Green Hydrogen Production in Microgrids

Yes. So just give us a minute. I'm just trying to bring up the screen. I'll just go ahead and introduce our first speaker, Mr. Chandrashekar Chincholkar. Mr. Chandrashekar Chincholkar has 32 years of experience in the area of corporate finance, capital markets, manufacturing, and consulting. He has done some significant level of work in corporate foundries-- roadshows for Indian companies in India and overseas for foundries. He's handled large manufacturing operations and has done consulting in his previous, various roles. He has worked with organizations of [AUDIO OUT]

He has also done significant [AUDIO OUT] work on policy [AUDIO OUT] levels, and [AUDIO OUT] white papers on water, bamboo policy, EV policy, carbon markets in the past.

Mr. Chandrashekar has been a part of new energy and new mobility issues and concerns, and he has contributed to various conferences in India on hydrogen, e-mobility, battery storage, and more. By qualification, Mr. Chincholkar is a cost accountant, company secretary, member of the Chartered Institute of Securities and Investments, UK and PG diploma holder in environmental law and policy. I would now like to bring upon Mr. Chandrashekar for the keynote, please. Mr. Chandrashekar, over to you.

Yeah. Can you share the presentation, Pooja?

Sure. [AUDIO OUT] Are you able to view the screen?

Yes, yes. Can you hear me properly?

All right. Yes, we can.

Yeah. OK. So thank you so much, Pooja and MathWorks team, for inviting me for this keynote address. My apologies. There are some delay with the login system at mine, and the entire program has been delayed. So moving ahead, looking at the particular topic-- powering the future with hydrogen and fuel cells. Can we go to the next slide?

Yeah. So background-- basically, all of us are aware, in terms of the Paris Agreement, which was signed in 2015, what kind of commitments, globally, people have in terms of whether you are corporate, you are a government, whether you are a fundhouse. And you know, the ultimate focus is emerging towards what people can do till 2030 as a basic case scenario, and probably moving towards net zero in the coming years.

So the aim of various governments, various corporates, and various fundhouses is to look at ensuring that decarbonization, as a process, happens properly, and we are able to raise money also for that in the coming two decades.

So the entire focus of the world has actually, you know, moved towards hydrogen in a big way, because of hydrogen batteries, which is a significant source of energy for short-term, short distance transportation. Hydrogen is actually emerging as a very large global scenario for replacement of various kinds of energies in the future. So the reason why hydrogen is very important for us is because it has high calorific value. Abundant availability. Obviously, it is not there in a separate form. It has to be basically extracted from various other sources.

It's a clean fuel. Whenever you run a hydrogen bus or a vehicle, obviously, the only output that comes out is water, and it has zero pollution. Then, an important parameter is that time is running out for us, as humanity, because the kind of pollution that has actually happened, the kind of carbon emission that has happened in the last so many years, and we've probably reached a level where the global emissions of carbon are actually now reached to around 35, 36 gigatons. So we, as humanity, are facing challenges in terms of how do we sustain ourselves for the next 30, 40 years, and also ensure that the life is good for our children in the future.

So this is a very important scenario. And the most important parameter that one should understand is that what has gone wrong in the last 70, 80 years needs to be probably corrected in the next 25, 30 years. So we need to run three times faster, with probably higher cost allocation or higher budgetary allocation in the coming years. So next slide, please.

So again, why hydrogen? Because if you look broadly, there are two kinds of scenarios from a decarbonization perspective. One is industrial decarbonization, and second is mobility-related. And from an industrial decarbonization point of view, when we talk about industrial decarbonization, out of 80%, roughly 30%, 35% is related to energy, which comes from coal or any other sources of energy, including renewables. The second most important parameter is what is process-related. So whether it is a steel manufacturing process or a cement manufacturing process, that probably contributes around 35% water.

And waste management is also an area which is an important parameter in terms of decarbonization. And process-related energy decarbonization is something which is very critical in terms of moving ahead with all the technological challenges which, probably, people like steel companies or cement companies will actually face. I hope, Pooja, people are able to see the screen. At least I am able to see it.

Yes. The screen is visible.

OK. Yeah. So moving on from there, mobility-related-- you know, when we talk about generally is around 20% decarbonization should actually happen from mobility-related areas. So when we talk about short distance, which is basically your two-wheelers, three-wheelers, or passenger cars and intercity buses-- sorry, intracity buses, which are basically running around 200, 250 kilometers on a daily basis. So most of these challenges will basically get addressed, to a large extent, through battery electric vehicles, whether you are using a 2 or 3-kilowatt hour battery for a scooter, or a 5-kilowatt hour battery for a three-wheeler, or probably a 30, 40 kilowatt hour battery for a car.

In terms of long distance, which is typically, say, 300 to 350 kilometers, 400 kilometers on a daily basis, whether it is intercity buses or whatever, the hydrogen mobility in the coming days will probably emerge as one of the best-case scenarios, because it is very, very highly viable scenario if a vehicle is probably running for around 400 kilometers plus on a daily basis.

And obviously, when we talk about people movement, whether it is intercity buses or whether it's goods moment, hydrogen probably seems to be the most logical scenario in the coming days. So next slide, Pooja.

So if you look at what the National Green Hydrogen Mission, which was announced in January 2023, what it has to actually bring to the table, it actually brings out a lot of subsidy in terms of outlay from the government, which is roughly 19,000 crores. Other than this, how this actually is going to help the country overall? It is going to help in terms of demand creation, incentivizing various supply scenarios.

At the same time, one of the key enablers that actually will come out from this policy is that it will help in terms of resources. It will also help a lot of R&D projects' prototyping. It will also help ease of doing business in India. It also helps create regulations and standards. It will also impact infrastructure and supply chain in the coming days.

And most important parameter is as we move ahead-- and you know, India is a very young country. Average age in the country is around 29 today. So you know, skilling is one very important area. And we, as CS India, or ISA, are contributing significantly towards skilling. We do a lot of work in the areas of energy storage and e-mobility training. And also, these are areas which can help India in a big way.

So if you look at the entire outlay, out of 19,000 crore, 17,000 crore program-- has been kept for the program SIGHT. Then, 1,466 crores has been kept for various pilot projects. Because what is actually happening is that globally, there are more than 200 pilot projects which are actually going on for various uses of hydrogen. But obviously, India needs to move in terms of more projects from a hydrogen perspective, and government is actually trying to do something in this area.

Government also announced Kalka-Shimla-- hydrogen train by 2030-- December 2023. And I hope they are able to really go there in terms of the declarations. So they also kept 400 crores for various R&D projects, and other mission components, they kept around 388 crores. So this is a broad government commitment towards what can actually be done from a hydrogen perspective.

So I think this is something that will help the industry in terms of looking at hydrogen as a good, challenging area to work upon in the future, and also create scenarios which will help propel the industry to the next level. Next slide.

So India probably has around 2 million to 2.8 million trucks are there, which actually run almost 100 billion kilometers on a yearly basis. So if you look at in terms of what they are in terms of the pollution that they actually contribute, if you look at the diesel consumption in a year, roughly a year before, the number was around 40, 45 million tons for diesel consumption for long distance trucks only. If you look at buses and trucks, it's roughly-- the figure is in the range of 80 million metric tons.

So India, as a country, probably looking look at long distance buses and trucks being replaced-- building used by using hydrogen as a fuel. And obviously, if you look at the kind of demand scenario that can get created with the Indian economy actually moving from, say $3, $3.5 trillion to, maybe, say $10 trillion in the first scenario, and probably around $27 trillion by 2047, as the government has actually budgeted for. And if you look at each truck, whether it is 18 ton or 35 ton truck, I think they will require minimum-- 18 ton truck will actually require 120 kilowatt sort of a fuel cell stack.

So these are the kind of opportunity which actually will come in India's way. Obviously, cost parameters in terms of cost per kilowatt and the stack level is something that needs to be really be the important factor. But the cost can only come down with the usage.

So if you look at public transport buses as a scenario, we have currently around 20 lakh buses in the country, out of which around 8 lakh buses are basically intercity buses. And all these buses can actually go more towards hydrogen, because they are practically running 350 kilometers on a daily basis. So if you just take a Pune-Borivali return trip from, say, Pune to Borivali and back, this entire scenario is that. The kilometers that are actually run is around 380 kilometers.

So average 9 meter typically requires anything between 40 to 50 kilowatt hour fuel cell stack, and for a 12-meter, it is roughly in the range of 80 to 200 kilowatt fuel cell stack.

So this is the kind of scenario where if you look at the urban population today, India currently has something like 30%, 35% of population staying in cities, whereas report-- it says that 60% of India's population will probably stay in cities by 2030. So there's going to be huge demand for buses. Also, the density of buses in the country today is around 1,500 buses per million. But if you look at some of the growth countries like India, like Brazil, South Africa, and other countries, the numbers are around 5,000 to 6,000 buses per million population.

So somewhere, some country like Thailand has around 8,500 buses per million, because obviously, Thailand is a tourist destination. So looking at all these upcoming scenarios, I think India will probably need something like 60 to 75 lakh buses in the coming years. And if you look at the incremental number of buses, I think the entire scenario looks very, very promising in the coming decade or so. Next slide.

So what are the outcomes that are expected from this government initiative? Government is also targeting 5 million metric tons of hydrogen to be supplied overseas, additional 125 gigawatt of capacity for renewables, probably investment of around 8 to 10 lakh crores, more significantly larger number of jobs, and in terms of fuel reduction, import reduction, obviously 1 lakh crore cumulative reduction in fuel imports, and abatement of around 50 million metric tons of annual greenhouse gas emissions.

In terms of the benefits for the industry, creation of export opportunities for Indian companies as well as government companies, private as well as public sector, decarbonization of industrial, mobility, and green sectors, reduction in import of dependence for fossil fuel, and indigenous manufacturing, as well as creation of large employment opportunities. Most important is development of cutting-edge technology, because when you talk about hydrogen or hydrogen fuel cell stack, or the newer technologies, obviously, you need to talk about balanced supply, and you need to talk about various supporting equipments.

And I think all those industries if they start doing much more localization, because any government policy actually requires large percentage of localization to be done, from the perspective of not being dependent on various other overseas countries. So I think this particular area will help in terms of putting the country on the right map for significant growth in the particular sector. So next slide.

So companies like Reliance are actually announced $1 per kg of hydrogen. But obviously, at the refinery level, when you use a smart process, the cost is in the range of $1 to $1.5, using hydrogen. But obviously, in the coming years, as green hydrogen scenario with costs of renewables coming down, we can probably see a significant reduction in the cost of hydrogen from the current green hydrogen pricing level of $4 to $5.

And with the production of low oil electrolyzers in the country-- currently, obviously, as we are aware, that electrolyzers are in great shortage worldwide. And that is one area which is of a big concern. But I think with local production of electrolyzers, also, I think these costs-- CapEx costs-- will also come down. And overall, it can lead to a reduction in terms of pricing for hydrogen.

Obviously, the demand scenario that can actually come from refining ammonia, steel, and transportation. Obviously, currently, India uses around 7 million metric tons of hydrogen, out of which around 80% is used for refining and fertilizers, and government is actually contemplating around putting 10% to 20% of these under green hydrogen production RPO sort of scenario-- renewable purchase obligation. But I think if that happens, then probably the demand for these kind of technologies as well as use of hydrogen, more green hydrogen, in the coming days will actually pick up more.

So if you look at the potential scenario, the other near-term benefits could be for pharmaceutical, speciality chemicals, and semiconductor industry, also. In space industry, also, we see a significant possibility in the coming days. So next slide.

So overall, you can see what kind of R&D has actually been going on from a hydrogen perspective, whether it is fuel cell membrane, fuel cell catalysts area, and hydrogen storage as well as hydrogen production. So these are the kind of mapping, in terms of the facilities that can possibly get developed in the coming days. And I think India probably is on the right track in terms of government, as well as larger corporate announcements to really move towards bigger usages of hydrogen. So next slide.

So from our R&D perspective, also, there are certain projects which can also be done on a PPP basis-- public-private partnership-- and these are the scenarios which can actually get developed in the coming days. Pilot scale manufacturing is one area where India needs to really push up. And obviously, we need to support also, SMEs, in terms of their ability to scale up faster. And some sort of technology [AUDIO OUT] is an area where government can actually support in terms of local centers, which can actually help in terms of more technology adoption in the coming days.

And research is important. But at the same time, large-scale manufacturing and deployment is very, very important. So how do we cross the bridge of the-- death valley, valley of death, is something which is critical for us to really judge. And I think the way things are happening, the way we are being able to look at the deployment and development, the way things are happening in the semiconductor industry, and large-scale government targets are also coming forward, we can possibly see a significant scenario for hydrogen in the coming days. Next.

So these are currently the broad export potential for hydrogen technologies. So global electrolyzer manufacturing. Current capacity is around one gigawatt, and requirement would be in the range of more than 200 gigawatts by 2030.

So all these supply chain-related opportunities for components as well as delivering to the global market is something that is very critical and important for India, also, because if we have to move from a $3.5, $4 trillion economy to around $27 trillion economy by 2047, I think these are the areas where India, if it can become, first, self-sufficient for the usage of hydrogen in the country, the usage of fuel cell in the country-- also, the export of potential that India can actually see in the coming days.

So broadly, the scenario looks very, very promising. Government is actually supporting through various incentives and schemes, also. And it is important for both public sector as well as private sector corporates to come forward and take up the challenging scenario to ensure that the technology adoption happens faster. So next slide.

So these are my coordinates. So anybody who wants to connect or have any questions or anything, I can take up those questions, also. So thank you so much, and my apologies for the initial trouble I had in terms of connecting with the system. Thank you so much, Pooja.

Thank you, Mr. Chandrashekar. We have some questions from our audiences. Let me just bring them up.

Sure.

So the first question is, are there any subsidies, incentives, announced by the government of India for fuel cell vehicles, like the FAME initiatives?

See, there has been-- Fame I, FAME II-- supports both battery electric cars-- actually supported battery electric vehicles. As far as buses are concerned, it supports buses, and there are incentives for two-wheelers and other vehicles. There are obviously no incentives for corporate buses, but we talk about hydrogen fuel cell buses, there was an appeal put up recently-- last year, in the Supreme Court-- for even more incentives for hydrogen fuel cell buses in the coming days.

So I think at an appropriate time when the industry actually comes up and there are more buses or vehicles that actually start running on the road, or nearer to running on the road. I think the government will definitely come out with some incentive scheme like FAME I or FAME II in the coming days.

Thank you. We can have two more questions. The next one is, the main issue for hydrogen storage-- how is that addressed when used in public transport vehicles?

See currently, whatever we have seen or studied globally. Typical 9-meter buses or 12-meter bus will have anything between two or four cylinders. And each cylinder can probably carry something like four to five cases of hydrogen. So storage is obviously an issue in terms of these storage cylinders. There are few companies in the world who actually manufacture these cylinders.

But I believe so that in India, there are a few companies, including Reliance, which are actually trying to develop these kinds of solutions by using or capturing carbon from the refining process and production, or production of blue hydrogen, and at the same time using the carbon for product manufacturing of cylinders with, obviously, some aluminum lining, or some sort of that material inside. So people are trying their effort in terms of finding out the solution for storage. But obviously, the current costs are very high, and large-scale production needs to happen so that these storage costs can actually come down significantly as far as transportation is concerned.

Any further questions, Pooja?

Yeah. Yeah. Just one last one. In the interest of time, I would request all the attendees to post in the questions, and we'll have it answered towards the end. But this will be the final one. Green hydrogen is preferred over green hydrogen due to cost. As you mentioned, it's $1.5. What would be the cost if we can opt for the blue hydrogen in place of green hydrogen? Do you want me to repeat it? Mr. Chandrashekar, can you hear us?

Yeah. I can hear you now. Yeah.

I'll just repeat the question quickly. The produce green hydrogen is much preferred over green hydrogen due to cost. As you mentioned, it's $1.5. What would be the cost if you can opt for the blue hydrogen in place of green?

It's difficult to say what will be the cost. But we believe the cost should not increase, go beyond $2, $2.50. But obviously, the time will only tell what can be the right cost. But obviously, usage of green hydrogen should start in some way for us to really start using hydrogen from a transportation perspective. And that should be the scenario, in terms of how things can actually improve in the coming days. And with more usage of hydrogen, more incentivization is going to happen, and likely chance of government also coming out with the FAME policy for fuel cell vehicles.

I think things should improve in the coming days. Cost parameters is very difficult to comment upon at this point of time. But refinery, I'm sure-- because I've worked with along with a few refineries in the past-- so I'm aware of the cost parameters in terms of green hydrogen. But blue hydrogen cost parameters is difficult to comment, but should not go beyond $2, $2.50. That's what I can understand.

Thank you, Dr. Chandrashekar for taking us through this insightful presentation, and also taking the time to answer all these queries. I hope this was quite beneficial to all our viewers.

My pleasure, and sorry for the initial fiasco in terms of connectivity. Yeah.

Thank you.

Thank you, sir. Thank you so much. And let's now call upon our second speaker, Mr. Ramanuja. He's a senior application engineer at MathWorks, and he's working on modeling simulation control design and simulation deployment for mechatronics applications. He has [AUDIO OUT]

And he's worked on projects-- planning and controls of railway projects. His academic background is in process control, and he is he's on his master's program at National Institute of Technology . Over to you, Ramanuja.

Yeah. Hi. Thanks, Pooja, for the introduction. Can somebody confirm if you can see my slides?

Yes, we can, Ramanuja.

OK. Good. Thank you. Thank you. Yeah. Hi. Good afternoon, all. So today, I'll be talking about the green hydrogen production in microgrids, and how do we make designs and do system assessments for such plants.

So let's talk about this webinar series. So today, we'll be talking about the hydrogen production side. So I'll be covering on how to convert your green electricity into hydrogen, and in tomorrow's session, my colleague, Raghu, will be covering how to convert the hydrogen back to electricity in order to power your transportation systems.

So the agenda of my presentation would be to walk you through the process of converting the electricity to hydrogen. How do we do multi-domain simulations? What are the key performance indicators? And then I will talk about how to perform technical economical analysis in setting up a hydrogen plant. And finally, I will wrap it up with a Q&A section.

So the basic process of hydrogen production is what we call it as an electrolysis. So here, we have electricity being pumped into the electrolyzer, where hydrogen and oxygen are produced from water. Now, if this electricity is produced from green sources, such as solar or wind, we call it as a green hydrogen. And there are many benefits to such hydrogen, because it's very sustainable and we can do storage and transportation.

But there are also some challenges here. Basically, in terms of the high energy consumption in order to produce hydrogen, of safety measures and storing hydrogen, and the overall cost of operating this plants. The idea is to do a simulation-based R&D in order to address these challenges and overcome these obstacles.

So in my tourist presentation, I'll be walking you through two scenarios. One scenario would be where we produce hydrogen from a microgrid where the primary source of supply is from wind. And the second scenario is the microgrid where the primary sources are solar. And in both these simulations, we will be using the multi-domain modeling capabilities of MATLAB and Simscape.

Another important factor in performing these simulations is to import external data. In case of wind, I need to import the wind speed in a particular location. And MATLAB does offer you utilities to make such external data import into MATLAB. So once you imported the data, then you might want to pass it on to a simulation model. And here is where I would be considering what fidelity should I model my system in?

For example, let's say if my focus is primarily on developing components-- let's say, an embedded controller-- then I would be building a high-fidelity model, because these simulations would usually takes timestamps at the order of milliseconds or microseconds.

But if my primary idea is to get some key performance indicators, then I would be building a systems-level model. Primarily, these models take the timestamp of seconds to minutes, and thus, they are medium-fidelity models. On the other extremity, if my intention is to perform some techno-economic analysis, such as finding return on investment, then these simulations typically run at a timestamp of days, months, and years. So we typically go ahead with the low-fidelity model and get these economic indicators.

At this point, let me put up a poll to understand what would be your interest in performing systems-level simulation. Is it primarily to get the techno-economic indicators? Or get the key performance indicators? Or do system sizing, or do controls design? Or it could be something else, which you could address in the chat.

So I'll wait for, like, 5 or 10 seconds. Please address the poll. I think the poll is also visible in your screen. And you could see what the other audience are thinking about the systems-level modeling requirements.

Right. So let me proceed. But the poll is open, and you can proceed answering that-- continue answering that. So let's talk about a component model of an electrolyzer. So here, what we see is a component-level model, which is very detailed. It captures thermodynamic and gas dynamics of the electrolysis process.

Now, this model can be accessed in Simscape using the command ssc_electrolyzer. And this is a very detailed, physics-based model of the electrolysis process. Another model could be the systems-level electrolyzer block, which usually considers multi-domain aspects. And these models are typically faster to simulate so that you can run multiple iterations and get some key performance indicators. So this is a medium-fidelity delivery model. And in my today's presentation, I will be using this particular block to carry out my simulations.

Another important component in these simulations are your energy conversion devices. So power electronic devices play an integral role in converting mechanical energy to hydrogen. Typically, the journey starts by converting mechanical energy to electrical energy in case of wind, where we have machine models such as induction and synchronous machines.

We also have converter models, such as DC-DC converter, and DC components such as electrolyzers and batteries. Additionally, we can also have AC components, such as the three-phase AC generator, if there is a requirement to connect the model to a grid.

Let's first get into the high-fidelity option, where our focus is on building components. A typical challenge when it comes to building components is designing them and trying to regulate these components.

Typically, these are your electrolyzer units, where you might want to do thermal regulations; your energy storage unit, such as batteries, where you might want to do BMS algorithms; power conversion units; and your generator models.

Also, you might want to capture detailed physical models of these components so that you are able to perform degradation and anomaly detection analysis. Also, you could be doing some prognosis development where you are usually doing quick maintenance and capturing remaining useful life of the component.

Now, typically, these models will use high-fidelity component models. And in this case, we would be using AC machines and DC machines, and we can also parametize these blocks using FAME parameters data to capture high-fidelity details, such as harmonics and spatial saturation effects.

It can also model the power converters, such as DC-DC converter, using detailed N-channel MOSFET models, also by capturing thermal options. When it comes to AC-DC converters, we have prebuilt blocks available, in which you can basically choose the switching device and also perform detailed harmonics and harmonic simulations.

So here is a systems-level model of your standard electrolyzer connected to the wind generator. And in this single model, you would see we have a high-fidelity component on this side, but we have a medium-fidelity component on the electrolyzer side. And usually, we combine models of different fidelities to perform simulations.

So let's walk through a video of a model which contains the standalone electrolyzer unit. So here, we have the electrolyzer connected to the wind generator. And if I go into the wind generator subsystem, I should be able to see my emissions model. So here, the generator has a permanent magnet synchronous machine, and we could parameterize the model using datasheet components.

The machine is excited by RPM, which is provided by a step input. The machine is also controlled using a field-oriented control algorithm. If I go into the implementation of the algorithm, I could see there is an outer loop and the inner current control loops. The outer loop basically provides set point reference. I think the screen-- OK. Let me share it. Is my slide still visible?

They're not up.

I think you need to re-share.

OK. Is it visible now?

Yes.

Yes.

OK. Thank you. Thank you. Yeah. Yeah. So as I was saying, we have two loops. One is the outer loop and the inner current loop. The outer loop provides the current references, the IDq current references for the inner loop. And the inner loop provides the voltage inferences-- the access voltage inferences. And these are built using VA control blocks

We can also model the PW model. Here, we have multiple options to model it, such as a continuous PW model-- this continuous PWM. Or we can make it to work in sinusoidal mode or a space vector modulation model. So once the model is done, let's go look into some of the key variables during simulations.

So here, we can observe the currents of both the generator and the electrolyzer. Similarly, I can look into voltage, power consumption, and the resulting voltage. Finally, I would also be interested in knowing what is the hydrogen produced, what amount of hydrogen is produced, and what is the energy consumed to produce one kg of hydrogen. And these are key performance indicators, which helps me understand how the system performs, and also to see if the control works as intended.

To summarize, the model we saw had the complete systems-level model of your electrolyzer unit. It also had a detailed implementation of the controls logic. And in simulations, we were able to verify the validity of these control algorithms. So once we are satisfied with the control logic, then we would typically go ahead and deploy it to the embedded platforms. And this is achieved, typically, using an automatic co-generation technology, which helps you to do code conversion very easily and quickly.

Let's get into another model. In this case, the electrolyzer is connected to the grid. The object of this model is to understand the effect of electrolyzers when there is a frequency change in the grid power supply. So here, the control unit consists of a PLL, which basically is used to detect the frequency of the grid. And here, you can see there are some fluctuations around the 50 Hertz base frequency.

Once we perform the simulation, we observe the key variables to understand the effects in the system. So here, you can see at the time of the load insertion. We could see there's a transient on the current site-- both the generator and the electrolyzer.

And Ramanuja, there's messages on the chat-- if you could go a little slow, and maybe try and cover up a few slides from previous. So there are messages saying if you could go a little slower with the presentation.

OK. Sure. Yeah. OK. Yeah. By the way, these slides will be provided to you, and the recording will be available, so you could go through later. But yeah. I'll try to go through slowly on this content.

So the change in frequency, that's-- I mean, we can see that there were some harmonics in the current, and we can also try to identify the spectral content of these harmonics. So here, I can look at it as a graph, or I can also view it as a list where we have different frequency components, and try to understand the spectral content of these components.

I can also go ahead and then change the base frequency and try to see how much these do changes. For example, let's see if I change base frequency to 50.4. Then the harmonic distortion reduces from 19 to 9. So these are some of the levers you could play around with the frequency spectral analysis.

So the key point here is that here, we are able to develop algorithms to detect frequency changes. And we were able to create detailed physics-based models, which were able to simulate the harmonics. Usually, these harmonics are an effect of-- I mean, they could be an indicator for some of the aging and fatigue in your competence. And these data can be used to perform predictive analysis, or to find out the remaining useful life of competence

My next topic-- let me move on to the key performance indicators, and let's see how we can use system simulation to address that. So when it comes to systems-level simulation, we have two concerns. One is to design your plan, and another is to design your algorithms. So on the plan side, we usually want to identify the best concept.

For example, the question could be, does AC or DC help me in generating hydrogen more efficiently? Should I have grid connection? Should I use solar or wind? Another requirement could be to size their components and see if they integrate well into a single system. Also, you might want to do some energy balance analysis and try to study the flow of energy across components

On the algorithmic side, you might want to design your supervisory logic where you are trying to find out the energy split between different components, and also do full selection, whether the energy is coming from a battery or from your renewable source. Another important point could be to provide set points for your batteries and electrolyzer voltages. And this could also be based upon historical data of your wind and solar patterns.

So while I build a simulation model, in this case, I would be building them using medium-fidelity models. So the machine model here is going to be a simple top speed characteristics-based model. And let's say if you have a DC-DC power converter, it could be a simple average value model. The same goes for a DC-AC power converter, where we also use average value models to do systems-level simulations.

So let's get into a model where we try to build a model of an overall hydrogen electricity system in a DC microgrid, and we try to assess different control strategies to understand how the key performance indicators gets affected.

And here, the primary source of power is produced from wind, and we have a DC generator placed over there. Here, we can see the mechanical and electrical components of the DC generator.

Another important component is your battery storage system. And here, we have a dynamic implementation of the battery systems. But you also have to produce your logic, which decide which source to choose from and also to decide the set points of an electrolyzer's voltage and battery voltage.

We can choose different control strategies. And we will revisit this in a while. We have DC-DC converter blocks, basically to translate power from your machines to your electrolyzer. And the electrolyzer model itself is a multi-domain model, considering your thermodynamics and gas dynamics models.

To the model, I'm able to look into the key variables, such as voltage, current, the hydrogen produced, and energy consumed and produced in various components. But this is the voltage-regulated mode. And here, I get about 38 kilograms of hydrogen in a single simulation.

The battery is utilized, and we can see the source is at 50% voltage. But when I change the control strategy from voltage more to current energy mode, I can see there is an increase in hydrogen production from 38 kgs to 50 kgs. I can also look into some of the key variables. And here, I see the battery does get some time to charge itself.

Another strategy could be to keep the battery in a charging mode so that we don't use it often. But the trade-off here is that I'm losing a little bit amount of hydrogen being produced. So here, we see the battery has is charged at 60%-- slightly higher than the previous use case. We had different strategies, such as old age and current mode, and we also had strategies on the usage of the batteries.

So if I were to tabulate the entire simulation for a day in different conditions, here are the results. So in an energy-based mode, I see that we produce more hydrogen. So we can see clearly 48 gauges of hydrogen being produced in an energy-based mode, compared to 38 being produced in voltage-based mode. But the trade-off here is that we see, also, energy-based mode draws more current from your emissions. And this could mean the lifetime of the machine could be deteriorated, and that's a trade-off decision we have to make.

Here is also a comparison between different battery charging characteristics. And we could see, as the battery is getting more and more charged, the hydrogen is slightly getting reduced as well.

So performing such simulations will help you to understand what is the hydrogen produced, what is the water being consumed, which control algorithm is best for your systems. You can also do component sizing for your systems, and also you can plan your operations where you would typically plan when to collect your hydrogen, when to replace water, and perform other maintenance activities.

Finally, let me jump onto the techno-economic analysis. By definition, techno-economic analysis is basically a way of analyzing economic performance of your plant, involving petroleum and energy. Now typically, here, we use a software model to include a financial data, such as capital cost, operating costs, some technical details. And the idea is to basically understand what is the revenue from your operation so that you get better ROI.

So for today's demonstration, I will take an example that I would want to identify a proper place to install my hydrogen production plant. So there are 242 locations, and then I want to understand which location gives me better savings in terms of grid cost and gives better usage of solar energy. The first step in doing such analysis is to build the model of your system. So in this case, the system has your solar array, which converts solar energy to the electricity. We have energy storage devices, and then we also have the electrolyzer unit.

So here we perform a simulation for three days. And as I mentioned before, we choose low-fidelity components so that the entire simulation is basically run within 10 seconds. Once I run the simulation, I can get the key variables of interest. For example, since this is a simulation run for three days, I can see on the top right that my current is fluctuating for three different days. So this reflects, basically, the solar pattern from sunrise to sunset for three separate days.

But still, I can see that my electrolyzer is consuming power during nighttime, because it's taking power from the battery. On the bottom left, I can also see my hydrogen consumed. And this gives me a quantitative effect of how much hydrogen is being produced by the solar plant operations.

So the question is, how did I build such models? So typically, when we build complex models, we go through a certain sequence of steps. So we start by building standard components. We typically start from a low-fidelity model and then incrementally build more details into the models. Each component models are simulated, and the behavior is well-understood. And once we test each component, we integrate them and perform overall system simulations.

So once it's completed, the model is the battery component. The battery component could be modeled using the Thevenin equivalent model, which has the open circuit voltage and your internal resistances. But this block can be parameterized using the lookup table, which depends upon where the open circuit voltage depends upon and temperature. And to validate if this parameter position is right, you can run the simulation model in parallel with your lab data and then verify if the data overlaps. If there is a mismatch, you can use some parameter estimation techniques to fine-tune the lookup table values.

The next component is the solar array. So here, you build up harness, which has the solar array. And that, you can subject you to certain voltage loads. So when I subject it to a different voltage load, I can get some power profiles and the voltage output of the solar gas, solar array, and I could validate the performance of the simulation model.

The solar array itself consists of the photovoltaic cell, and it has the buck boost converter and the MPPT algorithm. The photovoltaic cell is parameter is using the discrete component values. And also, we mentioned the number of series connected strings and the parallel connected strings.

An important part of the solar array is to define the MPPT algorithm. So on the left, you see the profile of your typical photovoltaic cell. And here, when the voltage exceeds a certain value, you see the current declining drastically. But that's the reason we see the power increasing, and it peaks at certain value. For efficient operation, we might want to operate the solar array at this peak power point value.

This is further complicated when there is a change in the environment conditions, such as temperature and irradiance, where this curve could shift up and down. So typically, we use an MPPT algorithm, which finds out the optimal point where we get maximum power. The overall concept of this process is defined using the impedance matching.

So in order to get maximum power from your source, we have studied that usually with a load impedance, you should match the force impedance. And that's what the buck boost converter is trying to do, and the algorithm is helping the converter do that. So here is a test case model where we study the effect of the buck boost converter to obtain maximum power for the load.

So we have two models over here. The model on the top left is a simple model, where we have a DC internal resistance, and we also have a variable load, which is subjected for different loading conditions. On the left bottom, you can see a model which has an MPPT block inserted between your source and load components. Now, this converter block would typically modulate signals in such a way we get maximum power from the input and distribute it to the output.

On examining the simulation data, on the top right, we can see the red color curve, which is basically the power produced by the-- the power consumed by the load. And you can see that peaks out at some midpoint value and then declines. But whereas when I use the MPPT algorithm, you can see that the power is almost consistent at 100 watts, and it is almost remains constant through the operation of the load changes.

One more component to validate would be your electrolyzer block. And here, I would give the block with some ideal and see what is the hydrogen produced by this particular component.

So once I validated all these components, now what I would do is integrate them in a single model, and then perform my simulations. But the simulation model, what we have seen here is purely considering the technical details only. But it hasn't still considered any economical or financial details.