Techno-Economic Analysis of a Solar-Powered Green Hydrogen Production System
Learn how to model a solar-powered DC microgrid with a green hydrogen production system, run simulations, and analyze the economic performance of the system at 242 different locations with MATLAB®, Simulink®, and Simscape™.
Watch how you can:
- Perform system analysis on a physical model of a localized DC microgrid with a battery-based energy storage system, electrolyzer loads, and a solar array as the energy generation unit.
- Make modifications at a component level, such as battery sizing and solar cell parameters, and try out different scenarios by running multiple simulations.
- Vary the simulation time scale ranging from days to years.
- Obtain key operational metrics such as the amount of power generated, consumed, and stored; the amount of water consumed; and the amount of hydrogen generated.
- Import real-world solar irradiance data (9760 TMY3 from NREL) with 242 locations in the data set and electricity price data to analyze grid cost and solar resources at each location.
- Speed up simulations for the techno-economic analysis by using quasi-steady state simulations, reduced-order modeling, and parallel simulations.
- Identify locations with the highest and lowest grid costs as well as the highest and lowest solar resources.
Tadele Shiferaw is an application engineer at MathWorks Benelux, based in Eindhoven. He coordinates the engagement on power electronics design and control with customers from multiple industries within the Benelux area. His technical focus areas include physical modeling of systems, controller design, systems engineering, and hardware-in-the loop simulations. Tadele completed a graduate study in control systems and a PhD on controller design for safe robotic manipulation at University of Twente in the Netherlands.
Published: 16 Sep 2022
In this session, I will focus on how we can perform techno-economic analysis using models and the power of simulation. For the discussion, I will consider a use case where a solar energy is used as the main renewable source for our green hydrogen production. So let's start with a definition. What is techno-economic analysis? And, in short, it is an analytical approach that introduces economic aspects, like cost and revenue, into applied technologies like energy, petroleum, bioprocessing, and et cetera. In most scenarios, a techno-economic analysis relies on a software model to calculate estimates of capital or operational costs using inputs from either technical or non-technical financial data.
As we explore the concept, we'll consider an example techno-economic analysis for setting up a green hydrogen production from solar energy at 242 different locations. Using our analysis, we'll answer typical questions like which locations have the highest or the lowest energy, or which of those 242 locations have the highest and the lowest cost. As I mentioned earlier, a model is the basis of techno economic analysis. And in the case of a solar powered green hydrogen production, we need to have a model that includes three important components-- the solar arrays, the storage battery, and the electrolyzer.
Here I have a complete system model of a solar powered green hydrogen production with the solar array, the energy battery, and electrolyzer. And if you look at the parameter which is defined as the stop time, I'm running a full three-day simulation in this scenario. One thing you will immediately observe is that the simulation is fast, and then I'm able to run a full three day simulation in less than 10 seconds. I'm also able to capture some important measurements so I can inspect what happened during these three-days simulation scenario.
So I can use my Data Inspector to look at some signals. For example, let's start with the power which was generated from the solar arrays. So here you can see that there were these three daily peaks, peak power during the daytime and no power generated during the nighttimes. And let's compare that against the power which was consumed by the electrolyzer, for example.
Here, in pink, you can see the power consumed by the electrolyzer. And then, for example, at some instance where there is no power which was generated by the solar panel, the electrolyzer was consuming power. And as you might expect, that actually came from the energy which was stored in the battery.
We can also look at some other parameters. For example, the amount of hydrogen that was captured during those three-day simulations. And then also the amount of water, for example, consumed to generate that hydrogen. With this complete system model available, we can make modifications to each of the components and try out a number of different scenarios that could likely happen in the actual system. One of the components making up our system is a storage battery, and we can use our physical modeling language, Simscape, to model the battery as it's an equivalent circuit, or use a complete standard library block for modeling the complete battery.
The second building block in our system is a solar array that we can model separately and verify it's performance via simulation. Let's switch to MATLAB and explore the model of this solar array. Here I have a lexical model of a solar array connected to a load, and I can use this model to run multiple simulations to validate the performance of the solar arrays. If I explore what's under the hood, I can identify three main components. So the solar cell and two additional extending components here. The major component, the solar cell, is the block that converts the irradiance, or the power from the sun, into electrical energy that flows through the rest of the system.
While we are defining the cell behavior here in the configuration, we can also define how a number of these cells could be connected, either in series or in parallel, to create a panel or an array configuration. While this manual parameterization is a possibility here, there is also an option to actually select a manufacturer. And then also a part number, so that there is an automatic parameterization based on the data obtained from manufacturer datasheet. Once we parameterize the solar cell, we have to extend it with additional components, MPPT, and a boost converter that we will discuss shortly, and then finally connect it to a load so that we can create a performance simulation of a system.
Here, in this case, I'm running a full simulation for a 24 hours using the solar cell. And then I'm also capturing important measurements that I can inspect afterwards. For example, I can check the maximum power that I was able to generate from the system, and then the power that was consumed by the grid, which was obviously less than the solar power. Then I can also look at the voltage and the current that was produced from the solar cells.
The last major component in our green hydrogen production system is the electrolyzer, which converts water into hydrogen using electricity. Let's explore the model and run some simulations to understand how it operates. Here is a standalone model of the electrolyzer connected to a standard voltage source.
I can run multiple simulations with different input voltage sources, for example, to understand its detailed behavior. Let's look at how the electrolyzer is implemented in detail. One thing we can see is that this model is constructed from components that span a number of engineering disciplines, and it's indicated by the different colors that are used in the model here.
For example, we can see the electrical domain here in blue, the thermal liquid domain here in light orange, and also a purple color indicating here the gas domain. Once the detail electrolyzer is implemented, then we can run simulations to understand and investigate its detailed operation and performance. For example, we can use the Data Inspector of the simulation to look at the amount of hydrogen that we are able to capture for a given input voltage, and then run multiple simulations to compare how much performance we can get for increasing or decreasing the input voltages. Once the three main components are modeled and verified separately using simulation, then we can start integrating them towards creating a complete system model.
Once we have the system model, we can run simulations to understand and evaluate the operational performance of the system. If you remember, in the beginning I said our objective was to do a techno-economic analysis on setting up green hydrogen production from solar energy on 242 different locations. But so far, the models that we have created were used for purely technical analysis. Can we re-use the models for techno-economic analysis, or is there some special consideration for models that are relevant for techno-economic analysis?
Well, it turns out that there are some spatial considerations for models that are used for techno-economic analysis. The first consideration is that the models are executed for a long duration of time, and we are interested in data points at time interval ranging from 1 minute to 1 hour. A second important consideration is that they rely on steady state operations on those specific time intervals. This means that they are not interested in the dynamics that happens between two different time intervals. And for that, we use a quasi steady-state simulation, which assumes that the system reaches at a steady state during those specific time intervals of interest.
The quasi steady-state simulation is implemented by replacing components with the reduced order model equivalent, and this reduced order model is realized by using a lookup table that captures quasi steady-state values at desired time intervals. For example, in this reduced order model of a solar cell, the detail dynamics is replaced by three different lookup tables that capture the output voltage, the output impedance, and the maximum power point tracking implementation output as a duty cycle for input into the back post-converter. And this way, we'll have a quasi-steady state simulation possible with these kind of models.
Once we have replaced other components like the energy storage and also the grid with the reduced order model equivalent, then we can create a complete system model that's ready for techno-economic analysis. The input to our model are two things, the irradiance of that specific location, and then the electricity cost at that location. Once we have these two as an input, then we can run multiple simulations for those 242 locations to understand and answer these important two questions-- the highest and lowest grid cost, and the highest and lowest solar energy captured from the system.
A full year irradiance data for the 242 locations in North America was captured from National Renewable Energy Laboratory. Data not only contains the solar irradiance, it also contains other informations relevant for renewable energy study, such as wind speed and temperature. And for the electricity price data, representative one day data per location was used, and it was simply repeated 365 times to calculate the cost for a given year. With both input data available for all locations, we can make use of parallelization to speed up simulation for the 242 different locations.
With such an option enabled, we are able to run the simulation in less than 10 minutes, and are able to answer the four questions that we started with. For example, we were able to find out the lowest cost was at Phoenix Sky Harbor International Airport, and the highest solar resource was available at the Daggett Barstow Airport. There are also the highest grid costs observed at Quillayute State Airport, and then the lowest solar resource cost were captured at a similar location. So in total, we are able to simulate 242 years in less than 10 minutes and answer our technical economic question.