Producing Green Hydrogen with Renewable Energy Powering Hydrolysis - MATLAB
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    Producing Green Hydrogen with Renewable Energy Powering Hydrolysis

    In a green hydrogen production system, electric power harvested from renewable energy sources (such as wind and solar) is converted into hydrogen gas through electrolysis, with the excess energy stored in an energy storage system.

    Modeling and simulation of a green hydrogen production system enables engineers to optimize component configurations and system performance. A high-fidelity model captures complex physical behaviors of the component and informs component sizing. A medium-fidelity model enables fast simulations, multi-domain integration, and grid-connection studies.

    Watch how Simulink® and Simscape™ are used to:

    • Create models of a green hydrogen production system from Simscape blocks with different levels of fidelity
    • Perform detailed design and analysis on components, such as the electrolyzer, the battery system, and the power converter unit, with a high-fidelity model
    • Conduct system-level performance evaluation by running simulations and assessing key operational metrics, such as the level of hydrogen production and water consumption, with a medium-fidelity model
    • Study system operations in standalone mode or in grid-connected mode
    • Fine-tune control algorithms, adjust energy storage dimensioning, and plan for maintenance and operational cycles based on system-level simulations

    Juan Sagarduy is a senior application engineer in the control design and automation field. His specific focus is physical multidomain modeling and simulation. In his role, Juan provides technical expertise for successful adoption of plant modeling tools (Simscape platform) for model-based development. In recent years, he has led several initiatives within electrification for the Nordic region. Before joining MathWorks in 2011, Juan worked at ABB Corporate Research Center (Västerås, Sweden) in electrical machines and motion control projects. Juan holds an MS degree in industrial engineering (Bilbao, Spain) and a PhD in electrical engineering.

    Published: 13 Sep 2022

    I will start with a green hydrogen production through electrolysis. I will focus on energy conversion from wind to gas.

    So the chemical process to produce hydrogen is called electrolysis. Water and electrical energy are converted into hydrogen and oxygen. If the electrical energy to produce hydrogen comes from renewable energy sources, like wind and sun, then the hydrogen produced is labeled as green.

    So this technology has got many advantages when it comes to enabling electrification. So sustainability and versatility being two of them. But it does have quite a few challenges associated to it. And those are related to high energy consumption, the safety in managing hydrogen, and the high cost.

    We believe that simulation-based R&D can definitely mitigate the risk and then put you on a path to success. If we start with a micro-grid that is solar based, so those are the elements that you're going to find-- photovoltaic panels, energy storage units-- often with a battery-- and then the electrolyzer, whereby an electrical energy will be resulting in hydrogen. In my case, I will be using a microwave wind-based, then.

    So when it comes to green hydrogen production from renewables, the ability to import data from wind or sun into models is very important. And there are many available sources online that you can find. So in this case, you see a wind speed trace for almost one day from Inverness, in Scotland. And that can definitely be very well done in MATLAB and then reused by Simulink and Simscape models.

    So if you have now made the choice of using simulation for your development in green hydrogen, then of course fidelity is going to be a critical element into that. If you are focusing on a better developmental component, your target is going to be milliseconds or microseconds. You will not really need high-fidelity models.

    If you are, on the other hand, interested in assessing key system performance and the phenomena that lapse over seconds or minutes, then medium-fidelity will make a lot of sense. If your goal is, on the other hand, assess the feasibility of green hydrogen-- you are looking into velicle calculation for months and years, then very agile models with the lowest fidelity will be definitely the right choice for you.

    Let's say we focus on the electrolysis unit to start with. So I like to bring your attention to an open Simscape implementation of an electrolyzer and that captures the thermodynamics really in detail. But then, if you are once again a bit more focused on system level analysis-- our child simulation, you want to understand the technology and electrolyzer block-- an example in Simscape electrical-- are definitely very suitable for you. And this is going to be the case for the seminar. We are going to use that electrolyzer block.

    So let's get started with a high fidelity view of green hydrogen production embedded development and component analysis. So what are the challenges that we are going to meet at the physical unit level? So those are going to be related to the components themselves and how we regulate them.

    So the electrolytes will be a core element into that system. Energy storage-- often a battery-- BMS, Battery Management System, needs to interact with the rest of the elements and the grid. Power converters-- they need fault management, grid connection algorithms, regulation of power, and even cooling. And then the generator.

    So if we zoom in a bit into energy conversion-- so we can say that the Simscape electrical does give you a phenomenal library with universal and machine models that will make the modeling process intuitive and quick. If you were to reuse electromagnetic design data, you can do it with the two blocks that you see on the bottom of the screen, as well.

    A detailed AC/DC converter can be assembled from discrete elements, like diodes, N-channel MOSFETs, or IT repeted. They can even have a thermal option. A similar reflection applies to AC to DC converters. So we can use a prebuilt component where we can make a choice on what particular switch we want to use. But of course you can compose your own converter with discrete elements, as we saw before. In the end, you will be able to capture high frequency phenomena due to pulse width modulation in the converter.

    So it's important to emphasize that different levels of fidelity can and will coexist in models along the development cycle. So in this case, high-fidelity on the generator side, and then medium-fidelity on the electrolyzer, on the load side. This video is going to illustrate the first case of a standalone electrolyzer with high-fidelity.

    First, an overview of the architecture of the model with all the elements-- a permanent magnet, the generator with all the different parameters that are needed. Then, a signal of speed is continuous, applied onto the shaft, the control unit with a field oriented control algorithm for the generator-- see the architecture without any and the loop.

    The outer loop will regulate the DC link voltage and generate current reference. Inner controller will regulate the current and generate voltage references. Finally, the pulse width modulation is also given by the tool. There you have different options to customize your own PWM sampling modes. With that, let's get some results of current electrolyzer and generator voltage absorbed electrical power we seelink and voltage, and then energy consumption per kilogram of hydrogen, and then the estimate of hydrogen produced for one day.

    Now, let's look into the second case. In this case, the electrolyzer is not going to be a stand alone, but they're connected to the grid. In this case, what we are interested in is to see how the electrolyzer reacts to a change of frequency in the grid. So have phase locked loop that detects the frequency and then uses that for control purposes.

    Then, a transient is observed in the generator currents. The amplitude and the duration of that transient should be limited in time if the controller is doing a good job. So Simscape electrical does give you the possibility to do harmonic or FFT analysis. In this case, we're going to do harmonic analysis of a current 15 cycles after the transient. So results can be given as a bar diagram, or as a list of numerical values. Those can be exported through MATLAB to any other environment, like Excel.

    So what other challenges that we expect at the system level? I think those fall down into two categories-- plant and algorithmic design related. For the plant, the first question to ask yourself is, of course, what is the best concept to produce green hydrogen? Is it going to be AC versus DC generation, if you're going for wind? Or green versus remote-- is wind or solar the best choice or do you want to combine both?

    How can requirements for the different components be met? So the size of those components, the integration, the cost, and then even the scalability of your concept. And then, do you really understand energy balances in your system?

    From an algorithmic point of view, how can I architect the supervisory logic so that it accounts for all the different important functions-- so energy storage contribution, the asset management selection of the right sources. Also important-- how can you set up a relevant set point meeting very different circumstances, history, and meteorological conditions?

    So if we go back to energy conversion, then machine models that we're going to use in this type of work are going to be focusing more on energy flow rather than machine controlled. DC-DC converters are going to be modular and average. So those can be controlled with a duty cycle current or voltage reference. They account for losses and then they are going to be fast to simulate.

    Let's get started with a brief overview of the system-- the DC generator with the mechanical part and the electrical part. Then we have the energy storage with a dynamic battery. We have a supervisory logic where we set up switch logic electrolyzers and battery set points. It's worth mentioning that we have two ways of regulating that system-- energy or voltage based.

    DC-DC converter control with a voltage reference and then accounting for losses, then our electrolyzer multi-domain. Then we get some important KPIs from the system. So current, voltage, hydrogen mass produced, and energy consumption.

    So in this case, for the voltage based algorithm-- so production is around 38 kilograms of hydrogen. What will happen if we use the energy based method is that hydrogen production goes up significantly to 48 kilograms. Here we can see a trace of the currents generator electrolyzer.

    Just a brief recap on the results of simulation. We can draw two conclusions here. So the energy based method is more aggressive. It allows us to produce more hydrogen. But at the same time, the mean current in the generator is a lot higher, which means that the lifetime of the generator will be lower if we use it in that way.

    To wrap up-- expected hydrogen production, water consumption, the algorithm or the energy management solution that makes sense, how intensely you want to use the physical assets, energy storage based on a given capacity, and size. What is the contribution and the impact on the outcome of hydrogen?

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