In the last post, I have blogged about modeling dual-axis solar trackers in Energy3D. To be more precise, the trackers shown in that blog post are altitude-azimuth (Alt/Az), or altazimuth trackers, or AADAT in short. In this post, I will introduce a type of single-axis tracker -- the horizontal single-axis tracker, or HSAT in short.
Fig. 1 Solar panel arrays rotated by HSATs
Because single-axis trackers do not need to follow the sun exactly, there are many different designs. Most of them differ in the choice of the axis of rotation. If the axis is horizontal to the ground, the tracker is a HSAT. If the axis is vertical to the ground, the tracker is a vertical single-axis tracker, or VSAT in short. All trackers with axes of rotation between horizontal and vertical are considered tilted single-axis trackers, or TSAT in short. None of these single-axis trackers can help the solar panels capture 100% of the solar radiation that reaches the ground. Exactly which design to choose depends on the location of the solar farm, among other consideration such as the cost of the mechanical system.
Fig. 2 Compare daily outputs of HSAT, AADAT, and fixed in four seasons.
HSAT is the first type of single-axis tracker that has been implemented in Energy3D. HSAT is probably more common than VSAT and TSAT and is probably easier to construct and install. In most cases, the rotation axis of a HSAT aligns with the north-south direction and the solar panels follow the sun in an east-to-west trajectory, as is shown in the YouTube video embedded in this post and in Figure 1.
Fig. 3 Compare annual outputs of HSAT, AADAT, and fixed.
How much more energy can a HSAT help to generate? Figure 2 shows the comparison of the outputs of a HSAT system, an AADAT system, and an optimally fixed solar panel on March 22, June 22, September 22, and December 22, respectively, in the Boston area. The results suggest that the HSAT system is almost as good as the AADAT system in June but its performance declines in March and September and becomes the worst in December (in which case it can only capture a little more than half of the energy harvested by the AADAT system). Interestingly, also notice that there is a dip at noon in the energy graphs for March, September, and December. Why so? I will leave the question for you to figure out. If you have a hard time imagining this, perhaps the visualizations in Energy3D can help.
Fig. 4 Compare wide- and narrow-spacing of HSAT arrays
Figure 3 shows the annual result, which suggests that, over the course of a year, the HSAT system -- despite of its relatively unsatisfactory performance in spring, fall, and winter -- still outperforms any fixed solar panel, but it captures about 86% of the energy captured by the AADAT system.
An important factor to consider in solar farm design is the choice of the inter-row spacing to avoid significant energy loss due to shading of adjacent rows in early morning and late afternoon. But you don't want the distance between two rows to be too far as the rows will occupy a large land area that makes no economic sense. With Energy3D, we can easily investigate the change of the energy output with regard to the change of the inter-row spacing. Figure 4 shows the gain from HSAT is greatly reduced when the rows are too close, essentially eliminating the advantages of using solar trackers. Despite of their ability to track the sun, HSATs still require space to achieve the optimal performance.
A solar tracker is a system that automatically turns a solar panel or a reflector toward the sun in order to maximize the energy output of a solar power station. It is often said to be inspired by the sunflower.
In general, trackers can be categorized into two types: single-axis trackers and dual-axis trackers. Single-axis trackers have one degree of freedom that acts as an axis of rotation. The axis of rotation of single-axis trackers typically points to true north. Dual-axis trackers, on the other hand, have two degrees of freedom that act as axes of rotation. These axes are typically perpendicular to each other such as those in the altazimuth system. Single-axis trackers cannot exactly follow the sun but dual-axis trackers can.
Dual-axis trackers have been implemented in our Energy3D software for photovoltaic (PV) solar panels, as is shown in the video embedded in this post.
Energy3D has a variety of built-in tools for creating PV array layouts and analyzing their daily and annual yields. Figure 2 shows the comparison of the output of a solar panel rotated by a dual-axis tracker and those of solar panels fixed at different tilt angles (0°, 15°, 30°, 45°, 60°, 75°, and 90°) on March 22, June 22, September 22, and December 22, respectively, in Boston, MA. Not surprisingly, the result shows that the solar panel produces the most energy in June and the least in December.
Fig.2 A tracking PV panel vs. fixed panels at different tilt angles
When analyzing the benefit of using a solar tracker, we found that in June, a fixed panel at the optimal tilt angle produces about 70% of the energy produced by a panel oriented by a dual-axis tracker. That percentage increases to about 75% in March and September and to about 90% in December. This means that the benefit of using a tracker, compared with the maximal output of a fixed panel with the optimal tilt angle, will be significant in the summer but gradually diminish when the winter comes.
Having to manually adjust the tilt angles for a lot of solar panels four times a year sounds like too laborious to be practical. If that is out of the question, it would then be fair to compare the output of a solar panel with a tracker and those fixed at the same tilt angle throughout the year. Figure 3 shows that the total annual yield of a solar panel at the best tilt angle produces only 70% of the energy produced by a solar panel rotated by a tracker. In other words, a solar panel rotated by a tracker generates about 42% more energy compared with a solar panel fixed at the optimal tilt angle on the annual basis.
Fig.3 Annual outputs: tracker vs. fixed
Does the additional energy that solar trackers help generate worth the money (initial investment plus maintenance of moving parts) they cost? You may have heard that, as solar panels get cheaper and cheaper, trackers become less and less favorable. I want to offer a different point of view.
Surely, the return of the investment on solar trackers depends on a number of factors such as the price of solar panels. But one of the most important factors is the solar cell efficiency of the solar panels they rotate. The higher the efficiency is, the more the extra electricity a tracker can yield to offset the cost and make a profit. With the solar cell efficiency for commercial panels breaks record every year (reportedly 31.6% in July 2016), what didn't make economic sense in the past looks lucrative now. The future of the market for solar trackers will only look brighter.
Concentrated solar power (CSP) systems generate electricity using arrays of mirrors to concentrate sunlight shed on a large area onto a small area. The concentrated light is converted into thermal energy, which then drives a heat engine connected to an electrical power generator. Put it simply, a CSP power station operates like a solar cooker that you might have made in a high school science project. You can think of it as a gigantic solar cooker.
But a small science idea like this could turn into big money. For example, the Ivanpah Solar Power Facility in the California Mojave Desert, which drew $2.2 billion of investment, generates 392 megawatts (MW) -- enough to power hundreds of thousands of homes. As of 2016, the largest CSP project in the world is the Ouarzazate Solar Power Station in Morocco, which is expected to output 580 MW at peak and cost about $9 billion. Globally, CSP power stations will generate 4,705 MW this year.
To support the exploration of all kinds of solar energy exploitation, we have added the initial capacity to model CSP power stations in our Energy3D software, which is intended to be a "one-stop-shop" for solar energy modeling and design. This includes the capability of adding mirrors, heliostats, and power towers and analyzing the outputs as a function of time, location, and weather. This article shows some of the graphic effects of solar power towers (with more than 500 reflectors, each of which has the size of 2 by 3 meters, amounting to a total reflective area of more than 3,000 square meters). The four images above demonstrate how heliostats change the orientations of the reflectors at different times of the day (the selected date is June 22 and the selected location is Phoenix, AZ). The images show a simple circular field layout. In reality, radial stagger layouts that minimize shadow loss and block loss and maximize cosine efficiency are commonly used.
In the months to come, we plan to enhance Energy3D's ability to support a variety of field layout designs for power towers and model various configurations of solar thermal power (e.g., parabolic trough and Fresnel reflectors).
We have just added new modeling capacities to our Energy3D software for simulating photovoltaic (PV) power stations. With these additions, the latest version of the software can now simulate rooftop solar panels, solar parks, and solar power plants. Our plan is to develop Energy3D into a "one stop shop" for solar simulations. The goal is to provide students an accessible (yet powerful) tool to learn science and engineering in the context of renewable energy and professionals an easy-to-use (yet accurate) tool to design, predict, and optimize renewable energy generation.
Users can easily copy and paste solar panels to create an array and then duplicate arrays to create more arrays. In this way, users can rapidly add many solar panels. Each solar panel can be rotated around three different axes (normal, zenith, and azimuth). With this flexibility, users can create a PV array in any direction and orientation. At any time, they can adjust the direction and orientation of any or all solar panels.
PV arrays that are oriented differently
What is in the design of a solar power plant? While the orientation is a no-brainer, the layout may need some thinking and planning, especially for a site that has a limited area. Another factor that affects the layout is the design of the solar tracking system used to maximize the output. Also, considering that many utility companies offer peak and off-peak prices for electricity, users may explore strategies of orienting some PV arrays towards the west or southwest for the solar power plant to produce more energy in the afternoon when the demand is high in the summer, especially in the south.
Rooftop PV arrays
In addition to designing PV arrays on the ground, users can do the same thing for flat rooftops as well. Unlike solar panels on pitched roofs of residential buildings, those on flat roofs of large buildings are usually tilted.
We are currently implementing solar trackers so that users can design solar power plants that maximize their outputs based on tracking the sun. Meanwhile, mirror reflector arrays will be added to support the design of concentrated solar power plants. These features should be available soon. Stay tuned!
The annual yield and cost benefit analyses of rooftop solar panels based on sound scientific and engineering principles are critical steps to the financial success of building solarization. Google's Project Sunroof provides a way for millions of property owners to get recommendations for the right solar solutions.
Another way to conduct accurate scientific analysis of solar panel outputs based on their layout on the rooftop is to use a computer-aided engineering (CAE) tool to do a three-dimensional, full-year analysis based on ab initio scientific simulation. Under the support of the National Science Foundation since 2010, we have been developing Energy3D, a piece of CAE software that has the goal of bringing the power of sophisticated scientific and engineering simulations to children and laypersons. To achieve this goal, a key step is to support users to rapidly sketch up their own buildings and the surrounding objects that may affect their solar potentials. We feel that most CAD tools out there are probably too difficult for average users to create realistic models of their own houses. This forces us to invent new solutions.
We have recently added countless new features to Energy3D to progress towards this goal. The latest version allows many common architectural styles found in most parts of the US to be created and their solar potential to be studied. The screenshots embedded in this article demonstrate this capability. With the current version, each of these designs took myself approximately an hour to create from scratch. But we will continue to push the limit. The 3D construction user interface has been developed based on the tenet of supporting users to create any structure using a minimum set of building blocks and operations. Once users master a relatively small set of rules, they are empowered to create almost any shape of building as they wish.
Solar yield analysis of the first house
The actual time-consuming part is to get the right dimension and orientation of a real building and the surrounding tall objects such as trees. Google's 3D map may provide a way to extract these data. Once the approximate geometry of a building is determined, users can easily put solar panels anywhere on the roof to check out their energy yield. They can then try as many different layouts as they wish to compare the yields and select an optimal layout. This is especially important for buildings that may have partial shades and sub-optimal orientations. CAE tools such as Energy3D can be used to do spatial and temporal analysis and report daily outputs of each panel in the array, allowing users to obtain fine-grained, detailed results and thus providing a good simulation of solar panels in day-to-day operation.
The engineering principles behind this solar design, assessment, and optimization process based on science is exactly what the Next Generation Science Standards require K-12 students in the US to learn and practice. So why not ask children for help to solarize their own homes, schools, and communities, at least virtually? The time for doing this can never be better. And we have paved the road for this vision by creating one of easiest 3D interfaces with compelling scientific visualizations that can potentially entice and engage a lot of students. It is time for us to test the idea.
Visual analytics provides a powerful way for people to see patterns and trends in data by visualizing them. In real life, we use both our eyes and ears. So can we hear patterns and trends if we listen to the data?
Note that the data sonification capabilities of VPA is very experimental at this point. To make the matter worse, I am not a musician by any stretch of the imagination. So the generated sounds in the latest version of VPA may sound horrible to you. But this represents a step forward to better interactions with complex learner data. As my knowledge about music improves, the data should sound less terrifying.
The first test feature added to VPA is very simple: It just converts a time series into a sequence of notes and rests. To adjust the sound, you can change a number of parameters such as pitch, duration, attack, decay, and oscillator types (sine, square, triangle, sawtooth, etc.). All these options are available through the context menu of a time series graph.
At the same time as the sound plays, you can also see a synchronized animation of VPA (as demonstrated by the embedded videos). This means that from now on VPA is a multimodal analytic tool. But I have no plan to rename it as data visualization is still and will remain dominant for the data mining platform.
The next step is to figure out how to synthesize better sounds from multiple types of actions as multiple sources or instruments (much like the Song from Pi). I will start with sonifying the scatter plot in VPA. Stay tuned.
Visual Process Analytics (VPA) is a data mining platform that supports research on student learning through using complex tools to solve complex problems. The complexity of this kind of learning activities of students entails complex process data (e.g., event log) that cannot be easily analyzed. This difficulty calls for data visualization that can at least give researchers a glimpse of the data before they can actually conduct in-depth analyses. To this end, the VPA platform provides many different types of visualization that represent many different aspects of complex processes. These graphic representations should help researchers develop some sort of intuition. We believe VPA is an essential tool for data-intensive research, which will only grow more important in the future as data mining, machine learning, and artificial intelligence play critical roles in effective, personalized education.
Several new features were added to Version 0.3, described as follows:
1) Interactions are provided through context menus. Context menus can be invoked by right-clicking on a visualization. Depending on where the user clicks, a context menu provides the available actions applicable to the selected objects. This allows a complex tool such as VPA to still have a simple, pleasant user interface.
2) Result collectors allow users to gather analysis results and export them in the CSV format. VPA is a data browser that allows users to navigate in the ocean of data from the repositories it connects to. Each step of navigation invokes some calculations behind the scenes. To collect the results of these calculations in a mining session, VPA now has a simple result collector that automatically keeps track of the user's work. A more sophisticated result manager is also being conceptualized and developed to make it possible for users to manage their data mining results in a more flexible way. These results can be exported if needed to be analyzed further using other software tools.
3) Cumulative data graphs are available to render a more dramatic view of time series. It is sometimes easier to spot patterns and trends in cumulative graphs. This cumulative analysis applies to all levels of granularity of data supported by VPA (currently, the three granular levels are Top, Medium, and Fine, corresponding to three different ways to categorize action data). VPA also provides a way for users to select variables from a list to be highlighted in cumulative graphs.
Many other new features were also added in this version. For example, additional information about classes and students are provided to contextualize each data set. In the coming weeks, the repository will incorporate data from more than 1,200 students in Indiana who have undertaken engineering design projects using our Energy3D software. This unprecedented large-scale database will potentially provide a goldmine of research data in the area of engineering design study.
Energy3D already provides a set of powerful analysis tools that users can use to analyze the annual energy performance of their designs. For experts, the annual analysis tools are convenient as they can quickly evaluate their designs based on the results. For novices who are trying to understand how the energy graphs are calculated (or skeptics who are not sure whether they should trust the results), the annual analysis is sometimes a bit like a black box. This is because if there are too many variables (which, in this case, are seasonal changes of solar radiation and weather) to deal with at once, we will be overwhelmed. The total energy data are the results of two astronomic cycles: the daily cycle (caused by the spin of the Earth itself) and the annual cycle (caused by the rotation of the Earth around the Sun). This is why novices have a hard time reasoning with the results.
Fig. 2: Daily light sensor data in four seasons.
To help users reduce one layer of complexity and make sense of the energy data calculated in Energy3D simulations, a new class of daily analysis tools has been added to Energy3D. These tools allow users to pick a day to do the energy analyses, limiting the graphs to the daily cycle.
For example, we can place three sensors on the east, south, and west sides of the house shown in Figure 1. Then we can pick four days -- January 1st, April 1st, July 1st, and October 1st -- to represent the four seasons. Then we run a simulation for each day to collect the corresponding sensor data. The results are shown in Figure 2. These show that in the winter, the south-facing side receives the highest intensity of solar radiation, compared with the east and west-facing sides. In the summer, however, it is the east and west-facing sides that receive the highest intensity of solar radiation. In the spring and fall, the peak intensities of the three sides are comparable but they peak at different times.
Fig. 3: Daily energy use and production in four seasons.
If you take a more careful look at Figure 2, you will notice that, while the radiation intensity on the south-facing side always peaks at noon, those on the east and west-facing sides generally go through a seasonal shift. In the summer, the peak of radiation intensity occurs around 8 am on the east-facing side and around 4 pm on the west-facing side, respectively. In the winter, these peaks occur around 9 am and 2 pm, respectively. This difference is due to the shorter day in the winter and the lower position of the Sun in the sky.
Energy3D also provides a heliodon to visualize the solar path on any given day, which you can use to examine the angle of the sun and the length of the day. If you want to visually evaluate solar radiation on a site, it is best to combine the sensor and the heliodon.
You can also analyze the daily energy use and production. Figure 3 shows the results. Since this house has a lot of south-facing windows that have a Solar Heat Gain Coefficient of 80%, the solar energy is actually enough to keep the house warm (you may notice that your heater runs less frequently in the middle of a sunny winter day if you have a large south-facing window). But the downside is that it also requires a lot of energy to cool the house in the summer. Also note the interesting energy pattern for July 1st -- there are two smaller peaks of solar radiation in the morning and afternoon. Why? I will leave that answer to you.
Camilo Vieira Mejia, a PhD student of Purdue University, recently brought our Energy3D software to a workshop, which is a part of Clubes de Ciencia -- an initiative where graduate students go to Colombia and share science and engineering concepts with high school students from small towns around Antioquia (a state of Colombia).
Students designed houses with Energy3D, printed them out, assemble them, and put them under the Sun to test their solar gains. They probably have also run the solar and thermal analyses for their virtual houses.
We are glad that our free software is reaching out to students in these rural areas and helping them to become interested in science and engineering. This is one of the many examples that a project funded by the National Science Foundation also turns out to benefit people in other countries and impact the world in many positive ways. In this sense, the National Science Foundation is not just a federal agency -- it is a global agency.
If you are also using Energy3D in your country, please consider contacting us and sharing your stories or thoughts.
Energy3D is intended to be global -- It currently includes weather data from 220 locations in all the continents. Please let us know you would like to include locations in your country in the software so that you can design energy solutions for your own area. As a matter of fact, this was exactly what Camilo asked me to do before he headed for Colombia. I would have had no clue which towns in Colombia should be added and where I could retrieve their weather data (which is often in a foreign language).
[With the kind permission of these participating students, we are able to release the photos in this blog post.]