Saturday, June 24, 2017

Robert F. Thinker (1941-2017)

It is in deep sadness that we mourned the passing of Dr. Robert Tinker on June 22, 2017. Bob was the founder of the Concord Consortium and the Virtual High School. For 18 years, he had been my mentor, friend, and supporter. It is hard to accept the fact that he is no longer with us.

My collaboration with Bob began in 1999, when I was doing a term of postdoc in the field of computational biophysics at the newly-established University of Cyprus. My job was to write computer code to simulate molecular motion and quantum transport in proteins. As it is difficult to imagine these nanoscopic processes from raw data generated in simulations, I had to resort to developing real-time, interactive visualizations of simulations so that I could make sense of the results. It was at this point that our trajectories merged. Around that time, Bob and colleague Dr. Boris Berenfeld just got a grant from the National Science Foundation to develop a tool that can visualize the motions of molecules and allow students to mess with them, hoping to create a powerful virtual "microscope" that can bring the obscure molecular dynamics to life on the computer screen for everyone. While Boris was surfing the then-barren Internet to find who had done what in this tiny niche, he came across my Java Molecular Dynamics applet that I created for the purpose of teaching myself Java while experimenting with interactive molecular dynamics. Boris, Bob, and Barbara (Bob's wife) immediately realized that the applet was exactly what they were looking for. After a few rounds of email exchanges, they hired me as a consultant for the project.

While we made progress on the development of what became the Molecular Workbench software later, the plan to employ me as a staff scientist at the Concord Consortium didn't go so well. For some reason, I couldn't come to the U.S. for a job interview (there was no video conference software at that time and it costed more than $3 per minute to make an international call). So Bob decided to stop by Cyprus on his way to an international conference in Israel to make sure that I wasn't just a cat that happened to know how to hit the keyboard in the right places. Even though I didn't know much about the American culture back then, the language of science needed no translation. So we hit it off at the meeting (except that it was kind of weird that the interviewee was actually the host and the interviewer was actually the guest). I made sure that he had enough authentic Mediterranean meze platters and got a chance to submerge himself in the pristine water of the Eastern Mediterranean Sea before he headed back to the States.

I arrived in the U.S. at the end of 2000, basically having nothing but a suitcase. Bob and Barbara welcomed me with an open house and gave me a room to stay for a while until I could find a place of my own. In the next eight years until he "retired," I was fortunate enough to be able to talk to him almost every workday as our offices were right next to each other. As we all remember, he was always optimistic, even in dark times such as September 11, 2001. As the years went by, funding at the Concord Consortium went up and down, but he was such a gifted grant writer that he could always manage to grab some money to keep me focused on the Molecular Workbench project until I became fully independent and found my own path and passion. After he and Barbara retreated to their retirement home in Amherst, they continued to invest their time and energy in the future of the organization. Bob went on to pen many proposals and secured a series of large grants to fund important work at the organization. Unlike many people who think programming and tinkering are "low level" jobs that the Principal Investigators should not have to do, Bob had always been creating his own prototypes and conducting his own experiments all the time to get firsthand experiences. This is probably the reason why he was so insightful with his ideas -- one cannot possibly have a deep understanding about the world if one does not bother to explore in it. He just loved science, programming, and teaching so much that he never stopped learning, thinking, and working until his final days. It is very hard for me to hold back my tears while writing about his last request to me just a few weeks ago, asking me to carry on some work on electronics that he couldn't complete because of illness. With that, he had completely dedicated his entire life to STEM.

Bob's vision about STEM education always put innovation first. He had transcribed the DNA of innovation into the Concord Consortium. His spirit had translated into a culture of innovation that is driving our research and development. With many new emerging technologies, the future ahead of us is full of exciting opportunities. With the combined power and promise of the Internet of Things (IoT), artificial intelligence (AI), and mixed reality (VR/AR/MR), the next decade will undoubtedly bring a new wave of innovation to propel STEM education to a higher level. As a pioneer of probeware for science education who completely understood the pivotal importance of sensors in IoT systems and embedded intelligence, Bob would have been thrilled to set out to explore these new territories with us.

I am deeply sad about this loss.

Thursday, June 22, 2017

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.

Also as of Version 7.0.5, Energy3D has supported 280 locations worldwide, as illustrated in the following map image. You can also view this map within Energy3D using "View Supported Locations on Map..." under the Help Menu. More locations are expected to be added in the following months.

Friday, June 16, 2017

Creating computer models for all solar thermal power plants in the world

Fig. 1: Energy3D models for six solar power towers
Fig. 2: The Gemasolar Plant
One of the unique features of Energy3D is its ability to model, design, and simulate solar power towers. Figure 1 shows the Energy3D models for six solar power towers: Gemosolar (Spain), PS10 (Spain), PS20 (Spain), Greenway (Turkey), Themis (France), and Badaling (China). To support the research and development on concentrated solar power (CSP) -- a solar power solution alternative to photovoltaic (PV) arrays that may be able to provide some baseload capacity, I have been working on creating a library of 3D models for all the existing and planned solar thermal power plants in the world. The ultimate goal is to develop Energy3D into a versatile CAD tool for all forms of CSP (and PV), based on accurate simulation of existing plants first. The acquisition of the capability of reliably modeling both CSP and PV will enable Energy3D to truly support our Solarize Your World Initiative.

Fig. 3: The Gemasolar Plant
Fig. 4: The Gemasolar plant (June 30)
This article shows a bit of progress towards that goal. I have recently added in Energy3D weather data for scores of sites that already have CSP plants or are planning to build CSP plants. Many of these new sites are in Africa, China, Europe, and South America (some of them were requested by our users in Algeria and Chile). These newly added locations bring the total number of sites supported in Energy3D to more than 250. This growing network should provide you weather data that are approximately applicable to your site (but let me know if your site is not currently covered by Energy3D to your satisfaction). When you import your Earth view in Energy3D, the software will automatically choose the supported location that is closest to your site. If there is already a power tower, you can use the length and direction of its shadow in the picture to estimate the date and time when the picture was taken -- this can be done by turning on the shadow and adjusting the date and time spinner of Energy3D until the calculated shadow approximately aligns with the real shadow. After this is done, the heliostats that you add to the scene will approximately point to the same direction as in the image.

In this article, I picked the impressive Gemosolar Thermosolar Plant near the city of Seville, Spain as a showcase. The plant has 2,650 heliostats on 520 acres of land, each of which is as large as 120 square meters. The tower is 140 meters tall. The annual output is approximately 110 GWh. With molten salt tanks, it can store up to 15 hours of energy. Using a low-resolution setting, it takes Energy3D 5-10 minutes to complete a daily simulation and up to a couple of hours to complete an annual simulation. If you can afford to wait longer, you can always increase the simulation resolution and improve the accuracy of results (e.g., more points on the reflectors better account for blocking and shadowing losses).

Wednesday, May 24, 2017

A Mickey Mouse-shaped solar farm

Fig. 1: An aerial view of the Mickey Mouse-shaped solar farm
Fig. 2: An Energy3D model of the Mickey Mouse-shaped solar farm
If I didn't tell you that this is an actual solar farm near the Epcot Theme Park in the Disney World in Orlando, Florida, you probably would think this is some kind of school project done by kids. But no, this 22-acre 5 MW project was designed and installed by Duke Energy and it has been powering Disney World's facilities since 2016 (Figure 1 is an image from Disney.com). So this is some kind of serious business and has drawn a lot of media attention. The solar farm is so new that even the latest version of Google Maps in May 2017 still does not show it (it is available through Google Maps API that we are using, though).

By shaping the beloved Mickey Mouse character with tens of thousands of solar panels, Disney World has delivered a strong message to the world that the company is committed to a sustainable future.

Fig. 3: A solar radiation heat map representation (June 22).
But who says that kids should not do this? Perhaps they couldn't do it because of the lack of appropriate support and tool. Not any more. Thanks to the support from the National Science Foundation, our powerful Energy3D software and our Solarize Your World curriculum can probably turn every wild imagination in solar power into virtual reality, particularly for children who may need more inquiry- and design-based activities that connect so deeply to their world and their future. Figure 2 shows a model of the Mickey Mouse-shaped solar farm in Energy3D and Figure 3 shows a heat map representation of the solar radiation onto the solar panel arrays.

Monday, May 22, 2017

Designing ground-mounted solar panel arrays: Part III

Fig. 1: Rows of solar panels on racks in a solar farm
The most common configuration of solar farms is perhaps arrays consisting of rows of solar panel racks such as shown in Figure 1. But have you ever thought about why? Can we challenge this conventional wisdom?

Fig.2: Cover the field with horizontally-placed solar panels
Obviously, some inter-row spacing allows for easier cleaning and maintenance and, perhaps, even integration with agricultural farming (e.g., growing mushrooms that prefer shaded areas). But let's put those benefits aside for now and just consider the energy part of the problem. Let me point out a fact: If we completely cover the entire field with solar panels with zero tilt angle and zero gap (Figure 2), we are guaranteed to capture almost every single photon that strikes the area regardless of time and location. Such a simple-minded "design" will produce the maximal output of any given field at any location and time and there is absolutely no such problem as inter-row shading. So what solar design?
Fig. 3: Comparing two hypothetical fields.

It turns out that, although the simple-minded design can surely generate maximum electricity, each individual solar panel in it does not necessarily generate a maximum amount of electricity over the course of a year, compared with other designs. In other words, it may just use more solar panels to generate more electricity. As engineering design must consider cost effectiveness and even put it as a top priority, an engineer's job is then to look for a better solution that maximizes the production of each solar panel.

Fig. 4: Compare outputs of single panels in two fields (Boston).
A great advantage of Energy3D is that it allows one to experiment with ideas rapidly. So let's create a field with tilted rows of solar panels and leave some gap between them and then use the Group Analysis Tools to compare the daily and annual outputs of individual solar panels in the two hypothetical fields (Figure 3). And let's assume the fields are in Boston.

Fig. 5: Compare outputs of single panels in two fields (Phoenix).
Figure 4 shows that the total annual output of a single solar panel in the field of tilted rows is nearly 20% higher than that of a single solar panel in the field of flat cover in Boston (42° N). In this simulation, the tilt angle was set to be equal to the latitude. This cost effectiveness is one of the main reasons why we choose tilted rows of solar panels in high-latitude areas (aside from the fact that tilted angles allow rain to wash panels more efficiently and snow to slide from them more quickly).

What about low-latitude locations?


Fig. 6: Compare outputs of single panels in two fields (Mexico).
Note that this result applies only to high-latitude areas such as Boston. If we are designing solar farms for tropical areas such as Singapore, the story may be completely different. In low-latitude areas, small or even zero tilt angles make sense. Therefore, the design principle may be to cover the field with as many solar panels as possible or to use trackers to increase individual outputs (whichever is more economic depends on the relative prices of solar panels and solar trackers that change all the time). You can experiment with Energy3D to find out at which latitude this principle starts to become dominant. Figure 5 shows that the results in cities with a lower latitude such as Phoenix (33° N) and Mexico City (19° N) in North America. In the case of Phoenix, AZ, the gain from the tilted rows drops to about 10%. In the case of Mexico City, it drops to 5%. So designing a ground-mounted solar array for Mexico may be very different from designing a ground-mounted solar array for Canada.

Thursday, May 18, 2017

National Science Foundation funds research and development of an IoT platform for smart schools

Fig. 1: A schematic illustration of IoT as a STEM learning integrator
Future sustainable and resilient infrastructure is expected to be powered by renewable energy, be able to respond intelligently to changes in the environment, and support smart and connected communities. We are pleased to announce that the National Science Foundation (NSF) has awarded our team a $2.9 million, four-year grant to explore the STEM education and workforce development challenges and opportunities in the coming transformation of our nation's infrastructure.

One of the core innovations will be a cyber-physical engineering platform for designing Internet of Things (IoT) systems that manage the resources, space, and processes of a community based on real-time analysis of data collected by various sensors. This innovation is potentially transformative as it can turn the entire building of a home, the entire campus of a school, or the entire area of a town into an engineering laboratory with virtually unlimited opportunities for learning, research, and exploration.

Fig. 2: A possible IoT system for managing a parking lot
Designing an IoT system provides plenty of opportunities to learn math, science, engineering, and computation practices in an integrated fashion, rather than in isolation. Working with sensors allows students to learn the science behind them through inquiry. For example, to calibrate an IoT system, students must understand what specific variables the sensor data represent scientifically. They must analyze the data to explore in what ranges the variables are supposed to vary in different scenarios in order to determine which type of response should be triggered, to what, and when. The acquired knowledge is then applied to the design of an IoT system, which requires engineering design thinking to make trade-off decisions, optimize system performance, and achieve cost effectiveness. Finally, the control, response, and integration of the entire system are realized through computer programming that deals with all foreseeable complexities. The overlaps among three basic skills—scientific reasoning, design thinking, and computational thinking—supported by the IoT platform provide researchers an opportunity to study their integration, as illustrated in Figure 1. (In fact, mathematical thinking is also involved, but let's just leave that out for now.)

This project is unique to engineering and computer science education because IoT is not only a crucial part of electrical engineering and information technology, but it is also one of the few ways through which computer programming can be directly linked to scientific inquiry and engineering design in the material world. Figure 2 provides an example.

This work is supported by the NSF under grant number 1721054. Any opinions, findings, and conclusions or recommendations expressed in this paper, however, are those of the author(s) and do not necessarily reflect the views of the NSF.

Monday, May 15, 2017

Energy2D included in the technology toolkit for sustainable design at a large architecture firm

A material thermal bridge
AECbytes just published an article written by its editor Dr. Lachmi Khemlani, which introduces the technology toolkit for sustainable design at Orcutt Winslow Partnership (OWP), one of the largest architecture firms in the Southwest and ranked in the top 100 firms in the U.S.

A geometric thermal bridge
Dr. Khemlani's article explores what these applications are and how OWP is deploying them to design more energy-efficient buildings. I am honored to learn from her article that Energy2D is part of the OWP toolkit for thermal bridge analysis. It is my great pleasure to know that the humble tool I created from scratch has found its way to professional workplaces. To some extent, it doesn't surprise me that engineers and architects have found it useful as the conduction part of the Energy2D simulation engine is pretty decent, highly accurate, and unconditionally stable.

The first image of this article shows an Energy2D simulation of the material thermal bridge (discontinuities in thermal conductivity of materials such as steel studs in walls). The second image shows an Energy2D simulation of the geometric thermal bridge (discontinuities in cross section of heat flow such as junctions of two planes).

The following is an excerpt from Dr. Khemlani's article:
"Another ArchiCAD feature that OWP uses extensively is its Thermal Bridging analysis tool, which allows a 2D heat-flow simulation to be run on any element to identify those parts of the building that are responsible for heat loss and might cause vapor condensation as well as other unwanted effects. Again, OWP uses this in conjunction with Energy2D, another tool that provides not only thermal bridging analysis but can also run sophisticated CFD (computation fluid dynamics) simulations, allowing OWP to test out different materials and composites for building components."

Saturday, May 13, 2017

A complete 3D model of the PS20 solar power plant

According to Wikipedia, the 20 MW PS20 Solar Power Plant in Seville, Spain consists of a solar field of 1,255 heliostats. Each heliostat, with a surface area of 120 square meters(!), automatically tracks the sun on two axes and reflects the solar radiation it receives onto the central receiver, located at the top of a tower that is as tall as 165 meters. The concentrated heat vaporizes water and produces steam that drives a turbine to generate electricity. The Wikipedia page mentions that PS20 uses a thermal storage system, but it is not clear whether it is a molten salt tank or not.

PS20 generates about 48,000 MWh per year, or roughly 132 MWh per day on average without considering seasonal variations.

The full 3D model of the PS20 plant is now available in Energy3D and can be downloaded from http://energy.concord.org/energy3d/designs/ps20-solar-tower.ng3. While it generally costs hundreds of millions of dollars to design and build such a futuristic power plant, it costs absolutely nothing to do so in the virtual space of Energy3D. In a way, Energy3D gives everyone, especially those in developing nations, a powerful tool to explore the solar potential of their regions. Whether you live in a desert or on the coast, near or far away from the equator, in cities or rural areas, you can imagine all sorts of possibilities with it.

I am working on heat transfer, energy conversion, and thermal storage models that can predict the electricity generation accurately. Right now, Energy3D estimates the raw solar radiation input to the receiver on June 22 to be about 656 MWh, considering all the shadowing and blocking losses. If the system efficiency of heat transfer and energy conversion is in the range of 30-50%, then Energy3D's prediction will fall into a reasonable range.

Wednesday, May 10, 2017

Artificial intelligence research for engineering design

Have you ever thought about what a pity it is when a senior engineer with 40 years of problem-solving experience retires? Have you ever thought about what a loss it is when a senior teacher with 40 years of teaching experience retires? Imagine what we could do for humanity if we find a way to somehow preserve their experience, expertise, and intelligence automatically before these incredible treasures are taken to the graveyard...

Heat map visualizations of different patterns of design task transition
Funded by the National Science Foundation, I have been working on the research and development of artificial intelligence (AI) for engineering design for a number of years and have been developing the Visual Process Analytics for visualizing and analyzing engineering design process data. This exciting intersection among AI (basically everything about how intelligence can be realized), engineering (basically a generative and creative discipline), and cognitive science (basically everything about how humans acquire intelligence) is full of tremendous challenges, but it also creates unprecedented opportunities that constantly entice and enlighten me.

I have recently written a short article to explain my research to the lay people (mostly educators, but the implications are not limited only to education). Check it out at http://energy.concord.org/~xie/papers/aired.pdf

Saturday, May 6, 2017

Designing ground-mounted solar panel arrays: Part II

To design a solar panel array, we need to understand the specifications of the type of solar panel that we are going to use (here is an example of the specs of SunPower's X21-series). Although all solar panels provide nominal maximum power outputs (Pmax or Pnom), those numbers specify the DC power outputs under the Standard Test Conditions (STC) or PVUSA Test Conditions (PTC). Those numbers only provide some standardized values for customers' reference and cannot be used to calculate the electricity generation in the real world. Although each brand of solar panel may be designed in different ways and the specs vary, there are a few scientific principles that govern most of them. The calculation of power generation can therefore be drawn upon these fundamental principles. This article covers some of these principles.

The first parameter for solar power calculation is the solar cell efficiency, which defines the percentage of incident sunlight that can be converted into electricity by a cell of the solar panel. This property is usually determined by the semiconductor materials used to make the cell. Monocrystalline silicon-based materials tend to have a higher efficiency than polycrystalline ones. As of 2017, the solar cell efficiency for most solar panels in the market typically ranges from 15% to 25%. The higher the efficiency, the more expensive the solar panel.

Figure 1: All cells in a series (left) and diode bypasses (right)
The solar cell efficiency generally decreases when the temperature increases. To reflect this relationship, solar panels usually specify the Nominal Operating Cell Temperature (NOCT) and the Temperature Coefficient of Pmax. The former describes how high the temperature of the cell rises to under the sun. The latter describes how much the solar cell efficiency drops as the cell temperature rises. If we know the solar cell efficiency under STC, the NOCT, the Temperature Coefficient of Pmax, the air temperature, and the solar radiation density on the surface of the cell, we can compute the actual efficiency of the solar cell at current time.

Now, in order to compute the actual power output of the cell, we will need to know two more things: the area of the cell and the angle between the surface of the cell and the direction of the sun. The area of the cell is related to the packing density of the cells on a solar panel. Polycrystalline solar cells can have nearly 100% of packing density as they are usually rectangular, whereas monocrystalline ones have less packing density as they usually have round corners (therefore, they can't use up the entire surface area of a solar panel). The angle between the cell and the sun depends on how the solar panel is installed. This usually comes down to its tilt angle and azimuth.

Figure 2: Landscape vs. portrait (diode bypasses, location: Boston)
All these parameters are needed in Energy3D's solar radiation simulation. As a user, what you have to do is to understand the meaning of these parameters while designing your solutions and set the parameters correctly for your simulations. As Energy3D hasn't provided a way to select a solar panel model and then automatically import all of its specs, you still have to define a solar panel brand by setting its properties manually.

The next thing we must consider is a little tricky. A solar panel is made of many cells, arranged in an array of, for example 6 × 10. In order for the cells to produce usable voltage, they are usually connected in a series (the left image in Figure 1). In this case, the electric current flowing through each cell is the same but the voltage adds up. However, the problem with a series circuit is that, if one cell gets shaded by, say, a leaf that falls on it, and as a result generates a weaker current, every other cell of the panel will end up generating a smaller output (worse, all the generated electricity that cannot flow freely will turn into heat and damage the cells). To mitigate this problem, most solar panels today use diode bypasses (the right image in Figure 1) or similar technologies to allow the part of the solar panel that is not shaded to be able to contribute to the overall output. However, if the shade is not as spotty as is in the case of a leaf, even the diode bypasses will not be able to prevent complete loss (this video nicely demonstrates the problem). Therefore, our design of solar arrays must consider the actual wiring of the solar cells on the solar panel that we choose.

Figure 3: Month-by-month outputs of four arrays in Figure 2.
What are the implications of the cell wiring? Figure 2 shows four solar panel arrays with two different inter-row distances but the same number of identical solar panels that connect their cells with diode bypasses. The size of each solar panel is about 1 meter × 2 meters. On the racks of two arrays, the solar panels are placed in the landscape orientation -- each rack has therefore four rows of solar panels. On the racks of the other two arrays, they are placed in the portrait orientation -- each rack has therefore two rows of solar panels. When the inter-row spacing between two adjacent racks is the same, our simulation suggests that the landscape array always generates more electricity than the portrait array. This difference demonstrates the effect of the cell wiring using diode bypasses. In the front part of Figure 2 for arrays with narrower inter-row spacing, the simulation shows that about a quarter of the area on the racks after the first one is shaded during the course of the day (as indicated by their blue coloring). When the solar panels at the bottom of a rack is shaded, a portrait orientation reduces the output of 50% of the solar panels (there are two rows of solar panels on each rack in the portrait array shown in Figure 2), while a landscape orientation reduces the output of 25% of the solar panels (there are four rows of solar panels on each rack in the landscape array shown in Figure 2). The difference becomes less when the inter-row distance is longer. So when you have a limited space to place your solar arrays, you should probably favor the landscape orientation.

Figure 4: Shadow analysis shows inter-row shading in four seasons.
Of course, the output of a solar array depends also on the season. When the sun is high in the sky in the summer, the inter-row shading becomes less a problem. It is during the winter months when the shading loss becomes significant. This is shown in Figure 3. A snapshot of the shadow analysis (Figure 4) illustrates the difference visually.

For sites in the snowy north, another factor in the winter that favors the landscape orientation is the effect of snow accumulation on the panels. As soon as snow slides off the upper third of a solar panel in the landscape arrangement, it will start to generate some electricity. In the case of the portrait arrangement, it has to wait until all the snow comes off the panel.

Note that this article is concerned only with the cell wiring on a solar panel. The wiring of solar panels in an array is another important topic that we will cover later.

Sunday, April 30, 2017

Introducing the Virtual Solar Decathlon

Hypothetical solar power near Hancock Tower in Boston
At the ACE Hackathon event on April 28, we introduced the concept of the Virtual Solar Decathlon to students at Phillips Academy who are interested in sustainable development.

Hypothetical solar canopies at Andover High School
The U.S. Department of Energy's Solar Decathlon challenges 20 collegiate teams to design, build, and operate solar-powered houses that are forward-thinking and cost-effective. Such a project, however, may take up to a year to complete and cost up to $250,000.

PS20 solar power tower in Seville, Spain
For a few years, I have been thinking about creating a high school equivalent of the Solar Decathlon that costs nothing, takes a much shorter time, and allows everyone to participate. The result of this thinking process is the Virtual Solar Decathlon that can now be supported by our Energy3D CAD software (and increasingly so as we added new features to allow more clean energy technologies to be simulated and designed). The goal of the Virtual Solar Decathlon is to turn the entire Google Earth into a simulation-based engineering lab of renewable energy and engage students to change their world by tackling energy problems (at least virtually) that matter deeply to their lives.

Here is the link to our presentation at Phillips Academy.

Thursday, April 20, 2017

Designing ground-mounted solar panel arrays: Part I

Fig. 1: Inter-row shadowing (daily total)
Designing a ground-mounted solar panel array is one of the challenges in our Solarize Your World curriculum, in addition to other challenges such as rooftop solar power systems, solar canopies, building-integrated photovoltaics, and concentrated solar power plants. With the support of our intuitive Energy3D software, designing a solar panel array appears to be a small and simple job as students can easily add, drag, and drop solar panels to cover up a site with many solar panels. But things are not always as simple as they seem.
Fig. 2: Solar radiation on an array in four reasons.

The design of a photovoltaic solar farm is, in fact, a typical engineering problem that requires the designer to find a solution that generates as much electricity as possible with a limited number of solar panels on a given piece of land, among many other constraints and criteria. Such an engineering project mandates iterative design and optimization in a solution space that has scores of variables. And the more the variables we have to deal with, the more complicated the design challenge becomes.

Fig. 3: Annual outputs vs. row spacing and tilt angle
This sequence of articles will walk you through the essential steps for designing photovoltaic solar farms under a variety of conditions. To get you started, let's assume that 1) we have a rectangular area for the solar farm; 2) the edges of the area are perfectly aligned with the north-south and east-west axes; and 3) the area is perfectly flat. This kind of site is probably uncommon in reality (unless the site is in a desert). But let's begin with a very simple scenario like this.


Fig. 4: Surface plot of solar output (ideal)
One of the first things that we have to decide is the number of solar panels. This is usually dictated by the budget. Suppose we have a fixed quantity of solar panels that we can install at a site large enough to space them (i.e., let's assume that we are not constrained by the area of the site for the time being). As people usually put solar panels on racks (a rack of solar panels is often referred to as a row -- but don't confuse it with the rows of solar panels you put on each rack), the next things we have to decide are 1) how many solar panels we want to place on each rack, 2) whether these solar panels are placed in "portrait" or "landscape" orientation on the rack, and 3) how long each rack is. From these information, we know the number of rows for the array. For example, the array in Figure 1 has four rows, each of which has 88 solar panels stacked up in a 4x22 landscape configuration. Since the shorter side of each panel is about one meter long, each rack is about four meters wide.

Fig. 5: Surface plot of solar output (using bypass diodes)
How far should the distance between two adjacent rows be? If the solar panels are tilted towards the sun, the rows cannot be too close to one another as the inter-row shadowing (Figure 1) will reduce the total output (sometimes severely, depending on the wiring of the solar cells on the solar panels -- we will investigate this in the next article), but they cannot be too far away from one another, either, as a longer distance between rows will decrease the efficiency of land use. Determining the optimal inter-row spacing for the solar array under design depends on a number of confounding factors such as the tilt angles, location, solar cell wiring, time of year, use of trackers, type of inverters, and shape of the site that greatly complicate the problem (Figure 2). This is a case in which a thorough understanding of the domain knowledge per se does not suffice to solve the problem. As there is no exact solution, we have to come up with a procedure and a strategy to search for an optimal one in the solution space. And, sometimes, this solution space can be so vast that manual search becomes infeasible.

Fig. 6: Line graph of solar output (using bypass diodes)
To simplify the search for now, let's assume that we only have to decide on the optimal values for the tilt angle and the inter-row spacing. This assumption reduces the solution space to only two dimensions. The most straightforward way to nail them down is to gradually vary the tilt angle and the inter-row spacing and then compute the total annual output of the solar panels at each step (Figure 3), a tedious job that took me a couple of hours to do. Once we have the results, we can use Excel to create a surface plot that shows different zones of outputs as a function of the inter-row spacing and tilt angle (Figures 4 and 5 -- we will discuss their differences in the next article; for now, you just need to know that Figure 5 is a more accurate result). The yellow zones in the surface plots are the reduced solution space where we should zero in to find our solution, taking trade-offs with other criteria such as the efficiency of land use into account. To have a clearer view, Figure 6 shows a 2D line graph of the solar outputs as a function of the tilt angle for six values of inter-row spacing.

The conclusions are that a tilt angle that is approximately equal to the latitude of the site (about 42 degrees in the case of Boston, MA) is the best when the rows are relatively far apart (say, 10 meters away center-to-center or 6 meters way edge to edge when the tilt angle is zero) and when the rows become closer, a smaller tilt angle should be more favorable. For instance, with the center-to-center inter-row spacing reduced to 8 and 7 meters, 35 and 26 degrees are the optimal choices for the tilt angle, respectively. With the optimal tilt angles, we will lose about 2% and 4% of electricity output when we reduce the inter-row spacing from 10 meters to 8 meters and 7 meters, respectively. If we don't change the tilt angles, the losses will increase to 3% and 9%, respectively. These findings apply to fixed solar panel arrays that do not track or "backtrack" the sun.

The analyses we have done so far just barely scratched the surface of the problem. We have many other design topics to cover and design factors to consider. But the volume of work thus far should speak aloud for itself that this is not a simple problem. At the same time Energy3D greatly simplifies an engineering task and empowers anyone to tackle it, it could also create an illusion as if engineering were simple. Yes, a What-You-See-Is-What-You-Get (WYSIWYG) 3D design and construction program like Energy3D may be entertaining in ways similar to playing with Minecraft, but no, engineering is not gaming -- it differs from gaming in many fundamental ways.

Wednesday, April 5, 2017

A demo of the Infrared Street View

An infrared street view
The award-winning Infrared Street View program is an ambitious project that aims to create something similar to Google's Street View, but in infrared light. The ultimate goal is to develop the world's first thermographic information system (TIS) that allows the positioning of thermal elements and the tracking of thermal processes on a massive scale. The applications include building energy efficiency, real estate inspection, and public security monitoring, to name a few.
An infrared image sphere


The Infrared Street View project is based on infrared cameras that work with now ubiquitous smartphones. It takes advantages of the orientation and location sensors of smartphones to store information necessary to knit an array of infrared thermal images taken at different angles and positions into a 3D image that, when rendered on a dome, creates an illusion of immersive 3D effects for the viewer.

The project was launched in 2016 and later joined by three brilliant computer science undergraduate students, Seth Kahn, Feiyu Lu, and Gabriel Terrell, from Tufts University, who developed a primitive system consisting of 1) an iOS frontend app to collect infrared image spheres, 2) a backend cloud app to process the images, and 3) a Web interface for users to view the stitched infrared images anchored at selected locations on a Google Maps application.

The following YouTube video demonstrates an early concept played out on an iPhone:



Friday, March 31, 2017

High school students to solarize the city of Lowell -- virtually


In April, high school students in Lowell, Massachusetts will start exploring various solarization possibilities in the city of Lowell -- famously known as the Cradle of American Industrial Revolution. Many municipal properties and apartment buildings in Lowell have large roofs that are ideal for rooftop solar installations. Public parking facilities also provide space for installing solar canopies, which serve the dual purpose of generating clean energy and providing shade for parked cars. Students will discover the solar potential of their city and calculate the amount of electricity that can generated based on it.

This project is made possible by our Energy3D software, which supports engineering-grade solar design, simulation, and analysis. The Lowell High School, local business owners, and town officials have been very supportive about this initiative. They provided a number of public and private sites for students to pick and choose. Some of them have even agreed to serve as the "clients" for students to provide specifications, inputs, and feedback to students while they are carrying out this engineering project.

Among the available sites, five public parking garages managed by the municipal authority, which have not installed solar canopies, will be investigated by students through feasibility studies that include 3D modeling, solar energy simulation, and financial planning. Through the project work, students will author reports addressed to the property owners, in which they will recommend appropriate solar solutions and financial options.

Solving real-world problems like these creates a meaningful and compelling context and pathway for students to learn science and engineering knowledge and skills. Hopefully, their work will also help inform the general public about the solar potential of their city and the possibility of transitioning it to 100% renewable energy in the foreseeable future, which is a goal recently set by Massachusetts lawmakers.

Saturday, February 25, 2017

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.