Generative Design of Concentrated Solar Power Towers

In a sense, design is about choosing parameters. All the parameters available for adjustment form the basis of the multi-dimensional solution space. The ranges within which the parameters are allowed to change, often due to constraints, sets the volume of the feasible region of the solution space where the designer is supposed to work. Parametric design is, to some extent, a way to convert design processes or subprocesses into algorithms for varying the parameters in order to automatically generate a variety of designs. Once such algorithms are established, users can easily create new designs by tweaking parameters without having to repeat the entire process manually. The reliance on computer algorithms to manipulate design elements is called parametricism in modern architecture.

Parametricism allows people to use a computer to generate a lot of designs for evaluation, comparison, and selection. If the choice of the parameters is driven by a genetic algorithm, then the computer will also be able to spontaneously evolve the designs towards one or more objectives. In this article, I use the design of the heliostat field of a concentrated solar power tower as an example to illustrate how this type of generative design may be used to search for optimal designs in engineering practice. As always, I recorded a screencast video that used the daily total output of such a power plant on June 22 as the objective function to speed up the calculation. The evaluation and ranking of different solutions in the real world must use the annual output or profit as the objective function. For the purpose of demonstration, the simulations that I have run for writing this article were all based on a rather coarse grid (only four points per heliostat) and a pretty large time step (only once per hour for solar radiation calculation). In real-world applications, a much more fine-grained grid and a much smaller time step should be used to increase the accuracy of the calculation of the objective function.

Video: The animation of a generative design process of a heliostat field on an area of 75m×75m for a hypothetical solar power tower in Phoenix, AZ.

 Figure 1: A parametric model of the sunflower.
Heliostat fields can take many forms (the radial stagger layout with different heliostat packing density in multiple zones seems to be the dominant one). One of my earlier (and naïve) attempts was to treat the coordinates of every heliostat as parameters and use genetic algorithms to find optimal coordinates. In principle, there is nothing wrong with this approach. In reality, however, the algorithm tends to generate a lot of heliostat layouts that appear to be random distributions (later on, I realized that the problem is as challenging as protein folding if you know what it is -- when there are a lot of heliostats, there are just too many local optima that can easily trap a genetic algorithm to the extent that it would probably never find the global optimum within the computational time frame that we can imagine). While a "messy" layout might in fact generate more electricity than a "neat" one, it is highly unlikely that a serious engineer would recommend such a solution and a serious manager would approve it, especially for large projects that cost hundreds of million of dollars to construct. For one thing, a seemingly stochastic distribution would not present the beauty of the Ivanpah Solar Power Facility through the lens of the famed photographers like Jamey Stillings.

In this article, I chose a biomimetic pattern proposed by Noone, Torrilhon, and Mitsos in 2012 based on Fermat's spiral as the template. The Fermat spiral can be expressed as a simple parametric equation, which in its discrete form has two parameters: a divergence parameter β that specifies the angle the next point should rotate and a radial parameter b that specifies how far the point should be away from the origin, as shown in Figure 1.

 Figure 2: Possible heliostat field patterns based on Fermat's spiral.
When β = 137.508° (the so-called golden angle), we arrive at Vogel's model that shows the pattern of florets like the ones we see in sunflowers and daisies (Figure 1). Before using a genetic algorithm, I first explored the design possibilities manually by using the spiral layout manager I wrote for Energy3D. Figure 2 shows some of the interesting patterns I came up with that appear to be sufficiently distinct. These patterns may give us some ideas about the solution space.
 Figure 3: Standard genetic algorithm result.
 Figure 4: Micro genetic algorithm result.

Then I used the standard genetic algorithm to find a viable solution. In this study, I allowed only four parameters to change: the divergence parameter β, the width and height of the heliostats (which affect the radial parameter b), and the radial expansion ratio (the degree to which the radial distance of the next heliostat should be relative to that of the current one in order to evaluate how much the packing density of the heliostats should decrease with respect to the distance from the tower). Figure 3 shows the result after evaluating 200 different patterns, which seems to have converged to the sunflower pattern. The corresponding divergence parameter β was found to be 139.215°, the size of the heliostats to be 4.63m×3.16m, and the radial expansion ratio to be 0.0003. Note that the difference between β and the golden angle cannot be used alone as the criterion to judge the resemblance of the pattern to the sunflower pattern as the distribution also depends on the size of the heliostat, which affects the parameter b.

I also tried the micro genetic algorithm. Figure 4 shows the best result after evaluating 200 patterns, which looks quite similar to Figure 3 but performs slightly less. The corresponding divergence parameter β was found to be 132.600°, the size of the heliostats to be 4.56m×3.17m, and the radial expansion ratio to be 0.00033.

In conclusion, genetic algorithms seem to be able to generate Fermat spiral patterns that resemble the sunflower pattern, judged from the looks of the final patterns.

Virtual Solar Grid adds Crescent Dunes Solar Tower

 The Crescent Dues Solar Tower as modeled in Energy3D
 A light field visualization in Energy3D
 A top view
The Crescent Dunes Solar Power Tower is a 110 MW utility-scale concentrated solar power (CSP) plant with 1.1 GWh of molten salt energy storage, located about 190 miles northwest of Las Vegas in the United States (watch a video about it). The plant includes a whopping number of 10,347 large heliostats that collect and focus sunlight onto a central receiver at the top of a 195-meter tall tower to heat 32,000 tons of molten salt. The molten salt circulates from the tower to some storage tanks, where it is then used to produce steam and generate electricity. Excess thermal energy is stored in the molten salt and can be used to generate power for up to ten hours, providing electricity in the evening or during cloudy hours. Unlike other CSP plants, Crescent Dunes' advanced storage technology eliminates the need for any backup fossil fuels to melt the salt and jumpstart the plant in the morning. Each heliostat is made up of 35 6×6 feet (1.8 m) mirror facets, adding up to a total aperture of 115.7 square meters. The total solar field aperture sums to an area of 1,196,778 square meters, or more than one square kilometer, in a land area of 1,670 acres (6.8 square kilometers). That is, the plant is capable of potentially collecting one seventh of all the solar energy that shines onto the field. Costing about \$1 billion to construct, it was commissioned in September 2015.

 A close-up view of accurate modeling of heliostat tracking
Since its inception in January 2018, our Virtual Solar Grid has included the Energy3D models of nearly all the existing large CSP power plants in the world. That covers more than 80 large CSP plants capable of generating more than 11 TWh per year. The ultimate goal of the Virtual Solar Grid is to mirror every solar energy system in the world in the computing cloud through crowdsourcing involving a large number of students interested in engineering, creating an unprecedentedly detailed computational model for learning how to design a reliable and resilient power grid based completely on renewable energy (solar energy in this phase). The modeling of the Crescent Dunes plant has put our Energy3D software to a stress test. Can it handle such a complex project with so many heliostats in such a large field?
 A side view

 Near the base of the tower
 Over the shoulder of the tower
 The solar field

 The solar field
After making numerous other improvements for Energy3D, the latest version (V7.8.4) was finally capable of modeling this colossal power plant. This includes the capability of being able to divide the whole project into nine smaller projects and then allow Energy3D to stitch the smaller 3D models together to create the full model using the Import Tool. This divide-and-conquer method makes the user interface a lot faster as neither you nor Energy3D need to deal with 9,000 existing heliostats while you are adding the last 1,000. The predicted annual output of the plant by Energy3D is 462 GWh, as opposed to the official projection of 500 GWh, assuming 90% of mirror reflectance and 25% of thermal-to-electric conversion.

One thing I had to do, though, was to double the memory requirement for the software from the default 256 MB to 512 MB for the Windows version (the Mac version is fine), which would make the software fail on really old computers that have only 256 MB of total memory (but I don't think such old computers would still work properly today anyways). The implication of this change is that, if you are a Windows user and have installed Energy3D before, you will need to re-install it using the latest installer from our website in order to take advantage of this update. If you are not sure, there is a way to know how much memory your Energy3D is allocated by checking the System Information and Preferences under the File Menu. If that number is about 250 MB, then you have to re-install the software -- if you really want to see the spectacular Crescent Dunes model in Energy3D without crashing it.

With basically only the three Ivanpah Solar Towers left to be modeled and uploaded, the Virtual Solar Grid has nearly incorporated all the operational solar thermal power plants in the world. We will continue to add new CSP plants as they come online and show up in Google Maps. In our next phase, we will move to add more photovoltaic (PV) solar power plants to the Virtual Solar Grid. At this point, the proportion of the modeled capacity from PV stands at only 8% in the Virtual Solar Grid, compared with 92% from CSP. Adding PV power plants will really require crowdsourcing as there are many more PV projects in the world -- there are potentially millions of small rooftop systems in existence. On a separate avenue, the National Renewable Energy Laboratory (NREL) has estimated that, if we add solar panels to every square feet of usable roof area in the U.S., we could meet 40% of our total electricity need. Is their statement realistic? Perhaps only time can tell, but by adding more and more virtual solar power systems to the Virtual Solar Grid, we might be able to tell sooner.

Virtual Solar Grid comes online

 Fig. 1: Modeled output of the Virtual Solar Grid
 Fig. 2: A residential rooftop PV system.
If you care about finding renewable energy solutions to environmental problems, you probably would like to join an international community of Energy3D users to model existing or design new solar power systems in the real world and contribute them to the Virtual Solar Grid — a hypothetical power grid that I am developing from scratch to model and simulate interconnected solar energy systems and storage. My ultimate goal is to crowdsource an unprecedented fine-grained, time-dependent, and multi-scale computational model for anyone, believer or skeptic of renewables, to study how much of humanity's energy need can be met by solar power generation on the global scale — independent of any authority and in the spirit of citizen science. I have blogged about this ambitious plan before and I am finally pleased to announce that an alpha version of the Virtual Solar Grid has come online, of course, with a very humble beginning.

 Fig. 3: The Micky Mouse solar farm in Orlando, FL.
 Fig. 4: NOOR-1 parabolic troughs in Morocco.
As of the end of January, 2018, the Virtual Solar Grid has included 3D models of only a bit more than 100 solar energy systems, ranging from small rooftop photovoltaic solar panel arrays (10 kW) to large utility-scale concentrated solar power plants (100 MW) in multiple continents. At present, the Virtual Solar Grid has a lot of small systems in Massachusetts because we are working with many schools in the state.

With this initial capacity, the Virtual Solar Grid is capable of generating roughly 4 TWh per year, approximately 0.02% of all the electricity consumed by the entire world population in 2016 (a little more than 2 PWh). Although 0.02% is too minuscule to count, it nonetheless marks the starting point of our journey towards an important goal of engaging and supporting anyone to explore the solar energy potential of our planet with serious engineering design. In a sense, you can think of this work as inventing a "Power Minecraft" that would entice people to participate in a virtual quest for switching humanity's power supply to 100% renewable energy.

 Fig. 5: Khi Solar One solar power tower in South Africa.
 Fig. 6: PS 10 and PS 20 in Spain.
The critical infrastructure underlying the Virtual Solar Grid is our free, versatile Energy3D software that allows anyone from a middle school student to a graduate school student to model or design any photovoltaic or concentrated solar power systems, down to the exact location and specs of individual solar panels or heliostats. Performance analysis of solar power systems in Energy3D is based on a growing database of solar panel brand models and weather data sets for nearly 700 regions in every habitable continent. To construct a grid, micro or global, an Energy3D model can be geotagged — the geolocation is automatically set when you import a Google Maps image into an Energy3D model. Such a virtual model, when uploaded to the Virtual Solar Grid, will be deployed to a Google Maps application that shows exactly where it is in the world and how much electricity it produces at a given hour on a given day under average weather conditions. This information will be used to investigate how solar power and other renewables, with thermal and electric storage, can be used to provide base loads and meet peak demands for a power grid of an arbitrary size, so to speak.

Finally, it is important to note that the Virtual Solar Grid project is generously funded by the U.S. National Science Foundation through grant number #1721054. Their continuous support of my work is deeply appreciated.

Modeling parabolic dish Stirling engines in Energy3D

 Fig. 1: A parabolic dish Stirling engine
 Fig. 2: The Tooele Army Depot solar project in Utah
A parabolic dish Stirling engine is a concentrated solar power (CSP) generating system that consists of a stand-alone parabolic dish reflector focusing sunlight onto a receiver positioned at the parabolic dish's focal point. The dish tracks the sun along two axes to ensure that it always faces the sun for the maximal input (for photovoltaic solar panels, this type of tracker is typically known as dual-axis azimuth-altitude tracker, or AADAT). The working fluid in the receiver is heated to 250–700 °C and then used by a Stirling engine to generate power. A Stirling engine is a heat engine that operates by cyclic compression and expansion of air or other gas (the working fluid) at different temperatures, such that there is a net conversion of thermal energy to mechanical work. The amazing Stirling engine was invented 201 years ago(!). You can see an infrared view of a Stirling engine at work in a blog article I posted early last year.

Although parabolic dish systems have not been deployed at a large scale -- compared with its parabolic trough cousin and possibly due to the same reason that AADAT is not popular in photovoltaic solar farms because of its higher installation and maintenance costs, they nonetheless provide solar-to-electric efficiency above 30%, higher than any photovoltaic solar panel in the market as of 2017.

In Version 7.2.2 of Energy3D, I have added the modeling capabilities for designing and analyzing parabolic dish engines (Figure 1). Figure 2 shows an Energy3D model of the Tooele Army Depot project in Utah. The solar power plant consists of 429 dishes, each having an aperture area of 35 square meters and outputting 3.5 kW of power.

 Fig. 3: All four types of real-world CSP projects modeled in Energy3D
With this new addition, all four types of main CSP technologies -- solar towers, linear Fresnel reflectors, parabolic troughs, and parabolic dishes, have been supported in Energy3D (Figure 3). Together with its advancing ability to model photovoltaic solar power, these new features have made Energy3D one of the most comprehensive and powerful solar design and simulation software tools in the world, delivering my promise made about a year ago to model all major solar power engineering solutions in Energy3D.

An afterthought: We can regard a power tower as a large Fresnel version of a parabolic dish and the compact linear Fresnel reflectors as a large Fresnel version of a parabolic trough. Hence, all four concentrated solar power solutions are based on parabolic reflection, but with different nonimaging optical designs that strike the balance between cost and efficiency.

Analyzing the linear Fresnel reflectors of the Sundt solar power plant in Tucson

 Fig. 1: The Sundt solar power plant in Tucson, AZ
 Fig. 2: Visualization of incident and reflecting light beams
Tucson Electric Power (TEP) and AREVA Solar constructed a 5 MW compact linear Fresnel reflector (CLFR) solar steam generator at TEP’s H. Wilson Sundt Generating Station -- not far from the famous Pima Air and Space Museum. The land-efficient, cost-effective CLFR technology uses rows of flat mirrors to reflect sunlight onto a linear absorber tube, in which water flows through, mounted above the mirror field. The concentrated sunlight boils the water in the tube, generating high-pressure, superheated steam for the Sundt Generating Station. The Sundt CLFR array is relatively small, so I chose it as an example to demonstrate how Energy3D can be used to design, simulate, and analyze this type of solar power plant. This article will show you how various analytic tools built in Energy3D can be used to understand a design principle and evaluate a design choice.

 Fig. 3: Snapshots
One of the "strange" things that I noticed from the Google Maps of the power station (the right image in Figure 1) is that the absorber tube stretches out a bit at the northern edge of the reflector assemblies, whereas it doesn't at the southern edge. The reason that the absorber tube was designed in such a way becomes evident when we turn on the light beam visualization in Energy3D (Figure 2). As the sun rays tend to come from the south in the northern hemisphere, the focal point on the absorber tube shifts towards the north. During most days of the year, the shift decreases when the sun rises from the east to the zenith position at noon and increases when the sun lowers as it sets to the west. This shift would have resulted in what I call the edge losses if the absorber tube had not extended to the north to allow for the capture of some of the light energy bounced off the reflectors near the northern edge. This biased shift becomes less necessary for sites closer to the equator.

Energy3D has a way to "run the sun" for the selected day, creating a nice animation that shows exactly how the reflectors turn to bend the sun rays to the absorber pipe above them. Figure 3 shows five snapshots of the reflector array at 6am, 9am, 12pm, 3pm, and 6pm, respectively, on June 22 (the longest day of the year).

As we run the radiation simulation, the shadowing and blocking losses of the reflectors can be vividly visualized with the heat map (Figure 4). Unlike the heat maps for photovoltaic solar panels that show all the solar energy that hits them, the heat maps for reflectors show only the reflected portion (you can choose to show all the incident energy as well, but that is not the default).

There are several design parameters you can explore with Energy3D, such as the inter-row spacing between adjacent rows of reflectors. One of the key questions for CLFR design is: At what height should the absorber tube be installed? We can imagine that a taller absorber is more favorable as it reduces shadowing and blocking losses. The problem, however, is that, the taller the absorber is, the more it costs to build and maintain. It is probably also not very safe if it stands too tall without sufficient reinforcements. So let's do a simulation to get in the ballpark. Figure 5 shows the relationship between the daily output and the absorber height. As you can see, at six meters tall, the performance of the CLFR array is severely limited. As the absorber is elevated, the output increases but the relative gain decreases. Based on the graph, I would probably choose a value around 24 meters if I were the designer.
 Fig. 4: Heat map visualization

An interesting pattern to notice from Figure 5 is a plateau (even a slight dip) around noon in the case of 6, 12, and 18 meters, as opposed to the cases of 24 and 30 meters in which the output clearly peaks at noon. The disappearance of the plateau or dip in the middle of the output curve indicates that the output of the array is probably approaching the limit.

 Fig. 5: Daily output vs. absorber height
If the height of the absorber is constrained, another way to boost the output is to increase the inter-row distance gradually as the row moves away from the absorber position. But this will require more land. Engineers are always confronted with this kind of trade-offs. Exactly which solution is the optimal depends on comprehensive analysis of the specific case. This level of analysis used to be a professional's job, but with Energy3D, anyone can do it now.

Modeling linear Fresnel reflectors in Energy3D

 Fig. 1: Fresnel reflectors in Energy3D.
 Fig. 2: An array of linear Fresnel reflectors
Linear Fresnel reflectors use long assemblies of flat mirrors to focus sunlight onto fixed absorber pipes located above them, thus capable of concentrating sunlight to as high as 30 times of its original intensity (Figures 1 and 2). This concentrated light energy is then converted into thermal energy to heat a fluid in the pipe to a very high temperature. The hot fluid gives off the heat through a heat exchanger to power a steam generator, like in other concentrated solar power plants such as parabolic troughs and power towers.

 Fig. 3: Heap map view of reflector gains
Compared with parabolic troughs and power towers, linear Fresnel reflectors may be less efficient in generating electricity, but they may be cheaper to build. According to Wikipedia and the National Renewable Energy Laboratory, Fresnel reflectors are the third most used solar thermal technology after parabolic troughs and power towers, with about 15 plants in operation or under construction around the world. To move one small step closer to our goal of providing everyone a one-stop-shop solar modeling software program for solarizing the world, I have added the design, simulation, and analysis capabilities of this type of concentrated solar power technology in Version 7.1.8 of Energy3D.

 Fig. 4: Compact linear Fresnel reflectors.
 Fig. 5: Heat map view of linear Fresnel reflectors for two absorber pipes.
Like parabolic troughs, Fresnel reflectors are usually aligned in the north-south axis and rotate about the axis during the day for maximal efficiency (interestingly enough, however, some of the current Fresnel plants I found on Google Maps do not stick to this rule -- I couldn't help wondering the rationale behind their design choices). Unlike parabolic troughs, however, the reflectors hardly face the sun directly, as they have to bounce sunlight to the absorber pipe. The reflectors to the east of the absorber start the day with a nearly horizontal orientation and then gradually turn to face west. Conversely, those to the west of the absorber start the day with an angle that faces east and then gradually turn towards the horizontal direction. Due to the cosine efficiency similar to the optics related to heliostats for power towers, the reflectors to the east collect less energy in the morning than in the afternoon and those to the west collect more energy in the morning and less in the afternoon.

Like heliostats for power towers, Fresnel reflectors have both shadowing and blocking losses (Figure 3). Shadowing losses occur when a part of a reflector is shadowed by another. Blocking losses occur when a part of a reflector that receives sunlight cannot reflect the light to the absorber due to the obstruction of another reflector. In addition, Fresnel reflectors suffer from edge losses -- the focal line segments of certain portions near the edges may fall out of the absorber tube and their energy be lost, especially when the sun is low in the sky. In the current version of Energy3D, edge losses have not been calculated (they are relatively small compared with shadowing and blocking losses).

Linear Fresnel reflectors can focus light on multiple absorbers. Figure 4 shows a configuration of a compact linear Fresnel reflector with two absorber pipes, positioned to the east and west of the reflector arrays, respectively. With two absorber pipes, the reflectors may be overall closer to the absorbers, but the downside is increased blocking losses for each reflector (Figure 5).

Simulation-based analysis of parabolic trough solar power plants around the world

 Fig. 1: 3D heat map of the Keahole Plant in Hawaii
 Fig. 2: SEGS-8 in California and NOOR-1 in Morocco
In Version 7.1.7 of Energy3D, I have added the basic functionality needed to perform simulation-based analysis of solar power plants using parabolic trough arrays. These tools include 24-hour yield analysis for any selected day, 12-month annual yield analysis, and the 3D heat map visualization of the solar field for daily shading analysis (Figure 1). The heat map representation makes it easy to examine where and how the design can be optimized at a fine-grained level. For instance, the heat map in Figure 1 illustrates some degree of inter-row shadowing in the densely-packed Keahole Solar Power Plant in Hawaii (also known as Holaniku). If you are curious, you can also add a tree in the middle of the array to check out its effect (most solar power plants are in open space with no external obstruction to sunlight, so this is just for pure experimental fun).
 Fig. 3: Hourly outputs near Tuscon in four seasons

 Fig. 4: Hourly outputs near Calgary in four seasons
As of July 12, I have constructed the Energy3D models for nine such solar power plants in Canada, India, Italy, Morocco, and the United States (Arizona, California, Florida, Hawaii, and Nevada) using the newly-built user interface for creating and editing large-scale parabolic trough arrays (Figure 2). This interface aims to support anyone, be she a high school student or a professional engineer or a layperson interested in solar energy, to design this kind of solar power plant very quickly. The nine examples should sufficiently demonstrate Energy3D's capability of and relevance in designing realistic solar power plants of this type. More plants will be added in the future as we make progress in our Solarize Your World Initiative that aims to engage everyone to explore, model, and design renewable energy solutions for a sustainable world.
 Fig. 5: Hourly outputs near Honolulu in four seasons

An interesting result is that the output of parabolic troughs actually dips a bit at noon in some months of the year (Figure 3), especially at high altitudes and in the winter, such as Medicine Hat in Canada at a latitude of about 51 degrees (Figure 4). This is surprising as we perceive noon as the warmest time of the day. But this effect has been observed in a real solar farm in Cary, North Carolina that uses horizontal single-axis trackers (HSATs) to turn photovoltaic solar panels. Although I don't currently have operation data from solar farms using parabolic troughs, HSAT-driven photovoltaic solar arrays that align in the north-south axis work in a way similar to parabolic troughs. So it is reasonable to expect that the outputs from parabolic troughs should exhibit similar patterns. This also seems to agree with the graphs in Figure 6 of a research paper by Italian scientists that compares parabolic troughs and Fresnel reflectors.

The effect is so counter-intuitive that folks call it "Solar Array Surprises." It occurs only in solar farms driven by HSATs (fixed arrays do not show this effect). As both the sun and the solar collectors move in HSAT solar arrays, exactly how this happens may not be easy to imagine at once. Some people suggested that the temperature effect on solar cell efficiency might be a possible cause. Although it is true that the decrease of solar cell efficiency at noon when temperature rises to unfavorable levels in the summer of North Carolina can contribute to the dip, the theory cannot explain why the effect is also pronounced in other seasons. But Energy3D accurately predicts these surprises, as I have written in an article about a year before when I added supports for solar trackers to Energy3D. I will think about this more carefully and provide the explanation later in an article dedicated to this particular topic. For now, I would like to point out that Energy3D shows that the effect diminishes for sites closer to the equator (Figure 5).

Energy3D turns the globe into a powerful engineering design lab for everyone

 Fig. 1: Dots represent regions supported in Energy3D.
Many of the readers of my blog may not know Energy3D is, in fact, also a Google Maps application. Energy3D allows users to import a satellite image of a site through the Google Maps API as the "ground image" in its 3D coordinate system, on top of which users can draw 3D structures such as buildings or power plants. Built-in simulation engines can then be used to test and analyze these structures without having to switch to another tool and leave the scene (something known as "concurrent analysis" in the CAD industry). These engines use large geographical and weather datasets for the site as inputs for simulations to accurately take environmental factors such as air temperature and solar radiation into account. As the climate is probably the single most important factor that drives the energy usage in buildings where we live and work, it is important to use weather data from a typical meteorological year (TMY) in a simulation. If no weather data is available for the site, Energy3D will automatically select the nearest location from a network of more than 525 supported worldwide regions (Figure 1) when you import the satellite image from Google Maps. The following table lists the numbers of regions in 176 countries that are currently supported in Energy3D. The United States is covered by a network of 164 nodes. So if you are in the United States, you will have a better chance to find a location that may represent the climate of your area.

 Afghanistan 1 Albania 1 Algeria 6 Angola 1 Argentina 3 Armenia 1 Australia 11 Austria 1 Azerbaijan 1 Bahamas 1 Bahrain 1 Bangladesh 1 Belarus 1 Belgium 1 Belize 1 Bolivia 1 Bosnia & Herzegovina 1 Botswana 1 Brazil 8 Brunei 1 Bulgaria 1 Burkina Faso 1 Burundi 1 Cambodia 1 Cameroon 1 Canada 10 Cape Verde 1 Central African Republic 1 Chad 1 Chile 12 China 42 Colombia 2 Comoros 1 Congo 1 Costa Rica 1 Croatia 1 Cuba 1 Cyprus 2 Czech 1 DR Congo 1 Denmark 1 Djibouti 1 Dominica 1 Dominican Republic 1 East Timor 1 Ecuador 1 Egypt 1 El Salvador 1 Equatorial Guinea 1 Eritrea 1 Estonia 1 Ethiopia 1 Fiji 2 Finland 1 France 8 Gabon 1 Gambia 1 Georgia 1 Germany 12 Ghana 1 Greece 2 Guatemala 1 Guinea 1 Guinea-Bissau 1 Guyana 1 Haiti 1 Honduras 1 Hungary 1 Iceland 1 India 11 Indonesia 4 Iran 3 Iraq 1 Ireland 1 Israel 1 Italy 5 Ivory Coast 1 Jamaica 1 Japan 6 Jerusalem 1 Jordan 1 Kazakhstan 1 Kenya 1 Kosovo 1 Kuwait 1 Kyrgyzstan 1 Laos 1 Latvia 1 Lebanon 1 Lesotho 1 Liberia 1 Libya 2 Liechtenstein 1 Lithuania 1 Luxembourg 1 Macedonia 1 Madagascar 1 Malawi 1 Malaysia 2 Maldives 1 Mali 1 Malta 1 Marshall Islands 1 Mauritania 1 Mauritius 1 Mexico 4 Moldova 1 Monaco 1 Mongolia 1 Montenegro 1 Morocco 3 Mozambique 1 Myanmar 2 Namibia 1 Nepal 1 Netherlands 1 New Zealand 2 Nicaragua 1 Niger 1 Nigeria 1 North Korea 1 Norway 1 Oman 1 Pakistan 4 Panama 1 Papua New Guinea 1 Paraguay 1 Peru 2 Philippines 1 Poland 7 Portugal 2 Qatar 1 Republic of China 2 Romania 1 Russia 7 Rwanda 1 Saudi Arabia 2 Senegal 1 Serbia 2 Sierra Leone 1 Singapore 1 Slovakia 1 Slovenia 1 Solomon Islands 1 Somalia 1 South Africa 8 South Korea 2 South Pole 1 South Sudan 1 Spain 8 Sri Lanka 1 Sudan 1 Sweden 1 Switzerland 3 Syria 2 Tajikistan 1 Tanzania 2 Thailand 2 Togo 1 Trinidad & Tobago 1 Tunisia 1 Turkey 3 Turkmenistan 1 Uganda 1 Ukraine 2 United Arab Emirates 2 United Kingdom 6 United States 164 Uruguay 1 Uzbekistan 1 Venezuela 1 Vietnam 2 Western Sahara 1 Yemen 1 Zambia 1 Zimbabwe 1

 Fig. 2: Solar sites in Fitchburg, MA.
Energy3D's capability of turning Google Maps into a gigantic virtual engineering design lab has tremendous potential in STEM education and energy revolution. It allows students to pick and choose sites for designing renewable energy and energy efficiency solutions that are most relevant to their lives, such as their home and school buildings (Figure 2). It gives students an authentic tool that supports them to scientifically investigate all sorts of possibilities to design a more sustainable world and effectively communicate their ideas to the public. And, most importantly, with Energy3D being a free tool that anyone can use at zero cost, this can happen at the global scale to engage every student in the world to act now and make a difference!

This global vision is not new. Back in 1995, the National Science Foundation funded my colleagues Boris Berenfeld, Bob Tinker, and Dan Barstow, who were at TERC at that time, a grant to develop a curriculum that they touted as the Globe Lab. The Global Lab Curriculum meant to provide an interdisciplinary, one-year course at the secondary level that supports science standards and school reform through intercultural, scientifically meaningful, and collaborative student investigations in environmental studies. Students were given the opportunity to experience all aspects of genuine scientific research: problem identification, background study, project design, collaboration, data analysis, and communication.

 Fig. 3: Solar power plants around the world.
More than 20 years later, technology has advanced so much that we now have many more resources and tools to rethink about this idea. With Google Maps and weather data for countless regions in the world, Energy3D is poised to become a true example of Globe Lab for science and engineering. The integration of the software and our Solarize Your World Curriculum with the current, unstoppable waves of renewable energy innovation and movement worldwide will create numerous exciting possibilities for youth to become truly involved and engaged in shaping their world and their future (Figure 3). While we undertake this grand challenge, it is utterly important to keep in mind that renewable energy does not just stand for some kind of green ideology related only to potential tax hikes -- it also represents trillions of dollars worth of business opportunities and investment in the coming decades committed by almost all governments on the planet to revamp the world's energy infrastructure to provide cleaner air and healthier environment for their citizens. Given this level of global significance, our work will only become more essential and the implications will only become more profound.

As we are mourning the loss of Bob Tinker, one of the architects of the Global Lab Curriculum, carrying on this line of work will be the best way to remember his visions, honor his contributions, and celebrate his life.

Modeling parabolic troughs in Energy3D

 Fig. 1. The absorber tube of a parabolic trough
A parabolic trough is a type of concentrated solar collector that is straight in one dimension and curved as a parabola in the other two, lined with mirrors. Sunlight that enters the trough is focused on an absorber tube aligned along the focal line of the parabola, heating up the fluid in the tube (Figures 1 and 2). If the parabolic trough is for generating electricity, the heated fluid is then used to vaporize water and drive a turbine engine. A power plant usually consists of many rows of parabolic troughs.

 Fig. 2. A view from the absorber tube.
Parabolic troughs are another common form of concentrated solar power (CSP), in addition to solar power towers that Energy3D has already supported (there are two other types of CSP technologies: Dish Stirling and Fresnel reflectors, but they are not very common). According to Wikipedia, there are currently more parabolic trough-based CSP plants than tower-based ones.

In the latest version of Energy3D (V7.0.6), users can now add any number of parabolic troughs of any shape and size to design a solar thermal power plant.

 Fig. 3: Parabolic troughs at different times of the day

Parabolic troughs are most commonly aligned in the north-south axis so that they can rotate to track the sun from east to west during the day. This kind of trackers for parabolic troughs works in a way similar to the horizontal single-axis tracker (HSAT) for driving photovoltaic solar panel arrays. You can observe their motions when you change the time or date or animate the movement of the sun in Energy3D. Figure 3 illustrates this.

Like photovoltaic solar panel arrays, parabolic troughs have the inter-row shadowing problem as well. So the distance between adjacent rows of parabolic troughs cannot be too small, either. But unlike solar power towers, parabolic troughs do not have reflection blocking issues among mirrors. Figure 4 shows this.

This new addition greatly enhances Energy3D's capability of modeling CSP plants, moving the software closer to the goal of being a one-stop shop for exploring all sorts of solar solutions. In the coming weeks, we will start to build 3D models for parabolic troughs in the real world.
 Fig. 4: Inter-row shadowing in parabolic trough arrays

Khi Solar One

Khi Solar One (KSO) is a 50 MW solar power tower plant located in Upington, South Africa, which was commissioned in February, 2016. KSO has 4,120 heliostats on 346 acres of land. Each heliostat is as large as 140 square meters, reflecting sunlight to a tower as tall as 205 meters. KSO has two hours of thermal storage. The power plant is expected to generate a total of 180 GWh per year.

A low-resolution simulation of Energy3D predicts that on February 28 (close to when the Google Maps image was most likely taken) and June 28 (a winter day in the southern hemisphere), the total daily input to the solar tower (not the output of electricity generated by the turbines) is about 2.6 MWh and 1.9 MWh, respectively, as is shown in the graphs below.

The Energy3D model of the KSO can be downloaded from this web page, along with other solar power plants.