Hello, everybody. Welcome back another quick AI video. This time we're in CapCut and I'm going to show you how to do a split screen and not just any split screen. I'm going to show you how to do a four way split screen. The most popular kind. So here's an example here. I've got four different YouTube videos to sort of do on their own thing. How did I do it? It should take about two minutes max. Let's go. All right. So the first step is an easy one. We're just going to import the videos. So I've got four videos here and I'm just going to drag and drop them in. You can drop in as many as you want all at once. And you'll see here they are now inside our project panel. Now the next step is you want to just drag each video onto the timeline, but you want to stack them on top of each other. So in this case, I've got videos that I think are all the same size. Make sure they start at the beginning, all of them. And then Presto, we've got four videos all stacked on top of each other. Now I'm going to show you the next step. All right, we are rolling. The next step is optional. I'm just going to make them all the same length because I guess I've got OCD to some degree. So there we go. They're all the same length. But here is the critical step. Now here we go. I'm going to click on the top video and then what I'm going to do here is I'm going to go over to the right side. It says video basic. And then under scale, what you need to do is you need to type in 50%. And what happens is it basically it reduces it by half, obviously, but it makes it into a nice beautiful quarter piece. So look at this.  But here we go. I'm now that I've reduced the scale. I'm just going to grab it and move it into the top left corner or whichever corner you want it to be. Keep in mind that these videos are the exact same size. If you have different size videos, you may have to scale it a little differently. This one here, I'm going to change it from 100% scale to 50% scale. Now I'm going to move this one. I'm just going to click on it. I'm going to move it until you see that it clicks in. These are basically magnetic. They are snapping. We're going to go to the third one. And I'm going to double click on this one here. I don't even need to do that. I'm just going to go ahead and click on the scale, drop it down to 50%. And there we go. We've got the third one here. I'm just going to move it into the bottom right quadrant. And when I let go, you will see that the light blue lines are touching each other. So this is an exact fit. And this is the fourth and final one. The bottom one, I'm going to go ahead and change that scale to 50%. And presto, this will work as well. Now I'm just going to snap that into place. And then if I move the timeline and I hit play, this is perfect. All right. So that's how you do it. I'm going to show you a second technique in case you want to do like a split screen in two. So let's delete all of these videos. I'm going to drag and drop the first video here on the left, right into the timeline. I'll drop a video right on top of it. And then watch this. If I go over to the right side here, you'll see under masking, video, basic, switched to mask. And under mask, we can drag and drop down any of these different styles. So if I wanted a horizontal split screen, I would just click on that and presto, we've split the screen. And you can actually go ahead and adjust it, I believe too. So you can feather it and stuff like that. But there you go. This is a second option and you can move it up and down. And you can also readjust the videos as you see fit. Those are the two techniques in CapCut. Thanks for watching.


Many it's me Yeah, where are you calling from? So I'm from in Thailand And where are you from? Are you from Thailand originally? Well Nay, welcome to talk to us. I'm Avi R. Didi from via way learning English and I'm here in Seoul, South Korea joined by Two English teachers. Hey, I'm so he more and I'm from your mom Yeah, it's me. Yeah, nice to meet you And I'm from Vietnam Sometimes that I'm missing there like a present party present part of the way and a part of the work Sometimes they made me difficult to understand Especially the first part is so I'll give us an example of the past participle and the present part So a sentence where that's being used so so people can understand what he's talking about It's it's only a matter of chances because as you said participle It's a well to make you more understand You are talking about the ING form. Am I right? Yeah, I am formed that we we call it ING form in our So yes, so what? Yeah That the action you are doing at the moment you we use the present participle like I am eating I am talking to you. I am writing a letter But this action is happened in the past we use I was writing. I was reading I was listening but in that sense, there should be another action happening at the same time You were you were talking to someone else while while you were reading the newspaper something like that So there's two action happening the same time in the past And so an example a present participle you're saying I am eating yes, and then past participle would be I was I was eating but this this structure this grandma structure is used when two actions happening at the same time I was eating while I was doing this and that something like I was eating while I was talking Yes, well, I was because if you want to see the past action happened in the past you usually use past it. I I read newspaper Yes, but you can use I was reading newspaper Yesterday while I while my mother came into my house or when my mother called me Time, you know, like a person Who made the two action in the same time like they ever now, yeah, we use like the ever but if at the present participle it doesn't make me Complex but yeah, it could be possible. Yes Yeah, some yeah some time because some people can choose whether to use the past tense or the past Yeah Like I read the newspaper But if it was I read the newspaper while watching television, then you would use the ING But if you were only doing one thing, I read the newspaper The past 10 and The difference is that When we talk about the completed Action in the past we use the past tense for example, I read a very interesting book yesterday that when we focus on the When we focus on the action that something is in progress at a particular time in the past use the In participle Yes, for example, I I was having dinner at 7 p.m So the action is in the progress It's so hard to remember You have any suggestions for In my country Burma we use the vocabulary books written by Raymond Mafie Deacons from Elementary level intermediate level advanced level so different level so if you can find these books with the author name Raymond Mafie, then I think it's the idea you can start with the Elementary one then you can move on to be advanced level that that's my suggestion Yes, when you learn vocabulary you should learn it in context. Do you have any difficulties or problems? Yes, I Actually What is your biggest problem? I think it's about speaking Yes Any strategies you would recommend How often do you use speaking English? Not much. I don't have friends that are Spare units, so I'm just a Just watching movie to improve my speaking I'm losing me watching movie with a subtitle Focus listening when you watch the movies because the more the more you Listening do listening and you will be able to How has it been for you learning English is it hard is it easy? Yes, sometimes hard something easy Why do you know? Yeah I'm going by band by myself and from internet and I reading from your Practice in Facebook Glad it's been helpful. I'm learning also from internet and by myself and now I'm get Learning about Germany language Now English has a lot of words from German Do you find that the English you've learned is helping you study German? Yeah Yeah Because that's my teacher from the German use be use English also to Explain to us. Are you planning to go to Germany or how did you pick German as your next language? No, yeah, I want to know because I see that Germany is so Complicated so I want to know why like that, but in English all people all know about English Yeah, so I want to try Germany language Interesting because you're saying because everyone is learning English you want to learn something different Hmm. Yeah, I want to have a plan for learning English with you Firstly, you should think about your goal of learning English So first start with your goal Watch level and what do you want to achieve and then you can consider how much time do you have a day? to reach that goal Do you want to focus on Speaking or listening what Mohammed? What's what's your Huang just talked about a goal? Setting a goal. What what goal do you want to reach in terms of learning English? I am nurse practitioner and many of My textbook is in English language. I want to I want Yeah, I want to read my book completely and understand them How much of your books do you currently understand? About 70 percent


Hello and welcome to tomorrow. My name is Lisa and I'm gonna be your host today. This is tomorrow's STEAM Discovery 2.05. I'm super excited to get you guys talking to someone who I find incredibly inspiring. Today we're gonna be talking to Julie Angus. She is the CEO and co-founder of Open Ocean Robotics. They're building a fleet of autonomous ocean drones and these drones are packed with sensors that are gonna help us understand our planet a whole lot better. But she was also the first woman to row across the Atlantic from mainland to mainland and a recipient of the National Geographic Adventure of the Year Award. But here's the thing that I find incredibly interesting to me. She's a Bachelor of Science in Biology and Psychology and a Master of Science in Molecular Biology. And I actually have a degree in Molecular Biology as well. I remember being totally stuck in a lab, getting a sore wrist from perpeting chemicals all the time. So, Julie, I wanna ask you, how did you cope from being in a molecular biology lab to being out on the oceans, building these incredible craft that can totally revolutionize the way we understand our planet? Well, thanks for that question, Lisa. And thanks for having me on the show. It's a real pleasure to be here. So yes, I would definitely say my career has not taken a linear path. So I really loved molecular biology. I love genetics. And for my masters, I was researching a rare genetic disorder called Gauchat disease and how to make the enzyme replacement therapeutic for it. So I became very interested in that intersection of where science meets the real world and commercializing the things people develop in universities. So instead of staying in research after my masters, I moved into business, first working in technology transfer and then working in business development. And I've always loved the outdoors and I love the ocean. I lived in Victoria, BC in Canada and that's also where I did my masters. So when I was working in business development, you know, very nine to five desk job, I decided to take some time off to row across the Atlantic Ocean. And there were a lot of reasons why that inspired me and I wanted to do it in part because I love the ocean. I have a fascination with it. I wanted to, you know, push that boundary. And then that just led to this completely different career going on expeditions, writing books, producing documentaries and eventually founding this autonomous boat company, Open Ocean Robotics. So let's get back to the fact that you sailed, that you rode across the Atlantic being the first woman to do so mainland to mainland. Did that rowing experience help guide the work that you're doing with these boats? Because you are going to attempt to be the first people to send an ocean going drone across the Atlantic for the first time. So how did those experiences kind of line up and yeah, what advantages do you have that you've actually been sitting in there in that seat knowing what those drones are going to go through? Yeah, so we actually spent five months rowing across the Atlantic. So it's not a fast way to cross the ocean. It is a truly incredible experience though. When you spend that much time on the open ocean, you know, you're looking at blue sea and blue sky and gray sea, you know, for months on end there is nothing else out there except the ocean and the wildlife in it. And, you know, you really, it hits home how huge our ocean is and how poorly understood it is, you know, very few people will see the middle of the ocean. And we really still know very little about our oceans. I mean, there's that saying that we know more about, you know, our moon than our oceans. And I think that is very true. And part of the reason is because it is so hard to explore our oceans. It's so expensive, it's dangerous, they're so huge. So I think really the idea for this company started when we were in that rowboat thinking about ways in which we could better understand our oceans. I think you touched on a really important point there of being able to understand our oceans and how they affect our planet. So to do that, your company is building these autonomous drones. Could you tell us a little bit about, you currently got two that I was looking at, the Force 12 Explorer and the Solar Explorer. Could you tell us how they differ and just a little bit about what's special about each of them? Yeah, so I think for ocean exploration and data collection offshore, it would be incredible to have a vessel that can go for months on end. So that means it has to harvest energy from the environment around it. So that could be wind energy, solar energy, wave energy. So the first boat that we developed is the Force 12 Explorer, which is our autonomous sailboat. So it uses the wind, it has two rigid wing sails and it can go out on the ocean for months at a time. It's equipped with sensors and communication devices so it can flood ocean data and send that back. And then the second boat we developed was a solar boat, Solar Explorer. And it has a, this is that early prototype on top of those three calls, the trimer and shape, solar deck goes on top of it. And since then we've actually developed a smaller solar boat, Data Explorer. And that's the first boat that we're commercially manufacturing. And we feel that solar is great in many ways for an autonomous boat. Just like the sailboat, it can go out on the ocean for months at a time. But the advantage with solar is that you have complete control over it. Like with a sailboat for, if you've ever been a sailboat, you know you're dependent on the wind. You have to tack, you can't go in the exact direction you want. But a solar boat is very much like driving a car.  You can go even when the sun doesn't shine because you have those reserves. So we're really excited about the potential for solar in terms of endurance offshore autonomous boats. Yeah, I think you hit another good point there. The fact of using solar, being able to store that energy and not having to do any additional kind of engineering in that sense so that these boats can be out there for months at a time. We've got a nice question from our chat room here. We've got Peter Yannick on YouTube who wants to know, he wants to know how the weather was during your travel on the Atlantic. And I want to talk about that in regards to these drones that you're building. What kind of weather can they handle out on the ocean? Yeah, so we actually had terrible weather. So we spent two years looking at historical weather data, speaking to experts. So we would plan our route and our timing to, reduce our chances of encountering storms and definitely not hurricanes. But the year we did it was the year of Hurricane Katrina, which was the worst hurricane season in all of history. So there were more hurricanes than ever before. There were more powerful hurricanes. There were hurricanes outside of the hurricane season. There were hurricanes and parts of the ocean that were thought to be too cold for hurricanes. So we were actually hit by two hurricanes when we were in that low boat. The first one was when we were three weeks out, Hurricane Vince, it was the most northern and most eastern hurricane in all of history. And it came directly over us. And that was a completely terrifying situation.  So that was the first hurricane and then the second one as was after the end of hurricane season. So the conditions are pretty intense, hurricane force winds, waves that are, you know, as high as a five-story building. So we definitely understand the power of the ocean, what it takes for a boat to go through those kinds of conditions. And you know, the rowboat is really designed well. It's designed so it's self-writing. It's designed to be able to endure those big seas and you can design small boats very well to endure heavy seas. And so that's what we've done with our autonomous boats. And so our goal is that they can go through hurricanes as well because when you put a boat out onto the ocean for months at a time, it is going to have that heavy weather hit it. I have extreme respect for you to be able to be out there and hit not just one, but two hurricanes. That must've been incredibly frightening and I just props to you because that's so scary. But you mentioned that, you know, you've gone through all this weather data and we're like, yep, we're gonna roll across now, the weather's gonna be fine. Like this is the best chance of having like the most smooth weather that we can for this trip. And to be hit by not one, but two hurricanes and intense hurricanes, you know, we're seeing this pattern of our, the climate on our earth changing hurricanes becoming more frequent, they're becoming stronger. And we see that in the news today with, you know, like Dorian that's been all over the news. So can your autonomous boats help in understanding how that weather is changing and what kind of like data can we get from using these boats that might be able to help us maybe even have better warning systems for hurricane systems or anything like that. Yeah, I think a better data collection from the ocean is key to making better predictions about our weather, better understanding our oceans, the impact of climate change and helping, you know, maintain the sustainability of our oceans, allowing industry to function more effectively. So there are lots of things that our autonomous boats can measure. As you said, they can measure weather conditions. So they can measure things like wind speed and direction, wave height and period, water, surface temperature, currents, dissolved oxygen. So those are all parameters that are really important for understanding the weather of our oceans but also the health of our oceans. And you can go beyond that. You can put a whole range of sensors. Our boats are basically platforms that can carry different sensors to measure ocean parameters. So you can measure other things. You can look at the population of fish. You can listen for marine mammals. You can look for pollutants. You can better understand microplastic pollutants. So there's a lot of opportunity to better understand our oceans using technology like this and especially vessels that can be out there for months at a time because right now it's really difficult to get data from the middle of the ocean. Research ships are very expensive. Offshore ships cost like $75,000 a day. They're limited to the track that they can travel on whereas an autonomous boat, it's, you don't have to worry about the weather. There's no crew on it and it's just really a fraction of the cost. I love that you talk about having these boats in the middle of nowhere. We've got a question coming up from YouTube who wants to know, we've got Mike McHugh who's saying, do the robot boats use Arduino or some other off-the-shelf kind of components? Yeah, so they use off-the-shelf and custom components with the autonomous vessels. So we've been working on this technology for almost two years now. We're at the stage where we're moving from prototype to commercialization. So we have four prototypes that we've been testing in the waters off Vancouver Island and we're currently making five commercial vessels. So those will be the data explorer, the smaller solar powered vessel and we're going to start commercial sort of data collection. So data that has real scientific value. Speaking of the data collection and collaborating with scientists, do you have any like universities or research institutions that you're working with to kind of tailor make these boats so that they can have the best conditions for getting the best science data? Yeah, absolutely. So we've been working with the University of British Columbia as well as a commotion technology access center to design the boats and the hull and look at the composite manufacturing. We're also working with the Department of Fisheries and Oceans in Canada to look at measuring ocean variables that they're interested in. So two of the things that we're measuring with them is ocean currents. So looking at the current profile in the vertical water column, so through the depths. And that's really important for a number of things. One of the things they're interested in is being able to make better models of what they predict the currents will do for ports like the port of Vancouver. Other things it's important for is predicting oil spill movement so you can tailor your response. And then the other thing we're doing with them is marine mammal monitoring. So using acoustics to listen for whales as well as the background noise. So marine mammals are very affected by marine noise and here in Victoria and off the West Coast we have a southern resident killer whale population that has numbers that are in the 70 individuals. So it's really endangered and we're looking at ways in which we can better understand them and put measures in place to better protect them. And then the other organization we're working with is the Canadian Coast Guard. So we'll be doing survey work for them. So sea floor mapping. So that's another thing that the boats are good at is you can put a multi beam sonar on the bottom of the boat and it basically draws a picture of what the sea floor looks like and that's really important for creating charts for boaters so that they don't run on ground. It's amazing that you've already got so many people coming on board to jump on this opportunity. It just goes to show how many uses there are for this kind of product out there and how much science data and just data that can help everyday people like people that are coming into a port and maybe they have to usually rely on getting a pilot to get their boat to dock to a berth and then now they can just have this data from these autonomous drones and they don't need to have to rely on someone else to come and get them and guide them in. So I think that's pretty cool. We've got another couple of questions here from our chat room. A lot of people are pretty interested in how these boats actually operate. So I'm gonna go with a sparker on YouTube wants to know how good can the drones operate in polar regions considering that they rely on solar power? Yeah, so polar regions have less light especially in the winter and also the light comes at a lower angle so it doesn't have the same intensity. So for a solar vessel, it's not really the ideal area to capture sunlight. So it would be better for a sailboat where you still have solar panels on the sailboat but you're not using the solar energy for propulsion you're using the wind for propulsion instead the solar powers, the sensors and the navigation and the rudder. So I think you could still use a strictly solar boat. I would say our larger solar vessel which is the one that we're building next would be a better option because it has a 1200 watt array so it's able to capture more light so in those low level conditions. And then it also depends on the sensors that you put on board. So our solar boat, it's designed to run with very minimal energy input with just 30 watts. You can move the boat at about two knots so that's about four kilometers an hour and run your basic navigation and communication equipment. And then depending on the sensors you put on board that will increase the power consumption. You mentioned that it takes just 30 watts of power to be able to run all of that stuff. So how much power goes into maintaining the communications link back to your mission control and how do your boats actually communicate? Do they use cell towers? Do they use satellite? So the boats use both cellular and satellite and there's also radio communication when it's line of sight. So when it's in cell service it will use the cellular network and then when it moves out of that it will seamlessly switch into satellite. So you are always communicating with the vessel. You can send information to the vessel and you can take information off the vessel. So for example, when you're in a cell area that has cellular coverage we have 360 degree cameras so you can visually see everything that's going around the boat and you can take over control of the boat from autonomous to remotely operated and you can drive it with a joystick. And then when it moves to the satellite you can still update the navigation of the boat but it's lines of code instead because you don't have that ability to send those high data files. And the same thing with the information you're getting from the boat. So you can get high bandwidth data from the boat. So even like acoustics, other high data sensor information when you're on satellite but once you move into, sorry, when you're on cellular once you move into satellite it's far more limited but we do, we're working with a number of IoT and communication experts and there are ways in which you can optimize these systems to increase the data that you're able to get off of it and also as we move forward you can do more onboard processing using artificial intelligence and machine learning to be able to figure out what is the important data to be able to compress it more. So there's endless things you can do to optimize that. That's incredible. I love that you've been able to get such a robust communications link with those boats. Ideally, in my perfect world I would love to see a fleet of these boats out potentially out off the say east coast of Florida with the ability to string back video of potentially exciting, the very powerful machines coming back from space and maybe not have communications dropouts but we've got a lot more questions in our chat room. So Johnny Spacer from YouTube wants to know could drones such as these be available for search and rescue purposes when needed? I think they could, yeah. So it depends on where you are in terms of communication and what you're depending on and what you're searching for. So obviously if you're searching for a downed plane or a shipwreck you might be using underwater acoustics so that would be like the multi-beam sonar that we're using for our sea floor mapping. So definitely for that, if you're looking for a vessel or people in the water it would depend on how you're gonna be able to detect those. So I mean, you might rely on LiDAR or visual. I think it would be more challenging especially when you're in a satellite area but you could have a system where say you have a main boat and then you use these boats on the water as a force multiplier so they could communicate with the main vessel. So you're taking away that need for satellite communication you're still able to get that high bandwidth data. So there's definitely lots of opportunity and I think like in the future we're going to increasingly be able to utilize autonomous technology like this for a range of applications. The charm is just so totally enthralled with this all of your technology. They're coming up with so many ideas of like things that potentially you guys might be able to do in the future. So I'm gonna go through a couple here and maybe we can talk about whether these could be a possibility or maybe there's some technical challenges as to why maybe you can't do these things. So Jared Elvin on YouTube wants to know how about using a fleet of those smaller autonomous vessels to collect plastics in the ocean. Like I'm guessing they're envisioning them coming together and kind of shepherding the plastics together and then maybe those get lifted up onto a collection vessel so that they can be recycled. Yeah I think autonomous vessels to collect plastics is a great idea and there are companies that are developing autonomous boats for that. I think right now it's mostly like imports and harbor uses and they're able to sift through large quantities of water and then sort of hold on to the plastic debris. So I think there are options. It would depend on the design of the vessel. I think it'd be pretty easy to put sensors on it to detect micro plastics in areas with high plastic pollution. Other things that some of the, a lot of plastic debris in the ocean is actually discarded fishing gear or lost fishing gear, ghost fishing, which continues to fish. And these are quite large items of plastic and by some statistics it's like 40 to 60%. So I think there might be ways to go out and detect areas like that that have a lot of the ghost fishing gear and then working with a support vessel it would go and get that larger plastic and deliver it to a depot, I guess, a floating depot. Cool. Plastics are such a huge problem in the ocean and to have even just the data of where the plastic is not necessarily collecting them. As you say, that's totally a way to tackle that problem as well and any more information we can get on that is always beneficial. Another really cool idea from our chat room comes from Smokey Doug. Have you ever given thought to including a quadcopter drone on board for aerial surveys? Yes, we have. And that is a great idea and that is something that we're actively thinking about. There's a huge advantage to be able to have that aerial perspective, especially because our boats are pretty small. They're low to the water. And if you're in big seas, not that you would necessarily be able to launch an aerial drone in big seas, it's hard to get that vantage. Like I know from our time out in the rowboat, sometimes you feel like you're in a valley surrounded by these mountains of water. So to be able to get that aerial perspective, if you're looking for something, if you're looking for marine mammals, if you just want to get a better vantage would be amazing. And there's so much innovation in drone technology and you could definitely envision ways in which it could land on the vessel and be able to use like induction charging or something like that to power the drone. And then you could go out for a month at a time with this drone and well, two drones. I love the idea of combining drones with autonomous ocean craft. A few episodes ago, we talked to Dr. Vanessa Perotta and she works on for marine mammal monitoring for whale monitoring. She has a drone that she's put a petri dish on and they actually fly the drone. They open up the petri dish and fly it through the whale. I don't remember the technical term, but when they like spout out the water and so they fly it through and collect these petri dishes and monitor like the health of the whales like without even like hurting them. So I could just imagine those drones being like put on a fleet of your craft and going out into the middle of the ocean and getting all this data from like all these like whales and marine mammals out there. I think that'd be incredibly cool. Yeah, it would be incredibly cool. I've heard about that research. I think they're also using that on the east coast of Canada to study the right whales, but it is amazing. It's like they're collecting kind of whale snot basically and doing all this analysis on it. Yeah, so another question related to monitoring animals animals out in the oceans. We've got Smoky Dog wants to know what sort of instruments will be aboard these platforms and whether they will have cameras above. And we did mention that your platforms do have cameras above, but also below the waterline. So like underwater cameras. Yeah, it's interesting. We don't have underwater cameras yet, but that is definitely something that is possible. And in terms of the sensors that will be on it, it'll be environmental sensors as well as ocean sensors. So in our solar boat that we're making, it has a self-riding roll bar at the back end. And that also acts as a platform for some of those sensors. So kind of a weather station, things to measure wind speed, wind direction, air temperature, solar radiation. Then on the vessel, you can measure things like wave height and period. In the water you can have sensors where the water's flowing past the sensors, just detecting things like dissolved oxygen, temperature, conductivity. You can look for pollutants. And those are sort of the lower power sensors. And then you can have things like a hydrophone to listen for whales or an ADCP, which is an acoustic Doppler current profiler to take those ocean current readings or the multi-beam sonar to take a picture of the seafloor bottom. So there's a lot of different sensors. And the thing with sensor technology is it's constantly becoming smaller and less power hungry. So it's always growing what you can measure. I mean, even with these boats, you can even measure EDNA. So taking small samples of water and then finding out what fish have passed through those waters by just detecting the DNA. Wow, I didn't even think that that was an application. But now that you say that, that makes complete sense.  So there are just endless possibilities. But that also means that a lot of data is gonna be generated. And so we did have a question here talking about kind of the business aspect of open ocean robotics. Through my research, I kind of saw that you guys have had nearly a million dollars worth of support so far and investment. So Andy Law wants to know, are you selling the drones or are you selling the data? Like what's an example of your custom use case and from the revenue kind of earning aspect? How does that kind of side work? Yeah, so starting out we're a data as a service company. So we're not selling our boats but we'll go out there and collect the data that the customer needs for however long they need it. So that can be a range of data. So I mean, some of those applications are see for mapping, ocean currents, marine mammal monitoring and that data goes into our cloud platform where some of it is in real time. Some of it is after the mission is complete. And so we'll have some analytic tools for the researchers there. And having the data in a cloud platform also enables us to collect additional data. In some cases, things we're really interested in and starting to build that database of proprietary ocean insights. And then thinking about ways we can use that to even better understand our oceans. Maybe looking at that data set with satellite data or other ocean data that's collected by universities and using advanced data analysis and artificial intelligence and machine learning and all those techniques to draw more insights from the ocean. And I think we don't even know yet what some of those things might be that we could discover but I think by having this data, the potential is there. That's so cool. I'm sad that I can't go and buy one of these vessels for myself. I just put like a camera on it and have a live stream. It could be for our next show. So this is our STEAM science, technology, engineering, art, mathematics show. But eventually we want to have a show all about oceans. And I mean, maybe in the future, way off, it would be maybe cool to buy a drone from you guys and control it with a joystick but just have a camera on it and be live streaming video from the middle of the ocean. I think that would be really cool. Maybe in the future. That would be amazing. And definitely the goal is also to be able to put these boats into people's hands so that they can control them and they can use them in exactly this way that they want to. And the advantage of a solar electric boat is that it is easier to control than say a sailboat or something that, you drive it like a car, right? I mean, there's still learning and training that has to go into operating it properly but that is something that you can launch it from a boat launch ramp. You don't have to have a research ship. So it's easy to use autonomous vessel and it has the power to, you know, not only do coastal waters, but offshore waters. And like thinking about these boats in the middle of the ocean, I think one of the exciting things is just how rapidly technology is evolving and especially in communication. Like when we think about low earth orbit satellites and those really widespread and having the ability to get data, like video data from the middle of the ocean using those low earth orbit satellites, I mean, it will be incredible. You'll just be able to, you know, have instant access to the ocean anywhere. I think that's incredibly important. We're huge space fans here at tomorrow and you've hit the nail on the head there. There are so many technologies that you guys are utilizing not just like space and low earth orbit constellations, but also, you know, battery technology. I believe you guys use lithium batteries to store your power on board. These technologies are gonna continue to get better without any investment from you guys. And so you reap the benefits of using this technology. The same thing with, you know, machine learning and artificial intelligence. That's gonna improve the data products that you guys are able to put out there. So you guys are taking a really, really intelligent path there to be using those technologies that are gonna improve and help your product in the long run. Let's talk a little bit about where your company is gonna head in the future. So we've talked about the different sensors, the different data that you guys can get, the science and the things we can learn about our planet, but where do you kind of see your boats heading in the next, say, let's look even kind of short term in the next two or three, four years. What's the next big hurdle for you guys? I think right now it's, you know, getting our boats on the water, getting a lot of testing time, seeing where there are issues. I mean, it's really challenging building an autonomous boat. You know, there's lots of technological hurdles. There's lots of ways we can continue to improve them.  I think once we have a lot of those issues sorted and have our boats working well, spending more time thinking about the data, you know, what kind of onboard processing we can do, improving our collision avoidance, looking at ways to, you know, do real-time processing, things for like, you know, maybe detecting whales and sending out an alert saying, hey, there's a whale here, you know, so shipping can be alerted. So I think, like, as we focus away from the autonomous vessels and more to the data, I think that's the way the technology arc will go for us. We had a fantastic question from Mandanar on YouTube. How do you actually go about collision avoidance so you don't run into ships or whales or anything like that? So right now, the surveys we're doing are in the cellular network, so we use our cameras. We have 360-degree cameras, and so we can visually see. We're also starting to build a collision avoidance system with onboard processing and are putting the sensors in place so it'll use things like LiDAR, optical data, pre-programmed variables like, you know, the geography depth sounder. So those will be some of the variables that will go into that. And, you know, right now, it's still, you know, at the very early stages, like our collision avoidance system will allow our boat to stop, you know, see something ahead of it using the LiDAR. So that's why, you know, we want to develop our technology in incremental steps. We want to ensure that at these early stages, we do have that complete control of it. We operate it in areas where we can ensure the safety of it. And it also uses AIS for collision avoidance. That's Automatic Identification System for vessels and offshore where it's generally just commercial vessels. Almost all vessels have it. So it will be able to see other vessels using that, and also other vessels will be able to detect it. That's really cool. You mentioned AIS. I used to work for a rocket laboratory. And for that company, we launched some satellites that tracked AIS. So, I mean, to be out in the middle of the ocean and using that system, I guess you can kind of have all that data working together to help improve your collision avoidance systems. But another question here from the YouTube chat from Mordekis. They want to know, what's the current life expectancy of your drone? And then I just want to add, how do you think that that life expectancy is going to improve as your technology and your processes and your operations improve? So right now, I think our drones, they're designed to be able to go out onto the ocean for months at a time. So we're saying about six months. And then they'll have to be pulled in for maintenance. In particular, biofouling will be a challenge. Things are just going to grow on it, grow on the hull, grow on the sensors. In terms of the life expectancy, just like another boat, these autonomous boats will be able to be used for 10 years or more. And I don't think we can really say the life expectancy yet. I mean, think that we'll need to be maintenance. But the pod motor will need to be maintained regularly. The sensors will need to be recalibrated. Maybe the solar panels will lose some of their ability. But I think with regular maintenance, you're going to be able to use these boats for a pretty long time. Yeah. One of the design things that I noticed came to play with how efficient your boats are out on the sea. You actually use, there's no hole in the hull that connects the propulsion module from where that mode of force is generated. Could you tell us a little bit about why that was so important? Because I read that on your website. And I was like, huh, that's pretty neat. There's no way that water can actually get into the craft. And I don't think I fully understood why that was such a cool and efficient system. So could you explain that a little bit? Yeah. So we've gone through a few different iterations for our propulsion system. And the goal was we wanted to be extremely energy efficient. We wanted to be able to go for months at a time without any kind of maintenance. And we wanted it to have enough power to be able to move the vessel at a good speed. So we designed a system that used a magnetic coupler instead of a through-haul drive shaft. So this allows us to have the motor inside the vessel. And then instead of the drive shaft going through the vessel and having to have the bearing seals and other mechanisms in place to ensure that there's no water leaking through, which caused friction and need to be replaced, it's just magnetic forces. It's a magnetic coupler. And that will turn the drive shaft and spin the propeller. So that is the system that we have for our larger vessels. And, I mean, as far as we know, I'm not sure if there's really any other boats that use that kind of a system. But it was a great solution for what we needed. So that's for a larger solar explorer. And then for the smaller data explorer, we're actually also using a magnetic coupler. But it's in a pod style motor. So it is the motor and the propeller are outside of the vessel. So that same thing, we don't have a full in the hull. That's really cool. I love that you've taken that kind of unique design approach and ran with it and get all these extra benefits that go along with it. So coming around to that kind of technical side and that kind of expertise, are there any skills or job roles that you're trying to fill at the moment at your company or any skill shortages that you have? Thank you for asking. Yes, we are. We're actually hiring right now for an autonomous systems engineer. So this would be, most likely, an electrical engineer who has experience with embedded software to do a lot of the system design for our autonomous systems, power management, starting to integrate sensors. And as we start to move from prototype to production level. So we are. And we're always looking for great people. Right now, we're still really small. We're six people. With this hire will be seven. But yeah, if you have anybody out there who is interested, please get in touch. Obviously, we're in Canada. So it helps if you're in Canada or able to work in Canada. Awesome. We have a lot of viewers out there that are quite technically minded. So hopefully if any of them are interested, maybe they could send along their resume and see how they go. But I just want to say how enthralled I am with this technology. There's just so many applications out there to not only to help us understand the weather and weather patterns and potentially things like hurricanes, but also what's going on underneath the oceans, what's going on on the ocean floor, the way that ships are moving about in the ocean, as well as plant and animal life as well. So your technology is so cool and it can just affect so many different things. So I just want to say a huge thank you for taking the time out of your Saturday to come along and join us on our show. And before we go, if people want to learn more about what you do and what OpenOcean Robotics does, where can they go to find that information? Yeah, so you can go to our website, openoceanrobotics.com and just connect with me, I guess, on LinkedIn. And yeah, that's the best way to stay in touch and find out what we're doing. And I just want to thank you for having me on the show and thank your viewers for all their great questions. You definitely have a really technically-minded audience and it's fun to be able to share our story with people like that. Perfect, all right. And if it wasn't for our viewers and our people that contribute to our show, we wouldn't be able to do what we do and bring to you every week. So from the bottom of my heart, thank you so much to all of these citizens of tomorrow that contribute to this show and make us able to bring on cool people like Julie. We're able to spin up tomorrow's steam and down the road, maybe we will have tomorrow's oceans. Maybe we can pick up one of these drones and start live streaming from the ocean to 24 seven. If you'd like to contribute, head on over to patreon.com slash T-M-R-O or you can even use YouTube now youtube.com slash T-M-R-O slash join. So that's gonna wrap up our tomorrow's steam show this month and until next time, keep on discovering.


Hi, everyone. Welcome to the Smart Grid Seminar. This is the last one for this quarter at the end of this presentation. We also talk about this seminar for the next quarter. So our speaker today is Professor Bolenso from Columbia University. Before we introduce him, I would like to go over some of the features that you can use during the presentation. So you can use the chat feature to communicate any technical difficulties to us. So for example, if you cannot hear the speaker, you can let us know. And you can use raise hand if you have a clarification question during the presentation. And if you have any in-depth questions, you should use the Q&A feature. And all the questions will be addressed and answered at the end of the presentation. Now, this is the list of speakers who have visited us online, at least this quarter. So I just want to remind everyone that the presentations were recorded. So if you miss any of the presentations, you can view the video. Professor Bolenso is an assistant professor of Earth and Environmental Engineering at Columbia University. He's also affiliated with the EE department and is a core member at the Electromechanical Energy Center. His research areas are on design and operation of sustainable energy systems, spending from battery modeling to your city market design. He received his PhD from the University of Washington, Seattle. And he was a post-doc at MIT before joining Columbia University. Yeah, let's welcome the speaker. Professor Sui can share your slides. Okay, sharing right now. Thanks, Ching Wu, for the introduction and thanks everyone for attending. I'm seeing my slide on my presentation, presenter Wu, I guess the slides, okay. Okay, cool. Yeah, yeah, good afternoon. I'm very glad to be here and talk about my research. And I really cover the work I started since my PhD to my postal and even some of my very recent work here at Columbia. I'm doing operation at lifetime valuation for battery, okay. Okay, so battery, battery, battery, right, battery is becoming a key topic at the start of the show for the future energy system, and it has many different applications, all right. The most popular one is battery powers electric vehicle, right. And now many region California and countries UK have declared our targeting date that then the sale of traditional fuel vehicles. Battery is also a key enabling technology to improve utilization to integrate renewable energy right by charging discharge it mitigates the fluctuation from wind and solar. Another interesting development with battery is it actually demonstrate a show proved to be a very reliable resource for improving the power system reliability and resilience and the industry this is called nonwire alternative, and especially in some rural areas of mountain or coastal areas, and battery showing to be more cost effective than building a new transmission line, and battery is also much more resilient to extreme weather like wildfire or or hurricane. So we will see a dramatic increase of behind the meter batteries and these batteries will, including electric vehicle, and this will provide vast opportunities to promote demand response program. And some of you, some of you may know there's a very recent development from FERC, a FERC order 2022, which encouraged the participation of demand aggregation electricity markets. Okay. So here let's take a quick overview of what benefit batteries bringing us sustainability. Okay, battery is making our system more sustainable by integrating renewable energy. Also reliability battery is making our grid more reliable. The third pillar here in power system design operation is economics, right, and I hear the key focus of my research and also this talk is how do we improve the economic utilization of battery resources in the grid. And this is essentially a problem of valuation dispatch and control. So let's take a step back and just a quick review of what is how would that economics mean in power system and the basic, the most basic concept here is economic dispatch. Okay, and many of you may be familiar with concept is basically we are figuring how to use multiple generators to meet the demand, and each of the generator has a cost curve and also has incremental cost. Right. And then we can formulate this into either a linear programming of mixing linear programming problem. The objective is minimize the total generation cost. And we have constraint supply, demand and balance, unique constraints, network security, and this is a traditional definition of a generator based economic dispatch. And now we'd like to see how do we include battery into this framework. Okay, and in a very high level, the solution is pretty intuitive, you add battery into the constraint, and you add a battery operating cost into the objective function. Right, but this is just a high level overview but in detail that this may not be so trivial has the same right. So let's start from the constraints first. Right, the battery of depending on various technology are very have very complicated later chemical process going inside the battery, but seeing from the power system perspective, battery are actually very simple devices model. Right. It doesn't have a complicated ramping or other, you know, governance dynamics, no mean up on time, no, no minimum generation limit right it's just a power rating. Right, the power rating or the power electronic device. And energy rating, right energy dynamics the changing the stored energy equals to the energy or charge or discharge, and also efficiency. Right, it can either be a constant, or it can even be a piece by sling your approximation. And here, this is a recent paper, just showing how the seemingly complicated battery charge and discharge efficiency dynamics can be represented use a piece by sling your approximation. So we can very easily formulate battery operation constraints into a linear programming framework and putting the dog dispatch. Okay. However, to characterize the cost of putting costs but battery is not so tribute. Okay, and here we are looking at two components. One is capacity opportunity cost. Right, battery has limited capacity and actually compared to most other resources in the power system it has a very, very limited capacity usually one, one to four hours. Right. And that means to operate the battery we must plan ahead and determine what need what we need to do now based on the future expectation of the price. And here are two examples. In the upper price here, we have relatively high somewhere around $100 but pretty flat price profile. Okay, so this will be a good day for a generator because for for conventional generator if the price is higher than the generator fuel cost. I just generate but for battery. There's not so much profitable opportunity but battery profit, not by generating energy but for up a charging price differences. Okay. On the lower plot here, we have much lower average price somewhere lower than 50% $50 per megawatt hour, but this will be a pretty nice day for battery because well, that's high fluctuation right, especially here during this a few price valleys and the battery would would be it would be very good opportunity for battery to charge and later it has the opportunity to sell this energy charge here as somewhere around $50 per megawatt hour. Right. So this is what we mean by capacity opportunity. Right. So it's a trade off to save your capacity now now or use it and sell your energy later. Right. So this is the best opportunity. On the other hand, we have degradation. Right. And some of you may have heard of this term before that this basically means for battery is an electrochemical device. The useful energy capacity decreases slowly with time and recycling meaning charge and discharge. Okay, so I guess we all have a phone on iPhone or any other phone and we all know how the battery looks after two or three years of use right even now. And now iPhone even has a has the function to estimate the remaining lifetime of your battery in your phone. Okay. But here I have an example of battery warranty and it's very similar to the ones I saw in the industry is well the warranty will specify that your battery has a 10 year calendar life. Okay, if you do not use a battery at all it lasts for 10 years, or if you use the battery cycle one cycle per day it lasts for five years and at the end of the warranty, it guarantees you'd have 80% remaining capacity. Right. And there is even a fancier way to define this but you get 10 calendar at 10 years of warranty days for a battery. And then every cycle you perform reduce the warranty time by one day. Okay. So how do we, how does the relation impact our economic decisions. Okay. And here, it's again, an opportunity, right, but it's not no longer opportunity of capacity is a community of your lifetime. You know, if you, if I cycle my battery now, I got my money now, or any other benefit but the drawback here is I have less capacity to use in the future and I shortest my battery life. And here is a bigger from a battery testing data from one of our earlier publications that really shows the remaining battery capacity is crucially dependent on how you use the battery, how the batteries are charged discharge so it's very correlated highly correlated with the battery operation. Okay. And so here's the problem we have capacity opportunity cause we have lifetime opportunity cause how they are, and they are correlated and how do we get the value of both of them. Right. I'm here. I'm just do a quick summary and a bottom up introduction on this problem. We start right with a daily operation. They're short term capacity opportunity cost model right, which we had the pricing for the day we put it in, and it generates the revenue and the control of it. Right. So this is for one day. And also, we can go one day prior right this is from widely prior, and also these two days are weekly coupled energy you left from yesterday is energy of service from today, right. And also, the day before yesterday and going on and on right and this is a coupled sequential operation problem. And it's also similar it's a very similar problem to inventory management, or even our hydro storage planning. Okay, comp hydro storage, but it was unique about battery is a degradation model, right. So, all this operation we perform will cause incremental degradation, right in the battery. And also, the remaining capacity of the battery will be impacted. And that will also impact how you operate the battery here and here and here. And this is another layer of coupling. And this is unique in electrochemical batteries and this and our long term by long term life opportunity cost, we are trying to capture all of these interactions, like to capture an opportunity value of the battery degradation. Okay. Right, and here is a summary of the three blocks we considered here I think this really is a good summary of the work of the research, a main focus of my research since my PhD, especially my PhD I've worked a lot on degradation modeling. And then during my postdoc at MIT, I spent a lot of time doing the short term battery operation. And here, during my recent time at Columbia, Columbia, mostly during the COVID lockdown, also got some very interesting results on long term lifetime opportunity costs. But let's just start step by step, and from the very basic degradation model here, right, and degradation model basically takes in the lifetime opportunity cost and has an input and translate that into an operating cost for the short term capacity evaluation. Okay. However, we have to start somewhere right we don't know the lifetime opportunity cost yet but remember, this is the model for technically, it can work with any, any number, right, any number opportunity cost. And here, a preliminary approach we take is to use the cost to pour it the battery replacement cost has the value of lifetime right and this is actually a very common approach in the industry and also used by many other publications. And a quick example here we have the battery degradation curve. This is the incremental battery life loss. And this is a long linear function with respect to how deep you cycle the battery. This is from the degradation from single cycle. Right, which we have the life loss here, and we have a cell replacement replacement cost here, we multiply them together we get the cost of degradation. Right. So that is, and following this, this assumption, right, and we'll revisit this topic later. And we, we did a modeling on the piece by senior approximation to a long linear degradation model, doing to model better degradation in optimization and this is a joint work with folks at I so in England. And a piece by linear approximation. Okay, and eventually we convert the long linear cycle battery cycle degradation model into a linear programming that can be using any of my different problems and we use, we test this you see a very simple arbitrage example, which we have where we have the price data here. Here is the seed of charge response the resulting seed of charge from the arbitrage using different resolutions. Okay, using different approximate level approximation. And you wish we can see with a higher number of segments. Okay. So the long linear variation start to be more responsive to the small price variation where you have big cycle with big price change. Well, you also from on top of they also have small cycle responding to small price variation because of the long linear here. And our model achieves a diminution arrow compared to the benchmark nonlinear model with more number of approximation blocks. So the work I'd like to briefly talk about is a work with actually with Wilson, some, some of you may know him, and on calculating battery degradation into control and specifically specifically tracking control, right, and this for frequency regulation, in which you better think to regulate a stochastic signal. But the arrow, right, the arrow in the response will be charged for a penalty. And we show that the optimal response is really a threshold policy on the seed of charge you just follow the signal until you reach an upper lower bound. Okay. And then we show that this dress code can actually be analytically calculated. Using the market tariff and the degradation mechanism and intuition is really when you follow the single or you have degradation cause if you don't follow the single or you have penalty and that's a trade off there, again, due to the nonlinearity, the degradation. Okay. And we squeeze out to actually to publication out of this one focus on frequency participation to fix the regulation market, and the other is on control policy itself, which although it's pretty elegant here is actually take a quite, quite, quite amount of effort to to prove to do the proof. So that's from the degradation model and the less moving on to the next component, which is a short term capacity opportunity cost model. Okay, so what is the value of the short of the capacity of storage of the energy stored in the battery. And there's definitely many different framework the way we can show this but I just want to today, talk about a pretty interesting work I did when I was at MIT it's using a stock testing dynamic programming framework to do this. So if you were familiar with dynamic programming, you will know the dynamic programming itself is evaluation framework because we need to get a value function of the state and state here is the battery state of charge. So we automate by doing a dynamic programming way automatically get the value function for storage. And here just a quick summary about the formulation and this is the single stage of my objective function separate power in a state of charge limits. We have a single stage profit, the arbitrage profit, and also the cost of regulation, and also the value to go function life from from from the future. Okay, and for stochastic dynamic programming is really the optimized value here goes into the expectation function right and then our value depends on the expectation for the future profit. And here the stochastic rival is the price lambda, which is the real time market price. And this is basically the stochastic dynamic programming framework here. So just a quick over insurance stochastic dynamic programming and for folks who worked with stochastic dynamic program before. I think we all know this not a very pleasant experience, and simply because inherent complexity, right, and the combination of uncertainty and a state just just just blow up exponentially right especially for real time price arbitrage. So five minute market clearance of 288 uncertainty stages per day. Right. And here just a quick overview review about some prior method of showing solving spochastic dynamic programming. One is Markov decision process, right, value, a very map your action as a based on your states. And also following that Markov dishes and process we also have reinforcement learning. Okay, also use Markov decision process and also approximate dynamic program. Okay, but, but here, a big limitation from MVP based approach is it's very accurate to model efficiency, because the battery has no idea efficiency, you know, 92%, 93%, 94%. And it's really difficult to be captured by transition of discretized states. Okay. Or you can do a very fine when you let it be of state but that will increase your computation expense by quite a lot. Okay. And on the other hand, there's another algorithm called stochastic do dynamic programming and this is actually the classic algorithm for scheduling hydro storage. Okay, pump hydro storage, which, which actually a more complicated problem than battery because for hydro storage scheduling was a many environmental temperature factors, right. The inflow or even the flow of the salmon the migrate of salmon if you live in Washington state. Okay. And MVP is in many cases computationally expensive. Okay. It just need to do it still do a lot of iteration back and forth. Okay, and here are some of the prior literatures as it tackled on this problem. Okay. But for the algorithm here, a proposed organism is this study is we develop a analytical organism tailored to storage price of a trash. And we use a piecewise linear approximation to the value function. And as a result, we don't need to discretize anything no discretization of states control or distribution of the of the uncertainty. Okay. So here, this is a quick summary of the technical approach we do with this. And the main theory we use is, it's the same theory foundation to stochastic do dynamic program is which do decomposition, which we decompose to do with respect to the state of charge evolution constraint, right. And then we use a relationship between these two sub problem to derive to derive the analytical representation of the distribution of small q, which is the derivative of your profit of your profit. And what you hear is the maximize the profit, your stage wise maximize profit and here this is the derivative of it, and we are able to show it is a closed form on distribution function directly based on the distribution of the price. Okay. And just know that here we have a gradient update rule and I'm just want to clarify that we use this for the proof, not for design the algorithm itself right and we use this as a way to prove this room. Okay. And as a result, our. So we have the distribution and then by taking the expectation we can analytically cutter eyes, the derivative of the value function, it's a small thing, right. It has a function of the value function derivative from the next time period, right, because in dynamic for me work backwards so this is a function, this is the result from our prior step. Okay. And also a function of the price distribution function right CDF PDF and this is this integral here is basically the conditional expectation. Okay. So storage for a coefficient cost efficiency power limit. Okay. And although this function looks pretty pretty ugly pretty complicated it's actually very simple to calculate. You know, if this is, this is basically can be can be finished, but can be calculated on the most software or hardware platform. So here is, we don't need to criticize anything. The pro that assume, especially no discretization of distribution right we can deal with distribution function directly is analytical form, or you can use samples, right, that you have discretize you have this great distribution function. It has extremely fast computation speed with one backwards trip you just start from the last time period go backwards to solve the problem for. It's a simple hardware or software, but as long as you can finish this computation, you're good to go. You can implement anywhere. And also we have a current limitation is we still assume statewide independent distribution for the price which is not realistic but you know, but based on our preliminary computation performance we're pretty confident that this can be easily adopted into more complicated and less dependent distribution. And here this is a comparison with the benchmark as DTP solution in solving average watch, which 24 uncertainty stages. And here we use 24 no 288 is at least this as DTP cannot solve 288 stages on my computer, right. I have to do 24. So here we see, we consider different number of state price, price note or different number of uncertainty notes per stage. Okay, and the solution time for STP goes up to nearly an hour, right, but with our method. So everything is solved somewhere around a below 25 milliseconds, which is a huge improvement. And also the two methods achieve identical performance in exposed Monte Carlo simulations based on the derived the value function. Okay. And here this is the case study of the battery, the value of the battery capacity on the under different price divisions. Okay. And here we have, we have price series that they had quite also different. We model real time price has a normal distribution around it with different standard divisions, which give us different value of the energy storing the battery. And just to note here, this is the value and just stored in the battery not the battery profit right if you if you should the battery profit, higher price duration will give you much higher profit, but here, this is how much is the energy stored in my bag. And it also shows interesting that with higher price division, the energy stored in the battery, versus more. It's kind of interesting because the US because because the expectation of all these prices are the same. If you only think about the one stage where you store, you have energy storing the battery, and you want to sell them in the next time period.  But here, if we consider multi stage uncertainty, it does show a difference here that battery values more with higher uncertainty. And another advantage here is this curve can be directly used has to be done bidding curve into the into the electricity market for battery. Okay. Which is pretty difficult to achieve which is with Markov decision process based methods. Okay. So we have degradation model we have short term creation model and let's go back to where we started the long term life opportunity cost model right and just bring everybody back by long term life opportunity. It's a trade off between use your battery now and shorten this lifetime, or save your battery to use it now and you get a little bit longer lifetime right it's again a trade off problem. And it turned out this problem has not be very commonly studied. In fact, the first and only publication command knowledge is written by actually a very good friend of mine. And we both worked at MIT. Dr one and her, he's still at MIT now, and he publishes paper in major energy that tackle this problem. But there's a theory because we are targeting a very long term operation here. There's some inherent computation difficulties, and he has to meet and made an assumption here that the marginal cost of battery degradation also incremental cost of battery degradation. It's a time invariant life state independent term. Okay. But here. If we introduce a dynamic is supposedly more accurate dynamic programming framework into this we do show that this number this degradation but it's highly time. Okay, and life dependent. Okay, so it's still, it's still dynamic programming. It's just another, another version of dynamic programming here to do the valuation of lifetime. And here we started with the daily operating problem. And now the our value function is longer the value of the stored energy, but the value of remaining battery lifetime. Okay, all the remaining battery capacity. So we have the battery, the battery revenue from operating day. Okay. And here we're still targeting arbitrage application but essentially, it can be any battery operations that can, as long as it can be formulated as a complex problem. It can be anything but here we just focus still for simplicity we use arbitrage. Okay. And, and we have, we get revenue. Okay, from from today, right from today, they end by operating battery and the battery operation passing through a cycle degradation model generates tells us what's the incremental degradation of the capacity. And this will change in my lifetime in the in the remaining capacity of the battery, and this goes into the future battery value. Okay, and then we do a trade off between our profit now, today, and the remaining value of the battery. The daily operating problem. And also we have a alter loop, or the intraday value function update model because the optimized value here. Okay. It is essentially the optimized battery value from today. Okay, to the project that line. We assume there's a deadline of the battery project that after that, the battery project has to be terminated. Okay.  And if we can add daily discount ratio, right, the money tomorrow versus a little bit less seen from today, we can also include a benchmark resale value right off the battery, which means, well, we are targeting the battery arbitrage application right now, but I can still sell my battery source to to say backup power supply or two electric buses right but we still worth a little bit of money there so we can even consider a benchmark resale. I'll show you that later. And that will get included into our future value function. And here is the formulation again, and our daily objective is maximize arbitrage income and the remaining value of the battery right and here. This is the value function from today, but optimize the value here is the value from from today to the future right and and and this will be the value from tomorrow to the future. So we add, we can add discount factor to it, right, daily discount factor, and also if our resale value benchmark resale value turned out to be higher than this. This term, we used to benchmark resale value and the physical and practically this basically mean we should just sell our battery and terminate the project. And so we have power rating system chart evolution rating this is pretty standard way to write this. And a special constraint here is the battery lifetime evolution. Okay, then I'm where my maximum energy capacity, okay, my battery energy capacity depends on the battery energy capacity from yesterday, minus cycle degradation the function of my power. Okay, of my power, and also my calendar degradation this model has a constant value my battery lost a fixed amount of life of capacity everything. Right. And this term defined limits how much energy we can charge this chart and also this term goes into the value function. For tomorrow. And, and here. This will be our initial state. Okay, which is the energy remain the capacity of the battery from yesterday. Okay. This, this dynamic programming problem is actually solve using a very similar approach to the previous one and using piecewise linear approximation, right, it looks like I'm really a lover of piecewise linear approximation. But it does show that it's in many cases a very good trick to so many, many nonlinear problems. Okay, and I'd like to jump to the results directly. Okay. Very similar to the previous one here in the simulation setup is we got we use real time Christ data from line of New York from 2010 to 2019. Okay, and we do the simulation backwards. And using a determinist framework, we assume the battery one meg about two meg bar battery and this is a popular setting for a grisco battery now one to one to our battery 85% long trip efficiency. And here, this is our valuation result, right, and the 3d plot during the value of battery depends on the remaining capacity, right, of the battery, and also we assume the battery last for 10 years. So, so, so the value is also dependent on how many remaining project years we have. Okay. And, sorry. And at the end of the project with what we left is a resell value. And here we assume the resell value to be assume we have a battery warranty until reaching 80% degradation. So that means a battery with valid warranty with still some warranty that has some priority to resell value. And once the word expired the battery can still usable until 60% remaining lifetime, but it has no sale value because it's no longer have a warranty, but this is considered to be a second life battery. Okay. So here, we show the second life battery, you know, the result is zero but if you do that, do the valuation issues, it actually show that second life battery do worse. Quite a lot has with more project duration remain. Okay. Another interesting angle to look at is the surplus bed, which is we minus the resell value, what do we minus the resell value from the total value which we get the difference, right, which means additional value we can get from this battery from this resell right, which we can assume that we can buy any kind of battery at its resell value, right. And here does show that once we consider that 80% lifetime which is the, you know, the newest end of the life battery, the battery that just had a expired warranty. Give us the most value here, right, in all scenarios. Okay. And of course, you see here by approaching the battery deadline and with a newer battery, it has zero surplus value, which means he has a value is basically dependent on the resell value. So another good angle to look at this is take this look at an incremental degradation cost or the marginal cost, basically you take the derivative, we take the derivative of this plot, right. And this is the actual this should be the actual term the marginal cost of dignity, this will be the extra term to be plugged in into market optimization. Right. And does show here that's a very strong dependency of the marginal cost, with respect to the remaining battery capacity right a new battery has somewhere around zero to $20 marginal cost, but once the battery approaches end of life, the marginal cost to even five foot or even ten foot. Okay, to even to up to $100, somewhere over $100 per megawatt hour, right. And this, and it also get more sensitive to the price dynamics. But here I'm showing the, the Saturday moving average price deviation in Lyle, right. And this is 2010 to 2020. And I can say it's very similar to the shape here so it's highly coupled become highly sensitive. And that's a very interesting takeaway from this is the battery. So we, with, with aged battery we should use it much more conservatively with a much higher marginal cost. And this actually very much in line with the car with, with using second line batteries because the second line battery has high impedance, and also less stable internal performance. So we as we so physically, we should use it more conservatively and we also show that economically, we also should use it more conservatively so it's actually a pretty positive result here. It shows that second line battery is really a viable concept here. And then Lyle and next thing we just want to compare the battery performance in different price phones. And here we consider four different prices of New York. And here it shows you have very different dynamics in terms of average price and the price deviation because they have very different price portfolio, New York State has very strong congestion, south to west, south, sorry, east to west, and north to south. And that costs a price to be very different before in the four zones. Okay. And here is the comparison of the value of a second line battery versus new battery and here, again, just remind, second line battery is the battery with 80% capacity, we, which we assume that no longer has a ready warranty. And to continue the uncertainty of better end of life right in reality we are not sure what is the true end of life of battery without a warranty. We assume it can be, it can be distributed uniformly anywhere between 75% 50% so we assume here end of life is uncertain, and that really shows as a, a shaded range here. This is blue in the second line battery, new is, it's, it's, it's, it's for a new batch and we also here for new battery, we assume, we can still use the new battery after the run takes by the new battery covers included the second line battery here. And also just to be fair, we do not consider resale that anymore. So the key takeaway here is, you see the battery value is very much dependent on the price realization the frozen. But the ratio between these two guys, like between us like every, it's pretty stable in all these four zones despite they are very different price dynamics. It also said that the value, the ratio between second life to new batteries go somewhere around 50, 50, 57%, right, all the way to around nearly 100%. And when you are approaching battery life at that point, and this is a pretty encouraging result considering first, second life battery doesn't have any, I'm not supposed to have any resale value compared to new battery. And even you compare the energy, lifetime energy throughput, right, every second life, second life battery can only provide less than 40% lifetime energy throughput compared to new bed. Okay, and here, remember, for new batteries, we assume new batteries include a second life portion right. And so this is a very preliminary work I just uploaded to archive this this Wednesday I believe. I think this is also pretty promising and currently result, especially we have a very new study from from the Kenzie scene by 2030. Our work will have 200 gigabytes of of second life that was pretty. Okay. Now let's go back to where we started we want to include battery into economic dispatch right. We have a valuation framework we can run the valuation from framework on some red with low dimension data right price, or demand forecast regulation signal right. And we have our economic dispatch, which we optimize will be meaning my generation costs, separate the country. And then we have the value and it's pretty simple here how that way, how do we include a result into the economy dispatch is basically the value function. So the cost function is a negative to a very fun right. And if we hear we have storage cost, and it's a function, a battery state of charge and battery lifetime. And also the degradation model will go both into constraint and also objective function. So here we can optimize battery operation using single period route single period real time dispatch. Which is basically existing route and dispatch framework in most system operations. And we can directly incorporate this cost function into economic dispatch or dysfunction can be used to design bidding curves, and with supporting market model. And now we're talking about market design. Okay, that's why many people now talking about marketing. Okay, just a little bit of outlook here. I think we talked about a lot about battery valuation, right. And one of my visions, I hope my on my future goal is to develop open source control and evaluation framework for battery storage by including modeling right we have all model we need operation tracking problems, more than predicting control stochastic programming and also valuation planning right we have the market signal here. Right. And this really to us, the social wide utilization of battery, right both distributed or even electric vehicle as a residential right. The key technical in me below here is we should be we are we are able to solve this using open source organism, right. And with minimal requirement on the software and hardware, right we can do this anywhere in home management devices on electric vehicles, right. And then we can, and this is the main engine of this valuation and then we can integrate everything to a holistic open source software platform, which for the utilization of industry in every social industry, I will say. Yeah, this will be the end of my talk, you for your intention and I really want to acknowledge many of my collaborators along the way, and also the companies that we work with. And yeah, this will be all for for today. Okay, and I'm glad to answer any questions. If you have any questions you can use the Q&A feature to type your questions, and you can also unmute yourself if you want to ask the question directly. I see there is one question. Yeah, about the resell value of first life batteries determined. Yeah. And here it is assumptions I made, but also I think there are some supportive literature of this is a new battery versus $200 per kilowatt hour. Okay. And then based on the remaining duration of the warranty. The resell value is prorated right here you have we have 50 we have half of the warranty left the battery versus $100 per megawatt hour, right. And then once your battery. It's the warranty expired, we have no more resell value. But on the other hand, I will say this just the framework is able to take into consideration of any possible. Assumption on second life, trading back, sorry, resell. There's a second question. Can you talk about the intuition why second life batteries are less economical than new ones. Yeah, so do me is. Yes, you mean the battery of second life battery. Well, let me go. This is here. Second life battery versus less than your battery as well, because we have less lifetime and less capacity right for new battery in this framework. For example, I can my battery can I can use my bed for 100% capacity to anywhere between 75 to 50% capacity, right. And for the battery of a second life battery, I'm starting at 80%. I'm starting at 80%. Right. So, I can, I can arrive. So I have much smaller battery in terms of capacity and in terms of available lifetime. All right. So here's the intuition for the second life batteries really aiming about any at batteries that retired from electric vehicles. They probably are are are degraded is not enough to beat a many, many, many mileage requirement or performance requirement, but the system can have hope can can can charge and discharge. Not good enough for electric vehicle, but still good enough for electric for electric power system applications. Okay, let me. Does the model characterize the value from recycling the battery. Very good point. Yeah. Yeah. Yeah. Yeah. This is a very good point is this actually part of our ongoing effort. So the recycling is another. Big topic, right. And actually I just talked with some battery people working on recycling. So it turns out the battery state of life, right, whether your battery is at, you know, 90% 80% 70% 60% the procedure and the quality of the recycling is exactly the same. Right. So really is think about to to drink the full potential out of the battery before recycling. It's a big, it's a big thing here, right, because because it makes no difference recycling. And on the other hand, we are trying to look into the problem further. In terms of recycling, as well as, as well as production is a mine. We have many different battery technology, and they have many different requirement on the material. I think I would. And you may know that cobalt is critical is the critical material for for AMC on many car batteries to stick to prolonged the left time and stabilizes the battery performance, but cobalt has its head is a very limited. Mineral right and definitely may not be sufficient to power up power system electrification for power sustainable power system the future right so and so really I think one of the future that we are looking into this is how do we incorporate life cycle right recycle production or even logistics into this framework so we can profile different types of battery technology and identify like like the best technology for for for grid. There's one more question. Yeah, this is interesting. Do you model second life batteries from primary usage differently from second life batteries from grid storage primary usage. Oh, good question. No, in this framework. No, okay, in this framework, we both new second life battery be assumed to be operating for grid arbitrage. Okay, so so so so so it's always great. But as I mentioned, cobalt is general. Okay, so so so here is a day and revenue but we can insert a different application right into this we can. add the utilization of our benefit of electric vehicle into this. And so that's I think that's the economic perspective and then back to your point. I see another perspective here is a battery retired from electric vehicle may be different right from battery, battery that has been on the grid is operating very different conditions right and and also an electric vehicle, supposedly, lives in a much more dynamic, less stable environment, so the battery can be different and right and you have the battery collected from different cars, they may be different right. So I think this goes into another very important topic here is for utilizing second life battery is to develop a dynamic adaptive battery management system right and right now I believe most battery management system are still based on are still basically based on the manufacturer said right. So I think that is a critical topic that batteries second life battery special from electric vehicles they are very diverse very different. Okay, but go back to the one of the results here is a good side of partly good news is we show that these batteries are second life batteries should be used very conservatively. Right. So if you consider, like here, the battery may have a marginal production costs of somewhere around 50 to $60. And this is the definition cost. This is, we have the counting opportunity, the capacity opportunity cost. Yeah, right. So if you can't do that we will expect the actual cost right actual operating cost discharge second life battery on this from framework, maybe it's likely $80 to $100. So that means the battery are operated very, very conservatively only get this chart a few times in the day or even week. And then this actually a good news to utilize a second life battery that means that the operation that we put, okay, on the second life battery is much more simple, simpler, right, much more conservative than new batteries, which means the second we can deal this presumably using a sloppier BMS right. Yeah, so this is the multi factor problem. And I haven't going to the standard right I think there are a lot of people discussing we should make more standard about how to diagnose second life. So, but this is a, again, I think this is also very important topic. So, are there any more questions. Okay, so, thank you for giving such an interesting presentation. Yeah, we look forward to another opportunity when you can visit us in person. Yeah, yeah, thank you for the invitation. And yeah, really, really looking forward to come to come to the campus and see, see all right. I think Dr. Liang Ming has an announcement. Are you sharing your screen right now, Professor Su. Yeah, okay. Before I do that, I'd like to thank a bullet again for the excellent presentation. So I think next quarter I'd like to connect you with some of the researchers we hear doing research in the degradation model. You know today is the last seminar for this quarter. I'd like to give a special thanks to Dr. Chin Wu Tan and Wahilla Wilkie for coordinating and organizing the seminar series for this quarter really appreciate. And I'd like to quickly announce what we're going to have in the next quarter. So next quarter we're going to highlight the Stanford postdoc program. So we will have a five postdocs. They are doing research with different professors at Stanford on different topics and power electronic, e-com policy, climate change, and power market, etc. And each of them will bring different perspective from their previous organization universities or even from different countries. So we'll be very exciting seminar series for the next quarter. And we will still deliver the smart grid seminar series next quarter through the zoom through online. And with that, I'd like to wish all the students good luck with your exam and the final report and for all the attendees and happy holidays. Thank you.


The aim of this project is to monitor underwater acoustic fields over large areas using distributed optical fiber sensor systems. Typically, sub-C acoustic sensing is performed with point sensors, and when large areas need to be monitored, arrays of these sensors are connected together. In optical fibers, each one meter long section corresponds to one sensor. Therefore, 40 kilometers of fiber can provide an equivalent output to 40,000 point sensors. And the beauty of this approach is that all of them can be monitored at a single end by a system which looks like this. This system was developed within my group at the Optoleutronic Research Center in Southampton. The initial experiments were carried out at the A.B. Wood tank at the Institute of Sound Evaporation at the University of Southampton, which you can see in the top right corner. This tank is about 5 meters deep. We plan to perform further experiments in the towing tank at the engineering, which is nearly 100 meters long. This graph summarizes the measurement performed using the optical fiber underwater, and it is possible to observe that the optical fiber experience vibrations in the sections between 1560 meters, 90100 meters, and at 110 meters, with frequency in the region of 1000 Hz. The ultimate aim is to reconstruct the 3D acoustic field from these measurements. The next test will be performed at the Empress Deck. Just outside the National Oceanographic Center in downtown Southampton, we plan to have optical fibers on the seabed and monitor both traffic on top of that. And why do we do that? Because the optical fiber network is vast and a significant fraction of it is underwater. In this map, you can see that there are significant sections under the Irish Sea, some of them go under the North Sea, and some of them are under the channel. It is therefore possible to place our units where the cables land and monitor from those locations 50 kilometers under the water from single locations. We hope then to be able to use these to monitor mammals movements, nautical traffic, and possibly even sea level.