Tag Archives: Computer-aided design

Designing building-integrated photovoltaics with Energy3D

Fig. 1: An example of solar facade.
Building-integrated photovoltaics (BIPV) represents an innovative way to think and design buildings as both human dwellings and power plants. In BIPV, solar panels or photovoltaic thin films are used to replace conventional constructional materials in parts of the building envelope such as roofs, walls, and even windows. Designing new buildings nowadays increasingly includes BIPV elements to offset operational costs. Existing buildings can also be retrofitted with BIPV (e.g., replacing glass curtain walls with solar panels). BIPV is expected to grow more important in architectural design and building engineering.

Fig. 2: An example of solar curtain walls
We are developing modeling capabilities in Energy3D to support the design, simulation, and analysis of BIPV. Figures 1 and 2 in this article show a few cases that demonstrate these capabilities in their primitive forms. Considering BIPV is relatively new and a lot of research is still under way to develop and test new ideas and technologies, we expect the development of these capabilities in Energy3D will be a long-term effort that will be integrated with latest research and development in the industry.
Fig.3: Power balancing throughout the day.

As the first step towards that long-term vision, the current version of Energy3D has already allowed you to add solar panel racks to any planar surface, being it horizontal, vertical, or slanted. Running a simulation for any day, you will be able to predict the daily output of all the solar panels. You can also compare the outputs of selected arrays. For example, if you want to track down on which side solar panels produce the most at a given time during the day, you can compare them in a graph. Figure 3 shows a comparison of the solar arrays in the model shown in Figure 1. As you can see, the east-facing array produces peak energy in the morning whereas the west-facing array produces peak energy in the afternoon. In this case, the BIPV solution ensures that the photovoltaic system generates some electricity at different times of the day.

Video tutorial: Solarize an imported building in Energy3D

We are pleased to announce that the solar panel and analysis tools in Energy3D (version 6.5.6 or higher) are now fully applicable to arbitrary imported structures. We hope that the new capabilities can help engineers who design rooftop solar systems and building solar facades to get their jobs done more efficiently and students who are interested in engineering to learn the theory and practice in an inquiry-based fashion. The six-minute video in this article demonstrates how easy it is to perform solar panel design and analysis in Energy3D. (Note: Unfortunately, the annotations in the video do not show if you are watching this on a smartphone.)

One of the handiest features is the automatic, real-time detection of the angle of the surface under a solar panel while the user is moving it. This feature basically allows the user to drag and drop a solar panel or a solar panel rack anywhere (on top of roofs, walls, or other surfaces) without having to set its tilt angle manually.

Solar heat map of a house with solar panels
Copy and paste a house with solar panels
Solar panels are "first-class citizens" in Energy3D as they are readily recognized by the built-in simulation engines. Energy3D provides a comprehensive list of properties that you can choose for each solar panel or solar panel array. For example, even the temperature coefficient of Pmax, a parameter that specifies the change of solar cell efficiency with regard to ambient temperature change, is supported. The software also has a variety of analytic tools for predicting the hourly, daily, and annual outputs of each solar panel and their sums. Interactive graphs are available to intuitively show the trends and allow the user to compare the outputs of different solar panels, of different arrays, on different days, or with different environmental settings (e.g., with or without a tree nearby).

These "native" solar panels are now completely blended into the "alien" meshes of structures imported into Energy3D from other CAD software or Google Earth. For example, once you drop a solar panel on a surface of the structure, it will stick to it. In other words, if you move or rotate the structure, the solar panel will go with it as if it were part of the original design. When you copy and paste the entire building, the solar panels will be copied and pasted as well (by the way, it takes only four clicks to copy and paste a building in Energy3D through the pop-up menu: one click to pick which one to act upon, one click to select the "Copy" action, one click to pick where to paste, and one last click to select the "Paste" action). That is to say, the native and alien meshes are completely meshed.

Automatic remeshing in Energy3D for solar analysis

A headache in the practice of simulation-based engineering or computer-aided engineering is the incompatibility of the meshes used to create and render structures (let's call them the drawing meshes) and the meshes needed to simulate and analyze certain functions (let's call them the analysis meshes). This incompatibility stems deeply from the fundamental differences in computer graphics for visual rendering and computer simulation for physics modeling.

When importing a model from a CAD tool into a simulation tool, engineering analysts often have to recreate new analysis meshes for their computation, which requires fine-grained discretization such is in the case of finite element analysis and other methods. It becomes a nightmare when the conversion from the drawing meshes to the analysis meshes can only be done manually. Even if only a few drawing meshes need to be taken care by hand while the majority of the meshes can be converted automatically, the analyst still would have to check all the meshes to make sure that every mesh is good for simulation. So we really need a tool that can deal with all sorts of scenarios. And this kind of tool is by no mean easy to develop.

Mesh incompatibility was (and may remain for a long time) a problem for Energy3D when it imports structure models created by other tools such as SketchUp. For example, in most architectural CAD models, a structural element such as a wall or a roof (or a part of them) is represented by two planar meshes that have exactly opposite normal vectors, representing the interior and exterior surfaces, respectively. These two meshes have identical coordinates for their vertices, though the orders of their definition are different (one goes clockwise and the other anticlockwise in order for their normal vectors to be exactly opposite). Whatever they represent has therefore no thickness, but with the two faces, we can apply two different textures to them so that the viewer can tell if they are looking at its interior or exterior surface.

While this sounds good to people who use such models in 3D games, it spells troubles to Energy3D. The first problem is that it increases the number of vertices that Energy3D has to load and, therefore, the memory footprint of such models. In a physics simulation, a structural element without thickness is meaningless. If we don't really need the two faces in most cases (we do in some other cases, but I will skip that line of discussion in the article), why bother to import them in the first place? Despite spending days on researching on the Internet and checking the Collada specification, I couldn't figure out how to force SketchUp to export only the exterior meshes (SketchUp's Colloda export function does provide the option of exporting two-sided faces or not, but it sometime exports only the interior sides for some meshes, mixed with exterior sides for others). So it occurred to me that I had to deal with them in the Energy3D code.

If we don't treat them and use the meshes as they are, we then have the second problem: which mesh should we pick to be the side that receives solar radiation? While it is self-evident which mesh of the two identical ones faces outside when we look at the 3D model after it is rendered on the computer screen, we absolutely have no way to tell from their coordinates alone in our code. Somehow, we have to invent an algorithm that simulates how people discern it when they look at the 3D view of the model on a computer screen.

Picking and choosing the right meshes, of course, is crucial to the correctness of the simulation results. In order to test the new algorithms in Energy3D, I selected the lower Manhattan island and the US Capitol Building as two test cases. The results of the lower Manhattan island have been reported in an earlier article. But the Capitol Building has turned out to be a harder case as -- with over 15,000 meshes -- it is geometrically more complicated and it has so many details that really put our code to test. After more than a week of my work on solving a myriad of problems, it seems that Energy3D has passed the test (or has it?). The screenshots of the solar irradiance heat maps of the Capitol Building from different angles show that severe anomalies are practically non-existent across the heat maps.

The heat maps occasionally suffer from a flickering effect called "Z-fighting" when two planar surfaces are visually close, especially when being looked at from a distance sufficiently far away. For the Z-fighting between identical meshes, this can be mitigated by increasing the offset of their distance. But, as this doesn't affect the simulation result, it is less a concern for the time being.

Automatic remeshing for solar analysis may also need to include automatic repair of models that contain errors. For example, some models may contain surfaces that have only one mesh with the normal vector pointing inward (this could happen if the original designer accidentally used the "Reverse Faces" feature in SketchUp without realizing that the normal vectors would be flipped). This kind of mesh is tolerated in SketchUp because it does not affect rendering. But they cannot be tolerated in Energy3D because such a single-face mesh with a north-pointing normal vector, which appears to the viewer as south-facing, will show little to zero solar radiation when the sun shines on it. Luckily, due to the visualization in Energy3D, we can quickly spot those incorrect surfaces and fix them by reversing their faces manually. But we will need to find a way to automate this process (not easy, but not impossible).

Solar analysis of metropolitan areas using Energy3D

Fig. 1: Sunshine at the lower Manhattan island
Energy3D can be used to analyze the solar radiation on houses, buildings, and solar power plants to help engineers design strategies for exploiting useful solar energy or mitigating excessive solar heating. This blog post shows that Energy3D may also be used to analyze the solar radiation in large urban areas (e.g., to study the effect of urban heat islands).

Fig. 2: Solar irradiance heat map of Manhattan on 4/25
To demonstrate this application, I chose a 3D model of a section of the lower Manhattan island as a test. The 3D model was downloaded from SketchUp's 3D Warehouse. It was supposedly created after the lower Manhattan island in 2008. I didn't bother to check for accuracy as this was supposed to be a test of Energy3D. Figure 1 shows the model of the Manhattan area.

Fig. 3: More solar irradiance heat maps.
The model has more than 8,000 meshes of various sizes (a mesh is a polygon area for computational analysis and graphical rendering in Energy3D). The entire area is so big that even a low-resolution daily simulation took more than five hours to complete on my Surface Book computer. Figures 2 and 3 show the rendering of the solar irradiance heat map on top of the 3D model after the computation completes.

Our next step is to figure out how to optimize our simulation engine to speed up the calculations. The latest version of Energy3D already includes some optimizations that allow faster re-rendering of the solar irradiance heat map by re-generating the texture images without re-calculating the solar irradiance distribution.

Importing and analyzing models created by other CAD software in Energy3D: Part 2

Fig.1: The Gherkin (London, UK)
In Part I, I showed that Energy3D can import COLLADA models and perform some analyses. This part shows that Energy3D (Version 6.3.5 or higher) can conduct full-scale solar radiation analysis for imported models. This capability officially makes Energy3D a useful daylight and solar simulation tool for sustainable building design and analysis. Its ability to empower anyone to analyze virtually any 3D structure with an intuitive, easy-to-use interface and speedy simulation engines opens many opportunities to engage high school and college students (or even middle school students) in learning science and engineering through solving authentic, interesting real-world problems.
Fig. 2: Beverly Hills Tower (Qatar)

There is an ocean of 3D models of buildings, bridges, and other structures on the Internet (notably from SketchUp's 3D Warehouse, which provides thousands of free 3D models that can be exported to the COLLADA format). These models can be imported into Energy3D for analyses, which greatly enhances Energy3D's applicability in engineering education and practice.

Fig. 3: Solar analysis of various houses
The images in this post show examples of different types of buildings, including 30 St Mary Axe (the Gherkin) in London, UK (Figure 1) and the Beverly Hills Tower in Qatar (Figure 2). Figure 3 shows the analyses of a number of single-family houses. All the solar potential heat maps were calculated and generated based on the total solar radiation that each unit area on the building surfaces receive during the selected day (June 22).

These examples should give you some ideas about what the current version of Energy3D is already capable of doing in terms of solar energy analysis to support, for example, the design of rooftop solar systems and building solar facades.

In the months to come, I will continue to enhance this analytic capacity to provide even more powerful simulation and visualization tools. Optimization, which will automatically identify the boundary meshes (meshes that are on the building envelope), is currently on the way to increase the simulation speed dramatically.

Importing and analyzing models created by other CAD software in Energy3D: Part 1

Fig. 1: Solarize a COLLADA model in Energy3D
Fig.2: A house imported from SketchUp's 3D Warehouse
Energy3D is a relatively simple CAD tool that specializes in building simulation and solar simulation. Its current support for architectural design is fine, but it has limitations. It is never our intent to reinvent the wheel and come up with yet another CAD tool for architecture design. Our primary interest is in physics modeling, artificial intelligence, and computational design. Many users have asked if we can import models created in other CAD software such as SketchUp and then analyze them in Energy3D.

Fig. 3: A house imported from SketchUp's 3D Warehouse
I started this work yesterday and completed the first step today. Energy3D can now import any COLLADA models (*.dae files) on top of a foundation. The first step was the inclusion of the mesh polygons in the calculation of solar radiation. The polygons should be able to cast shadow on any object existing in an Energy3D model. This means that, if you have a 3D model of a neighboring building to the target building, you can import it into Energy3D so that it can be taken into consideration when you design solar solutions for your target. Once you import a structure, you can always translate and rotate it in any way you want by dragging its foundation, like any existing class of object in Energy3D.

Fig. 4: A house at night in Energy3D
Due to some math difficulties, I haven't figured out how to generate a solar radiation heat map overlaid onto the external surfaces of an imported structure that are exposed to the sun. This is going to be a compute-intensive task, I think. But there is a shortcut -- we can add Energy3D's solar panels to the roof of an imported building (Figure 1). In this way, we only have to calculate for these solar panels and all the analytic capabilities of Energy3D apply to them. And we can get pretty good results pretty quickly.

Fig. 5: A 3D tree imported from SketchUp's 3D Warehouse
Figures 2-4 show more examples of how houses designed with SketchUp look like in Energy3D after they are imported. This interoperability makes it possible for architects to export their work to Energy3D to take advantage of its capabilities of energy performance analysis.

Being able to import any structure into Energy3D also allows us to use more accurate models for landscapes. For instance, we can use a real 3D tree model that has detailed leaves and limbs, instead of a rough approximation (Figure 5). Of course, using a more realistic 3D model of a tree that has tens of thousands of polygons slows down the graphic rendering and simulation analysis. But if you can afford to wait for the simulation to complete, Energy3D will eventually get the results for you.

Why is Israel building the world’s tallest solar tower?

Fig. 1: Something tall in Negev desert (Credit: Inhabitat)
The Ashalim solar project (Figure 1) in the Negev desert of Israel will reportedly power 130,000 homes when it is completed in 2018. This large-scale project boasts the world’s tallest solar tower -- at 250 meters (820 feet), it is regarded by many as a symbol of Israel’s ambition in renewable energy.

Solar thermal power and photovoltaic solar power are two main methods of generating electricity from the sun that are somewhat complementary to each other. Solar tower technology is an implementation of solar thermal power that uses thousands of mirrors to focus sunlight on the top of a tower, producing intense heat that vaporizes water to spin a turbine and generate electricity. The physics principle is the same as a solar cooker that you have probably made back in high school.

Why does the Ashalim solar tower have to be so tall?

Surrounding the tower are approximately 50,000 mirrors that all reflect sun beams to the top of the tower. For this many mirrors to "see" the tower, it has to be tall. This is easy to understand with the following metaphor: If you are speaking to a large, packed crowd in a square, you had better stand high so that the whole audience can see you. If there are children in the audience, you want to stand even higher so that they can see you as well. The adults in this analogy represent the upper parts of mirrors whereas the children the lower parts. If the lower parts cannot reflect sunlight to the tower, the efficiency of the mirrors will be halved.

Fig. 2: Visualizing the effect of tower height
An alternative solution for the children in the crowd to see the speaker is to have everyone stay further away from the speaker (assuming that they can hear well) -- this is just simple trigonometry. Larger distances among people, however, mean that the square with a fixed area can accommodate less people. In the case of the solar power tower, this means that the use of the land will not be efficient. And land, even in a desert, is precious in countries like Israel. This is why engineers chose to increase the height of tower and ended up constructing the costly tall tower as a trade-off for expensive land.

Fig. 3: Daily output graphs of towers of different heights
But how tall is tall enough?

Fig. 4: Energy output vs. tower height
This depends on a lot of things such as the mirror size and field layout. The analysis is complicated and reflects the nature of engineering. With our Energy3D software, however, complicated analyses such as this are made so easy that even high school students can do. Not only does Energy3D provide easy-to-use 3D graphical interfaces never seen in the design of concentrated solar power, but it also provides stunning "eye candy" visualizations that clearly spell out the science and engineering principles in design time. To illustrate my points, I set up a solar power tower, copied and pasted to create an array of mirrors, linked the heliostats with the tower, and copied and pasted again to create another tower and another array of mirrors with identical properties. None of these tasks require complicated scripts or things like that; all they take are just some mouse clicks and typing. Then, I made the height of the second tower twice as tall as the first one and run a simulation. A few seconds later, Energy3D showed me a nice visualization (Figure 2). With only a few more mouse clicks, I generated a graph that compares the daily outputs of towers of different heights (Figure 3) and collected a series of data that shows the relationship between the energy output and the tower height (Figure 4). The graph suggests that the gain from raising the tower slows down after certain height. Engineers will have to decide where to stop by considering other factors, such as cost, stability, etc.

Note that, the results of the solar power tower simulations in the current version of Energy3D, unlike their photovoltaic counterparts, can only be taken qualitatively. We are yet to build a heat transfer model that simulates the thermal storage and discharge accurately. This task is scheduled to be completed in the first half of this year. By that time, you will have a reliable prediction software tool for designing concentrated solar power plants.

Ten research papers utilizing Energy2D published in the past two years

Screenshots from recent papers that use Energy2D
Energy2D simulation of fire
Energy2D is a multiphysics simulation program that was created from scratch and is still under development (though its progress has slowed down significantly because my priority has been given to its Energy3D cousin). The software was originally intended to be a teaching and learning tool for high school students who are interested in studying engineering. Over the past two years, however, we have seen 10 research papers published in various journals and conferences that involved significant applications of Energy2D as a scientific research tool for modeling natural phenomena and engineering systems. The problems that these researchers simulated range from solar energy, industrial processes, geophysics, and building science. The authors come from universities from all over the world, including top-notch institutions in US, Europe, and China.

Energy2D simulation of thermal bridge
Among them, researchers from Delft University of Technology, Technical University of Darmstadt, and Eindhoven University of Technology wrote in their recent paper about the validity of Energy2D: "The software program Energy2D is used to solve the dynamic Fourier heat transfer equations for the Convective Concrete case. Energy2D is a relatively new program (Xie, 2012) and is not yet widely used as a building performance simulation tool. To gain more confidence in the predictions with Energy2D, an analytical validation study was therefore carried out first, inspired by the approach described in Hensen and Nakhi (1994). Those analytical solutions and the simulation results of the dynamic response to a 20°C temperature step change on the surface of a concrete construction with the following properties were compared for this research." They concluded that "the simulation results never divert from the exact solution more than 0.45°C and it is therefore considered acceptable to further use this model."

The publication of these papers and very positive user feedback suggest that Energy2D seems to have found itself an interesting niche market. Many scientists and engineers are unable to invest a lot of time and money on its complicated commercial counterparts. But they nonetheless need a handy simulation tool that is much more flexible, intuitive, and capable than formulas in books to deal with realistic geometry -- at least in 2D. This is where Energy2D comes into play.

Reaching this milestone is critically important to the free and open-source Energy2D software, whose future will be reliant on community support. Its modest popularity among scientists is a valid demonstration of the broader impact expected by the National Science Foundation that funded its development. One can only imagine that there are many more users who used the software in their workplace but didn't publish. Now that good words about it have spread, we expect the usage to continue and even accelerate. To better support our users, we have added a community forum recently. We also plan to work with Professor Bob Hanson to port the Java code to JavaScript through his SwingJS translator so that the program can run on more devices.

The list of these papers is as follows:
  1. Mahfoud Abderrezek & Mohamed Fathi, Experimental Study of the Dust Effect on Photovoltaic Panels' Energy Yield, Solar Energy, Volume 142, pp 308-320, 2017
  2. Dennis de Witte, Marie L. de Klijn-Chevalerias, Roel C.G.M. Loonen, Jan L.M. Hensen, Ulrich Knaack, & Gregor Zimmermann, Convective Concrete: Additive Manufacturing to Facilitate Activation of Thermal Mass, Journal of Facade Design and Engineering, Volume 5, No. 1, 2017
  3. Javier G. Monroy & Javier Gonzalez-Jimenez, Gas Classification in Motion: An Experimental Analysis, Sensors and Actuators B: Chemical, Volume 240, pp 1205-1215, 2017
  4. Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, & Frank Wood, Bayesian Optimization for Probabilistic Programs, 30th Conference on Neural Information Processing Systems, Barcelona, Spain, 2016
  5. E. Rozos, I. Tsoukalas, & C. Makropoulos, Turning Black into Green: Ecosystem Services from Treated Wastewater, 13th IWA Specialized Conference on Small Water and Wastewater Systems, Athens, Greece, 2016
  6. W. Taylor Shoulders, Richard Locke, & Romain M. Gaume, Elastic Airtight Container for the Compaction of Air-Sensitive Materials, Review of Scientific Instruments, Volume 87, 063908, 2016
  7. Zachary R. Adam, Temperature Oscillations near Natural Nuclear Reactor Cores and the Potential for Prebiotic Oligomer Synthesis, Origins of Life and Evolution of Biospheres, Volume 46, Issue 2, pp 171-187, 2016
  8. Jiarui Chen, Shuyu Qin, Xinglong Wu, & Paul K Chu, Morphology and Pattern Control of Diphenylalanine Self-Assembly via Evaporative Dewetting, ACS Nano, Volume 10, No. 1, pp 832-838, 2016
  9. Atanas Vasilev, Geothermal Evolution of Gas Hydrate Deposits: Bulgarian Exclusive Economic Zone in the Black Sea, Comptes rendus de l‘Académie bulgare des Sciences, Volume 68, No. 9, pp 1135-1144, 2015
  10. Pedro A. Hernández, et al., Magma Emission Rates from Shallow Submarine Eruptions Using Airborne Thermal Imaging, Remote Sensing of Environment, Volume 154, pp 219-225, November 2014

Accelerating solar farm design in Energy3D with a new model of solar panel racks

Fig. 1: A solar farm of 5,672 solar panels on 8/16 in Boston
The solar simulation in Energy3D is based on discretizing a solar panel, a reflector, a solar water heater, a window, or any other surface into many small cells (mesh), calculating the solar radiation to the centers of the cells, and then summing the results up to obtain the total energy output. For example, a photovoltaic solar panel can be divided into 6x10 cells (this is also because many residential versions of solar panels are actually designed to have 6x10 solar cells). The simulation runs speedily when we have only a few dozen solar panels such is in the case of rooftop solar systems.

Fig. 2: Simulation of 5,672 solar panels on 8/16 in Boston
Unlike rooftop solar systems, large-scale solar farms typically involve thousands of solar panels (mega utility-scale solar farms may have hundreds of thousands of solar panels). If we use the same discretization method for each panel, the simulation would run very slowly (e.g., the speed drops to 1% when the number of solar panels are 100 times more). This slowdown basically makes Energy3D impractical to use by those who cannot afford to wait such as students in the classroom who need to get the results quickly.

Fig. 3: The result of the accelerated model
Fig. 4: The result of the original model
Luckily, solar panel arrays are often installed on parallel long racks in many solar farms (Figure 1). For such solar panel arrays, a lot of calculations could be spared without compromising the overall accuracy of the simulation too much. This allows us to develop a more efficient model of numeric simulation to do solar radiation calculation and even explore methods that use non-uniform meshes to better account for areas that are more likely to be shaded, such as the lower parts of the solar panel arrays. By implementing this new model, we have succeeded in speeding up the calculation dramatically. For example, the daily solar simulation of a solar farm consisting of more than 5,000 solar panels took about a second on my Surface Book computer (Figure 2 -- in this scene I deliberately added a couple of trees so that you can see the result of shading). With the previous model I would probably have to wait for hours to see the result and the graphics card of my computer would take a very deep breath to render more than 5,000 dynamic textures. This is a huge improvement.

Figures 3 and 4 show a comparison of the simulation results between the new and old models. Quantitatively, the total output of the new model is 93.63 kWh for the selected day of June 22 in Boston, compared with 93.25 kWh from the original model. Qualitatively, the color shading patterns that represent the distribution of solar radiation in the two cases are also similar.

The new rack model supports everything about solar panels. It has a smart user interface that allows the user to draw racks of any size and in any direction -- it automatically trims off any extra length so that you will never see a partial solar panel on a rack. When tracking systems are used with long, linear racks, there is only one way to do it -- horizontal single-axis tracker (HSAT). The new model can handle HSAT with the same degree of speed-up. For other trackers such as the vertical single-axis tracker (VSAT) or the altazimuth dual-axis trackers (AADAT), the speed-up will not be as significant, however, as the inter-rack shading is more dynamically complex and each rack must be treated independently.

Designing solar farms and solar canopies with Energy3D

Fig. 1: Single rack
Many solar facilities use racking systems to hold and move arrays of solar panels. Support of racks is now available in our Energy3D software. This new feature allows users to design many different kinds of solar farm, solar park, and solar canopy, ranging from small scale (a few dozen) to large scale (a few thousand).

Fig. 2: Multiple racks
Mini solar stations often use a single rack to hold an array of solar panels (Figure 1). This may be the best option when we cannot install solar panels on the building's roof. You probably have seen this kind of setup at some nature centers where the buildings are often shadowed by surrounding trees.

If you have more space, you probably can install multiple racks (Figure 2), especially when you are considering using altazimuth dual-axis solar trackers to drive them. This configuration is also seen in some large photovoltaic power stations.

Fig. 3: Rack arrays
Larger solar farms typically use arrays of long racks (Figure 3). Each rack can be driven by a horizontal single-axis tracker. Using taller racks usually requires larger inter-rack spacing, which may be an advantage as it allows maintenance trucks to drive through. In a recent experiment, SunPower experimented with how to grow crops or raise animals in the inter-rack space with their Oasis 3.0 system. So arrays of taller racks may be desirable if you want to combine green energy with green agriculture.

Fig. 4: Solar canopy above a parking lot
If you raise the height of a rack, it becomes a so-called solar canopy that provides shading for human activities like the green canopies of trees do. The most common type of solar canopy converts parking lots into power stations and provides shelters from the sun for cars in the summer (Figure 4).

Designing solar canopies for schools' parking lots may be a great engineering project for students to undertake. This is being integrated into our Solarize Your School Project. In fact, Figure 4  shows a real project in Natick High School in Massachusetts. The hypothetical design has more than 1,500 solar panels (each of them has the size of 0.99 x 1.96 m) and costs over a million dollars.