform that has different microservices as Marcello just explained so today I'm let's say presenting to you the project that is ongoing with the Kille University in UK it's a really big campus in the UK about 600 acres and the main objective of this project is to let's say demonstrate the possibility to have a huge smart grid playing a role in terms of CO2 minimization and in terms of flexibility management inside the campus through the controllable assets so of course this enables also a great collaboration between researchers and us since researchers are utilizers of the platform and as Marcello already highlighted one of the main purpose is research being let's say enabled also by the data the wide amount of data we are monitoring and let's say visualizing inside the platform so just a couple of numbers about this project we have a huge campus as I mentioned where 25 substations are let's say configured so it's quite a big grid in which there is a main encumber and smaller substations some of which are controlled by microgrid controllers so RTUs let's say interacting directly at substation level there are six rooftop PV plants around the campus which are we are monitoring six flexible buildings which provide flexibility for for CO2 minimization and for grid balancing services and we are also let's say gathering data from something around 500 meters around the campus so these meters are let's say acquired and monitored through different platforms and all of them via SFTP service through an interface directly to the op we are collecting all these meter readings into the op these type of meters are let's say monitoring energy vectors such as gas meter readings electrical meter readings and heat meter readings of the different buildings so this enables of course the analysis the multi vector energy analysis for the buildings and also an understanding of for the flexible buildings for example how much they are playing a role in CO2 minimization we are also monitoring and we will start controlling soon CHP and electric gas boilers in which let's say dedicated logics will also implement CO2 minimization according to an interface that we have with the GRPC with the sorry API carbon intensity provider so there is a service in UK that we can call through APIs that provides a carbon intensity of the grid in terms of kilograms of CO2 per kilowatt hour forecasted so that we can use that in terms of let's say logics and in order to define how to use the flexibility available from the assets to minimize you to consumption in the campus also charging stations will be installed in the campus some of them are already there and ECRC will be the platform that will talk to the op and implement modulation comments in order to fulfill demand response signals coming from from let's say the platforms that are identifying grid constraints inside this huge campus so about the functionalities as I already mentioned the demand response is of course one of the functionalities that is needed in such a big and complicated network where many substations are controlled and where there are platforms that are also playing a role in identifying constraints in the network and let's say calling flexibility to us in order to balance again demand and supply inside this network so flexibility management is also another functionality in which the target is to use the flexibility available from the controllable assets in order to implement the co2 minimization logics we are already doing that in the flexible buildings in which we have a forecast of flexibility available for the for the following day and we are comparing the co2 so the carbon intensity of the grid in kilograms of co2 per kilowatt hour in order to define when this value is overcoming a certain limit to activate flexibility of the building of course functionality is also involved a huge contribution from researchers that implement formulas analytics and digital twin capabilities within the platform because as we will see in the demo there is the possibility to customize formulas inside the assets and to elaborate the signals coming from from fields in order to define new parameters either for analytics or either for digital twin validation so you can model your assets and you can validate it if you have meters let's say enabling this validation a grpc interface is another possibility so this new protocol that allows communication between our system with external algorithms that let's say talk grpc protocol so the exchange of data real-time in read mode or write mode is let's say allowed by this type of interface and we are able to define set points to be sent also to the field devices api interfaces of course something really fundamental in this project since we are for example calling apis so we have customized a gateway that is implementing api calls to carbon intensity providers and we are also talking to other platforms like Dems as Marcello was explaining earlier or to Spectrum which is a grid control platform in order to exchange data about flexibility available for the different assets about the our request so flexibility requested to balance the grid we all manage this inside the op and we design customized the logics in order to meet the requests in terms of demand response for example so this is a quick overview of how the different let's say different assets are located inside the campus so we have six flexible buildings we have eight microgrid controllers we have different pv plants and chp installed in here and several other assets are to be installed so a wind and pv plant as well as a storage system that can enable additional optimization logics inside the platform so i'll now jump to the actual demo of the system so this is how it looks so how the op looks when you log into the system you have a page in which user can customize the visualization of dashboards so in this case for example we are monitoring the different substations we have a marker showing what are the co2 savings for the current day for example and we can also have a a dedicated dashboard to the single microgrid controllers monitoring so as you can see i've configured a dashboard in which each microgrid controller is showing me the current status of energy imported from the grid frequency and all the parameters that the microgrid controller is currently monitoring of course the objective of the project as i mentioned is a lot about using flexibility to minimize co2 impact so we have also dedicated widgets in the dashboards that can associate energy consumption of a building during a day to co2 savings in the same in the same day and down here we can see that we can also implement algorithms such as consumption forecast that can enable additional optimization functions inside the platform and i would like to show you how we have organized the the navigation inside the platform for this type of system which is quite huge and which might have several ways of let's say being displayed in terms of assets and in terms of monitoring so as you can see here is a map of the system and in the geographical view we can navigate in the different areas and jump down in the navigation tree to the buildings we are most interested in for example if we take the the example of the library building as you can see we have meters that are showing the electric consumption meters that are showing the thermal consumption so in this type of geographical view we are going to show the balance of the different energy vectors in terms of say total consumption inside the system so right now we are monitoring electric consumption for for nodes such as student union for example i've configured let's say a visualization so that we can see the balance between the different energy vectors in the system one additional feature that i was mentioning is the possibility to use flexible buildings flexibility in order to respond to the requests coming from the other platforms so as you can see this is not a simple load having only power consumption parameters and signals but we have many properties describing this type of flexible load like flexibility available in the system flexibility requested and most of all the CO2 saved the accumulated daily so in order to determine this value it's possible to elaborate in a formula for example the flexibility request that we determine with our algorithm and to take into account the grid carbon intensity of the grid so these two values are computed in order to determine what's instantly the CO2 value saved inside this building when we sum up the contribution of all the different flex have a total CO2 saving of the campus under the electrical view for example we don't stick anymore to the geographical navigation but we we decided to support the user in understanding from an electrical perspective what's the actual configuration of the network so we have the main encumber of the entire campus and we have the different substations some of which may have two transformers and under each transformer we have the main meters that are being fed by that substation node in particular so under each node we will have the configuration of the energy balance in terms of import export energy consumption and the PV production in this way each node will will show in a clear way to the user what's the current status of energy balance at that level one last thing about algorithms i wanted to show you how algorithm play a role inside the platform so this is like a one instance of an algorithm configured for a flexible building like the library in this case we have this algorithm which is doing what i was explaining earlier so it's taking into account the flexibility available forecasted for the following day and it's evaluating what's the carbon intensity forecasted for for the following day in order to decide when to activate this flexibility of course when it comes to manage a single building that's an easy task what Marcello was explaining is that in the end each one of these controllable assets will play a role in co2 minimization so when we will put together a flexible buildings together with the chp and the boilers that are controllable together with the for example the storage system and the charging units we will have a an overall optimization purpose looking at a co2 minimization objective in order to be controlled and forecasted to reach this goal in this example i've configured for example the orange line is showing what is the flexibility request for a specific building the green value is showing what was the carbon intensity forecasted for that specific moment and the purple line is showing how much kilograms of co2 have been saved in in that specific moment for thanks to this flexibility activation okay thank you for for listening and we're happy to answer any questions you might have thank you Maglena we do have one question and it is from Satish who comments that this is a great presentation thank you i'm curious whether in your practical projects you are starting to see peak forming rather than peak leveling also could you outline optimization methods that you use algorithms and tools okay in the case is explained also introduced by Marcello and in this case as well we are using let's say algorithms implemented in matlab and gums so in this type of algorithms the optimization objective as we mentioned earlier is either cost minimization or co2 minimization or to define a reliability of the grid in order not to overcome a maximum limit of import from the grid so in these models each type of asset has been modeled with its specific constraints and specific technological let's say values and the algorithm let's say thinks in a forecasting direction so takes into account pv forecasting takes into account energy consumption forecast and is determining an optimal scheduling of the different assets according to an optimization objective wonderful thank you for that thorough answer it looks like that was the main question and just a reminder to everybody please type any questions that you do have for the panelists in the q&a box and we will give the remaining amount of time to answering those questions if anyone has them go ahead and type them in it doesn't look like we have any other questions bill would you like to bill or selena would you like to close this out um well i thank everybody from Siemens for a really great presentation and as you can see on the slide here our next webinar will be July 22nd from new to one on mountain time and it should be quite interesting frank curry is a new hire there at centipede community college he was hired through fscore funding and he'll be leading a discussion of the new workforce development program at centipede community college um i do see one last question yep just side over that and then i'll say you do right now but go ahead and cover the other question all right we do have one last minute question from steam steven gomez um what scale is the system optimization for and is there a minimum or maximum size of grid that this will work on uh just i can give you a feedback about this question a there is no specific limit in term of grid size that we can manage most of the time the assets that we are managing we are talking around the medium level voltage system so we are not used to integrate high voltage system not because we are not able to do this but because our you know business is more focusing on the medium and low voltage system so in term of size uh i don't see a specific limit on our management is only is only a matter of the purpose and the goals that we want to see with our with our users okay even in term of sizing about the amount of assets that we can manage again here we don't have any specific limit because the our capability to scale up using our cloud infrastructure that can be really scaled in term of amount of let me say it resources resources to be used that can be really fulfill the request to scale the amount of assets to manage within our within our platform outstanding thank you you're welcome okay um with that we would obviously like to thank all of the wonderful panelists who joined us today and um we again welcome you to join us for the next webinar on July 22nd have a wonderful day everybody thanks everybody thank you bye thank you