Tag Archives: Solar energy

Modeling solar thermal power using heliostats in Energy2D

An array of heliostats in Energy2D (online simulation)
A new class of objects was added in Energy2D to model what is called a heliostat, a device that can automatically turn a mirror to reflect sunlight to a target no matter where the sun is in the sky. Heliostats are often used in solar thermal power plants or solar furnaces that use mirrors. With an array of computer-controlled heliostats and mirrors, the energy from the sun can be concentrated on the target to heat it up to a very high temperature, enough to vaporize water to create steam that drives a turbine to generate electricity.

Image credit: Wikipedia
The Ivanpah Solar Power Facility in California's Mojave Desert, which went online on February 13, 2014, is currently the world's largest solar thermal power plant. With a gross capacity of 392 megawatts, it is enough to power 140,000 homes. It deploys 173,500 heliostats, each controlling two mirrors.

A heliostat in Energy2D contains a planar mirror mounted on a pillar. You can drop one in at any location. Once you specify its target, it will automatically reflect any sunlight beam hitting on it to the target.

Strictly speaking, heliostats are different from solar trackers that automatically face the sun like sunflowers. But in Energy2D, if no target is specified, as is the default case, a heliostat becomes a solar tracker. Unlike heliostats, solar trackers are often used with photovoltaic (PV) panels that absorb, instead of reflecting, sunlight that shine on them. A future version of Energy2D will include the capacity of modeling PV power plants as well.

Accurate prediction of solar radiation using Energy3D: Part III

Predicted and measured average daily insolation for 80 cities.
In Parts I and II, we have documented our progress on solar radiation modeling with our Energy3D CAD software. In the past few weeks, our summer interns Siobhan Bailey from Rensselaer Polytechnic Institute and Shiyan Jiang from University of Miami, and I have collected data for 167 worldwide locations. We analyzed 100 US locations among them and compared the insolation data calculated by Energy3D for a horizontal surface and a south-face vertical surface with 30 years of data collected by the US Department of Energy. The results show that, on average, the calculated mean daily insolation is within ±14% of error range compared with the measured results for a horizontal surface and ±10% of error range compared with the measure results for a south-facing vertical surface, respectively. The calculation of the average accuracy is based on both temporal data of 12 months over a year and spatial data of 100 locations in the US.

With this crystal ball in the hand to predict solar radiation anywhere anytime with a reasonable accuracy, Energy3D can be used by professional engineers for real-world applications related to solar energy, such as passive solar architecture, urban planning, solar park optimization, solar thermal power plants, and so on. Stay tuned for our future reports of those applications.

Go to Part I and Part II.

Accurate prediction of solar radiation using Energy3D: Part II

About a week ago, I reported our progress in modeling worldwide solar radiation with our Energy3D software. While our calculated insolation data for a horizontal surface agreed quite well with the data provided by the National Solar Radiation Data Base, those for a south-facing vertical surface did not work out as well. I suspected that the discrepancy was partly caused by missing the reflection of short-wave radiation: not all sunlight is absorbed by the Earth. A certain portion is reflected. The ability of a material to reflect sunlight is known as albedo. For example, fresh snow can reflect up to 90% of solar energy. People who live in the northern part of the country often experience strong reflection from snow or ice in the winter.

Figure 1. Calculated and measured insolation on a south-facing surface.
In the summer, the Sun is high in the sky. A south-facing plate doesn't get as much energy as in other seasons, especially near the Equator where the Sun is just above your head (such as Honolulu as included in the figures above). However, the ambient reflection can be significant. After incorporating this component into our equations following the convention in the ASHRAE solar radiation model, the agreement between the calculated and measured results significantly improves -- you can see this big improvement by comparing Figure 1 (new algorithm) with Figure 2 (old algorithm).

Figure 2. Results without considering reflected short-wave radiation.
This degree of accuracy is critically important to supporting meaningful engineering design projects on renewable energy sources that might be conducted by students across the country. We are working to refine our computational algorithms further based on 50 years' research on solar science. This work will lend Energy3D the scientific integrity needed for rational design, be it about sustainable architecture, urban planning, or solar parks.

Go to Part I and Part III.

Accurate prediction of solar radiation using Energy3D: Part I

Solar engineering and building design rely on accurate prediction of solar radiation at any given location. This is a core functionality of our Energy3D CAD software. We are proud to announce that, through continuous improvements of our mathematical model, Energy3D is now capable of modeling solar radiation with an impressive precision.

Figure 1. Comparison of measured and calculated solar radiation on a horizontal plate at 10 US locations.
Figure 1 shows that Energy3D's calculated results of solar energy density on a horizontal plate agree remarkably well with, the National Solar Radiation Database that houses 30 years of data measured by the National Renewable Energy Laboratory of the U.S. Department of Energy -- for 10 cities across the US. One striking success is the prediction of a dip of solar radiation in June for Miami, FL (see the second image of the first row). Overall, the predicted results are slightly smaller than the measured ones. 

Note that these results are theoretical calculations, not numerical fits (such as using an artificial neural network to predict based on previous data). It is pretty amazing if you think about this: Through some complex calculations the number for each month and each city come very close to the data measured for three decades at those weather stations scattered around the country! This is the holy grail of computer simulation. This success lays a solid foundation for our Energy3D software to be scientifically and engineeringly relevant.

Figure 2. Comparison of measured and calculated solar radiation on a south-facing plate at 10 US locations.
The National Renewable Energy Laboratory also measured the solar radiation on surfaces that tilt at different angles. The predicted trends for the solar energy density on an upright south-facing plate agree reasonably well (Figure 2) with the measured data. For example, both measured and calculated data show that solar radiation on a south-facing plate peaks in the spring and fall for most northern locations and in the winter for tropical locations. It is amazing that Energy3D also correctly predicts the exception --  Anchorage in Alaska, where the solar data peak only in the spring!

Quantitatively, Energy3D seems to underestimate the solar radiation more than in the horizontal case shown in Figure 1, especially for the summer months. We suspect that this is because a vertical plate has a larger contribution from the ambient radiation and reflection than a horizontal plate (which faces the sky). We are now working towards a better model to correct this problem.

For Energy3D to serve a global audience, we have collected geographical and climate data of more than 150 domestic and foreign locations and integrated them into the software (Version 3.2). If you live in the US, you are guaranteed to find at least one location in your state.

Go to Part II and Part III.

Global pattern of insolation predicted by Energy3D

Figure 1. Global insolation pattern from Pole to Pole
The Sun's power drives the climate of the Earth. Accurately modeling the incident solar radiation, namely, insolation, at a given location is important to the design of high-performance buildings. As I have blogged last week, the insolation calculation in our Energy3D software considers the incident angle of the Sun to the surface, the duration of the day, and the air mass. And we have recently incorporated the effect of altitude and the ambient inputs.

Figure 2. Real data for the three locations (source)
In Energy3D, we can easily investigate the global pattern of insolation by horizontally placing a sensor module on the ground and then collecting the sensor data throughout the year. We can easily change the latitude and collect a new set of data. Figure 1 shows the global insolation pattern from the North Pole to the South Pole. The time integral of each curve represents the total solar energy a location at the corresponding latitude receives. There is an interesting observation from Figure 1: The Equator doesn't actually have the highest peak value and its peak values are not in the summer but in the spring and fall. However, because the insolation does not differ very much from season to season in the Equator, its time integral is much larger, which is the Equator is hot all year round.
Figure 3. Energy3D's prediction

How accurate are the predictions of Energy3D? Let's pick three locations that someone has collected real data, as shown in Figure 2. More insolation data can be found on this website. (Surprisingly, the peak solar energy at the South Pole is higher than the peak solar energy at the Equator.)

Figure 3 shows that the insolation values predicted by Energy3D. As you can see, the predicted trend agrees reasonably well with the trend in the real data. Overall, Energy3D tends to underestimate the insolation by about 50% (after unit conversion), however.